id	sid	tid	token	lemma	pos
esrj-91195	1	1	geoid	geoid	ADJ
esrj-91195	1	2	undulation	undulation	NOUN
esrj-91195	1	3	prediction	prediction	NOUN
esrj-91195	1	4	using	use	VERB
esrj-91195	1	5	anns	ann	NOUN
esrj-91195	1	6	(	(	PUNCT
esrj-91195	1	7	rbfnn	rbfnn	NOUN
esrj-91195	1	8	and	and	CCONJ
esrj-91195	1	9	grnn	grnn	NOUN
esrj-91195	1	10	)	)	PUNCT
esrj-91195	1	11	,	,	PUNCT
esrj-91195	1	12	multiple	multiple	ADJ
esrj-91195	1	13	linear	linear	ADJ
esrj-91195	1	14	regression	regression	NOUN
esrj-91195	1	15	(	(	PUNCT
esrj-91195	1	16	mlr	mlr	NOUN
esrj-91195	1	17	)	)	PUNCT
esrj-91195	1	18	,	,	PUNCT
esrj-91195	1	19	and	and	CCONJ
esrj-91195	1	20	interpolation	interpolation	NOUN
esrj-91195	1	21	methods	method	NOUN
esrj-91195	1	22	:	:	PUNCT
esrj-91195	2	1	a	a	DET
esrj-91195	2	2	comparative	comparative	ADJ
esrj-91195	2	3	study	study	NOUN
esrj-91195	2	4	berkant	berkant	ADJ
esrj-91195	2	5	konakoglua	konakoglua	PROPN
esrj-91195	2	6	*	*	PROPN
esrj-91195	2	7	and	and	CCONJ
esrj-91195	2	8	alper	alper	PROPN
esrj-91195	2	9	akarb	akarb	PROPN
esrj-91195	2	10	atechnical	atechnical	ADJ
esrj-91195	2	11	sciences	sciences	PROPN
esrj-91195	2	12	vocational	vocational	ADJ
esrj-91195	2	13	school	school	NOUN
esrj-91195	2	14	,	,	PUNCT
esrj-91195	2	15	amasya	amasya	NOUN
esrj-91195	2	16	university	university	NOUN
esrj-91195	2	17	,	,	PUNCT
esrj-91195	2	18	amasya	amasya	NOUN
esrj-91195	2	19	,	,	PUNCT
esrj-91195	2	20	turkey	turkey	PROPN
esrj-91195	2	21	bvocational	bvocational	PROPN
esrj-91195	2	22	school	school	PROPN
esrj-91195	2	23	,	,	PUNCT
esrj-91195	2	24	erzincan	erzincan	PROPN
esrj-91195	2	25	binali	binali	PROPN
esrj-91195	2	26	yıldırım	yıldırım	PROPN
esrj-91195	2	27	university	university	PROPN
esrj-91195	2	28	,	,	PUNCT
esrj-91195	2	29	erzincan	erzincan	ADJ
esrj-91195	2	30	,	,	PUNCT
esrj-91195	2	31	turkey	turkey	NOUN
esrj-91195	2	32	*	*	PUNCT
esrj-91195	2	33	corresponding	correspond	VERB
esrj-91195	2	34	author	author	NOUN
esrj-91195	2	35	:	:	PUNCT
esrj-91195	2	36	berkantkonakoglu@amasya.edu.tr	berkantkonakoglu@amasya.edu.tr	PROPN
esrj-91195	2	37	keywords	keyword	NOUN
esrj-91195	2	38	:	:	PUNCT
esrj-91195	2	39	generalized	generalized	ADJ
esrj-91195	2	40	regression	regression	NOUN
esrj-91195	2	41	neural	neural	ADJ
esrj-91195	2	42	network	network	NOUN
esrj-91195	2	43	(	(	PUNCT
esrj-91195	2	44	grnn	grnn	PROPN
esrj-91195	2	45	)	)	PUNCT
esrj-91195	2	46	;	;	PUNCT
esrj-91195	2	47	radial	radial	ADJ
esrj-91195	2	48	basis	basis	NOUN
esrj-91195	2	49	function	function	NOUN
esrj-91195	2	50	neural	neural	ADJ
esrj-91195	2	51	network	network	NOUN
esrj-91195	2	52	(	(	PUNCT
esrj-91195	2	53	rbfnn	rbfnn	PROPN
esrj-91195	2	54	)	)	PUNCT
esrj-91195	2	55	;	;	PUNCT
esrj-91195	2	56	multiple	multiple	ADJ
esrj-91195	2	57	linear	linear	ADJ
esrj-91195	2	58	regression	regression	NOUN
esrj-91195	2	59	(	(	PUNCT
esrj-91195	2	60	mlr	mlr	NOUN
esrj-91195	2	61	)	)	PUNCT
esrj-91195	2	62	;	;	PUNCT
esrj-91195	2	63	interpolation	interpolation	NOUN
esrj-91195	2	64	methods	method	NOUN
esrj-91195	2	65	;	;	PUNCT
esrj-91195	2	66	geoid	geoid	ADJ
esrj-91195	2	67	determination	determination	NOUN
esrj-91195	2	68	palabras	palabra	NOUN
esrj-91195	2	69	clave	clave	PROPN
esrj-91195	2	70	:	:	PUNCT
esrj-91195	2	71	red	red	ADJ
esrj-91195	2	72	neuronal	neuronal	ADJ
esrj-91195	2	73	de	de	PROPN
esrj-91195	2	74	regresión	regresión	PROPN
esrj-91195	2	75	generalizada	generalizada	NOUN
esrj-91195	2	76	;	;	PUNCT
esrj-91195	2	77	red	red	ADJ
esrj-91195	2	78	neuronal	neuronal	ADJ
esrj-91195	2	79	de	de	X
esrj-91195	2	80	base	base	NOUN
esrj-91195	2	81	radial	radial	NOUN
esrj-91195	2	82	;	;	PUNCT
esrj-91195	2	83	regresión	regresión	NOUN
esrj-91195	2	84	lineal	lineal	PROPN
esrj-91195	2	85	múltiple	múltiple	NOUN
esrj-91195	2	86	;	;	PUNCT
esrj-91195	2	87	métodos	métodos	PROPN
esrj-91195	2	88	de	de	X
esrj-91195	2	89	interpolación	interpolación	PROPN
esrj-91195	2	90	;	;	PUNCT
esrj-91195	2	91	determinación	determinación	PROPN
esrj-91195	2	92	geoide	geoide	PROPN
esrj-91195	2	93	.	.	PUNCT
esrj-91195	3	1	issn	issn	PROPN
esrj-91195	3	2	1794	1794	NUM
esrj-91195	3	3	-	-	SYM
esrj-91195	3	4	6190	6190	NUM
esrj-91195	3	5	e	e	NOUN
esrj-91195	3	6	-	-	NOUN
esrj-91195	3	7	issn	issn	PROPN
esrj-91195	3	8	2339	2339	NUM
esrj-91195	3	9	-	-	SYM
esrj-91195	3	10	3459	3459	NUM
esrj-91195	3	11	https://doi.org/10.15446/esrj.v25n4.91195	https://doi.org/10.15446/esrj.v25n4.91195	NOUN
esrj-91195	3	12	earth	earth	PROPN
esrj-91195	3	13	sciences	sciences	PROPN
esrj-91195	3	14	research	research	PROPN
esrj-91195	3	15	journal	journal	PROPN
esrj-91195	3	16	earth	earth	PROPN
esrj-91195	3	17	sci	sci	PROPN
esrj-91195	3	18	.	.	PUNCT
esrj-91195	4	1	res	res	PROPN
esrj-91195	4	2	.	.	PUNCT
esrj-91195	5	1	j.	j.	PROPN
esrj-91195	5	2	vol	vol	PROPN
esrj-91195	5	3	.	.	PROPN
esrj-91195	6	1	25	25	NUM
esrj-91195	6	2	,	,	PUNCT
esrj-91195	6	3	no	no	INTJ
esrj-91195	6	4	.	.	NOUN
esrj-91195	6	5	4	4	NUM
esrj-91195	6	6	(	(	PUNCT
esrj-91195	6	7	december	december	PROPN
esrj-91195	6	8	,	,	PUNCT
esrj-91195	6	9	2021	2021	NUM
esrj-91195	6	10	):	):	PUNCT
esrj-91195	6	11	371	371	NUM
esrj-91195	6	12	-	-	SYM
esrj-91195	6	13	382	382	NUM
esrj-91195	6	14	g	g	NOUN
esrj-91195	6	15	eo	eo	PROPN
esrj-91195	6	16	ph	ph	PROPN
esrj-91195	6	17	ys	ys	PROPN
esrj-91195	6	18	ic	ic	PROPN
esrj-91195	6	19	s	s	PROPN
esrj-91195	6	20	record	record	NOUN
esrj-91195	6	21	manuscript	manuscript	NOUN
esrj-91195	6	22	received	receive	VERB
esrj-91195	6	23	:	:	PUNCT
esrj-91195	6	24	27/10/2020	27/10/2020	NUM
esrj-91195	6	25	accepted	accept	VERB
esrj-91195	6	26	for	for	ADP
esrj-91195	6	27	publication	publication	NOUN
esrj-91195	6	28	:	:	PUNCT
esrj-91195	6	29	12/06/2021	12/06/2021	NUM
esrj-91195	6	30	abstract	abstract	ADJ
esrj-91195	6	31	this	this	DET
esrj-91195	6	32	study	study	NOUN
esrj-91195	6	33	evaluated	evaluate	VERB
esrj-91195	6	34	different	different	ADJ
esrj-91195	6	35	methods	method	NOUN
esrj-91195	6	36	for	for	ADP
esrj-91195	6	37	geoid	geoid	ADJ
esrj-91195	6	38	undulation	undulation	NOUN
esrj-91195	6	39	prediction	prediction	NOUN
esrj-91195	6	40	and	and	CCONJ
esrj-91195	6	41	included	include	VERB
esrj-91195	6	42	two	two	NUM
esrj-91195	6	43	types	type	NOUN
esrj-91195	6	44	of	of	ADP
esrj-91195	6	45	artificial	artificial	ADJ
esrj-91195	6	46	neural	neural	ADJ
esrj-91195	6	47	networks	network	NOUN
esrj-91195	6	48	(	(	PUNCT
esrj-91195	6	49	anns	anns	PROPN
esrj-91195	6	50	)	)	PUNCT
esrj-91195	6	51	the	the	DET
esrj-91195	6	52	radial	radial	ADJ
esrj-91195	6	53	basis	basis	NOUN
esrj-91195	6	54	function	function	NOUN
esrj-91195	6	55	neural	neural	ADJ
esrj-91195	6	56	network	network	NOUN
esrj-91195	6	57	(	(	PUNCT
esrj-91195	6	58	rbfnn	rbfnn	PROPN
esrj-91195	6	59	)	)	PUNCT
esrj-91195	6	60	and	and	CCONJ
esrj-91195	6	61	the	the	DET
esrj-91195	6	62	generalized	generalized	ADJ
esrj-91195	6	63	regression	regression	NOUN
esrj-91195	6	64	neural	neural	ADJ
esrj-91195	6	65	network	network	NOUN
esrj-91195	6	66	(	(	PUNCT
esrj-91195	6	67	grnn	grnn	PROPN
esrj-91195	6	68	)	)	PUNCT
esrj-91195	6	69	as	as	ADV
esrj-91195	6	70	well	well	ADV
esrj-91195	6	71	as	as	ADP
esrj-91195	6	72	conventional	conventional	ADJ
esrj-91195	6	73	methods	method	NOUN
esrj-91195	6	74	including	include	VERB
esrj-91195	6	75	multiple	multiple	ADJ
esrj-91195	6	76	linear	linear	ADJ
esrj-91195	6	77	regression	regression	NOUN
esrj-91195	6	78	(	(	PUNCT
esrj-91195	6	79	mlr	mlr	NOUN
esrj-91195	6	80	)	)	PUNCT
esrj-91195	6	81	and	and	CCONJ
esrj-91195	6	82	ten	ten	NUM
esrj-91195	6	83	different	different	ADJ
esrj-91195	6	84	interpolation	interpolation	NOUN
esrj-91195	6	85	techniques	technique	NOUN
esrj-91195	6	86	.	.	PUNCT
esrj-91195	7	1	in	in	ADP
esrj-91195	7	2	this	this	DET
esrj-91195	7	3	work	work	NOUN
esrj-91195	7	4	,	,	PUNCT
esrj-91195	7	5	k	k	ADJ
esrj-91195	7	6	-	-	ADJ
esrj-91195	7	7	fold	fold	ADJ
esrj-91195	7	8	cross	cross	NOUN
esrj-91195	7	9	-	-	ADJ
esrj-91195	7	10	validation	validation	NOUN
esrj-91195	7	11	was	be	AUX
esrj-91195	7	12	used	use	VERB
esrj-91195	7	13	to	to	PART
esrj-91195	7	14	evaluate	evaluate	VERB
esrj-91195	7	15	the	the	DET
esrj-91195	7	16	model	model	NOUN
esrj-91195	7	17	and	and	CCONJ
esrj-91195	7	18	its	its	PRON
esrj-91195	7	19	behavior	behavior	NOUN
esrj-91195	7	20	on	on	ADP
esrj-91195	7	21	the	the	DET
esrj-91195	7	22	independent	independent	ADJ
esrj-91195	7	23	dataset	dataset	NOUN
esrj-91195	7	24	.	.	PUNCT
esrj-91195	8	1	with	with	ADP
esrj-91195	8	2	this	this	DET
esrj-91195	8	3	validation	validation	NOUN
esrj-91195	8	4	method	method	NOUN
esrj-91195	8	5	,	,	PUNCT
esrj-91195	8	6	each	each	PRON
esrj-91195	8	7	of	of	ADP
esrj-91195	8	8	a	a	DET
esrj-91195	8	9	k	k	ADJ
esrj-91195	8	10	number	number	NOUN
esrj-91195	8	11	of	of	ADP
esrj-91195	8	12	groups	group	NOUN
esrj-91195	8	13	has	have	VERB
esrj-91195	8	14	the	the	DET
esrj-91195	8	15	chance	chance	NOUN
esrj-91195	8	16	to	to	PART
esrj-91195	8	17	be	be	AUX
esrj-91195	8	18	divided	divide	VERB
esrj-91195	8	19	into	into	ADP
esrj-91195	8	20	training	training	NOUN
esrj-91195	8	21	and	and	CCONJ
esrj-91195	8	22	testing	testing	NOUN
esrj-91195	8	23	data	datum	NOUN
esrj-91195	8	24	.	.	PUNCT
esrj-91195	9	1	the	the	DET
esrj-91195	9	2	performances	performance	NOUN
esrj-91195	9	3	of	of	ADP
esrj-91195	9	4	the	the	DET
esrj-91195	9	5	methods	method	NOUN
esrj-91195	9	6	were	be	AUX
esrj-91195	9	7	evaluated	evaluate	VERB
esrj-91195	9	8	in	in	ADP
esrj-91195	9	9	terms	term	NOUN
esrj-91195	9	10	of	of	ADP
esrj-91195	9	11	the	the	DET
esrj-91195	9	12	root	root	NOUN
esrj-91195	9	13	mean	mean	VERB
esrj-91195	9	14	square	square	ADJ
esrj-91195	9	15	error	error	NOUN
esrj-91195	9	16	(	(	PUNCT
esrj-91195	9	17	rmse	rmse	NOUN
esrj-91195	9	18	)	)	PUNCT
esrj-91195	9	19	,	,	PUNCT
esrj-91195	9	20	mean	mean	VERB
esrj-91195	9	21	absolute	absolute	ADJ
esrj-91195	9	22	error	error	NOUN
esrj-91195	9	23	(	(	PUNCT
esrj-91195	9	24	mae	mae	PROPN
esrj-91195	9	25	)	)	PUNCT
esrj-91195	9	26	,	,	PUNCT
esrj-91195	9	27	nash	nash	PROPN
esrj-91195	9	28	–	–	PUNCT
esrj-91195	9	29	sutcliffe	sutcliffe	PROPN
esrj-91195	9	30	efficiency	efficiency	NOUN
esrj-91195	9	31	coefficient	coefficient	NOUN
esrj-91195	9	32	(	(	PUNCT
esrj-91195	9	33	nse	nse	NOUN
esrj-91195	9	34	)	)	PUNCT
esrj-91195	9	35	,	,	PUNCT
esrj-91195	9	36	correlation	correlation	NOUN
esrj-91195	9	37	coefficient	coefficient	NOUN
esrj-91195	9	38	(	(	PUNCT
esrj-91195	9	39	r2	r2	PROPN
esrj-91195	9	40	)	)	PUNCT
esrj-91195	9	41	,	,	PUNCT
esrj-91195	9	42	and	and	CCONJ
esrj-91195	9	43	using	use	VERB
esrj-91195	9	44	graphical	graphical	ADJ
esrj-91195	9	45	indicators	indicator	NOUN
esrj-91195	9	46	.	.	PUNCT
esrj-91195	10	1	the	the	DET
esrj-91195	10	2	evaluation	evaluation	NOUN
esrj-91195	10	3	of	of	ADP
esrj-91195	10	4	the	the	DET
esrj-91195	10	5	performance	performance	NOUN
esrj-91195	10	6	of	of	ADP
esrj-91195	10	7	the	the	DET
esrj-91195	10	8	datasets	dataset	NOUN
esrj-91195	10	9	obtained	obtain	VERB
esrj-91195	10	10	using	use	VERB
esrj-91195	10	11	cross	cross	NOUN
esrj-91195	10	12	-	-	ADJ
esrj-91195	10	13	validation	validation	NOUN
esrj-91195	10	14	was	be	AUX
esrj-91195	10	15	performed	perform	VERB
esrj-91195	10	16	in	in	ADP
esrj-91195	10	17	two	two	NUM
esrj-91195	10	18	ways	way	NOUN
esrj-91195	10	19	.	.	PUNCT
esrj-91195	11	1	when	when	SCONJ
esrj-91195	11	2	the	the	DET
esrj-91195	11	3	method	method	NOUN
esrj-91195	11	4	having	have	VERB
esrj-91195	11	5	the	the	DET
esrj-91195	11	6	minimum	minimum	ADJ
esrj-91195	11	7	error	error	NOUN
esrj-91195	11	8	result	result	NOUN
esrj-91195	11	9	was	be	AUX
esrj-91195	11	10	accepted	accept	VERB
esrj-91195	11	11	as	as	ADP
esrj-91195	11	12	the	the	DET
esrj-91195	11	13	most	most	ADV
esrj-91195	11	14	appropriate	appropriate	ADJ
esrj-91195	11	15	method	method	NOUN
esrj-91195	11	16	,	,	PUNCT
esrj-91195	11	17	the	the	DET
esrj-91195	11	18	natural	natural	ADJ
esrj-91195	11	19	neighbor	neighbor	NOUN
esrj-91195	11	20	(	(	PUNCT
esrj-91195	11	21	nn	nn	NOUN
esrj-91195	11	22	)	)	PUNCT
esrj-91195	11	23	gave	give	VERB
esrj-91195	11	24	better	well	ADJ
esrj-91195	11	25	results	result	NOUN
esrj-91195	11	26	than	than	ADP
esrj-91195	11	27	the	the	DET
esrj-91195	11	28	other	other	ADJ
esrj-91195	11	29	methods	method	NOUN
esrj-91195	11	30	(	(	PUNCT
esrj-91195	11	31	rmse	rmse	NOUN
esrj-91195	11	32	=	=	PUNCT
esrj-91195	11	33	0.142	0.142	NUM
esrj-91195	11	34	m	m	NOUN
esrj-91195	11	35	,	,	PUNCT
esrj-91195	11	36	mae	mae	PROPN
esrj-91195	11	37	=	=	PROPN
esrj-91195	11	38	0.097	0.097	NUM
esrj-91195	11	39	m	m	NOUN
esrj-91195	11	40	,	,	PUNCT
esrj-91195	11	41	nse	nse	NOUN
esrj-91195	11	42	=	=	SYM
esrj-91195	11	43	0.98986	0.98986	NUM
esrj-91195	11	44	,	,	PUNCT
esrj-91195	11	45	and	and	CCONJ
esrj-91195	11	46	r2	r2	PROPN
esrj-91195	11	47	=	=	SYM
esrj-91195	11	48	0.99011	0.99011	NUM
esrj-91195	11	49	)	)	PUNCT
esrj-91195	11	50	.	.	PUNCT
esrj-91195	12	1	on	on	ADP
esrj-91195	12	2	the	the	DET
esrj-91195	12	3	other	other	ADJ
esrj-91195	12	4	hand	hand	NOUN
esrj-91195	12	5	,	,	PUNCT
esrj-91195	12	6	it	it	PRON
esrj-91195	12	7	was	be	AUX
esrj-91195	12	8	observed	observe	VERB
esrj-91195	12	9	that	that	SCONJ
esrj-91195	12	10	on	on	ADP
esrj-91195	12	11	average	average	ADJ
esrj-91195	12	12	,	,	PUNCT
esrj-91195	12	13	the	the	DET
esrj-91195	12	14	grnn	grnn	NOUN
esrj-91195	12	15	exhibited	exhibit	VERB
esrj-91195	12	16	the	the	DET
esrj-91195	12	17	best	good	ADJ
esrj-91195	12	18	performance	performance	NOUN
esrj-91195	12	19	(	(	PUNCT
esrj-91195	12	20	rmse	rmse	NOUN
esrj-91195	12	21	=	=	PROPN
esrj-91195	12	22	0.185	0.185	NUM
esrj-91195	12	23	m	m	PROPN
esrj-91195	12	24	,	,	PUNCT
esrj-91195	12	25	mae	mae	PROPN
esrj-91195	12	26	=	=	PROPN
esrj-91195	12	27	0.137	0.137	NUM
esrj-91195	12	28	m	m	NOUN
esrj-91195	12	29	,	,	PUNCT
esrj-91195	12	30	nse	nse	NOUN
esrj-91195	12	31	=	=	SYM
esrj-91195	12	32	0.98229	0.98229	NUM
esrj-91195	12	33	,	,	PUNCT
esrj-91195	12	34	and	and	CCONJ
esrj-91195	12	35	r2	r2	PROPN
esrj-91195	12	36	=	=	SYM
esrj-91195	12	37	0.98249	0.98249	NUM
esrj-91195	12	38	)	)	PUNCT
esrj-91195	12	39	.	.	PUNCT
esrj-91195	13	1	predicción	predicción	PROPN
esrj-91195	13	2	de	de	PROPN
esrj-91195	13	3	ondulación	ondulación	PROPN
esrj-91195	13	4	geoide	geoide	PROPN
esrj-91195	13	5	con	con	PROPN
esrj-91195	13	6	redes	redes	PROPN
esrj-91195	13	7	neuronales	neuronales	PROPN
esrj-91195	13	8	artificiales	artificiale	NOUN
esrj-91195	13	9	(	(	PUNCT
esrj-91195	13	10	base	base	NOUN
esrj-91195	13	11	radial	radial	ADJ
esrj-91195	13	12	y	y	PROPN
esrj-91195	13	13	regresión	regresión	PROPN
esrj-91195	13	14	generalizada	generalizada	PROPN
esrj-91195	13	15	)	)	PUNCT
esrj-91195	13	16	,	,	PUNCT
esrj-91195	13	17	regresión	regresión	NOUN
esrj-91195	13	18	lineal	lineal	ADJ
esrj-91195	13	19	múltiple	múltiple	NOUN
esrj-91195	13	20	y	y	PROPN
esrj-91195	13	21	métodos	métodos	PROPN
esrj-91195	13	22	de	de	PROPN
esrj-91195	13	23	interpolación	interpolación	PROPN
esrj-91195	13	24	:	:	PUNCT
esrj-91195	13	25	estudio	estudio	PROPN
esrj-91195	13	26	comparativo	comparativo	PROPN
esrj-91195	13	27	resumen	resumen	PROPN
esrj-91195	13	28	este	este	PROPN
esrj-91195	13	29	estudio	estudio	PROPN
esrj-91195	13	30	evalúa	evalúa	NOUN
esrj-91195	13	31	los	los	PROPN
esrj-91195	13	32	métodos	métodos	PROPN
esrj-91195	13	33	diferentes	diferentes	PROPN
esrj-91195	13	34	de	de	PROPN
esrj-91195	13	35	predicción	predicción	PROPN
esrj-91195	13	36	de	de	PROPN
esrj-91195	13	37	ondulación	ondulación	PROPN
esrj-91195	13	38	geoide	geoide	PROPN
esrj-91195	13	39	donde	donde	PROPN
esrj-91195	13	40	se	se	PROPN
esrj-91195	13	41	incluye	incluye	PROPN
esrj-91195	13	42	dos	dos	PROPN
esrj-91195	13	43	tipos	tipos	PROPN
esrj-91195	13	44	de	de	PROPN
esrj-91195	13	45	redes	redes	PROPN
esrj-91195	13	46	neuronales	neuronale	VERB
esrj-91195	13	47	artificiales	artificiales	PROPN
esrj-91195	13	48	-la	-la	X
esrj-91195	13	49	red	red	ADJ
esrj-91195	13	50	neuronal	neuronal	ADJ
esrj-91195	13	51	de	de	X
esrj-91195	13	52	base	base	NOUN
esrj-91195	13	53	radial	radial	NOUN
esrj-91195	13	54	y	y	PROPN
esrj-91195	13	55	la	la	X
esrj-91195	13	56	red	red	ADJ
esrj-91195	13	57	neuronal	neuronal	PROPN
esrj-91195	13	58	de	de	PROPN
esrj-91195	13	59	regresión	regresión	PROPN
esrj-91195	13	60	generalizadaal	generalizadaal	ADJ
esrj-91195	13	61	igual	igual	X
esrj-91195	13	62	que	que	X
esrj-91195	13	63	métodos	métodos	PROPN
esrj-91195	13	64	convencionales	convencionale	NOUN
esrj-91195	13	65	donde	donde	PROPN
esrj-91195	13	66	se	se	PROPN
esrj-91195	13	67	incluyen	incluyen	PROPN
esrj-91195	13	68	la	la	PROPN
esrj-91195	13	69	regresión	regresión	PROPN
esrj-91195	13	70	lineal	lineal	PROPN
esrj-91195	13	71	múltiple	múltiple	NOUN
esrj-91195	13	72	y	y	PROPN
esrj-91195	13	73	diez	diez	PROPN
esrj-91195	13	74	técnicas	técnicas	PROPN
esrj-91195	13	75	diferentes	diferentes	PROPN
esrj-91195	13	76	de	de	X
esrj-91195	13	77	interpolación	interpolación	PROPN
esrj-91195	13	78	.	.	PUNCT
esrj-91195	14	1	en	en	ADP
esrj-91195	14	2	este	este	X
esrj-91195	14	3	trabajo	trabajo	PROPN
esrj-91195	14	4	,	,	PUNCT
esrj-91195	14	5	la	la	PROPN
esrj-91195	14	6	validación	validación	NOUN
esrj-91195	14	7	cruzada	cruzada	PROPN
esrj-91195	14	8	de	de	PROPN
esrj-91195	14	9	k	k	PROPN
esrj-91195	14	10	iteraciones	iteraciones	PROPN
esrj-91195	14	11	se	se	X
esrj-91195	14	12	usó	usó	PROPN
esrj-91195	14	13	para	para	PROPN
esrj-91195	14	14	evaluar	evaluar	PROPN
esrj-91195	14	15	el	el	PROPN
esrj-91195	14	16	modelo	modelo	PROPN
esrj-91195	14	17	y	y	PROPN
esrj-91195	14	18	su	su	PROPN
esrj-91195	14	19	comportamiento	comportamiento	PROPN
esrj-91195	14	20	en	en	PROPN
esrj-91195	14	21	un	un	PROPN
esrj-91195	14	22	conjunto	conjunto	PROPN
esrj-91195	14	23	de	de	PROPN
esrj-91195	14	24	datos	datos	X
esrj-91195	14	25	independiente	independiente	X
esrj-91195	14	26	.	.	PUNCT
esrj-91195	15	1	con	con	PROPN
esrj-91195	15	2	este	este	X
esrj-91195	15	3	método	método	X
esrj-91195	15	4	de	de	X
esrj-91195	15	5	evaluación	evaluación	PROPN
esrj-91195	15	6	,	,	PUNCT
esrj-91195	15	7	cada	cada	PROPN
esrj-91195	15	8	grupo	grupo	PROPN
esrj-91195	15	9	de	de	PROPN
esrj-91195	15	10	números	números	PROPN
esrj-91195	15	11	k	k	PROPN
esrj-91195	15	12	tiene	tiene	PROPN
esrj-91195	15	13	la	la	PROPN
esrj-91195	15	14	posibilidad	posibilidad	PROPN
esrj-91195	15	15	de	de	PROPN
esrj-91195	15	16	dividirse	dividirse	PROPN
esrj-91195	15	17	entre	entre	PROPN
esrj-91195	15	18	datos	datos	X
esrj-91195	15	19	de	de	PROPN
esrj-91195	15	20	entrenamiento	entrenamiento	PROPN
esrj-91195	15	21	y	y	PROPN
esrj-91195	15	22	datos	datos	X
esrj-91195	15	23	de	de	PROPN
esrj-91195	15	24	evaluación	evaluación	PROPN
esrj-91195	15	25	.	.	PUNCT
esrj-91195	16	1	el	el	PROPN
esrj-91195	16	2	desempeño	desempeño	PROPN
esrj-91195	16	3	de	de	PROPN
esrj-91195	16	4	los	los	PROPN
esrj-91195	16	5	métodos	métodos	PROPN
esrj-91195	16	6	se	se	PROPN
esrj-91195	16	7	evaluó	evaluó	PROPN
esrj-91195	16	8	en	en	X
esrj-91195	16	9	términos	términos	PROPN
esrj-91195	16	10	de	de	PROPN
esrj-91195	16	11	la	la	PROPN
esrj-91195	16	12	raíz	raíz	PROPN
esrj-91195	16	13	del	del	PROPN
esrj-91195	16	14	error	error	NOUN
esrj-91195	16	15	cuadrático	cuadrático	NOUN
esrj-91195	16	16	medio	medio	NOUN
esrj-91195	16	17	(	(	PUNCT
esrj-91195	16	18	rmse	rmse	PROPN
esrj-91195	16	19	,	,	PUNCT
esrj-91195	16	20	del	del	PROPN
esrj-91195	16	21	inglés	inglés	PROPN
esrj-91195	16	22	root	root	NOUN
esrj-91195	16	23	mean	mean	VERB
esrj-91195	16	24	square	square	NOUN
esrj-91195	16	25	error	error	NOUN
esrj-91195	16	26	)	)	PUNCT
esrj-91195	16	27	,	,	PUNCT
esrj-91195	16	28	el	el	PROPN
esrj-91195	16	29	error	error	PROPN
esrj-91195	16	30	absoluto	absoluto	PROPN
esrj-91195	16	31	medio	medio	X
esrj-91195	16	32	(	(	PUNCT
esrj-91195	16	33	mae	mae	PROPN
esrj-91195	16	34	,	,	PUNCT
esrj-91195	16	35	mean	mean	VERB
esrj-91195	16	36	absolute	absolute	ADJ
esrj-91195	16	37	error	error	NOUN
esrj-91195	16	38	)	)	PUNCT
esrj-91195	16	39	,	,	PUNCT
esrj-91195	16	40	el	el	PROPN
esrj-91195	16	41	coeficiente	coeficiente	PROPN
esrj-91195	16	42	de	de	PROPN
esrj-91195	16	43	eficiencia	eficiencia	PROPN
esrj-91195	16	44	nash	nash	NOUN
esrj-91195	16	45	-	-	PUNCT
esrj-91195	16	46	sutcliffe	sutcliffe	PROPN
esrj-91195	16	47	(	(	PUNCT
esrj-91195	16	48	nse	nse	NOUN
esrj-91195	16	49	,	,	PUNCT
esrj-91195	16	50	nash	nash	PROPN
esrj-91195	16	51	–	–	PUNCT
esrj-91195	16	52	sutcliffe	sutcliffe	PROPN
esrj-91195	16	53	efficiency	efficiency	NOUN
esrj-91195	16	54	coefficient	coefficient	NOUN
esrj-91195	16	55	)	)	PUNCT
esrj-91195	16	56	y	y	PROPN
esrj-91195	16	57	el	el	PROPN
esrj-91195	16	58	coeficiente	coeficiente	PROPN
esrj-91195	16	59	de	de	PROPN
esrj-91195	16	60	correlación	correlación	PROPN
esrj-91195	16	61	(	(	PUNCT
esrj-91195	16	62	r2	r2	PROPN
esrj-91195	16	63	,	,	PUNCT
esrj-91195	16	64	correlation	correlation	NOUN
esrj-91195	16	65	coefficient	coefficient	NOUN
esrj-91195	16	66	)	)	PUNCT
esrj-91195	16	67	a	a	DET
esrj-91195	16	68	través	través	PROPN
esrj-91195	16	69	de	de	PROPN
esrj-91195	16	70	indicadores	indicadores	PROPN
esrj-91195	16	71	gráficos	gráficos	PROPN
esrj-91195	16	72	.	.	PUNCT
esrj-91195	17	1	la	la	PROPN
esrj-91195	17	2	evaluación	evaluación	PROPN
esrj-91195	17	3	del	del	PROPN
esrj-91195	17	4	desempeño	desempeño	PROPN
esrj-91195	17	5	de	de	X
esrj-91195	17	6	los	los	PROPN
esrj-91195	17	7	grupos	grupos	PROPN
esrj-91195	17	8	de	de	PROPN
esrj-91195	17	9	datos	datos	X
esrj-91195	17	10	obtenidos	obtenido	NOUN
esrj-91195	17	11	con	con	X
esrj-91195	17	12	la	la	PROPN
esrj-91195	17	13	validación	validación	PROPN
esrj-91195	17	14	cruzada	cruzada	PROPN
esrj-91195	17	15	se	se	PROPN
esrj-91195	17	16	realizó	realizó	PROPN
esrj-91195	17	17	en	en	PROPN
esrj-91195	17	18	dos	dos	PROPN
esrj-91195	17	19	vías	vías	PROPN
esrj-91195	17	20	.	.	PUNCT
esrj-91195	18	1	cuando	cuando	PROPN
esrj-91195	18	2	el	el	PROPN
esrj-91195	18	3	método	método	PROPN
esrj-91195	18	4	que	que	PROPN
esrj-91195	18	5	tiene	tiene	PROPN
esrj-91195	18	6	el	el	PROPN
esrj-91195	18	7	resultado	resultado	VERB
esrj-91195	18	8	de	de	X
esrj-91195	18	9	mínimo	mínimo	NOUN
esrj-91195	18	10	error	error	NOUN
esrj-91195	18	11	es	es	X
esrj-91195	18	12	aceptado	aceptado	PROPN
esrj-91195	18	13	como	como	PROPN
esrj-91195	18	14	el	el	PROPN
esrj-91195	18	15	método	método	PROPN
esrj-91195	18	16	más	más	PROPN
esrj-91195	18	17	apropiado	apropiado	PROPN
esrj-91195	18	18	,	,	PUNCT
esrj-91195	18	19	el	el	PROPN
esrj-91195	18	20	vecino	vecino	PROPN
esrj-91195	18	21	natural	natural	PROPN
esrj-91195	18	22	ofrece	ofrece	PROPN
esrj-91195	18	23	mejores	mejores	PROPN
esrj-91195	18	24	resultados	resultado	VERB
esrj-91195	18	25	que	que	PROPN
esrj-91195	18	26	otros	otros	NOUN
esrj-91195	18	27	métodos	método	NOUN
esrj-91195	18	28	(	(	PUNCT
esrj-91195	18	29	rmse	rmse	NOUN
esrj-91195	18	30	=	=	PUNCT
esrj-91195	18	31	0.142	0.142	NUM
esrj-91195	18	32	m	m	NOUN
esrj-91195	18	33	,	,	PUNCT
esrj-91195	18	34	mae	mae	PROPN
esrj-91195	18	35	=	=	PROPN
esrj-91195	18	36	0.097	0.097	NUM
esrj-91195	18	37	m	m	NOUN
esrj-91195	18	38	,	,	PUNCT
esrj-91195	18	39	nse	nse	NOUN
esrj-91195	18	40	=	=	SYM
esrj-91195	18	41	0.98986	0.98986	NUM
esrj-91195	18	42	,	,	PUNCT
esrj-91195	18	43	and	and	CCONJ
esrj-91195	18	44	r2	r2	PROPN
esrj-91195	18	45	=	=	SYM
esrj-91195	18	46	0.99011	0.99011	NUM
esrj-91195	18	47	)	)	PUNCT
esrj-91195	18	48	.	.	PUNCT
esrj-91195	19	1	por	por	PROPN
esrj-91195	19	2	otro	otro	PROPN
esrj-91195	19	3	lado	lado	PROPN
esrj-91195	19	4	,	,	PUNCT
esrj-91195	19	5	se	se	AUX
esrj-91195	19	6	observó	observó	PROPN
esrj-91195	19	7	que	que	X
esrj-91195	19	8	en	en	X
esrj-91195	19	9	promedio	promedio	PROPN
esrj-91195	19	10	la	la	PROPN
esrj-91195	19	11	red	red	PROPN
esrj-91195	19	12	neuronal	neuronal	PROPN
esrj-91195	19	13	de	de	PROPN
esrj-91195	19	14	regresión	regresión	PROPN
esrj-91195	19	15	generalizada	generalizada	PROPN
esrj-91195	19	16	presentó	presentó	PROPN
esrj-91195	19	17	un	un	PROPN
esrj-91195	19	18	mejor	mejor	PROPN
esrj-91195	19	19	desempeño	desempeño	PROPN
esrj-91195	19	20	(	(	PUNCT
esrj-91195	19	21	rmse	rmse	NOUN
esrj-91195	19	22	=	=	PROPN
esrj-91195	19	23	0.185	0.185	NUM
esrj-91195	19	24	m	m	PROPN
esrj-91195	19	25	,	,	PUNCT
esrj-91195	19	26	mae	mae	PROPN
esrj-91195	19	27	=	=	PROPN
esrj-91195	19	28	0.137	0.137	NUM
esrj-91195	19	29	m	m	NOUN
esrj-91195	19	30	,	,	PUNCT
esrj-91195	19	31	nse	nse	NOUN
esrj-91195	19	32	=	=	SYM
esrj-91195	19	33	0.98229	0.98229	NUM
esrj-91195	19	34	,	,	PUNCT
esrj-91195	19	35	and	and	CCONJ
esrj-91195	19	36	r2	r2	PROPN
esrj-91195	19	37	=	=	SYM
esrj-91195	19	38	0.98249	0.98249	NUM
esrj-91195	19	39	)	)	PUNCT
esrj-91195	19	40	.	.	PUNCT
esrj-91195	20	1	how	how	SCONJ
esrj-91195	20	2	to	to	PART
esrj-91195	20	3	cite	cite	VERB
esrj-91195	20	4	item	item	NOUN
esrj-91195	20	5	:	:	PUNCT
esrj-91195	20	6	konakoglu	konakoglu	PROPN
esrj-91195	20	7	,	,	PUNCT
esrj-91195	20	8	b.	b.	PROPN
esrj-91195	20	9	,	,	PUNCT
esrj-91195	20	10	&	&	CCONJ
esrj-91195	20	11	akar	akar	PROPN
esrj-91195	20	12	,	,	PUNCT
esrj-91195	20	13	a.	a.	NOUN
esrj-91195	20	14	(	(	PUNCT
esrj-91195	20	15	2021	2021	NUM
esrj-91195	20	16	)	)	PUNCT
esrj-91195	20	17	.	.	PUNCT
esrj-91195	21	1	geoid	geoid	ADJ
esrj-91195	21	2	undulation	undulation	NOUN
esrj-91195	21	3	prediction	prediction	NOUN
esrj-91195	21	4	using	use	VERB
esrj-91195	21	5	anns	ann	NOUN
esrj-91195	21	6	(	(	PUNCT
esrj-91195	21	7	rbfnn	rbfnn	NOUN
esrj-91195	21	8	and	and	CCONJ
esrj-91195	21	9	grnn	grnn	NOUN
esrj-91195	21	10	)	)	PUNCT
esrj-91195	21	11	,	,	PUNCT
esrj-91195	21	12	multiple	multiple	ADJ
esrj-91195	21	13	linear	linear	ADJ
esrj-91195	21	14	regression	regression	NOUN
esrj-91195	21	15	(	(	PUNCT
esrj-91195	21	16	mlr	mlr	NOUN
esrj-91195	21	17	)	)	PUNCT
esrj-91195	21	18	,	,	PUNCT
esrj-91195	21	19	and	and	CCONJ
esrj-91195	21	20	interpolation	interpolation	NOUN
esrj-91195	21	21	methods	method	NOUN
esrj-91195	21	22	:	:	PUNCT
esrj-91195	21	23	a	a	DET
esrj-91195	21	24	comparative	comparative	ADJ
esrj-91195	21	25	study	study	NOUN
esrj-91195	21	26	.	.	PUNCT
esrj-91195	22	1	earth	earth	PROPN
esrj-91195	22	2	sciences	sciences	PROPN
esrj-91195	22	3	research	research	PROPN
esrj-91195	22	4	journal	journal	NOUN
esrj-91195	22	5	,	,	PUNCT
esrj-91195	22	6	25(4	25(4	PROPN
esrj-91195	22	7	)	)	PUNCT
esrj-91195	22	8	,	,	PUNCT
esrj-91195	22	9	371	371	NUM
esrj-91195	22	10	-	-	SYM
esrj-91195	22	11	382	382	NUM
esrj-91195	22	12	.	.	PUNCT
esrj-91195	22	13	https://doi.org/10.15446/esrj	https://doi.org/10.15446/esrj	NOUN
esrj-91195	22	14	.	.	PUNCT
esrj-91195	23	1	v25n4.91195	v25n4.91195	NOUN
esrj-91195	23	2	mailto:berkantkonakoglu@amasya.edu.tr	mailto:berkantkonakoglu@amasya.edu.tr	PROPN
esrj-91195	23	3	https://doi.org/10.15446/esrj.v25n1.74167	https://doi.org/10.15446/esrj.v25n1.74167	CCONJ
esrj-91195	23	4	https://doi.org/10.15446/esrj.v25n4.91195	https://doi.org/10.15446/esrj.v25n4.91195	NOUN
esrj-91195	23	5	https://doi.org/10.15446/esrj.v25n4.91195	https://doi.org/10.15446/esrj.v25n4.91195	VERB
esrj-91195	23	6	372	372	NUM
esrj-91195	23	7	berkant	berkant	PROPN
esrj-91195	23	8	konakoglu	konakoglu	PROPN
esrj-91195	23	9	,	,	PUNCT
esrj-91195	23	10	alper	alper	PROPN
esrj-91195	23	11	akar	akar	PROPN
esrj-91195	23	12	introduction	introduction	NOUN
esrj-91195	23	13	the	the	DET
esrj-91195	23	14	use	use	NOUN
esrj-91195	23	15	of	of	ADP
esrj-91195	23	16	global	global	ADJ
esrj-91195	23	17	navigation	navigation	NOUN
esrj-91195	23	18	satellite	satellite	NOUN
esrj-91195	23	19	systems	system	NOUN
esrj-91195	23	20	(	(	PUNCT
esrj-91195	23	21	gnss	gnss	NOUN
esrj-91195	23	22	)	)	PUNCT
esrj-91195	23	23	in	in	ADP
esrj-91195	23	24	engineering	engineering	NOUN
esrj-91195	23	25	and	and	CCONJ
esrj-91195	23	26	scientific	scientific	ADJ
esrj-91195	23	27	studies	study	NOUN
esrj-91195	23	28	has	have	AUX
esrj-91195	23	29	increased	increase	VERB
esrj-91195	23	30	considerably	considerably	ADV
esrj-91195	23	31	along	along	ADV
esrj-91195	23	32	with	with	ADP
esrj-91195	23	33	recent	recent	ADJ
esrj-91195	23	34	developing	develop	VERB
esrj-91195	23	35	technology	technology	NOUN
esrj-91195	23	36	.	.	PUNCT
esrj-91195	24	1	with	with	ADP
esrj-91195	24	2	this	this	DET
esrj-91195	24	3	technology	technology	NOUN
esrj-91195	24	4	,	,	PUNCT
esrj-91195	24	5	accurate	accurate	ADJ
esrj-91195	24	6	cartesian	cartesian	ADJ
esrj-91195	24	7	(	(	PUNCT
esrj-91195	24	8	x	x	NOUN
esrj-91195	24	9	,	,	PUNCT
esrj-91195	24	10	y	y	PROPN
esrj-91195	24	11	,	,	PUNCT
esrj-91195	24	12	z	z	NOUN
esrj-91195	24	13	)	)	PUNCT
esrj-91195	24	14	or	or	CCONJ
esrj-91195	24	15	geodetic	geodetic	ADJ
esrj-91195	24	16	(	(	PUNCT
esrj-91195	24	17	φ	φ	PROPN
esrj-91195	24	18	,	,	PUNCT
esrj-91195	24	19	λ	λ	PROPN
esrj-91195	24	20	,	,	PUNCT
esrj-91195	24	21	h	h	NOUN
esrj-91195	24	22	)	)	PUNCT
esrj-91195	24	23	coordinate	coordinate	NOUN
esrj-91195	24	24	information	information	NOUN
esrj-91195	24	25	for	for	ADP
esrj-91195	24	26	any	any	DET
esrj-91195	24	27	point	point	NOUN
esrj-91195	24	28	on	on	ADP
esrj-91195	24	29	the	the	DET
esrj-91195	24	30	earth	earth	NOUN
esrj-91195	24	31	is	be	AUX
esrj-91195	24	32	easily	easily	ADV
esrj-91195	24	33	available	available	ADJ
esrj-91195	24	34	(	(	PUNCT
esrj-91195	24	35	seeber	seeber	PROPN
esrj-91195	24	36	,	,	PUNCT
esrj-91195	24	37	2003	2003	NUM
esrj-91195	24	38	)	)	PUNCT
esrj-91195	24	39	.	.	PUNCT
esrj-91195	25	1	however	however	ADV
esrj-91195	25	2	,	,	PUNCT
esrj-91195	25	3	the	the	DET
esrj-91195	25	4	gnss	gnss	NOUN
esrj-91195	25	5	-	-	PUNCT
esrj-91195	25	6	derived	derive	VERB
esrj-91195	25	7	ellipsoidal	ellipsoidal	ADJ
esrj-91195	25	8	height	height	NOUN
esrj-91195	25	9	is	be	AUX
esrj-91195	25	10	not	not	PART
esrj-91195	25	11	used	use	VERB
esrj-91195	25	12	directly	directly	ADV
esrj-91195	25	13	in	in	ADP
esrj-91195	25	14	engineering	engineering	NOUN
esrj-91195	25	15	studies	study	NOUN
esrj-91195	25	16	.	.	PUNCT
esrj-91195	26	1	in	in	ADP
esrj-91195	26	2	order	order	NOUN
esrj-91195	26	3	to	to	PART
esrj-91195	26	4	use	use	VERB
esrj-91195	26	5	ellipsoidal	ellipsoidal	ADJ
esrj-91195	26	6	heights	height	NOUN
esrj-91195	26	7	obtained	obtain	VERB
esrj-91195	26	8	from	from	ADP
esrj-91195	26	9	gnss	gnss	NOUN
esrj-91195	26	10	data	datum	NOUN
esrj-91195	26	11	with	with	ADP
esrj-91195	26	12	orthometric	orthometric	ADJ
esrj-91195	26	13	heights	height	NOUN
esrj-91195	26	14	obtained	obtain	VERB
esrj-91195	26	15	from	from	ADP
esrj-91195	26	16	geometric	geometric	ADJ
esrj-91195	26	17	leveling	leveling	NOUN
esrj-91195	26	18	measurements	measurement	NOUN
esrj-91195	26	19	,	,	PUNCT
esrj-91195	26	20	geoid	geoid	ADJ
esrj-91195	26	21	undulations	undulation	NOUN
esrj-91195	26	22	must	must	AUX
esrj-91195	26	23	be	be	AUX
esrj-91195	26	24	determined	determine	VERB
esrj-91195	26	25	accurately	accurately	ADV
esrj-91195	26	26	.	.	PUNCT
esrj-91195	27	1	the	the	DET
esrj-91195	27	2	relationship	relationship	NOUN
esrj-91195	27	3	between	between	ADP
esrj-91195	27	4	the	the	DET
esrj-91195	27	5	ellipsoidal	ellipsoidal	ADJ
esrj-91195	27	6	and	and	CCONJ
esrj-91195	27	7	orthometric	orthometric	ADJ
esrj-91195	27	8	heights	height	NOUN
esrj-91195	27	9	of	of	ADP
esrj-91195	27	10	any	any	DET
esrj-91195	27	11	point	point	NOUN
esrj-91195	27	12	on	on	ADP
esrj-91195	27	13	earth	earth	NOUN
esrj-91195	27	14	can	can	AUX
esrj-91195	27	15	be	be	AUX
esrj-91195	27	16	calculated	calculate	VERB
esrj-91195	27	17	by	by	ADP
esrj-91195	27	18	using	use	VERB
esrj-91195	27	19	equation	equation	NOUN
esrj-91195	27	20	(	(	PUNCT
esrj-91195	27	21	1	1	NUM
esrj-91195	27	22	)	)	PUNCT
esrj-91195	27	23	(	(	PUNCT
esrj-91195	27	24	heiskanen	heiskanen	PROPN
esrj-91195	27	25	&	&	CCONJ
esrj-91195	27	26	moritz	moritz	PROPN
esrj-91195	27	27	,	,	PUNCT
esrj-91195	27	28	1967	1967	NUM
esrj-91195	27	29	)	)	PUNCT
esrj-91195	27	30	.	.	PUNCT
esrj-91195	28	1	(	(	PUNCT
esrj-91195	28	2	1	1	X
esrj-91195	28	3	)	)	PUNCT
esrj-91195	28	4	where	where	SCONJ
esrj-91195	28	5	n	n	PRON
esrj-91195	28	6	refers	refer	VERB
esrj-91195	28	7	to	to	PART
esrj-91195	28	8	geoid	geoid	VERB
esrj-91195	28	9	undulation	undulation	NOUN
esrj-91195	28	10	,	,	PUNCT
esrj-91195	28	11	h	h	NOUN
esrj-91195	28	12	is	be	AUX
esrj-91195	28	13	the	the	DET
esrj-91195	28	14	ellipsoidal	ellipsoidal	ADJ
esrj-91195	28	15	height	height	NOUN
esrj-91195	28	16	,	,	PUNCT
esrj-91195	28	17	and	and	CCONJ
esrj-91195	28	18	h	h	NOUN
esrj-91195	28	19	is	be	AUX
esrj-91195	28	20	the	the	DET
esrj-91195	28	21	orthometric	orthometric	ADJ
esrj-91195	28	22	height	height	NOUN
esrj-91195	28	23	.	.	PUNCT
esrj-91195	29	1	two	two	NUM
esrj-91195	29	2	different	different	ADJ
esrj-91195	29	3	methods	method	NOUN
esrj-91195	29	4	are	be	AUX
esrj-91195	29	5	used	use	VERB
esrj-91195	29	6	in	in	ADP
esrj-91195	29	7	geoid	geoid	ADJ
esrj-91195	29	8	modeling	modeling	NOUN
esrj-91195	29	9	:	:	PUNCT
esrj-91195	29	10	gravimetric	gravimetric	ADJ
esrj-91195	29	11	and	and	CCONJ
esrj-91195	29	12	geometric	geometric	ADJ
esrj-91195	29	13	(	(	PUNCT
esrj-91195	29	14	featherstone	featherstone	PROPN
esrj-91195	29	15	et	et	PROPN
esrj-91195	29	16	al	al	PROPN
esrj-91195	29	17	.	.	PROPN
esrj-91195	29	18	,	,	PUNCT
esrj-91195	29	19	1998	1998	NUM
esrj-91195	29	20	;	;	PUNCT
esrj-91195	29	21	kotsakis	kotsakis	PROPN
esrj-91195	29	22	&	&	CCONJ
esrj-91195	29	23	sideris	sideris	PROPN
esrj-91195	29	24	,	,	PUNCT
esrj-91195	29	25	1999	1999	NUM
esrj-91195	29	26	)	)	PUNCT
esrj-91195	29	27	.	.	PUNCT
esrj-91195	30	1	with	with	ADP
esrj-91195	30	2	the	the	DET
esrj-91195	30	3	geometric	geometric	ADJ
esrj-91195	30	4	approach	approach	NOUN
esrj-91195	30	5	,	,	PUNCT
esrj-91195	30	6	many	many	ADJ
esrj-91195	30	7	different	different	ADJ
esrj-91195	30	8	techniques	technique	NOUN
esrj-91195	30	9	such	such	ADJ
esrj-91195	30	10	as	as	ADP
esrj-91195	30	11	interpolation	interpolation	NOUN
esrj-91195	30	12	and	and	CCONJ
esrj-91195	30	13	leastsquares	leastsquare	NOUN
esrj-91195	30	14	collocation	collocation	NOUN
esrj-91195	30	15	(	(	PUNCT
esrj-91195	30	16	lsc	lsc	NOUN
esrj-91195	30	17	)	)	PUNCT
esrj-91195	30	18	methods	method	NOUN
esrj-91195	30	19	are	be	AUX
esrj-91195	30	20	used	use	VERB
esrj-91195	30	21	to	to	PART
esrj-91195	30	22	determine	determine	VERB
esrj-91195	30	23	a	a	DET
esrj-91195	30	24	local	local	ADJ
esrj-91195	30	25	geoid	geoid	NOUN
esrj-91195	30	26	(	(	PUNCT
esrj-91195	30	27	zhong	zhong	PROPN
esrj-91195	30	28	,	,	PUNCT
esrj-91195	30	29	1997	1997	NUM
esrj-91195	30	30	;	;	PUNCT
esrj-91195	30	31	zhan	zhan	PROPN
esrj-91195	30	32	-	-	PUNCT
esrj-91195	30	33	ji	ji	PROPN
esrj-91195	30	34	&	&	CCONJ
esrj-91195	30	35	yong	yong	PROPN
esrj-91195	30	36	-	-	PUNCT
esrj-91195	30	37	qi	qi	PROPN
esrj-91195	30	38	,	,	PUNCT
esrj-91195	30	39	1999	1999	NUM
esrj-91195	30	40	;	;	PUNCT
esrj-91195	30	41	yanalak	yanalak	PROPN
esrj-91195	30	42	&	&	CCONJ
esrj-91195	30	43	baykal	baykal	PROPN
esrj-91195	30	44	,	,	PUNCT
esrj-91195	30	45	2001	2001	NUM
esrj-91195	30	46	;	;	PUNCT
esrj-91195	30	47	erol	erol	NOUN
esrj-91195	30	48	&	&	CCONJ
esrj-91195	30	49	çelik	çelik	PROPN
esrj-91195	30	50	,	,	PUNCT
esrj-91195	30	51	2004	2004	NUM
esrj-91195	30	52	;	;	PUNCT
esrj-91195	30	53	zaletnyik	zaletnyik	PROPN
esrj-91195	30	54	et	et	PROPN
esrj-91195	30	55	al	al	PROPN
esrj-91195	30	56	.	.	PROPN
esrj-91195	30	57	,	,	PUNCT
esrj-91195	30	58	2004	2004	NUM
esrj-91195	30	59	;	;	PUNCT
esrj-91195	30	60	erol	erol	NOUN
esrj-91195	30	61	et	et	PROPN
esrj-91195	30	62	al	al	PROPN
esrj-91195	30	63	.	.	PROPN
esrj-91195	30	64	,	,	PUNCT
esrj-91195	30	65	2008	2008	NUM
esrj-91195	30	66	;	;	PUNCT
esrj-91195	30	67	tusat	tusat	VERB
esrj-91195	30	68	,	,	PUNCT
esrj-91195	30	69	2011	2011	NUM
esrj-91195	30	70	;	;	PUNCT
esrj-91195	30	71	rabah	rabah	PROPN
esrj-91195	30	72	&	&	CCONJ
esrj-91195	30	73	kaloop	kaloop	PROPN
esrj-91195	30	74	,	,	PUNCT
esrj-91195	30	75	2013	2013	NUM
esrj-91195	30	76	;	;	PUNCT
esrj-91195	30	77	doganalp	doganalp	PROPN
esrj-91195	30	78	,	,	PUNCT
esrj-91195	30	79	2016	2016	NUM
esrj-91195	30	80	;	;	PUNCT
esrj-91195	30	81	das	das	PROPN
esrj-91195	30	82	et	et	PROPN
esrj-91195	30	83	al	al	PROPN
esrj-91195	30	84	.	.	PROPN
esrj-91195	30	85	,	,	PUNCT
esrj-91195	30	86	2018	2018	NUM
esrj-91195	30	87	;	;	PUNCT
esrj-91195	30	88	ligas	ligas	PROPN
esrj-91195	30	89	&	&	CCONJ
esrj-91195	30	90	kulczycki	kulczycki	PROPN
esrj-91195	30	91	,	,	PUNCT
esrj-91195	30	92	2018	2018	NUM
esrj-91195	30	93	;	;	PUNCT
esrj-91195	30	94	tusat	tusat	PROPN
esrj-91195	30	95	&	&	CCONJ
esrj-91195	30	96	mikailsoy	mikailsoy	PROPN
esrj-91195	30	97	,	,	PUNCT
esrj-91195	30	98	2018	2018	NUM
esrj-91195	30	99	;	;	PUNCT
esrj-91195	30	100	dawod	dawod	PROPN
esrj-91195	30	101	&	&	CCONJ
esrj-91195	30	102	abdel	abdel	PROPN
esrj-91195	30	103	-	-	PUNCT
esrj-91195	30	104	aziz	aziz	PROPN
esrj-91195	30	105	,	,	PUNCT
esrj-91195	30	106	2020	2020	NUM
esrj-91195	30	107	)	)	PUNCT
esrj-91195	30	108	.	.	PUNCT
esrj-91195	31	1	for	for	ADP
esrj-91195	31	2	example	example	NOUN
esrj-91195	31	3	,	,	PUNCT
esrj-91195	31	4	doganalp	doganalp	PROPN
esrj-91195	31	5	&	&	CCONJ
esrj-91195	31	6	selvi	selvi	PROPN
esrj-91195	31	7	(	(	PUNCT
esrj-91195	31	8	2015	2015	NUM
esrj-91195	31	9	)	)	PUNCT
esrj-91195	31	10	,	,	PUNCT
esrj-91195	31	11	using	use	VERB
esrj-91195	31	12	gnss	gnss	NOUN
esrj-91195	31	13	/	/	SYM
esrj-91195	31	14	leveling	leveling	NOUN
esrj-91195	31	15	data	datum	NOUN
esrj-91195	31	16	(	(	PUNCT
esrj-91195	31	17	40	40	NUM
esrj-91195	31	18	reference	reference	NOUN
esrj-91195	31	19	points	point	NOUN
esrj-91195	31	20	and	and	CCONJ
esrj-91195	31	21	205	205	NUM
esrj-91195	31	22	test	test	NOUN
esrj-91195	31	23	points	point	NOUN
esrj-91195	31	24	)	)	PUNCT
esrj-91195	31	25	,	,	PUNCT
esrj-91195	31	26	determined	determine	VERB
esrj-91195	31	27	a	a	DET
esrj-91195	31	28	geoid	geoid	NOUN
esrj-91195	31	29	of	of	ADP
esrj-91195	31	30	the	the	DET
esrj-91195	31	31	57	57	NUM
esrj-91195	31	32	-	-	PUNCT
esrj-91195	31	33	km	km	NOUN
esrj-91195	31	34	-	-	PUNCT
esrj-91195	31	35	long	long	ADJ
esrj-91195	31	36	nurdagi	nurdagi	PROPN
esrj-91195	31	37	-	-	PUNCT
esrj-91195	31	38	gaziantep	gaziantep	NOUN
esrj-91195	31	39	highway	highway	NOUN
esrj-91195	31	40	project	project	NOUN
esrj-91195	31	41	.	.	PUNCT
esrj-91195	32	1	polynomial	polynomial	ADJ
esrj-91195	32	2	,	,	PUNCT
esrj-91195	32	3	lsc	lsc	PROPN
esrj-91195	32	4	,	,	PUNCT
esrj-91195	32	5	multiquadric	multiquadric	ADJ
esrj-91195	32	6	(	(	PUNCT
esrj-91195	32	7	mq	mq	NOUN
esrj-91195	32	8	)	)	PUNCT
esrj-91195	32	9	,	,	PUNCT
esrj-91195	32	10	and	and	CCONJ
esrj-91195	32	11	thin	thin	ADJ
esrj-91195	32	12	plate	plate	NOUN
esrj-91195	32	13	splines	spline	NOUN
esrj-91195	32	14	(	(	PUNCT
esrj-91195	32	15	tps	tps	NOUN
esrj-91195	32	16	)	)	PUNCT
esrj-91195	32	17	methods	method	NOUN
esrj-91195	32	18	were	be	AUX
esrj-91195	32	19	used	use	VERB
esrj-91195	32	20	for	for	ADP
esrj-91195	32	21	geoid	geoid	ADJ
esrj-91195	32	22	undulation	undulation	NOUN
esrj-91195	32	23	calculation	calculation	NOUN
esrj-91195	32	24	.	.	PUNCT
esrj-91195	33	1	the	the	DET
esrj-91195	33	2	findings	finding	NOUN
esrj-91195	33	3	determined	determine	VERB
esrj-91195	33	4	that	that	SCONJ
esrj-91195	33	5	using	use	VERB
esrj-91195	33	6	polynomial	polynomial	ADJ
esrj-91195	33	7	methods	method	NOUN
esrj-91195	33	8	with	with	ADP
esrj-91195	33	9	lsc	lsc	PROPN
esrj-91195	33	10	had	have	VERB
esrj-91195	33	11	a	a	DET
esrj-91195	33	12	positive	positive	ADJ
esrj-91195	33	13	effect	effect	NOUN
esrj-91195	33	14	on	on	ADP
esrj-91195	33	15	the	the	DET
esrj-91195	33	16	results	result	NOUN
esrj-91195	33	17	in	in	ADP
esrj-91195	33	18	strip	strip	NOUN
esrj-91195	33	19	projects	project	NOUN
esrj-91195	33	20	.	.	PUNCT
esrj-91195	34	1	it	it	PRON
esrj-91195	34	2	was	be	AUX
esrj-91195	34	3	concluded	conclude	VERB
esrj-91195	34	4	that	that	SCONJ
esrj-91195	34	5	mq	mq	PROPN
esrj-91195	34	6	and	and	CCONJ
esrj-91195	34	7	tps	tps	NOUN
esrj-91195	34	8	prediction	prediction	NOUN
esrj-91195	34	9	methods	method	NOUN
esrj-91195	34	10	along	along	ADP
esrj-91195	34	11	with	with	ADP
esrj-91195	34	12	the	the	DET
esrj-91195	34	13	lsc	lsc	PROPN
esrj-91195	34	14	method	method	NOUN
esrj-91195	34	15	performed	perform	VERB
esrj-91195	34	16	better	well	ADV
esrj-91195	34	17	than	than	ADP
esrj-91195	34	18	polynomial	polynomial	ADJ
esrj-91195	34	19	methods	method	NOUN
esrj-91195	34	20	in	in	ADP
esrj-91195	34	21	calculations	calculation	NOUN
esrj-91195	34	22	for	for	ADP
esrj-91195	34	23	strip	strip	NOUN
esrj-91195	34	24	projects	project	NOUN
esrj-91195	34	25	.	.	PUNCT
esrj-91195	35	1	they	they	PRON
esrj-91195	35	2	also	also	ADV
esrj-91195	35	3	commented	comment	VERB
esrj-91195	35	4	that	that	SCONJ
esrj-91195	35	5	if	if	SCONJ
esrj-91195	35	6	the	the	DET
esrj-91195	35	7	number	number	NOUN
esrj-91195	35	8	of	of	ADP
esrj-91195	35	9	reference	reference	NOUN
esrj-91195	35	10	points	point	NOUN
esrj-91195	35	11	had	have	AUX
esrj-91195	35	12	been	be	AUX
esrj-91195	35	13	increased	increase	VERB
esrj-91195	35	14	,	,	PUNCT
esrj-91195	35	15	the	the	DET
esrj-91195	35	16	results	result	NOUN
esrj-91195	35	17	might	might	AUX
esrj-91195	35	18	have	have	AUX
esrj-91195	35	19	been	be	AUX
esrj-91195	35	20	better	well	ADJ
esrj-91195	35	21	.	.	PUNCT
esrj-91195	36	1	karaaslan	karaaslan	PROPN
esrj-91195	36	2	et	et	PROPN
esrj-91195	36	3	al	al	PROPN
esrj-91195	36	4	.	.	PROPN
esrj-91195	37	1	(	(	PUNCT
esrj-91195	37	2	2016	2016	NUM
esrj-91195	37	3	)	)	PUNCT
esrj-91195	37	4	created	create	VERB
esrj-91195	37	5	a	a	DET
esrj-91195	37	6	local	local	ADJ
esrj-91195	37	7	geoid	geoid	ADJ
esrj-91195	37	8	model	model	NOUN
esrj-91195	37	9	in	in	ADP
esrj-91195	37	10	trabzon	trabzon	NOUN
esrj-91195	37	11	province	province	NOUN
esrj-91195	37	12	using	use	VERB
esrj-91195	37	13	geometric	geometric	ADJ
esrj-91195	37	14	methods	method	NOUN
esrj-91195	37	15	.	.	PUNCT
esrj-91195	38	1	polynomial	polynomial	ADJ
esrj-91195	38	2	surfaces	surface	NOUN
esrj-91195	38	3	were	be	AUX
esrj-91195	38	4	created	create	VERB
esrj-91195	38	5	using	use	VERB
esrj-91195	38	6	mq	mq	PROPN
esrj-91195	38	7	and	and	CCONJ
esrj-91195	38	8	weighted	weight	VERB
esrj-91195	38	9	average	average	ADJ
esrj-91195	38	10	(	(	PUNCT
esrj-91195	38	11	wa	wa	NOUN
esrj-91195	38	12	)	)	PUNCT
esrj-91195	38	13	interpolation	interpolation	NOUN
esrj-91195	38	14	methods	method	NOUN
esrj-91195	38	15	.	.	PUNCT
esrj-91195	39	1	in	in	ADP
esrj-91195	39	2	the	the	DET
esrj-91195	39	3	study	study	NOUN
esrj-91195	39	4	,	,	PUNCT
esrj-91195	39	5	a	a	DET
esrj-91195	39	6	600	600	NUM
esrj-91195	39	7	-	-	PUNCT
esrj-91195	39	8	point	point	NOUN
esrj-91195	39	9	dataset	dataset	NOUN
esrj-91195	39	10	was	be	AUX
esrj-91195	39	11	divided	divide	VERB
esrj-91195	39	12	into	into	ADP
esrj-91195	39	13	reference	reference	NOUN
esrj-91195	39	14	and	and	CCONJ
esrj-91195	39	15	test	test	NOUN
esrj-91195	39	16	data	datum	NOUN
esrj-91195	39	17	by	by	ADP
esrj-91195	39	18	looking	look	VERB
esrj-91195	39	19	at	at	ADP
esrj-91195	39	20	point	point	NOUN
esrj-91195	39	21	distributions	distribution	NOUN
esrj-91195	39	22	and	and	CCONJ
esrj-91195	39	23	orthometric	orthometric	ADJ
esrj-91195	39	24	height	height	NOUN
esrj-91195	39	25	values	value	NOUN
esrj-91195	39	26	.	.	PUNCT
esrj-91195	40	1	as	as	ADP
esrj-91195	40	2	a	a	DET
esrj-91195	40	3	result	result	NOUN
esrj-91195	40	4	of	of	ADP
esrj-91195	40	5	the	the	DET
esrj-91195	40	6	study	study	NOUN
esrj-91195	40	7	,	,	PUNCT
esrj-91195	40	8	the	the	DET
esrj-91195	40	9	best	good	ADJ
esrj-91195	40	10	geoid	geoid	ADJ
esrj-91195	40	11	model	model	NOUN
esrj-91195	40	12	was	be	AUX
esrj-91195	40	13	obtained	obtain	VERB
esrj-91195	40	14	with	with	ADP
esrj-91195	40	15	a	a	DET
esrj-91195	40	16	non	non	ADJ
esrj-91195	40	17	-	-	ADJ
esrj-91195	40	18	perpendicular	perpendicular	ADJ
esrj-91195	40	19	third	third	ADJ
esrj-91195	40	20	-	-	PUNCT
esrj-91195	40	21	degree	degree	NOUN
esrj-91195	40	22	polynomial	polynomial	ADJ
esrj-91195	40	23	surface	surface	NOUN
esrj-91195	40	24	.	.	PUNCT
esrj-91195	41	1	with	with	ADP
esrj-91195	41	2	the	the	DET
esrj-91195	41	3	development	development	NOUN
esrj-91195	41	4	of	of	ADP
esrj-91195	41	5	computer	computer	NOUN
esrj-91195	41	6	technology	technology	NOUN
esrj-91195	41	7	,	,	PUNCT
esrj-91195	41	8	studies	study	NOUN
esrj-91195	41	9	in	in	ADP
esrj-91195	41	10	the	the	DET
esrj-91195	41	11	field	field	NOUN
esrj-91195	41	12	of	of	ADP
esrj-91195	41	13	artificial	artificial	ADJ
esrj-91195	41	14	intelligence	intelligence	NOUN
esrj-91195	41	15	(	(	PUNCT
esrj-91195	41	16	ai	ai	AUX
esrj-91195	41	17	)	)	PUNCT
esrj-91195	41	18	have	have	AUX
esrj-91195	41	19	accelerated	accelerate	VERB
esrj-91195	41	20	and	and	CCONJ
esrj-91195	41	21	various	various	ADJ
esrj-91195	41	22	solution	solution	NOUN
esrj-91195	41	23	methods	method	NOUN
esrj-91195	41	24	have	have	AUX
esrj-91195	41	25	been	be	AUX
esrj-91195	41	26	developed	develop	VERB
esrj-91195	41	27	for	for	ADP
esrj-91195	41	28	different	different	ADJ
esrj-91195	41	29	problem	problem	NOUN
esrj-91195	41	30	types	type	NOUN
esrj-91195	41	31	.	.	PUNCT
esrj-91195	42	1	studies	study	NOUN
esrj-91195	42	2	have	have	AUX
esrj-91195	42	3	shown	show	VERB
esrj-91195	42	4	that	that	SCONJ
esrj-91195	42	5	application	application	NOUN
esrj-91195	42	6	of	of	ADP
esrj-91195	42	7	ai	ai	ADJ
esrj-91195	42	8	methods	method	NOUN
esrj-91195	42	9	in	in	ADP
esrj-91195	42	10	the	the	DET
esrj-91195	42	11	field	field	NOUN
esrj-91195	42	12	of	of	ADP
esrj-91195	42	13	geodesy	geodesy	PROPN
esrj-91195	42	14	has	have	AUX
esrj-91195	42	15	been	be	AUX
esrj-91195	42	16	increasing	increase	VERB
esrj-91195	42	17	in	in	ADP
esrj-91195	42	18	recent	recent	ADJ
esrj-91195	42	19	years	year	NOUN
esrj-91195	42	20	and	and	CCONJ
esrj-91195	42	21	that	that	SCONJ
esrj-91195	42	22	they	they	PRON
esrj-91195	42	23	have	have	AUX
esrj-91195	42	24	proven	prove	VERB
esrj-91195	42	25	to	to	PART
esrj-91195	42	26	be	be	AUX
esrj-91195	42	27	quite	quite	ADV
esrj-91195	42	28	useful	useful	ADJ
esrj-91195	42	29	to	to	ADP
esrj-91195	42	30	researchers	researcher	NOUN
esrj-91195	42	31	working	work	VERB
esrj-91195	42	32	in	in	ADP
esrj-91195	42	33	the	the	DET
esrj-91195	42	34	field	field	NOUN
esrj-91195	42	35	,	,	PUNCT
esrj-91195	42	36	especially	especially	ADV
esrj-91195	42	37	in	in	ADP
esrj-91195	42	38	the	the	DET
esrj-91195	42	39	formulating	formulating	NOUN
esrj-91195	42	40	of	of	ADP
esrj-91195	42	41	predictions	prediction	NOUN
esrj-91195	42	42	using	use	VERB
esrj-91195	42	43	fuzzy	fuzzy	ADJ
esrj-91195	42	44	logic	logic	NOUN
esrj-91195	42	45	as	as	ADP
esrj-91195	42	46	an	an	DET
esrj-91195	42	47	alternative	alternative	NOUN
esrj-91195	42	48	to	to	ADP
esrj-91195	42	49	classic	classic	ADJ
esrj-91195	42	50	methods	method	NOUN
esrj-91195	42	51	(	(	PUNCT
esrj-91195	42	52	yιlmaz	yιlmaz	PROPN
esrj-91195	42	53	&	&	CCONJ
esrj-91195	42	54	arslan	arslan	PROPN
esrj-91195	42	55	,	,	PUNCT
esrj-91195	42	56	2008	2008	NUM
esrj-91195	42	57	;	;	PUNCT
esrj-91195	42	58	yılmaz	yılmaz	PROPN
esrj-91195	42	59	,	,	PUNCT
esrj-91195	42	60	2010	2010	NUM
esrj-91195	42	61	;	;	PUNCT
esrj-91195	42	62	tusat	tusat	VERB
esrj-91195	42	63	,	,	PUNCT
esrj-91195	42	64	2011	2011	NUM
esrj-91195	42	65	;	;	PUNCT
esrj-91195	42	66	erol	erol	NOUN
esrj-91195	42	67	&	&	CCONJ
esrj-91195	42	68	erol	erol	PROPN
esrj-91195	42	69	,	,	PUNCT
esrj-91195	42	70	2012	2012	NUM
esrj-91195	42	71	;	;	PUNCT
esrj-91195	42	72	erol	erol	NOUN
esrj-91195	42	73	&	&	CCONJ
esrj-91195	42	74	erol	erol	PROPN
esrj-91195	42	75	,	,	PUNCT
esrj-91195	42	76	2013	2013	NUM
esrj-91195	42	77	)	)	PUNCT
esrj-91195	42	78	.	.	PUNCT
esrj-91195	43	1	one	one	NUM
esrj-91195	43	2	type	type	NOUN
esrj-91195	43	3	of	of	ADP
esrj-91195	43	4	ai	ai	NOUN
esrj-91195	43	5	technology	technology	NOUN
esrj-91195	43	6	is	be	AUX
esrj-91195	43	7	the	the	DET
esrj-91195	43	8	artificial	artificial	ADJ
esrj-91195	43	9	neural	neural	ADJ
esrj-91195	43	10	network	network	NOUN
esrj-91195	43	11	(	(	PUNCT
esrj-91195	43	12	ann	ann	PROPN
esrj-91195	43	13	)	)	PUNCT
esrj-91195	43	14	.	.	PUNCT
esrj-91195	44	1	the	the	DET
esrj-91195	44	2	ann	ann	PROPN
esrj-91195	44	3	produces	produce	VERB
esrj-91195	44	4	successful	successful	ADJ
esrj-91195	44	5	results	result	NOUN
esrj-91195	44	6	under	under	ADP
esrj-91195	44	7	conditions	condition	NOUN
esrj-91195	44	8	of	of	ADP
esrj-91195	44	9	multivariable	multivariable	ADJ
esrj-91195	44	10	and	and	CCONJ
esrj-91195	44	11	complex	complex	ADJ
esrj-91195	44	12	mutual	mutual	ADJ
esrj-91195	44	13	interaction	interaction	NOUN
esrj-91195	44	14	between	between	ADP
esrj-91195	44	15	variables	variable	NOUN
esrj-91195	44	16	or	or	CCONJ
esrj-91195	44	17	when	when	SCONJ
esrj-91195	44	18	there	there	PRON
esrj-91195	44	19	is	be	VERB
esrj-91195	44	20	no	no	DET
esrj-91195	44	21	single	single	ADJ
esrj-91195	44	22	solution	solution	NOUN
esrj-91195	44	23	set	set	VERB
esrj-91195	44	24	.	.	PUNCT
esrj-91195	45	1	with	with	ADP
esrj-91195	45	2	these	these	DET
esrj-91195	45	3	features	feature	NOUN
esrj-91195	45	4	,	,	PUNCT
esrj-91195	45	5	ann	ann	PROPN
esrj-91195	45	6	is	be	AUX
esrj-91195	45	7	seen	see	VERB
esrj-91195	45	8	as	as	ADP
esrj-91195	45	9	a	a	DET
esrj-91195	45	10	suitable	suitable	ADJ
esrj-91195	45	11	method	method	NOUN
esrj-91195	45	12	for	for	ADP
esrj-91195	45	13	geoid	geoid	ADJ
esrj-91195	45	14	undulation	undulation	NOUN
esrj-91195	45	15	determination	determination	NOUN
esrj-91195	45	16	.	.	PUNCT
esrj-91195	46	1	studies	study	NOUN
esrj-91195	46	2	have	have	AUX
esrj-91195	46	3	yielded	yield	VERB
esrj-91195	46	4	successful	successful	ADJ
esrj-91195	46	5	results	result	NOUN
esrj-91195	46	6	by	by	ADP
esrj-91195	46	7	using	use	VERB
esrj-91195	46	8	ann	ann	PROPN
esrj-91195	46	9	(	(	PUNCT
esrj-91195	46	10	stopar	stopar	NOUN
esrj-91195	46	11	et	et	PROPN
esrj-91195	46	12	al	al	PROPN
esrj-91195	46	13	.	.	PROPN
esrj-91195	46	14	,	,	PUNCT
esrj-91195	46	15	2006	2006	NUM
esrj-91195	46	16	;	;	PUNCT
esrj-91195	46	17	lin	lin	PROPN
esrj-91195	46	18	,	,	PUNCT
esrj-91195	46	19	2007	2007	NUM
esrj-91195	46	20	;	;	PUNCT
esrj-91195	46	21	akyilmaz	akyilmaz	PROPN
esrj-91195	46	22	et	et	PROPN
esrj-91195	46	23	al	al	PROPN
esrj-91195	46	24	.	.	PROPN
esrj-91195	46	25	,	,	PUNCT
esrj-91195	46	26	2009	2009	NUM
esrj-91195	46	27	;	;	PUNCT
esrj-91195	46	28	pikridas	pikridas	PROPN
esrj-91195	46	29	et	et	PROPN
esrj-91195	46	30	al	al	PROPN
esrj-91195	46	31	.	.	PROPN
esrj-91195	46	32	,	,	PUNCT
esrj-91195	46	33	2011	2011	NUM
esrj-91195	46	34	;	;	PUNCT
esrj-91195	46	35	veronez	veronez	NOUN
esrj-91195	46	36	et	et	PROPN
esrj-91195	46	37	al	al	PROPN
esrj-91195	46	38	.	.	PROPN
esrj-91195	46	39	,	,	PUNCT
esrj-91195	46	40	2011	2011	NUM
esrj-91195	46	41	;	;	PUNCT
esrj-91195	46	42	akcin	akcin	PROPN
esrj-91195	46	43	&	&	CCONJ
esrj-91195	46	44	celik	celik	PROPN
esrj-91195	46	45	,	,	PUNCT
esrj-91195	46	46	2013	2013	NUM
esrj-91195	46	47	;	;	PUNCT
esrj-91195	46	48	erol	erol	NOUN
esrj-91195	46	49	&	&	CCONJ
esrj-91195	46	50	erol	erol	PROPN
esrj-91195	46	51	,	,	PUNCT
esrj-91195	46	52	2013	2013	NUM
esrj-91195	46	53	;	;	PUNCT
esrj-91195	46	54	elshambaky	elshambaky	NOUN
esrj-91195	46	55	,	,	PUNCT
esrj-91195	46	56	2018	2018	NUM
esrj-91195	46	57	;	;	PUNCT
esrj-91195	46	58	kaloop	kaloop	NOUN
esrj-91195	46	59	et	et	PROPN
esrj-91195	46	60	al	al	PROPN
esrj-91195	46	61	.	.	PROPN
esrj-91195	46	62	,	,	PUNCT
esrj-91195	46	63	2018	2018	NUM
esrj-91195	46	64	;	;	PUNCT
esrj-91195	46	65	albayrak	albayrak	PROPN
esrj-91195	46	66	et	et	PROPN
esrj-91195	46	67	.	.	PUNCT
esrj-91195	47	1	al	al	PROPN
esrj-91195	47	2	,	,	PUNCT
esrj-91195	47	3	2020	2020	NUM
esrj-91195	47	4	;	;	PUNCT
esrj-91195	47	5	erol	erol	NOUN
esrj-91195	47	6	&	&	CCONJ
esrj-91195	47	7	erol	erol	NOUN
esrj-91195	47	8	,	,	PUNCT
esrj-91195	47	9	2021	2021	NUM
esrj-91195	47	10	)	)	PUNCT
esrj-91195	47	11	.	.	PUNCT
esrj-91195	48	1	for	for	ADP
esrj-91195	48	2	example	example	NOUN
esrj-91195	48	3	,	,	PUNCT
esrj-91195	48	4	seager	seager	PROPN
esrj-91195	48	5	et	et	PROPN
esrj-91195	48	6	al	al	PROPN
esrj-91195	48	7	.	.	PROPN
esrj-91195	49	1	(	(	PUNCT
esrj-91195	49	2	1999	1999	NUM
esrj-91195	49	3	)	)	PUNCT
esrj-91195	49	4	performed	perform	VERB
esrj-91195	49	5	local	local	ADJ
esrj-91195	49	6	geoid	geoid	NOUN
esrj-91195	49	7	modeling	modeling	NOUN
esrj-91195	49	8	using	use	VERB
esrj-91195	49	9	a	a	DET
esrj-91195	49	10	feedback	feedback	NOUN
esrj-91195	49	11	artificial	artificial	ADJ
esrj-91195	49	12	neural	neural	ADJ
esrj-91195	49	13	network	network	NOUN
esrj-91195	49	14	(	(	PUNCT
esrj-91195	49	15	fbann	fbann	PROPN
esrj-91195	49	16	)	)	PUNCT
esrj-91195	49	17	to	to	PART
esrj-91195	49	18	determine	determine	VERB
esrj-91195	49	19	the	the	DET
esrj-91195	49	20	geoid	geoid	NOUN
esrj-91195	49	21	.	.	PUNCT
esrj-91195	50	1	the	the	DET
esrj-91195	50	2	results	result	NOUN
esrj-91195	50	3	demonstrated	demonstrate	VERB
esrj-91195	50	4	that	that	SCONJ
esrj-91195	50	5	the	the	DET
esrj-91195	50	6	ann	ann	PROPN
esrj-91195	50	7	could	could	AUX
esrj-91195	50	8	be	be	AUX
esrj-91195	50	9	used	use	VERB
esrj-91195	50	10	as	as	ADP
esrj-91195	50	11	a	a	DET
esrj-91195	50	12	tool	tool	NOUN
esrj-91195	50	13	in	in	ADP
esrj-91195	50	14	geoid	geoid	ADJ
esrj-91195	50	15	determination	determination	NOUN
esrj-91195	50	16	and	and	CCONJ
esrj-91195	50	17	that	that	SCONJ
esrj-91195	50	18	it	it	PRON
esrj-91195	50	19	gave	give	VERB
esrj-91195	50	20	rapid	rapid	ADJ
esrj-91195	50	21	results	result	NOUN
esrj-91195	50	22	.	.	PUNCT
esrj-91195	51	1	kavzoglu	kavzoglu	PROPN
esrj-91195	51	2	&	&	CCONJ
esrj-91195	51	3	saka	saka	PROPN
esrj-91195	51	4	(	(	PUNCT
esrj-91195	51	5	2005	2005	NUM
esrj-91195	51	6	)	)	PUNCT
esrj-91195	51	7	performed	perform	VERB
esrj-91195	51	8	a	a	DET
esrj-91195	51	9	local	local	ADJ
esrj-91195	51	10	geoid	geoid	ADJ
esrj-91195	51	11	undulation	undulation	NOUN
esrj-91195	51	12	calculation	calculation	NOUN
esrj-91195	51	13	for	for	ADP
esrj-91195	51	14	istanbul	istanbul	PROPN
esrj-91195	51	15	using	use	VERB
esrj-91195	51	16	an	an	DET
esrj-91195	51	17	ann	ann	NOUN
esrj-91195	51	18	with	with	ADP
esrj-91195	51	19	gps	gps	PROPN
esrj-91195	51	20	/	/	SYM
esrj-91195	51	21	leveling	leveling	NOUN
esrj-91195	51	22	data	datum	NOUN
esrj-91195	51	23	.	.	PUNCT
esrj-91195	52	1	the	the	DET
esrj-91195	52	2	ann	ann	PROPN
esrj-91195	52	3	results	result	NOUN
esrj-91195	52	4	were	be	AUX
esrj-91195	52	5	compared	compare	VERB
esrj-91195	52	6	with	with	ADP
esrj-91195	52	7	the	the	DET
esrj-91195	52	8	polynomial	polynomial	ADJ
esrj-91195	52	9	and	and	CCONJ
esrj-91195	52	10	lsc	lsc	PROPN
esrj-91195	52	11	methods	method	NOUN
esrj-91195	52	12	.	.	PUNCT
esrj-91195	53	1	the	the	DET
esrj-91195	53	2	results	result	NOUN
esrj-91195	53	3	produced	produce	VERB
esrj-91195	53	4	by	by	ADP
esrj-91195	53	5	the	the	DET
esrj-91195	53	6	ann	ann	PROPN
esrj-91195	53	7	were	be	AUX
esrj-91195	53	8	just	just	ADV
esrj-91195	53	9	as	as	ADV
esrj-91195	53	10	accurate	accurate	ADJ
esrj-91195	53	11	as	as	ADP
esrj-91195	53	12	the	the	DET
esrj-91195	53	13	two	two	NUM
esrj-91195	53	14	classic	classic	ADJ
esrj-91195	53	15	methods	method	NOUN
esrj-91195	53	16	.	.	PUNCT
esrj-91195	54	1	cakir	cakir	PROPN
esrj-91195	54	2	&	&	CCONJ
esrj-91195	54	3	yilmaz	yilmaz	PROPN
esrj-91195	54	4	(	(	PUNCT
esrj-91195	54	5	2014	2014	NUM
esrj-91195	54	6	)	)	PUNCT
esrj-91195	54	7	determined	determine	VERB
esrj-91195	54	8	a	a	DET
esrj-91195	54	9	local	local	ADJ
esrj-91195	54	10	geoid	geoid	NOUN
esrj-91195	54	11	using	use	VERB
esrj-91195	54	12	polynomial	polynomial	ADJ
esrj-91195	54	13	,	,	PUNCT
esrj-91195	54	14	mq	mq	NOUN
esrj-91195	54	15	,	,	PUNCT
esrj-91195	54	16	radial	radial	ADJ
esrj-91195	54	17	basis	basis	NOUN
esrj-91195	54	18	function	function	NOUN
esrj-91195	54	19	(	(	PUNCT
esrj-91195	54	20	rbf	rbf	PROPN
esrj-91195	54	21	)	)	PUNCT
esrj-91195	54	22	,	,	PUNCT
esrj-91195	54	23	and	and	CCONJ
esrj-91195	54	24	multi	multi	ADJ
esrj-91195	54	25	-	-	ADJ
esrj-91195	54	26	layer	layer	ADJ
esrj-91195	54	27	perceptron	perceptron	PROPN
esrj-91195	54	28	neural	neural	ADJ
esrj-91195	54	29	network	network	NOUN
esrj-91195	54	30	(	(	PUNCT
esrj-91195	54	31	mlpnn	mlpnn	PROPN
esrj-91195	54	32	)	)	PUNCT
esrj-91195	54	33	methods	method	NOUN
esrj-91195	54	34	in	in	ADP
esrj-91195	54	35	kayseri	kayseri	PROPN
esrj-91195	54	36	province	province	PROPN
esrj-91195	54	37	and	and	CCONJ
esrj-91195	54	38	compared	compare	VERB
esrj-91195	54	39	their	their	PRON
esrj-91195	54	40	performances	performance	NOUN
esrj-91195	54	41	.	.	PUNCT
esrj-91195	55	1	compared	compare	VERB
esrj-91195	55	2	to	to	ADP
esrj-91195	55	3	the	the	DET
esrj-91195	55	4	rbf	rbf	PROPN
esrj-91195	55	5	,	,	PUNCT
esrj-91195	55	6	the	the	DET
esrj-91195	55	7	mq	mq	NOUN
esrj-91195	55	8	was	be	AUX
esrj-91195	55	9	more	more	ADV
esrj-91195	55	10	successful	successful	ADJ
esrj-91195	55	11	,	,	PUNCT
esrj-91195	55	12	whereas	whereas	SCONJ
esrj-91195	55	13	the	the	DET
esrj-91195	55	14	mlpnn	mlpnn	NOUN
esrj-91195	55	15	yielded	yield	VERB
esrj-91195	55	16	better	well	ADJ
esrj-91195	55	17	results	result	NOUN
esrj-91195	55	18	than	than	ADP
esrj-91195	55	19	all	all	DET
esrj-91195	55	20	the	the	DET
esrj-91195	55	21	other	other	ADJ
esrj-91195	55	22	methods	method	NOUN
esrj-91195	55	23	.	.	PUNCT
esrj-91195	56	1	in	in	ADP
esrj-91195	56	2	addition	addition	NOUN
esrj-91195	56	3	to	to	ADP
esrj-91195	56	4	the	the	DET
esrj-91195	56	5	methods	method	NOUN
esrj-91195	56	6	mentioned	mention	VERB
esrj-91195	56	7	above	above	ADV
esrj-91195	56	8	,	,	PUNCT
esrj-91195	56	9	regression	regression	NOUN
esrj-91195	56	10	methods	method	NOUN
esrj-91195	56	11	have	have	AUX
esrj-91195	56	12	also	also	ADV
esrj-91195	56	13	been	be	AUX
esrj-91195	56	14	used	use	VERB
esrj-91195	56	15	in	in	ADP
esrj-91195	56	16	geoid	geoid	ADJ
esrj-91195	56	17	undulation	undulation	NOUN
esrj-91195	56	18	determination	determination	NOUN
esrj-91195	56	19	(	(	PUNCT
esrj-91195	56	20	konakoglu	konakoglu	NOUN
esrj-91195	56	21	&	&	CCONJ
esrj-91195	56	22	akar	akar	PROPN
esrj-91195	56	23	,	,	PUNCT
esrj-91195	56	24	2021	2021	NUM
esrj-91195	56	25	)	)	PUNCT
esrj-91195	56	26	.	.	PUNCT
esrj-91195	57	1	for	for	ADP
esrj-91195	57	2	example	example	NOUN
esrj-91195	57	3	,	,	PUNCT
esrj-91195	57	4	kaloop	kaloop	NOUN
esrj-91195	57	5	et	et	PROPN
esrj-91195	57	6	al	al	PROPN
esrj-91195	57	7	.	.	PROPN
esrj-91195	57	8	(	(	PUNCT
esrj-91195	57	9	2020	2020	NUM
esrj-91195	57	10	)	)	PUNCT
esrj-91195	57	11	examined	examine	VERB
esrj-91195	57	12	the	the	DET
esrj-91195	57	13	usability	usability	NOUN
esrj-91195	57	14	of	of	ADP
esrj-91195	57	15	multivariate	multivariate	NOUN
esrj-91195	57	16	adaptive	adaptive	ADJ
esrj-91195	57	17	regression	regression	NOUN
esrj-91195	57	18	splines	spline	NOUN
esrj-91195	57	19	(	(	PUNCT
esrj-91195	57	20	mars	mar	NOUN
esrj-91195	57	21	)	)	PUNCT
esrj-91195	57	22	,	,	PUNCT
esrj-91195	57	23	gaussian	gaussian	ADJ
esrj-91195	57	24	process	process	NOUN
esrj-91195	57	25	regression	regression	NOUN
esrj-91195	57	26	(	(	PUNCT
esrj-91195	57	27	gpr	gpr	PROPN
esrj-91195	57	28	)	)	PUNCT
esrj-91195	57	29	,	,	PUNCT
esrj-91195	57	30	and	and	CCONJ
esrj-91195	57	31	kernel	kernel	PROPN
esrj-91195	57	32	ridge	ridge	PROPN
esrj-91195	57	33	regression	regression	PROPN
esrj-91195	57	34	(	(	PUNCT
esrj-91195	57	35	krr	krr	PROPN
esrj-91195	57	36	)	)	PUNCT
esrj-91195	57	37	methods	method	NOUN
esrj-91195	57	38	in	in	ADP
esrj-91195	57	39	geoid	geoid	ADJ
esrj-91195	57	40	undulation	undulation	NOUN
esrj-91195	57	41	modeling	modeling	NOUN
esrj-91195	57	42	using	use	VERB
esrj-91195	57	43	gps	gps	PROPN
esrj-91195	57	44	/	/	SYM
esrj-91195	57	45	leveling	leveling	NOUN
esrj-91195	57	46	observations	observation	NOUN
esrj-91195	57	47	.	.	PUNCT
esrj-91195	58	1	the	the	DET
esrj-91195	58	2	results	result	NOUN
esrj-91195	58	3	obtained	obtain	VERB
esrj-91195	58	4	with	with	ADP
esrj-91195	58	5	these	these	DET
esrj-91195	58	6	methods	method	NOUN
esrj-91195	58	7	were	be	AUX
esrj-91195	58	8	compared	compare	VERB
esrj-91195	58	9	with	with	ADP
esrj-91195	58	10	the	the	DET
esrj-91195	58	11	results	result	NOUN
esrj-91195	58	12	of	of	ADP
esrj-91195	58	13	the	the	DET
esrj-91195	58	14	ls	ls	ADJ
esrj-91195	58	15	-	-	PUNCT
esrj-91195	58	16	svm	svm	NOUN
esrj-91195	58	17	.	.	PUNCT
esrj-91195	59	1	according	accord	VERB
esrj-91195	59	2	to	to	ADP
esrj-91195	59	3	the	the	DET
esrj-91195	59	4	statistical	statistical	ADJ
esrj-91195	59	5	tests	test	NOUN
esrj-91195	59	6	,	,	PUNCT
esrj-91195	59	7	the	the	DET
esrj-91195	59	8	krr	krr	PROPN
esrj-91195	59	9	yielded	yield	VERB
esrj-91195	59	10	better	well	ADJ
esrj-91195	59	11	results	result	NOUN
esrj-91195	59	12	than	than	ADP
esrj-91195	59	13	the	the	DET
esrj-91195	59	14	other	other	ADJ
esrj-91195	59	15	methods	method	NOUN
esrj-91195	59	16	.	.	PUNCT
esrj-91195	60	1	one	one	NUM
esrj-91195	60	2	of	of	ADP
esrj-91195	60	3	the	the	DET
esrj-91195	60	4	factors	factor	NOUN
esrj-91195	60	5	affecting	affect	VERB
esrj-91195	60	6	the	the	DET
esrj-91195	60	7	accuracy	accuracy	NOUN
esrj-91195	60	8	of	of	ADP
esrj-91195	60	9	a	a	DET
esrj-91195	60	10	geoid	geoid	ADJ
esrj-91195	60	11	model	model	NOUN
esrj-91195	60	12	is	be	AUX
esrj-91195	60	13	how	how	SCONJ
esrj-91195	60	14	correctly	correctly	ADV
esrj-91195	60	15	it	it	PRON
esrj-91195	60	16	is	be	AUX
esrj-91195	60	17	represented	represent	VERB
esrj-91195	60	18	by	by	ADP
esrj-91195	60	19	the	the	DET
esrj-91195	60	20	selected	select	VERB
esrj-91195	60	21	training	training	NOUN
esrj-91195	60	22	set	set	NOUN
esrj-91195	60	23	used	use	VERB
esrj-91195	60	24	to	to	PART
esrj-91195	60	25	create	create	VERB
esrj-91195	60	26	it	it	PRON
esrj-91195	60	27	.	.	PUNCT
esrj-91195	61	1	the	the	DET
esrj-91195	61	2	geoid	geoid	ADJ
esrj-91195	61	3	undulation	undulation	NOUN
esrj-91195	61	4	determination	determination	NOUN
esrj-91195	61	5	studies	study	NOUN
esrj-91195	61	6	given	give	VERB
esrj-91195	61	7	above	above	ADP
esrj-91195	61	8	show	show	VERB
esrj-91195	61	9	that	that	SCONJ
esrj-91195	61	10	a	a	DET
esrj-91195	61	11	dataset	dataset	NOUN
esrj-91195	61	12	is	be	AUX
esrj-91195	61	13	divided	divide	VERB
esrj-91195	61	14	into	into	ADP
esrj-91195	61	15	two	two	NUM
esrj-91195	61	16	parts	part	NOUN
esrj-91195	61	17	as	as	ADP
esrj-91195	61	18	training	training	NOUN
esrj-91195	61	19	and	and	CCONJ
esrj-91195	61	20	testing	testing	NOUN
esrj-91195	61	21	data	datum	NOUN
esrj-91195	61	22	according	accord	VERB
esrj-91195	61	23	to	to	ADP
esrj-91195	61	24	the	the	DET
esrj-91195	61	25	spatial	spatial	ADJ
esrj-91195	61	26	homogeneity	homogeneity	NOUN
esrj-91195	61	27	distribution	distribution	NOUN
esrj-91195	61	28	criteria	criterion	NOUN
esrj-91195	61	29	.	.	PUNCT
esrj-91195	62	1	studies	study	NOUN
esrj-91195	62	2	are	be	AUX
esrj-91195	62	3	conducted	conduct	VERB
esrj-91195	62	4	in	in	ADP
esrj-91195	62	5	this	this	DET
esrj-91195	62	6	way	way	NOUN
esrj-91195	62	7	to	to	PART
esrj-91195	62	8	determine	determine	VERB
esrj-91195	62	9	the	the	DET
esrj-91195	62	10	performance	performance	NOUN
esrj-91195	62	11	of	of	ADP
esrj-91195	62	12	the	the	DET
esrj-91195	62	13	method	method	NOUN
esrj-91195	62	14	.	.	PUNCT
esrj-91195	63	1	the	the	DET
esrj-91195	63	2	model	model	NOUN
esrj-91195	63	3	is	be	AUX
esrj-91195	63	4	trained	train	VERB
esrj-91195	63	5	with	with	ADP
esrj-91195	63	6	the	the	DET
esrj-91195	63	7	training	training	NOUN
esrj-91195	63	8	dataset	dataset	NOUN
esrj-91195	63	9	before	before	SCONJ
esrj-91195	63	10	the	the	DET
esrj-91195	63	11	prediction	prediction	NOUN
esrj-91195	63	12	is	be	AUX
esrj-91195	63	13	made	make	VERB
esrj-91195	63	14	.	.	PUNCT
esrj-91195	64	1	the	the	DET
esrj-91195	64	2	correctness	correctness	NOUN
esrj-91195	64	3	of	of	ADP
esrj-91195	64	4	the	the	DET
esrj-91195	64	5	trained	train	VERB
esrj-91195	64	6	model	model	NOUN
esrj-91195	64	7	is	be	AUX
esrj-91195	64	8	statistically	statistically	ADV
esrj-91195	64	9	determined	determined	ADJ
esrj-91195	64	10	using	use	VERB
esrj-91195	64	11	the	the	DET
esrj-91195	64	12	testing	testing	NOUN
esrj-91195	64	13	dataset	dataset	NOUN
esrj-91195	64	14	.	.	PUNCT
esrj-91195	65	1	thus	thus	ADV
esrj-91195	65	2	,	,	PUNCT
esrj-91195	65	3	whether	whether	SCONJ
esrj-91195	65	4	or	or	CCONJ
esrj-91195	65	5	not	not	PART
esrj-91195	65	6	to	to	PART
esrj-91195	65	7	use	use	VERB
esrj-91195	65	8	the	the	DET
esrj-91195	65	9	established	establish	VERB
esrj-91195	65	10	model	model	NOUN
esrj-91195	65	11	can	can	AUX
esrj-91195	65	12	be	be	AUX
esrj-91195	65	13	decided	decide	VERB
esrj-91195	65	14	.	.	PUNCT
esrj-91195	66	1	in	in	ADP
esrj-91195	66	2	this	this	DET
esrj-91195	66	3	study	study	NOUN
esrj-91195	66	4	,	,	PUNCT
esrj-91195	66	5	the	the	DET
esrj-91195	66	6	k	k	ADJ
esrj-91195	66	7	-	-	ADJ
esrj-91195	66	8	fold	fold	ADJ
esrj-91195	66	9	cross	cross	NOUN
esrj-91195	66	10	validation	validation	NOUN
esrj-91195	66	11	was	be	AUX
esrj-91195	66	12	applied	apply	VERB
esrj-91195	66	13	for	for	ADP
esrj-91195	66	14	the	the	DET
esrj-91195	66	15	first	first	ADJ
esrj-91195	66	16	time	time	NOUN
esrj-91195	66	17	in	in	ADP
esrj-91195	66	18	a	a	DET
esrj-91195	66	19	local	local	ADJ
esrj-91195	66	20	geoid	geoid	ADJ
esrj-91195	66	21	determination	determination	NOUN
esrj-91195	66	22	study	study	NOUN
esrj-91195	66	23	,	,	PUNCT
esrj-91195	66	24	with	with	ADP
esrj-91195	66	25	the	the	DET
esrj-91195	66	26	aim	aim	NOUN
esrj-91195	66	27	of	of	ADP
esrj-91195	66	28	minimizing	minimize	VERB
esrj-91195	66	29	deviations	deviation	NOUN
esrj-91195	66	30	and	and	CCONJ
esrj-91195	66	31	errors	error	NOUN
esrj-91195	66	32	caused	cause	VERB
esrj-91195	66	33	by	by	ADP
esrj-91195	66	34	distribution	distribution	NOUN
esrj-91195	66	35	and	and	CCONJ
esrj-91195	66	36	division	division	NOUN
esrj-91195	66	37	.	.	PUNCT
esrj-91195	67	1	the	the	DET
esrj-91195	67	2	main	main	ADJ
esrj-91195	67	3	aim	aim	NOUN
esrj-91195	67	4	of	of	ADP
esrj-91195	67	5	this	this	DET
esrj-91195	67	6	study	study	NOUN
esrj-91195	67	7	was	be	AUX
esrj-91195	67	8	to	to	PART
esrj-91195	67	9	provide	provide	VERB
esrj-91195	67	10	a	a	DET
esrj-91195	67	11	comprehensive	comprehensive	ADJ
esrj-91195	67	12	comparative	comparative	ADJ
esrj-91195	67	13	analysis	analysis	NOUN
esrj-91195	67	14	of	of	ADP
esrj-91195	67	15	different	different	ADJ
esrj-91195	67	16	approaches	approach	NOUN
esrj-91195	67	17	for	for	ADP
esrj-91195	67	18	the	the	DET
esrj-91195	67	19	prediction	prediction	NOUN
esrj-91195	67	20	of	of	ADP
esrj-91195	67	21	the	the	DET
esrj-91195	67	22	geoid	geoid	ADJ
esrj-91195	67	23	undulation	undulation	NOUN
esrj-91195	67	24	.	.	PUNCT
esrj-91195	68	1	the	the	DET
esrj-91195	68	2	methods	method	NOUN
esrj-91195	68	3	examined	examine	VERB
esrj-91195	68	4	consisted	consist	VERB
esrj-91195	68	5	of	of	ADP
esrj-91195	68	6	two	two	NUM
esrj-91195	68	7	soft	soft	ADJ
esrj-91195	68	8	computing	computing	NOUN
esrj-91195	68	9	techniques	technique	NOUN
esrj-91195	68	10	(	(	PUNCT
esrj-91195	68	11	rbfnn	rbfnn	NOUN
esrj-91195	68	12	and	and	CCONJ
esrj-91195	68	13	grnn	grnn	NOUN
esrj-91195	68	14	)	)	PUNCT
esrj-91195	68	15	and	and	CCONJ
esrj-91195	68	16	eleven	eleven	NUM
esrj-91195	68	17	conventional	conventional	ADJ
esrj-91195	68	18	methods	method	NOUN
esrj-91195	68	19	including	include	VERB
esrj-91195	68	20	:	:	PUNCT
esrj-91195	68	21	1	1	X
esrj-91195	68	22	)	)	PUNCT
esrj-91195	68	23	multiple	multiple	ADJ
esrj-91195	68	24	linear	linear	ADJ
esrj-91195	68	25	regression	regression	NOUN
esrj-91195	68	26	(	(	PUNCT
esrj-91195	68	27	mlr	mlr	NOUN
esrj-91195	68	28	)	)	PUNCT
esrj-91195	68	29	,	,	PUNCT
esrj-91195	68	30	2	2	X
esrj-91195	68	31	)	)	PUNCT
esrj-91195	68	32	kriging	kriging	NOUN
esrj-91195	68	33	(	(	PUNCT
esrj-91195	68	34	krig	krig	PROPN
esrj-91195	68	35	)	)	PUNCT
esrj-91195	68	36	,	,	PUNCT
esrj-91195	68	37	3	3	X
esrj-91195	68	38	)	)	PUNCT
esrj-91195	68	39	inverse	inverse	NOUN
esrj-91195	68	40	distance	distance	NOUN
esrj-91195	68	41	to	to	ADP
esrj-91195	68	42	a	a	DET
esrj-91195	68	43	power	power	NOUN
esrj-91195	68	44	(	(	PUNCT
esrj-91195	68	45	idp	idp	PROPN
esrj-91195	68	46	)	)	PUNCT
esrj-91195	68	47	,	,	PUNCT
esrj-91195	68	48	4	4	X
esrj-91195	68	49	)	)	PUNCT
esrj-91195	68	50	triangulation	triangulation	NOUN
esrj-91195	68	51	with	with	ADP
esrj-91195	68	52	linear	linear	ADJ
esrj-91195	68	53	interpolation	interpolation	NOUN
esrj-91195	68	54	(	(	PUNCT
esrj-91195	68	55	tli	tli	NOUN
esrj-91195	68	56	)	)	PUNCT
esrj-91195	68	57	,	,	PUNCT
esrj-91195	68	58	5	5	X
esrj-91195	68	59	)	)	PUNCT
esrj-91195	68	60	minimum	minimum	ADJ
esrj-91195	68	61	curvature	curvature	NOUN
esrj-91195	68	62	surface	surface	NOUN
esrj-91195	68	63	(	(	PUNCT
esrj-91195	68	64	mcs	mcs	PROPN
esrj-91195	68	65	)	)	PUNCT
esrj-91195	68	66	,	,	PUNCT
esrj-91195	68	67	6	6	X
esrj-91195	68	68	)	)	PUNCT
esrj-91195	68	69	natural	natural	ADJ
esrj-91195	68	70	neighbor	neighbor	NOUN
esrj-91195	68	71	(	(	PUNCT
esrj-91195	68	72	nn	nn	PROPN
esrj-91195	68	73	)	)	PUNCT
esrj-91195	68	74	,	,	PUNCT
esrj-91195	68	75	7	7	X
esrj-91195	68	76	)	)	PUNCT
esrj-91195	68	77	nearest	near	ADJ
esrj-91195	68	78	neighbor	neighbor	NOUN
esrj-91195	68	79	(	(	PUNCT
esrj-91195	68	80	nrn	nrn	PROPN
esrj-91195	68	81	)	)	PUNCT
esrj-91195	68	82	,	,	PUNCT
esrj-91195	68	83	8)	8)	NUM
esrj-91195	68	84	local	local	ADJ
esrj-91195	68	85	polynomial	polynomial	ADJ
esrj-91195	68	86	(	(	PUNCT
esrj-91195	68	87	lp	lp	NOUN
esrj-91195	68	88	)	)	PUNCT
esrj-91195	68	89	,	,	PUNCT
esrj-91195	68	90	9	9	X
esrj-91195	68	91	)	)	PUNCT
esrj-91195	68	92	radial	radial	ADJ
esrj-91195	68	93	basis	basis	NOUN
esrj-91195	68	94	function	function	NOUN
esrj-91195	68	95	(	(	PUNCT
esrj-91195	68	96	rbf	rbf	PROPN
esrj-91195	68	97	)	)	PUNCT
esrj-91195	68	98	,	,	PUNCT
esrj-91195	68	99	10	10	NUM
esrj-91195	68	100	)	)	PUNCT
esrj-91195	68	101	polynomial	polynomial	ADJ
esrj-91195	68	102	regression	regression	NOUN
esrj-91195	68	103	(	(	PUNCT
esrj-91195	68	104	pr	pr	NOUN
esrj-91195	68	105	)	)	PUNCT
esrj-91195	68	106	,	,	PUNCT
esrj-91195	68	107	and	and	CCONJ
esrj-91195	68	108	11	11	NUM
esrj-91195	68	109	)	)	PUNCT
esrj-91195	68	110	modified	modify	VERB
esrj-91195	68	111	shepard	shepard	NOUN
esrj-91195	68	112	’s	’s	PART
esrj-91195	68	113	(	(	PUNCT
esrj-91195	68	114	ms	ms	NOUN
esrj-91195	68	115	)	)	PUNCT
esrj-91195	68	116	.	.	PUNCT
esrj-91195	69	1	the	the	DET
esrj-91195	69	2	performance	performance	NOUN
esrj-91195	69	3	of	of	ADP
esrj-91195	69	4	the	the	DET
esrj-91195	69	5	methods	method	NOUN
esrj-91195	69	6	was	be	AUX
esrj-91195	69	7	evaluated	evaluate	VERB
esrj-91195	69	8	using	use	VERB
esrj-91195	69	9	the	the	DET
esrj-91195	69	10	root	root	NOUN
esrj-91195	69	11	mean	mean	NOUN
esrj-91195	69	12	squared	square	VERB
esrj-91195	69	13	error	error	NOUN
esrj-91195	69	14	(	(	PUNCT
esrj-91195	69	15	rmse	rmse	NOUN
esrj-91195	69	16	)	)	PUNCT
esrj-91195	69	17	,	,	PUNCT
esrj-91195	69	18	mean	mean	VERB
esrj-91195	69	19	absolute	absolute	ADJ
esrj-91195	69	20	error	error	NOUN
esrj-91195	69	21	(	(	PUNCT
esrj-91195	69	22	mae	mae	PROPN
esrj-91195	69	23	)	)	PUNCT
esrj-91195	69	24	,	,	PUNCT
esrj-91195	69	25	the	the	DET
esrj-91195	69	26	nash	nash	NOUN
esrj-91195	69	27	-	-	PUNCT
esrj-91195	69	28	sutcliffe	sutcliffe	PROPN
esrj-91195	69	29	efficiency	efficiency	NOUN
esrj-91195	69	30	(	(	PUNCT
esrj-91195	69	31	nse	nse	NOUN
esrj-91195	69	32	)	)	PUNCT
esrj-91195	69	33	coefficient	coefficient	NOUN
esrj-91195	69	34	,	,	PUNCT
esrj-91195	69	35	and	and	CCONJ
esrj-91195	69	36	the	the	DET
esrj-91195	69	37	coefficient	coefficient	NOUN
esrj-91195	69	38	of	of	ADP
esrj-91195	69	39	determination	determination	NOUN
esrj-91195	69	40	(	(	PUNCT
esrj-91195	69	41	r2	r2	PROPN
esrj-91195	69	42	)	)	PUNCT
esrj-91195	69	43	.	.	PUNCT
esrj-91195	70	1	this	this	PRON
esrj-91195	70	2	is	be	AUX
esrj-91195	70	3	the	the	DET
esrj-91195	70	4	first	first	ADJ
esrj-91195	70	5	study	study	NOUN
esrj-91195	70	6	to	to	PART
esrj-91195	70	7	provide	provide	VERB
esrj-91195	70	8	a	a	DET
esrj-91195	70	9	comprehensive	comprehensive	ADJ
esrj-91195	70	10	comparison	comparison	NOUN
esrj-91195	70	11	of	of	ADP
esrj-91195	70	12	thirteen	thirteen	NUM
esrj-91195	70	13	methods	method	NOUN
esrj-91195	70	14	used	use	VERB
esrj-91195	70	15	in	in	ADP
esrj-91195	70	16	the	the	DET
esrj-91195	70	17	prediction	prediction	NOUN
esrj-91195	70	18	of	of	ADP
esrj-91195	70	19	geoid	geoid	ADJ
esrj-91195	70	20	undulation	undulation	NOUN
esrj-91195	70	21	and	and	CCONJ
esrj-91195	70	22	it	it	PRON
esrj-91195	70	23	is	be	AUX
esrj-91195	70	24	expected	expect	VERB
esrj-91195	70	25	that	that	SCONJ
esrj-91195	70	26	scientists	scientist	NOUN
esrj-91195	70	27	interested	interested	ADJ
esrj-91195	70	28	in	in	ADP
esrj-91195	70	29	geoid	geoid	ADJ
esrj-91195	70	30	computations	computation	NOUN
esrj-91195	70	31	may	may	AUX
esrj-91195	70	32	benefit	benefit	VERB
esrj-91195	70	33	from	from	ADP
esrj-91195	70	34	its	its	PRON
esrj-91195	70	35	findings	finding	NOUN
esrj-91195	70	36	.	.	PUNCT
esrj-91195	71	1	material	material	NOUN
esrj-91195	71	2	and	and	CCONJ
esrj-91195	71	3	methods	method	NOUN
esrj-91195	71	4	study	study	NOUN
esrj-91195	71	5	area	area	NOUN
esrj-91195	72	1	and	and	CCONJ
esrj-91195	73	1	dataset	dataset	VERB
esrj-91195	73	2	the	the	DET
esrj-91195	73	3	study	study	NOUN
esrj-91195	73	4	area	area	NOUN
esrj-91195	73	5	of	of	ADP
esrj-91195	73	6	approximately	approximately	ADV
esrj-91195	73	7	4664	4664	NUM
esrj-91195	73	8	km2	km2	NOUN
esrj-91195	73	9	is	be	AUX
esrj-91195	73	10	between	between	ADP
esrj-91195	73	11	the	the	DET
esrj-91195	73	12	40	40	NUM
esrj-91195	73	13	°	°	NOUN
esrj-91195	73	14	30	30	NUM
esrj-91195	73	15	'	'	PUNCT
esrj-91195	73	16	and	and	CCONJ
esrj-91195	73	17	41	41	NUM
esrj-91195	73	18	°	°	NOUN
esrj-91195	73	19	0	0	NUM
esrj-91195	73	20	'	'	PART
esrj-91195	73	21	latitudes	latitude	NOUN
esrj-91195	73	22	and	and	CCONJ
esrj-91195	73	23	39	39	NUM
esrj-91195	73	24	°	°	NOUN
esrj-91195	73	25	0	0	NUM
esrj-91195	73	26	'	'	PUNCT
esrj-91195	73	27	and	and	CCONJ
esrj-91195	73	28	40	40	NUM
esrj-91195	73	29	°	°	NOUN
esrj-91195	73	30	30	30	NUM
esrj-91195	73	31	'	'	PART
esrj-91195	73	32	longitudes	longitude	NOUN
esrj-91195	73	33	within	within	ADP
esrj-91195	73	34	the	the	DET
esrj-91195	73	35	borders	border	NOUN
esrj-91195	73	36	of	of	ADP
esrj-91195	73	37	the	the	DET
esrj-91195	73	38	province	province	NOUN
esrj-91195	73	39	of	of	ADP
esrj-91195	73	40	trabzon	trabzon	PROPN
esrj-91195	73	41	.	.	PUNCT
esrj-91195	74	1	figure	figure	NOUN
esrj-91195	74	2	1	1	NUM
esrj-91195	74	3	shows	show	VERB
esrj-91195	74	4	the	the	DET
esrj-91195	74	5	location	location	NOUN
esrj-91195	74	6	of	of	ADP
esrj-91195	74	7	the	the	DET
esrj-91195	74	8	study	study	NOUN
esrj-91195	74	9	area	area	NOUN
esrj-91195	74	10	.	.	PUNCT
esrj-91195	75	1	the	the	DET
esrj-91195	75	2	topography	topography	NOUN
esrj-91195	75	3	is	be	AUX
esrj-91195	75	4	irregular	irregular	ADJ
esrj-91195	75	5	,	,	PUNCT
esrj-91195	75	6	with	with	ADP
esrj-91195	75	7	orthometric	orthometric	ADJ
esrj-91195	75	8	height	height	NOUN
esrj-91195	75	9	varying	vary	VERB
esrj-91195	75	10	between	between	ADP
esrj-91195	75	11	22.87	22.87	NUM
esrj-91195	75	12	and	and	CCONJ
esrj-91195	75	13	3387.14	3387.14	NUM
esrj-91195	75	14	m	m	NOUN
esrj-91195	75	15	and	and	CCONJ
esrj-91195	75	16	geoid	geoid	ADJ
esrj-91195	75	17	undulations	undulation	NOUN
esrj-91195	75	18	between	between	ADP
esrj-91195	75	19	24.6	24.6	NUM
esrj-91195	75	20	and	and	CCONJ
esrj-91195	75	21	30.7	30.7	NUM
esrj-91195	75	22	m.	m.	NOUN
esrj-91195	75	23	(	(	PUNCT
esrj-91195	75	24	karaaslan	karaaslan	NOUN
esrj-91195	75	25	et	et	PROPN
esrj-91195	75	26	al	al	PROPN
esrj-91195	75	27	.	.	PROPN
esrj-91195	76	1	(	(	PUNCT
esrj-91195	76	2	2016	2016	NUM
esrj-91195	76	3	)	)	PUNCT
esrj-91195	76	4	.	.	PUNCT
esrj-91195	77	1	for	for	ADP
esrj-91195	77	2	this	this	DET
esrj-91195	77	3	application	application	NOUN
esrj-91195	77	4	,	,	PUNCT
esrj-91195	77	5	537	537	NUM
esrj-91195	77	6	c2	c2	PROPN
esrj-91195	77	7	(	(	PUNCT
esrj-91195	77	8	second	second	ADJ
esrj-91195	77	9	order	order	NOUN
esrj-91195	77	10	densification	densification	NOUN
esrj-91195	77	11	)	)	PUNCT
esrj-91195	77	12	and	and	CCONJ
esrj-91195	77	13	c3	c3	PROPN
esrj-91195	77	14	(	(	PUNCT
esrj-91195	77	15	third	third	ADJ
esrj-91195	77	16	order	order	NOUN
esrj-91195	77	17	densification	densification	NOUN
esrj-91195	77	18	)	)	PUNCT
esrj-91195	77	19	points	point	NOUN
esrj-91195	77	20	covering	cover	VERB
esrj-91195	77	21	the	the	DET
esrj-91195	77	22	entire	entire	ADJ
esrj-91195	77	23	area	area	NOUN
esrj-91195	77	24	of	of	ADP
esrj-91195	77	25	trabzon	trabzon	NOUN
esrj-91195	77	26	obtained	obtain	VERB
esrj-91195	77	27	from	from	ADP
esrj-91195	77	28	the	the	DET
esrj-91195	77	29	trabzon	trabzon	NOUN
esrj-91195	77	30	ix	ix	ADP
esrj-91195	77	31	regional	regional	ADJ
esrj-91195	77	32	directorate	directorate	NOUN
esrj-91195	77	33	of	of	ADP
esrj-91195	77	34	the	the	DET
esrj-91195	77	35	turkish	turkish	ADJ
esrj-91195	77	36	land	land	NOUN
esrj-91195	77	37	registry	registry	NOUN
esrj-91195	77	38	and	and	CCONJ
esrj-91195	77	39	cadastre	cadastre	NOUN
esrj-91195	77	40	were	be	AUX
esrj-91195	77	41	used	use	VERB
esrj-91195	77	42	.	.	PUNCT
esrj-91195	78	1	the	the	DET
esrj-91195	78	2	latitude	latitude	NOUN
esrj-91195	78	3	(	(	PUNCT
esrj-91195	78	4	j	j	NOUN
esrj-91195	78	5	)	)	PUNCT
esrj-91195	78	6	,	,	PUNCT
esrj-91195	78	7	longitude	longitude	NOUN
esrj-91195	78	8	(	(	PUNCT
esrj-91195	78	9	l	l	NOUN
esrj-91195	78	10	)	)	PUNCT
esrj-91195	78	11	,	,	PUNCT
esrj-91195	78	12	ellipsoidal	ellipsoidal	ADJ
esrj-91195	78	13	height	height	NOUN
esrj-91195	78	14	(	(	PUNCT
esrj-91195	78	15	h	h	NOUN
esrj-91195	78	16	)	)	PUNCT
esrj-91195	78	17	and	and	CCONJ
esrj-91195	78	18	orthometric	orthometric	ADJ
esrj-91195	78	19	height	height	NOUN
esrj-91195	78	20	(	(	PUNCT
esrj-91195	78	21	h	h	NOUN
esrj-91195	78	22	)	)	PUNCT
esrj-91195	78	23	values	value	NOUN
esrj-91195	78	24	of	of	ADP
esrj-91195	78	25	each	each	DET
esrj-91195	78	26	point	point	NOUN
esrj-91195	78	27	are	be	AUX
esrj-91195	78	28	known	know	VERB
esrj-91195	78	29	.	.	PUNCT
esrj-91195	79	1	the	the	DET
esrj-91195	79	2	orthometric	orthometric	ADJ
esrj-91195	79	3	heights	height	NOUN
esrj-91195	79	4	(	(	PUNCT
esrj-91195	79	5	h	h	NOUN
esrj-91195	79	6	)	)	PUNCT
esrj-91195	79	7	of	of	ADP
esrj-91195	79	8	the	the	DET
esrj-91195	79	9	points	point	NOUN
esrj-91195	79	10	were	be	AUX
esrj-91195	79	11	obtained	obtain	VERB
esrj-91195	79	12	via	via	ADP
esrj-91195	79	13	the	the	DET
esrj-91195	79	14	geometric	geometric	ADJ
esrj-91195	79	15	leveling	leveling	NOUN
esrj-91195	79	16	method	method	NOUN
esrj-91195	79	17	and	and	CCONJ
esrj-91195	79	18	the	the	DET
esrj-91195	79	19	ellipsoidal	ellipsoidal	ADJ
esrj-91195	79	20	heights	height	NOUN
esrj-91195	79	21	(	(	PUNCT
esrj-91195	79	22	h	h	NOUN
esrj-91195	79	23	)	)	PUNCT
esrj-91195	79	24	via	via	ADP
esrj-91195	79	25	gps	gps	PROPN
esrj-91195	79	26	/	/	SYM
esrj-91195	79	27	gnss	gnss	NOUN
esrj-91195	79	28	static	static	ADJ
esrj-91195	79	29	measurement	measurement	NOUN
esrj-91195	79	30	.	.	PUNCT
esrj-91195	80	1	the	the	DET
esrj-91195	80	2	spatial	spatial	ADJ
esrj-91195	80	3	density	density	NOUN
esrj-91195	80	4	of	of	ADP
esrj-91195	80	5	the	the	DET
esrj-91195	80	6	points	point	NOUN
esrj-91195	80	7	(	(	PUNCT
esrj-91195	80	8	1	1	NUM
esrj-91195	80	9	point	point	NOUN
esrj-91195	80	10	per	per	ADP
esrj-91195	80	11	9	9	NUM
esrj-91195	80	12	km2	km2	NOUN
esrj-91195	80	13	)	)	PUNCT
esrj-91195	80	14	demonstrated	demonstrate	VERB
esrj-91195	80	15	a	a	DET
esrj-91195	80	16	good	good	ADJ
esrj-91195	80	17	characterization	characterization	NOUN
esrj-91195	80	18	of	of	ADP
esrj-91195	80	19	the	the	DET
esrj-91195	80	20	topography	topography	NOUN
esrj-91195	80	21	.	.	PUNCT
esrj-91195	81	1	the	the	DET
esrj-91195	81	2	input	input	NOUN
esrj-91195	81	3	parameters	parameter	NOUN
esrj-91195	81	4	,	,	PUNCT
esrj-91195	81	5	comprised	comprise	VERB
esrj-91195	81	6	of	of	ADP
esrj-91195	81	7	the	the	DET
esrj-91195	81	8	latitude	latitude	NOUN
esrj-91195	81	9	(	(	PUNCT
esrj-91195	81	10	j	j	NOUN
esrj-91195	81	11	)	)	PUNCT
esrj-91195	81	12	and	and	CCONJ
esrj-91195	81	13	longitude	longitude	NOUN
esrj-91195	81	14	(	(	PUNCT
esrj-91195	81	15	l	l	NOUN
esrj-91195	81	16	)	)	PUNCT
esrj-91195	81	17	values	value	NOUN
esrj-91195	81	18	,	,	PUNCT
esrj-91195	81	19	were	be	AUX
esrj-91195	81	20	used	use	VERB
esrj-91195	81	21	to	to	PART
esrj-91195	81	22	develop	develop	VERB
esrj-91195	81	23	the	the	DET
esrj-91195	81	24	ann	ann	PROPN
esrj-91195	81	25	models	model	NOUN
esrj-91195	81	26	.	.	PUNCT
esrj-91195	82	1	the	the	DET
esrj-91195	82	2	geoid	geoid	ADJ
esrj-91195	82	3	undulation	undulation	NOUN
esrj-91195	82	4	value	value	NOUN
esrj-91195	82	5	(	(	PUNCT
esrj-91195	82	6	n	n	CCONJ
esrj-91195	82	7	)	)	PUNCT
esrj-91195	82	8	served	serve	VERB
esrj-91195	82	9	as	as	ADP
esrj-91195	82	10	the	the	DET
esrj-91195	82	11	output	output	NOUN
esrj-91195	82	12	parameter	parameter	NOUN
esrj-91195	82	13	.	.	PUNCT
esrj-91195	83	1	the	the	DET
esrj-91195	83	2	k	k	ADJ
esrj-91195	83	3	-	-	ADJ
esrj-91195	83	4	fold	fold	ADJ
esrj-91195	83	5	cross	cross	NOUN
esrj-91195	83	6	validation	validation	NOUN
esrj-91195	83	7	method	method	NOUN
esrj-91195	83	8	was	be	AUX
esrj-91195	83	9	used	use	VERB
esrj-91195	83	10	to	to	PART
esrj-91195	83	11	balance	balance	VERB
esrj-91195	83	12	the	the	DET
esrj-91195	83	13	features	feature	NOUN
esrj-91195	83	14	in	in	ADP
esrj-91195	83	15	the	the	DET
esrj-91195	83	16	dataset	dataset	NOUN
esrj-91195	83	17	used	use	VERB
esrj-91195	83	18	in	in	ADP
esrj-91195	83	19	the	the	DET
esrj-91195	83	20	study	study	NOUN
esrj-91195	83	21	and	and	CCONJ
esrj-91195	83	22	to	to	PART
esrj-91195	83	23	accurately	accurately	ADV
esrj-91195	83	24	measure	measure	VERB
esrj-91195	83	25	the	the	DET
esrj-91195	83	26	performance	performance	NOUN
esrj-91195	83	27	of	of	ADP
esrj-91195	83	28	the	the	DET
esrj-91195	83	29	method	method	NOUN
esrj-91195	83	30	in	in	ADP
esrj-91195	83	31	the	the	DET
esrj-91195	83	32	unbalanced	unbalanced	ADJ
esrj-91195	83	33	datasets	dataset	NOUN
esrj-91195	83	34	.	.	PUNCT
esrj-91195	84	1	the	the	DET
esrj-91195	84	2	value	value	NOUN
esrj-91195	84	3	of	of	ADP
esrj-91195	84	4	the	the	DET
esrj-91195	84	5	k	k	PROPN
esrj-91195	84	6	parameter	parameter	NOUN
esrj-91195	84	7	required	require	VERB
esrj-91195	84	8	for	for	ADP
esrj-91195	84	9	cross	cross	ADJ
esrj-91195	84	10	-	-	ADJ
esrj-91195	84	11	correcting	correcting	ADJ
esrj-91195	84	12	was	be	AUX
esrj-91195	84	13	determined	determine	VERB
esrj-91195	84	14	as	as	ADP
esrj-91195	84	15	5	5	NUM
esrj-91195	84	16	.	.	PUNCT
esrj-91195	85	1	thus	thus	ADV
esrj-91195	85	2	,	,	PUNCT
esrj-91195	85	3	the	the	DET
esrj-91195	85	4	dataset	dataset	NOUN
esrj-91195	85	5	was	be	AUX
esrj-91195	85	6	divided	divide	VERB
esrj-91195	85	7	into	into	ADP
esrj-91195	85	8	five	five	NUM
esrj-91195	85	9	parts	part	NOUN
esrj-91195	85	10	,	,	PUNCT
esrj-91195	85	11	with	with	ADP
esrj-91195	85	12	four	four	NUM
esrj-91195	85	13	used	use	VERB
esrj-91195	85	14	for	for	ADP
esrj-91195	85	15	training	training	NOUN
esrj-91195	85	16	and	and	CCONJ
esrj-91195	85	17	the	the	DET
esrj-91195	85	18	other	other	ADJ
esrj-91195	85	19	one	one	NOUN
esrj-91195	85	20	used	use	VERB
esrj-91195	85	21	to	to	PART
esrj-91195	85	22	test	test	VERB
esrj-91195	85	23	the	the	DET
esrj-91195	85	24	algorithm	algorithm	NOUN
esrj-91195	85	25	.	.	PUNCT
esrj-91195	86	1	to	to	PART
esrj-91195	86	2	perform	perform	VERB
esrj-91195	86	3	the	the	DET
esrj-91195	86	4	cross	cross	NOUN
esrj-91195	86	5	-	-	NOUN
esrj-91195	86	6	validation	validation	NOUN
esrj-91195	86	7	,	,	PUNCT
esrj-91195	86	8	the	the	DET
esrj-91195	86	9	dataset	dataset	NOUN
esrj-91195	86	10	was	be	AUX
esrj-91195	86	11	divided	divide	VERB
esrj-91195	86	12	into	into	ADP
esrj-91195	86	13	approximately	approximately	ADV
esrj-91195	86	14	80	80	NUM
esrj-91195	86	15	%	%	NOUN
esrj-91195	86	16	for	for	ADP
esrj-91195	86	17	training	training	NOUN
esrj-91195	86	18	sets	set	NOUN
esrj-91195	86	19	(	(	PUNCT
esrj-91195	86	20	reference	reference	NOUN
esrj-91195	86	21	points	point	NOUN
esrj-91195	86	22	)	)	PUNCT
esrj-91195	86	23	and	and	CCONJ
esrj-91195	86	24	20	20	NUM
esrj-91195	86	25	%	%	NOUN
esrj-91195	86	26	for	for	ADP
esrj-91195	86	27	testing	testing	NOUN
esrj-91195	86	28	sets	set	NOUN
esrj-91195	86	29	(	(	PUNCT
esrj-91195	86	30	test	test	NOUN
esrj-91195	86	31	points	point	NOUN
esrj-91195	86	32	)	)	PUNCT
esrj-91195	86	33	.	.	PUNCT
esrj-91195	87	1	table	table	NOUN
esrj-91195	87	2	1	1	NUM
esrj-91195	87	3	indicates	indicate	VERB
esrj-91195	87	4	the	the	DET
esrj-91195	87	5	statistical	statistical	ADJ
esrj-91195	87	6	properties	property	NOUN
esrj-91195	87	7	of	of	ADP
esrj-91195	87	8	the	the	DET
esrj-91195	87	9	geodetic	geodetic	ADJ
esrj-91195	87	10	data	datum	NOUN
esrj-91195	87	11	used	use	VERB
esrj-91195	87	12	in	in	ADP
esrj-91195	87	13	this	this	DET
esrj-91195	87	14	study	study	NOUN
esrj-91195	87	15	,	,	PUNCT
esrj-91195	87	16	including	include	VERB
esrj-91195	87	17	the	the	DET
esrj-91195	87	18	mean	mean	ADJ
esrj-91195	87	19	,	,	PUNCT
esrj-91195	87	20	minimum	minimum	ADJ
esrj-91195	87	21	,	,	PUNCT
esrj-91195	87	22	maximum	maximum	ADJ
esrj-91195	87	23	,	,	PUNCT
esrj-91195	87	24	standard	standard	ADJ
esrj-91195	87	25	deviation	deviation	NOUN
esrj-91195	87	26	,	,	PUNCT
esrj-91195	87	27	and	and	CCONJ
esrj-91195	87	28	skewness	skewness	NOUN
esrj-91195	87	29	values	value	NOUN
esrj-91195	87	30	of	of	ADP
esrj-91195	87	31	these	these	DET
esrj-91195	87	32	five	five	NUM
esrj-91195	87	33	different	different	ADJ
esrj-91195	87	34	datasets	dataset	NOUN
esrj-91195	87	35	(	(	PUNCT
esrj-91195	87	36	ds#1	ds#1	PROPN
esrj-91195	87	37	,	,	PUNCT
esrj-91195	87	38	ds#2	ds#2	PRON
esrj-91195	87	39	,	,	PUNCT
esrj-91195	87	40	ds#3	ds#3	X
esrj-91195	87	41	,	,	PUNCT
esrj-91195	87	42	ds#4	ds#4	PROPN
esrj-91195	87	43	,	,	PUNCT
esrj-91195	87	44	and	and	CCONJ
esrj-91195	87	45	ds#5	ds#5	PROPN
esrj-91195	87	46	)	)	PUNCT
esrj-91195	87	47	.	.	PUNCT
esrj-91195	88	1	the	the	DET
esrj-91195	88	2	corresponding	corresponding	ADJ
esrj-91195	88	3	average	average	ADJ
esrj-91195	88	4	latitude	latitude	NOUN
esrj-91195	88	5	and	and	CCONJ
esrj-91195	88	6	longitude	longitude	ADJ
esrj-91195	88	7	values	value	NOUN
esrj-91195	88	8	were	be	AUX
esrj-91195	88	9	determined	determine	VERB
esrj-91195	88	10	as	as	ADP
esrj-91195	88	11	40.73950	40.73950	NUM
esrj-91195	88	12	°	°	NOUN
esrj-91195	88	13	(	(	PUNCT
esrj-91195	88	14	40.47765	40.47765	NUM
esrj-91195	88	15	°	°	NOUN
esrj-91195	88	16	40.97722	40.97722	NUM
esrj-91195	88	17	°	°	NUM
esrj-91195	88	18	)	)	PUNCT
esrj-91195	88	19	and	and	CCONJ
esrj-91195	88	20	40.06419	40.06419	NUM
esrj-91195	88	21	°	°	NOUN
esrj-91195	88	22	(	(	PUNCT
esrj-91195	88	23	39.30994	39.30994	NUM
esrj-91195	88	24	°	°	NOUN
esrj-91195	88	25	40.49945	40.49945	NUM
esrj-91195	88	26	°	°	NUM
esrj-91195	88	27	)	)	PUNCT
esrj-91195	88	28	.	.	PUNCT
esrj-91195	89	1	negative	negative	ADJ
esrj-91195	89	2	distortion	distortion	NOUN
esrj-91195	89	3	was	be	AUX
esrj-91195	89	4	determined	determine	VERB
esrj-91195	89	5	in	in	ADP
esrj-91195	89	6	the	the	DET
esrj-91195	89	7	latitude	latitude	NOUN
esrj-91195	89	8	,	,	PUNCT
esrj-91195	89	9	longitude	longitude	NOUN
esrj-91195	89	10	,	,	PUNCT
esrj-91195	89	11	and	and	CCONJ
esrj-91195	89	12	geoid	geoid	ADJ
esrj-91195	89	13	undulation	undulation	NOUN
esrj-91195	89	14	dataset	dataset	VERB
esrj-91195	89	15	values	value	NOUN
esrj-91195	89	16	.	.	PUNCT
esrj-91195	90	1	the	the	DET
esrj-91195	90	2	distribution	distribution	NOUN
esrj-91195	90	3	of	of	ADP
esrj-91195	90	4	negative	negative	ADJ
esrj-91195	90	5	distortion	distortion	NOUN
esrj-91195	90	6	was	be	AUX
esrj-91195	90	7	indicated	indicate	VERB
esrj-91195	90	8	with	with	ADP
esrj-91195	90	9	an	an	DET
esrj-91195	90	10	asymmetrical	asymmetrical	ADJ
esrj-91195	90	11	tail	tail	NOUN
esrj-91195	90	12	extending	extend	VERB
esrj-91195	90	13	toward	toward	ADP
esrj-91195	90	14	the	the	DET
esrj-91195	90	15	more	more	ADV
esrj-91195	90	16	negative	negative	ADJ
esrj-91195	90	17	(	(	PUNCT
esrj-91195	90	18	lower	low	ADJ
esrj-91195	90	19	than	than	ADP
esrj-91195	90	20	373geoid	373geoid	PROPN
esrj-91195	90	21	undulation	undulation	NOUN
esrj-91195	90	22	prediction	prediction	NOUN
esrj-91195	90	23	using	use	VERB
esrj-91195	90	24	anns	ann	NOUN
esrj-91195	90	25	(	(	PUNCT
esrj-91195	90	26	rbfnn	rbfnn	NOUN
esrj-91195	90	27	and	and	CCONJ
esrj-91195	90	28	grnn	grnn	NOUN
esrj-91195	90	29	)	)	PUNCT
esrj-91195	90	30	,	,	PUNCT
esrj-91195	90	31	multiple	multiple	ADJ
esrj-91195	90	32	linear	linear	ADJ
esrj-91195	90	33	regression	regression	NOUN
esrj-91195	90	34	(	(	PUNCT
esrj-91195	90	35	mlr	mlr	NOUN
esrj-91195	90	36	)	)	PUNCT
esrj-91195	90	37	,	,	PUNCT
esrj-91195	90	38	and	and	CCONJ
esrj-91195	90	39	interpolation	interpolation	NOUN
esrj-91195	90	40	methods	method	NOUN
esrj-91195	90	41	:	:	PUNCT
esrj-91195	90	42	a	a	DET
esrj-91195	90	43	comparative	comparative	ADJ
esrj-91195	90	44	study	study	NOUN
esrj-91195	90	45	average	average	ADJ
esrj-91195	90	46	)	)	PUNCT
esrj-91195	90	47	values	value	NOUN
esrj-91195	90	48	.	.	PUNCT
esrj-91195	91	1	this	this	PRON
esrj-91195	91	2	showed	show	VERB
esrj-91195	91	3	that	that	SCONJ
esrj-91195	91	4	the	the	DET
esrj-91195	91	5	skewness	skewness	NOUN
esrj-91195	91	6	values	value	NOUN
esrj-91195	91	7	were	be	AUX
esrj-91195	91	8	generally	generally	ADV
esrj-91195	91	9	close	close	ADJ
esrj-91195	91	10	to	to	ADP
esrj-91195	91	11	zero	zero	NUM
esrj-91195	91	12	,	,	PUNCT
esrj-91195	91	13	and	and	CCONJ
esrj-91195	91	14	in	in	ADP
esrj-91195	91	15	this	this	DET
esrj-91195	91	16	case	case	NOUN
esrj-91195	91	17	,	,	PUNCT
esrj-91195	91	18	that	that	SCONJ
esrj-91195	91	19	the	the	DET
esrj-91195	91	20	data	datum	NOUN
esrj-91195	91	21	were	be	AUX
esrj-91195	91	22	in	in	ADP
esrj-91195	91	23	normal	normal	ADJ
esrj-91195	91	24	distribution	distribution	NOUN
esrj-91195	91	25	.	.	PUNCT
esrj-91195	92	1	the	the	DET
esrj-91195	92	2	statistical	statistical	ADJ
esrj-91195	92	3	parameters	parameter	NOUN
esrj-91195	92	4	given	give	VERB
esrj-91195	92	5	in	in	ADP
esrj-91195	92	6	table	table	NOUN
esrj-91195	92	7	1	1	NUM
esrj-91195	92	8	show	show	VERB
esrj-91195	92	9	that	that	SCONJ
esrj-91195	92	10	there	there	PRON
esrj-91195	92	11	were	be	VERB
esrj-91195	92	12	no	no	DET
esrj-91195	92	13	significant	significant	ADJ
esrj-91195	92	14	differences	difference	NOUN
esrj-91195	92	15	among	among	ADP
esrj-91195	92	16	the	the	DET
esrj-91195	92	17	datasets	dataset	NOUN
esrj-91195	92	18	created	create	VERB
esrj-91195	92	19	.	.	PUNCT
esrj-91195	93	1	the	the	DET
esrj-91195	93	2	geographical	geographical	ADJ
esrj-91195	93	3	distribution	distribution	NOUN
esrj-91195	93	4	of	of	ADP
esrj-91195	93	5	the	the	DET
esrj-91195	93	6	five	five	NUM
esrj-91195	93	7	different	different	ADJ
esrj-91195	93	8	datasets	dataset	NOUN
esrj-91195	93	9	(	(	PUNCT
esrj-91195	93	10	ds#1	ds#1	PROPN
esrj-91195	93	11	,	,	PUNCT
esrj-91195	93	12	ds#2	ds#2	PRON
esrj-91195	93	13	,	,	PUNCT
esrj-91195	93	14	ds#3	ds#3	X
esrj-91195	93	15	,	,	PUNCT
esrj-91195	93	16	ds#4	ds#4	PROPN
esrj-91195	93	17	and	and	CCONJ
esrj-91195	93	18	ds#5	ds#5	PROPN
esrj-91195	93	19	)	)	PUNCT
esrj-91195	93	20	is	be	AUX
esrj-91195	93	21	shown	show	VERB
esrj-91195	93	22	in	in	ADP
esrj-91195	93	23	figure	figure	NOUN
esrj-91195	93	24	2	2	NUM
esrj-91195	93	25	.	.	PUNCT
esrj-91195	93	26	table	table	NOUN
esrj-91195	93	27	1	1	NUM
esrj-91195	93	28	.	.	PUNCT
esrj-91195	93	29	statistical	statistical	ADJ
esrj-91195	93	30	parameters	parameter	NOUN
esrj-91195	93	31	of	of	ADP
esrj-91195	93	32	the	the	DET
esrj-91195	93	33	geodetic	geodetic	ADJ
esrj-91195	93	34	data	datum	NOUN
esrj-91195	93	35	(	(	PUNCT
esrj-91195	93	36	latitude	latitude	NOUN
esrj-91195	93	37	,	,	PUNCT
esrj-91195	93	38	longitude	longitude	NOUN
esrj-91195	93	39	,	,	PUNCT
esrj-91195	93	40	and	and	CCONJ
esrj-91195	93	41	geoid	geoid	ADJ
esrj-91195	93	42	undulation	undulation	NOUN
esrj-91195	93	43	)	)	PUNCT
esrj-91195	93	44	used	use	VERB
esrj-91195	93	45	in	in	ADP
esrj-91195	93	46	the	the	DET
esrj-91195	93	47	study	study	NOUN
esrj-91195	93	48	dataset	dataset	NOUN
esrj-91195	93	49	phase	phase	NOUN
esrj-91195	93	50	statistical	statistical	ADJ
esrj-91195	93	51	characteristics	characteristic	NOUN
esrj-91195	93	52	j	j	PROPN
esrj-91195	93	53	(	(	PUNCT
esrj-91195	93	54	°	°	ADP
esrj-91195	93	55	)	)	PUNCT
esrj-91195	93	56	l	l	NOUN
esrj-91195	93	57	(	(	PUNCT
esrj-91195	93	58	°	°	NOUN
esrj-91195	93	59	)	)	PUNCT
esrj-91195	93	60	n	n	CCONJ
esrj-91195	93	61	(	(	PUNCT
esrj-91195	93	62	m	m	PROPN
esrj-91195	93	63	)	)	PUNCT
esrj-91195	94	1	ds#1	ds#1	PROPN
esrj-91195	94	2	training	training	NOUN
esrj-91195	94	3	mean	mean	VERB
esrj-91195	94	4	40.739	40.739	NUM
esrj-91195	95	1	40.059	40.059	NUM
esrj-91195	95	2	28.418	28.418	NUM
esrj-91195	95	3	minimum	minimum	ADJ
esrj-91195	95	4	40.478	40.478	NUM
esrj-91195	95	5	39.340	39.340	NUM
esrj-91195	95	6	24.661	24.661	NUM
esrj-91195	95	7	maximum	maximum	ADJ
esrj-91195	95	8	40.969	40.969	NUM
esrj-91195	95	9	40.499	40.499	NUM
esrj-91195	95	10	30.748	30.748	NUM
esrj-91195	95	11	standard	standard	ADJ
esrj-91195	95	12	deviation	deviation	NOUN
esrj-91195	95	13	0.110	0.110	NUM
esrj-91195	95	14	0.294	0.294	NUM
esrj-91195	95	15	1.439	1.439	NUM
esrj-91195	95	16	skewness	skewness	NOUN
esrj-91195	95	17	-0.144	-0.144	PUNCT
esrj-91195	95	18	-0.663	-0.663	NUM
esrj-91195	95	19	-0.644	-0.644	PUNCT
esrj-91195	95	20	testing	testing	NOUN
esrj-91195	95	21	mean	mean	VERB
esrj-91195	96	1	40.743	40.743	NUM
esrj-91195	96	2	40.085	40.085	NUM
esrj-91195	96	3	28.327	28.327	NUM
esrj-91195	96	4	minimum	minimum	ADJ
esrj-91195	96	5	40.491	40.491	NUM
esrj-91195	96	6	39.310	39.310	NUM
esrj-91195	96	7	24.597	24.597	NUM
esrj-91195	96	8	maximum	maximum	NOUN
esrj-91195	96	9	40.977	40.977	NUM
esrj-91195	96	10	40.492	40.492	NUM
esrj-91195	96	11	30.673	30.673	NUM
esrj-91195	96	12	standard	standard	ADJ
esrj-91195	96	13	deviation	deviation	NOUN
esrj-91195	96	14	0.108	0.108	NUM
esrj-91195	96	15	0,28524	0,28524	PROPN
esrj-91195	96	16	1.372	1.372	NUM
esrj-91195	96	17	skewness	skewness	NOUN
esrj-91195	96	18	-0.259	-0.259	NOUN
esrj-91195	96	19	-0,79471	-0,79471	PROPN
esrj-91195	96	20	-0.503	-0.503	ADP
esrj-91195	96	21	ds#2	ds#2	NOUN
esrj-91195	96	22	training	training	NOUN
esrj-91195	96	23	mean	mean	VERB
esrj-91195	97	1	40.741	40.741	NUM
esrj-91195	97	2	40.072	40.072	NUM
esrj-91195	97	3	28.357	28.357	NUM
esrj-91195	97	4	minimum	minimum	ADJ
esrj-91195	97	5	40.478	40.478	NUM
esrj-91195	97	6	39.329	39.329	NUM
esrj-91195	97	7	24.597	24.597	NUM
esrj-91195	97	8	maximum	maximum	ADJ
esrj-91195	97	9	40.977	40.977	NUM
esrj-91195	97	10	40.499	40.499	NUM
esrj-91195	97	11	30.748	30.748	NUM
esrj-91195	97	12	standard	standard	ADJ
esrj-91195	97	13	deviation	deviation	NOUN
esrj-91195	97	14	0.110	0.110	NUM
esrj-91195	97	15	0.290	0.290	NUM
esrj-91195	97	16	1.431	1.431	NUM
esrj-91195	97	17	skewness	skewness	NOUN
esrj-91195	97	18	-0.194	-0.194	PUNCT
esrj-91195	97	19	-0.744	-0.744	NUM
esrj-91195	97	20	-0.590	-0.590	PUNCT
esrj-91195	97	21	testing	testing	NOUN
esrj-91195	97	22	mean	mean	NOUN
esrj-91195	97	23	40.733	40.733	NUM
esrj-91195	97	24	40.032	40.032	NUM
esrj-91195	97	25	28.571	28.571	NUM
esrj-91195	97	26	minimum	minimum	NOUN
esrj-91195	97	27	40.519	40.519	NUM
esrj-91195	97	28	39.310	39.310	NUM
esrj-91195	97	29	24.661	24.661	NUM
esrj-91195	97	30	maximum	maximum	ADJ
esrj-91195	97	31	40.969	40.969	NUM
esrj-91195	97	32	40.492	40.492	NUM
esrj-91195	97	33	30.689	30.689	NUM
esrj-91195	97	34	standard	standard	ADJ
esrj-91195	97	35	deviation	deviation	NOUN
esrj-91195	97	36	0.109	0.109	NUM
esrj-91195	97	37	0.301	0.301	NUM
esrj-91195	97	38	1.396	1.396	NUM
esrj-91195	97	39	skewness	skewness	NOUN
esrj-91195	97	40	-0.052	-0.052	PRON
esrj-91195	97	41	-0.477	-0.477	NOUN
esrj-91195	97	42	-0.731	-0.731	PUNCT
esrj-91195	97	43	ds#3	ds#3	ADJ
esrj-91195	97	44	training	training	NOUN
esrj-91195	97	45	mean	mean	VERB
esrj-91195	97	46	40.737	40.737	NUM
esrj-91195	97	47	40.073	40.073	NUM
esrj-91195	97	48	28.419	28.419	NUM
esrj-91195	97	49	minimum	minimum	ADJ
esrj-91195	97	50	40.478	40.478	NUM
esrj-91195	97	51	39.310	39.310	NUM
esrj-91195	97	52	24.597	24.597	NUM
esrj-91195	97	53	maximum	maximum	NOUN
esrj-91195	97	54	40.977	40.977	NUM
esrj-91195	97	55	40.499	40.499	NUM
esrj-91195	97	56	30.748	30.748	NUM
esrj-91195	97	57	standard	standard	ADJ
esrj-91195	97	58	deviation	deviation	NOUN
esrj-91195	97	59	0.110	0.110	NUM
esrj-91195	97	60	0.290	0.290	NUM
esrj-91195	97	61	1.424	1.424	NUM
esrj-91195	97	62	skewness	skewness	NOUN
esrj-91195	97	63	-0.188	-0.188	PUNCT
esrj-91195	97	64	-0.722	-0.722	PRON
esrj-91195	97	65	-0.600	-0.600	ADP
esrj-91195	97	66	testing	testing	NOUN
esrj-91195	97	67	mean	mean	VERB
esrj-91195	97	68	40.752	40.752	NUM
esrj-91195	97	69	40.030	40.030	NUM
esrj-91195	97	70	28.323	28.323	NUM
esrj-91195	97	71	minimum	minimum	NOUN
esrj-91195	97	72	40.520	40.520	NUM
esrj-91195	97	73	39.329	39.329	NUM
esrj-91195	97	74	24.661	24.661	NUM
esrj-91195	97	75	maximum	maximum	ADJ
esrj-91195	97	76	40.969	40.969	NUM
esrj-91195	97	77	40.466	40.466	NUM
esrj-91195	97	78	30.692	30.692	NUM
esrj-91195	97	79	standard	standard	ADJ
esrj-91195	97	80	deviation	deviation	NOUN
esrj-91195	97	81	0.106	0.106	NUM
esrj-91195	97	82	0.300	0.300	NUM
esrj-91195	97	83	1.433	1.433	NUM
esrj-91195	97	84	skewness	skewness	NOUN
esrj-91195	97	85	-0.042	-0.042	PUNCT
esrj-91195	97	86	-0.561	-0.561	PUNCT
esrj-91195	97	87	-0.684	-0.684	PUNCT
esrj-91195	97	88	figure	figure	NOUN
esrj-91195	97	89	1	1	NUM
esrj-91195	97	90	.	.	PUNCT
esrj-91195	97	91	study	study	NOUN
esrj-91195	97	92	area	area	NOUN
esrj-91195	97	93	dataset	dataset	NOUN
esrj-91195	97	94	phase	phase	NOUN
esrj-91195	97	95	statistical	statistical	ADJ
esrj-91195	97	96	characteristics	characteristic	NOUN
esrj-91195	97	97	j	j	PROPN
esrj-91195	97	98	(	(	PUNCT
esrj-91195	97	99	°	°	ADP
esrj-91195	97	100	)	)	PUNCT
esrj-91195	97	101	l	l	NOUN
esrj-91195	97	102	(	(	PUNCT
esrj-91195	97	103	°	°	NOUN
esrj-91195	97	104	)	)	PUNCT
esrj-91195	97	105	n	n	CCONJ
esrj-91195	97	106	(	(	PUNCT
esrj-91195	97	107	m	m	NOUN
esrj-91195	97	108	)	)	PUNCT
esrj-91195	97	109	ds#4	ds#4	VERB
esrj-91195	97	110	training	training	NOUN
esrj-91195	97	111	mean	mean	NOUN
esrj-91195	97	112	40.739	40.739	NUM
esrj-91195	97	113	40.062	40.062	NUM
esrj-91195	97	114	28.409	28.409	NUM
esrj-91195	97	115	minimum	minimum	ADJ
esrj-91195	97	116	40.478	40.478	NUM
esrj-91195	97	117	39.310	39.310	NUM
esrj-91195	97	118	24.597	24.597	NUM
esrj-91195	97	119	maximum	maximum	NOUN
esrj-91195	97	120	40.977	40.977	NUM
esrj-91195	97	121	40.492	40.492	NUM
esrj-91195	97	122	30.748	30.748	NUM
esrj-91195	97	123	standard	standard	ADJ
esrj-91195	97	124	deviation	deviation	NOUN
esrj-91195	97	125	0.110	0.110	NUM
esrj-91195	97	126	0.294	0.294	NUM
esrj-91195	97	127	1.435	1.435	NUM
esrj-91195	97	128	skewness	skewness	NOUN
esrj-91195	97	129	-0.133	-0.133	ADJ
esrj-91195	97	130	-0.685	-0.685	PUNCT
esrj-91195	97	131	-0.628	-0.628	X
esrj-91195	97	132	testing	testing	NOUN
esrj-91195	97	133	mean	mean	VERB
esrj-91195	97	134	40.741	40.741	NUM
esrj-91195	97	135	40.075	40.075	NUM
esrj-91195	97	136	28.362	28.362	NUM
esrj-91195	97	137	minimum	minimum	NOUN
esrj-91195	97	138	40.509	40.509	NUM
esrj-91195	97	139	39.365	39.365	NUM
esrj-91195	97	140	25.331	25.331	NUM
esrj-91195	97	141	maximum	maximum	ADJ
esrj-91195	97	142	40.919	40.919	NUM
esrj-91195	97	143	40.499	40.499	NUM
esrj-91195	97	144	30.658	30.658	NUM
esrj-91195	97	145	standard	standard	ADJ
esrj-91195	97	146	deviation	deviation	NOUN
esrj-91195	97	147	0.109	0.109	NUM
esrj-91195	97	148	0.283	0.283	NUM
esrj-91195	97	149	1.393	1.393	NUM
esrj-91195	97	150	skewness	skewness	NOUN
esrj-91195	97	151	-0.303	-0.303	X
esrj-91195	97	152	-0.697	-0.697	PUNCT
esrj-91195	97	153	-0.566	-0.566	NOUN
esrj-91195	97	154	ds#5	ds#5	NOUN
esrj-91195	97	155	training	training	NOUN
esrj-91195	97	156	mean	mean	VERB
esrj-91195	98	1	40.740	40.740	NUM
esrj-91195	99	1	40.065	40.065	NUM
esrj-91195	99	2	28.384	28.384	NUM
esrj-91195	99	3	minimum	minimum	NOUN
esrj-91195	99	4	40.478	40.478	NUM
esrj-91195	99	5	39.329	39.329	NUM
esrj-91195	99	6	24.661	24.661	NUM
esrj-91195	99	7	maximum	maximum	ADJ
esrj-91195	99	8	40.969	40.969	NUM
esrj-91195	99	9	40.499	40.499	NUM
esrj-91195	99	10	30.748	30.748	NUM
esrj-91195	99	11	standard	standard	ADJ
esrj-91195	99	12	deviation	deviation	NOUN
esrj-91195	99	13	0.109	0.109	NUM
esrj-91195	99	14	0.289	0.289	NUM
esrj-91195	99	15	1.429	1.429	NUM
esrj-91195	99	16	skewness	skewness	NOUN
esrj-91195	99	17	-0.187	-0.187	NOUN
esrj-91195	99	18	-0.712	-0.712	NOUN
esrj-91195	99	19	-0.579	-0.579	PUNCT
esrj-91195	99	20	testing	testing	NOUN
esrj-91195	99	21	mean	mean	VERB
esrj-91195	99	22	40.738	40.738	NUM
esrj-91195	100	1	40.061	40.061	NUM
esrj-91195	100	2	28.461	28.461	NUM
esrj-91195	100	3	minimum	minimum	ADJ
esrj-91195	100	4	40.491	40.491	NUM
esrj-91195	100	5	39.310	39.310	NUM
esrj-91195	100	6	24.597	24.597	NUM
esrj-91195	100	7	maximum	maximum	NOUN
esrj-91195	100	8	40.977	40.977	NUM
esrj-91195	100	9	40.492	40.492	NUM
esrj-91195	100	10	30.325	30.325	NUM
esrj-91195	100	11	standard	standard	ADJ
esrj-91195	100	12	deviation	deviation	NOUN
esrj-91195	100	13	0.112	0.112	NUM
esrj-91195	100	14	0.304	0.304	NUM
esrj-91195	100	15	1.414	1.414	NUM
esrj-91195	100	16	skewness	skewness	NOUN
esrj-91195	100	17	-0.085	-0.085	NOUN
esrj-91195	100	18	-0.605	-0.605	PUNCT
esrj-91195	100	19	-0.774	-0.774	PRON
esrj-91195	100	20	figure	figure	NOUN
esrj-91195	100	21	2	2	NUM
esrj-91195	100	22	.	.	PUNCT
esrj-91195	100	23	geographical	geographical	ADJ
esrj-91195	100	24	distribution	distribution	NOUN
esrj-91195	100	25	of	of	ADP
esrj-91195	100	26	the	the	DET
esrj-91195	100	27	reference	reference	NOUN
esrj-91195	100	28	and	and	CCONJ
esrj-91195	100	29	test	test	NOUN
esrj-91195	100	30	points	point	NOUN
esrj-91195	100	31	of	of	ADP
esrj-91195	100	32	the	the	DET
esrj-91195	100	33	5	5	NUM
esrj-91195	100	34	different	different	ADJ
esrj-91195	100	35	datasets	dataset	NOUN
esrj-91195	100	36	:	:	PUNCT
esrj-91195	100	37	(	(	PUNCT
esrj-91195	100	38	a	a	X
esrj-91195	100	39	)	)	PUNCT
esrj-91195	100	40	ds#1	ds#1	PROPN
esrj-91195	100	41	,	,	PUNCT
esrj-91195	100	42	(	(	PUNCT
esrj-91195	100	43	b	b	X
esrj-91195	100	44	)	)	PUNCT
esrj-91195	100	45	ds#2	ds#2	NOUN
esrj-91195	100	46	,	,	PUNCT
esrj-91195	100	47	(	(	PUNCT
esrj-91195	100	48	c	c	X
esrj-91195	100	49	)	)	PUNCT
esrj-91195	100	50	ds#3	ds#3	NOUN
esrj-91195	100	51	,	,	PUNCT
esrj-91195	100	52	(	(	PUNCT
esrj-91195	100	53	d	d	X
esrj-91195	100	54	)	)	PUNCT
esrj-91195	100	55	ds#4	ds#4	PROPN
esrj-91195	100	56	,	,	PUNCT
esrj-91195	100	57	(	(	PUNCT
esrj-91195	100	58	e	e	NOUN
esrj-91195	100	59	)	)	PUNCT
esrj-91195	100	60	ds#5	ds#5	NOUN
esrj-91195	100	61	data	datum	NOUN
esrj-91195	100	62	normalization	normalization	NOUN
esrj-91195	100	63	is	be	AUX
esrj-91195	100	64	a	a	DET
esrj-91195	100	65	pre	pre	ADJ
esrj-91195	100	66	-	-	ADJ
esrj-91195	100	67	processing	processing	ADJ
esrj-91195	100	68	stage	stage	NOUN
esrj-91195	100	69	that	that	PRON
esrj-91195	100	70	plays	play	VERB
esrj-91195	100	71	a	a	DET
esrj-91195	100	72	significant	significant	ADJ
esrj-91195	100	73	role	role	NOUN
esrj-91195	100	74	in	in	ADP
esrj-91195	100	75	the	the	DET
esrj-91195	100	76	performance	performance	NOUN
esrj-91195	100	77	of	of	ADP
esrj-91195	100	78	ann	ann	PROPN
esrj-91195	100	79	methods	method	NOUN
esrj-91195	100	80	.	.	PUNCT
esrj-91195	101	1	in	in	ADP
esrj-91195	101	2	order	order	NOUN
esrj-91195	101	3	to	to	PART
esrj-91195	101	4	obtain	obtain	VERB
esrj-91195	101	5	more	more	ADV
esrj-91195	101	6	accurate	accurate	ADJ
esrj-91195	101	7	results	result	NOUN
esrj-91195	101	8	,	,	PUNCT
esrj-91195	101	9	all	all	DET
esrj-91195	101	10	data	datum	NOUN
esrj-91195	101	11	should	should	AUX
esrj-91195	101	12	be	be	AUX
esrj-91195	101	13	normalized	normalize	VERB
esrj-91195	101	14	before	before	ADP
esrj-91195	101	15	proceeding	proceed	VERB
esrj-91195	101	16	with	with	ADP
esrj-91195	101	17	the	the	DET
esrj-91195	101	18	training	training	NOUN
esrj-91195	101	19	and	and	CCONJ
esrj-91195	101	20	testing	testing	NOUN
esrj-91195	101	21	stages	stage	NOUN
esrj-91195	101	22	.	.	PUNCT
esrj-91195	102	1	therefore	therefore	ADV
esrj-91195	102	2	,	,	PUNCT
esrj-91195	102	3	input	input	NOUN
esrj-91195	102	4	and	and	CCONJ
esrj-91195	102	5	output	output	NOUN
esrj-91195	102	6	parameters	parameter	NOUN
esrj-91195	102	7	were	be	AUX
esrj-91195	102	8	normalized	normalize	VERB
esrj-91195	102	9	to	to	ADP
esrj-91195	102	10	the	the	DET
esrj-91195	102	11	interval	interval	NOUN
esrj-91195	102	12	[	[	X
esrj-91195	102	13	-1	-1	X
esrj-91195	102	14	,	,	PUNCT
esrj-91195	102	15	1	1	NUM
esrj-91195	102	16	]	]	PUNCT
esrj-91195	102	17	and	and	CCONJ
esrj-91195	102	18	the	the	DET
esrj-91195	102	19	normalized	normalize	VERB
esrj-91195	102	20	value	value	NOUN
esrj-91195	102	21	(	(	PUNCT
esrj-91195	102	22	ynorm	ynorm	NOUN
esrj-91195	102	23	)	)	PUNCT
esrj-91195	102	24	for	for	ADP
esrj-91195	102	25	each	each	DET
esrj-91195	102	26	input	input	NOUN
esrj-91195	102	27	and	and	CCONJ
esrj-91195	102	28	output	output	NOUN
esrj-91195	102	29	parameter	parameter	NOUN
esrj-91195	102	30	(	(	PUNCT
esrj-91195	102	31	yi	yi	NOUN
esrj-91195	102	32	)	)	PUNCT
esrj-91195	102	33	was	be	AUX
esrj-91195	102	34	obtained	obtain	VERB
esrj-91195	102	35	using	use	VERB
esrj-91195	102	36	equation	equation	NOUN
esrj-91195	102	37	(	(	PUNCT
esrj-91195	102	38	2	2	NUM
esrj-91195	102	39	)	)	PUNCT
esrj-91195	102	40	.	.	PUNCT
esrj-91195	103	1	(	(	PUNCT
esrj-91195	103	2	2	2	X
esrj-91195	103	3	)	)	PUNCT
esrj-91195	103	4	where	where	SCONJ
esrj-91195	103	5	highvalue	highvalue	NOUN
esrj-91195	103	6	and	and	CCONJ
esrj-91195	103	7	lowvalue	lowvalue	NOUN
esrj-91195	103	8	are	be	AUX
esrj-91195	103	9	set	set	VERB
esrj-91195	103	10	to	to	ADP
esrj-91195	103	11	1	1	NUM
esrj-91195	103	12	and	and	CCONJ
esrj-91195	103	13	-1	-1	ADV
esrj-91195	103	14	,	,	PUNCT
esrj-91195	103	15	respectively	respectively	ADV
esrj-91195	103	16	.	.	PUNCT
esrj-91195	104	1	artificial	artificial	ADJ
esrj-91195	104	2	neural	neural	ADJ
esrj-91195	104	3	network	network	NOUN
esrj-91195	104	4	(	(	PUNCT
esrj-91195	104	5	ann	ann	PROPN
esrj-91195	104	6	)	)	PUNCT
esrj-91195	104	7	the	the	DET
esrj-91195	104	8	artificial	artificial	ADJ
esrj-91195	104	9	neural	neural	ADJ
esrj-91195	104	10	network	network	NOUN
esrj-91195	104	11	(	(	PUNCT
esrj-91195	104	12	ann	ann	PROPN
esrj-91195	104	13	)	)	PUNCT
esrj-91195	104	14	was	be	AUX
esrj-91195	104	15	inspired	inspire	VERB
esrj-91195	104	16	by	by	ADP
esrj-91195	104	17	the	the	DET
esrj-91195	104	18	working	working	ADJ
esrj-91195	104	19	principle	principle	NOUN
esrj-91195	104	20	of	of	ADP
esrj-91195	104	21	the	the	DET
esrj-91195	104	22	biological	biological	ADJ
esrj-91195	104	23	nervous	nervous	ADJ
esrj-91195	104	24	system	system	NOUN
esrj-91195	104	25	and	and	CCONJ
esrj-91195	104	26	was	be	AUX
esrj-91195	104	27	created	create	VERB
esrj-91195	104	28	by	by	ADP
esrj-91195	104	29	artificially	artificially	ADV
esrj-91195	104	30	simplifying	simplify	VERB
esrj-91195	104	31	and	and	CCONJ
esrj-91195	104	32	374	374	NUM
esrj-91195	104	33	berkant	berkant	ADJ
esrj-91195	104	34	konakoglu	konakoglu	PROPN
esrj-91195	104	35	,	,	PUNCT
esrj-91195	104	36	alper	alper	PROPN
esrj-91195	104	37	akar	akar	PROPN
esrj-91195	104	38	imitating	imitate	VERB
esrj-91195	104	39	the	the	DET
esrj-91195	104	40	nerve	nerve	NOUN
esrj-91195	104	41	cells	cell	NOUN
esrj-91195	104	42	(	(	PUNCT
esrj-91195	104	43	neurons	neuron	NOUN
esrj-91195	104	44	)	)	PUNCT
esrj-91195	104	45	in	in	ADP
esrj-91195	104	46	the	the	DET
esrj-91195	104	47	nervous	nervous	ADJ
esrj-91195	104	48	system	system	NOUN
esrj-91195	104	49	and	and	CCONJ
esrj-91195	104	50	then	then	ADV
esrj-91195	104	51	transferring	transfer	VERB
esrj-91195	104	52	them	they	PRON
esrj-91195	104	53	to	to	ADP
esrj-91195	104	54	a	a	DET
esrj-91195	104	55	computer	computer	NOUN
esrj-91195	104	56	(	(	PUNCT
esrj-91195	104	57	singh	singh	PROPN
esrj-91195	104	58	et	et	PROPN
esrj-91195	104	59	al	al	PROPN
esrj-91195	104	60	.	.	PROPN
esrj-91195	104	61	,	,	PUNCT
esrj-91195	104	62	2009	2009	NUM
esrj-91195	104	63	)	)	PUNCT
esrj-91195	104	64	.	.	PUNCT
esrj-91195	105	1	since	since	SCONJ
esrj-91195	105	2	anns	ann	NOUN
esrj-91195	105	3	are	be	AUX
esrj-91195	105	4	modeled	model	VERB
esrj-91195	105	5	after	after	ADP
esrj-91195	105	6	the	the	DET
esrj-91195	105	7	biological	biological	ADJ
esrj-91195	105	8	nervous	nervous	ADJ
esrj-91195	105	9	system	system	NOUN
esrj-91195	105	10	,	,	PUNCT
esrj-91195	105	11	they	they	PRON
esrj-91195	105	12	have	have	VERB
esrj-91195	105	13	the	the	DET
esrj-91195	105	14	advantage	advantage	NOUN
esrj-91195	105	15	of	of	ADP
esrj-91195	105	16	being	be	AUX
esrj-91195	105	17	capable	capable	ADJ
esrj-91195	105	18	of	of	ADP
esrj-91195	105	19	automatically	automatically	ADV
esrj-91195	105	20	,	,	PUNCT
esrj-91195	105	21	on	on	ADP
esrj-91195	105	22	their	their	PRON
esrj-91195	105	23	own	own	ADJ
esrj-91195	105	24	,	,	PUNCT
esrj-91195	105	25	realizing	realize	VERB
esrj-91195	105	26	skills	skill	NOUN
esrj-91195	105	27	such	such	ADJ
esrj-91195	105	28	as	as	ADP
esrj-91195	105	29	the	the	DET
esrj-91195	105	30	ability	ability	NOUN
esrj-91195	105	31	to	to	PART
esrj-91195	105	32	derive	derive	VERB
esrj-91195	105	33	and	and	CCONJ
esrj-91195	105	34	discover	discover	VERB
esrj-91195	105	35	new	new	ADJ
esrj-91195	105	36	information	information	NOUN
esrj-91195	105	37	through	through	ADP
esrj-91195	105	38	learning	learning	NOUN
esrj-91195	105	39	,	,	PUNCT
esrj-91195	105	40	which	which	PRON
esrj-91195	105	41	is	be	AUX
esrj-91195	105	42	one	one	NUM
esrj-91195	105	43	of	of	ADP
esrj-91195	105	44	the	the	DET
esrj-91195	105	45	features	feature	NOUN
esrj-91195	105	46	of	of	ADP
esrj-91195	105	47	the	the	DET
esrj-91195	105	48	human	human	ADJ
esrj-91195	105	49	brain	brain	NOUN
esrj-91195	105	50	.	.	PUNCT
esrj-91195	106	1	generalization	generalization	NOUN
esrj-91195	106	2	and	and	CCONJ
esrj-91195	106	3	working	work	VERB
esrj-91195	106	4	with	with	ADP
esrj-91195	106	5	an	an	DET
esrj-91195	106	6	unlimited	unlimited	ADJ
esrj-91195	106	7	number	number	NOUN
esrj-91195	106	8	of	of	ADP
esrj-91195	106	9	variables	variable	NOUN
esrj-91195	106	10	are	be	AUX
esrj-91195	106	11	also	also	ADV
esrj-91195	106	12	features	feature	NOUN
esrj-91195	106	13	of	of	ADP
esrj-91195	106	14	the	the	DET
esrj-91195	106	15	ann	ann	PROPN
esrj-91195	106	16	.	.	PUNCT
esrj-91195	107	1	supplying	supply	VERB
esrj-91195	107	2	the	the	DET
esrj-91195	107	3	learning	learning	NOUN
esrj-91195	107	4	and	and	CCONJ
esrj-91195	107	5	quick	quick	ADJ
esrj-91195	107	6	decision	decision	NOUN
esrj-91195	107	7	-	-	PUNCT
esrj-91195	107	8	making	make	VERB
esrj-91195	107	9	abilities	ability	NOUN
esrj-91195	107	10	of	of	ADP
esrj-91195	107	11	the	the	DET
esrj-91195	107	12	human	human	ADJ
esrj-91195	107	13	brain	brain	NOUN
esrj-91195	107	14	to	to	ADP
esrj-91195	107	15	anns	anns	NOUN
esrj-91195	107	16	enables	enable	VERB
esrj-91195	107	17	them	they	PRON
esrj-91195	107	18	to	to	PART
esrj-91195	107	19	solve	solve	VERB
esrj-91195	107	20	complex	complex	ADJ
esrj-91195	107	21	problems	problem	NOUN
esrj-91195	107	22	through	through	ADP
esrj-91195	107	23	training	training	NOUN
esrj-91195	107	24	.	.	PUNCT
esrj-91195	108	1	the	the	DET
esrj-91195	108	2	ann	ann	PROPN
esrj-91195	108	3	looks	look	VERB
esrj-91195	108	4	at	at	ADP
esrj-91195	108	5	examples	example	NOUN
esrj-91195	108	6	of	of	ADP
esrj-91195	108	7	problems	problem	NOUN
esrj-91195	108	8	,	,	PUNCT
esrj-91195	108	9	makes	make	VERB
esrj-91195	108	10	generalizations	generalization	NOUN
esrj-91195	108	11	about	about	ADP
esrj-91195	108	12	these	these	DET
esrj-91195	108	13	problems	problem	NOUN
esrj-91195	108	14	,	,	PUNCT
esrj-91195	108	15	sums	sum	VERB
esrj-91195	108	16	up	up	ADP
esrj-91195	108	17	the	the	DET
esrj-91195	108	18	information	information	NOUN
esrj-91195	108	19	,	,	PUNCT
esrj-91195	108	20	and	and	CCONJ
esrj-91195	108	21	then	then	ADV
esrj-91195	108	22	,	,	PUNCT
esrj-91195	108	23	when	when	SCONJ
esrj-91195	108	24	encountering	encounter	VERB
esrj-91195	108	25	new	new	ADJ
esrj-91195	108	26	examples	example	NOUN
esrj-91195	108	27	it	it	PRON
esrj-91195	108	28	has	have	AUX
esrj-91195	108	29	never	never	ADV
esrj-91195	108	30	seen	see	VERB
esrj-91195	108	31	before	before	ADV
esrj-91195	108	32	,	,	PUNCT
esrj-91195	108	33	it	it	PRON
esrj-91195	108	34	makes	make	VERB
esrj-91195	108	35	decisions	decision	NOUN
esrj-91195	108	36	using	use	VERB
esrj-91195	108	37	the	the	DET
esrj-91195	108	38	information	information	NOUN
esrj-91195	108	39	it	it	PRON
esrj-91195	108	40	has	have	AUX
esrj-91195	108	41	learned	learn	VERB
esrj-91195	108	42	.	.	PUNCT
esrj-91195	109	1	because	because	SCONJ
esrj-91195	109	2	of	of	ADP
esrj-91195	109	3	all	all	DET
esrj-91195	109	4	these	these	DET
esrj-91195	109	5	features	feature	NOUN
esrj-91195	109	6	,	,	PUNCT
esrj-91195	109	7	the	the	DET
esrj-91195	109	8	ann	ann	PROPN
esrj-91195	109	9	is	be	AUX
esrj-91195	109	10	used	use	VERB
esrj-91195	109	11	to	to	PART
esrj-91195	109	12	accomplish	accomplish	VERB
esrj-91195	109	13	goals	goal	NOUN
esrj-91195	109	14	in	in	ADP
esrj-91195	109	15	many	many	ADJ
esrj-91195	109	16	areas	area	NOUN
esrj-91195	109	17	,	,	PUNCT
esrj-91195	109	18	including	include	VERB
esrj-91195	109	19	classification	classification	NOUN
esrj-91195	109	20	,	,	PUNCT
esrj-91195	109	21	control	control	NOUN
esrj-91195	109	22	,	,	PUNCT
esrj-91195	109	23	image	image	NOUN
esrj-91195	109	24	processing	processing	NOUN
esrj-91195	109	25	,	,	PUNCT
esrj-91195	109	26	modeling	modeling	NOUN
esrj-91195	109	27	,	,	PUNCT
esrj-91195	109	28	feature	feature	NOUN
esrj-91195	109	29	determination	determination	NOUN
esrj-91195	109	30	,	,	PUNCT
esrj-91195	109	31	optimization	optimization	NOUN
esrj-91195	109	32	,	,	PUNCT
esrj-91195	109	33	prediction	prediction	NOUN
esrj-91195	109	34	,	,	PUNCT
esrj-91195	109	35	and	and	CCONJ
esrj-91195	109	36	more	more	ADJ
esrj-91195	109	37	.	.	PUNCT
esrj-91195	110	1	as	as	SCONJ
esrj-91195	110	2	there	there	PRON
esrj-91195	110	3	is	be	VERB
esrj-91195	110	4	no	no	DET
esrj-91195	110	5	limit	limit	NOUN
esrj-91195	110	6	to	to	ADP
esrj-91195	110	7	the	the	DET
esrj-91195	110	8	application	application	NOUN
esrj-91195	110	9	fields	field	NOUN
esrj-91195	110	10	of	of	ADP
esrj-91195	110	11	anns	ann	NOUN
esrj-91195	110	12	,	,	PUNCT
esrj-91195	110	13	they	they	PRON
esrj-91195	110	14	can	can	AUX
esrj-91195	110	15	be	be	AUX
esrj-91195	110	16	applied	apply	VERB
esrj-91195	110	17	to	to	ADP
esrj-91195	110	18	almost	almost	ADV
esrj-91195	110	19	any	any	PRON
esrj-91195	110	20	problem	problem	NOUN
esrj-91195	110	21	that	that	PRON
esrj-91195	110	22	can	can	AUX
esrj-91195	110	23	be	be	AUX
esrj-91195	110	24	converted	convert	VERB
esrj-91195	110	25	into	into	ADP
esrj-91195	110	26	fixed	fix	VERB
esrj-91195	110	27	input	input	NOUN
esrj-91195	110	28	and	and	CCONJ
esrj-91195	110	29	output	output	NOUN
esrj-91195	110	30	variables	variable	NOUN
esrj-91195	110	31	.	.	PUNCT
esrj-91195	111	1	in	in	ADP
esrj-91195	111	2	this	this	DET
esrj-91195	111	3	study	study	NOUN
esrj-91195	111	4	,	,	PUNCT
esrj-91195	111	5	two	two	NUM
esrj-91195	111	6	types	type	NOUN
esrj-91195	111	7	of	of	ADP
esrj-91195	111	8	ann	ann	PROPN
esrj-91195	111	9	(	(	PUNCT
esrj-91195	111	10	rbfnn	rbfnn	NOUN
esrj-91195	111	11	and	and	CCONJ
esrj-91195	111	12	grnn	grnn	NOUN
esrj-91195	111	13	)	)	PUNCT
esrj-91195	111	14	were	be	AUX
esrj-91195	111	15	applied	apply	VERB
esrj-91195	111	16	.	.	PUNCT
esrj-91195	112	1	the	the	DET
esrj-91195	112	2	development	development	NOUN
esrj-91195	112	3	of	of	ADP
esrj-91195	112	4	the	the	DET
esrj-91195	112	5	models	model	NOUN
esrj-91195	112	6	was	be	AUX
esrj-91195	112	7	coded	code	VERB
esrj-91195	112	8	in	in	ADP
esrj-91195	112	9	a	a	DET
esrj-91195	112	10	matlab	matlab	PROPN
esrj-91195	112	11	environment	environment	NOUN
esrj-91195	112	12	.	.	PUNCT
esrj-91195	113	1	an	an	DET
esrj-91195	113	2	overview	overview	NOUN
esrj-91195	113	3	and	and	CCONJ
esrj-91195	113	4	description	description	NOUN
esrj-91195	113	5	of	of	ADP
esrj-91195	113	6	all	all	DET
esrj-91195	113	7	the	the	DET
esrj-91195	113	8	applied	apply	VERB
esrj-91195	113	9	methods	method	NOUN
esrj-91195	113	10	are	be	AUX
esrj-91195	113	11	discussed	discuss	VERB
esrj-91195	113	12	in	in	ADP
esrj-91195	113	13	the	the	DET
esrj-91195	113	14	following	follow	VERB
esrj-91195	113	15	sections	section	NOUN
esrj-91195	113	16	.	.	PUNCT
esrj-91195	114	1	radial	radial	ADJ
esrj-91195	114	2	basis	basis	NOUN
esrj-91195	114	3	function	function	NOUN
esrj-91195	114	4	neural	neural	ADJ
esrj-91195	114	5	network	network	NOUN
esrj-91195	114	6	(	(	PUNCT
esrj-91195	114	7	rbfnn	rbfnn	PROPN
esrj-91195	114	8	)	)	PUNCT
esrj-91195	114	9	the	the	DET
esrj-91195	114	10	rbfnn	rbfnn	NOUN
esrj-91195	114	11	was	be	AUX
esrj-91195	114	12	developed	develop	VERB
esrj-91195	114	13	in	in	ADP
esrj-91195	114	14	1988	1988	NUM
esrj-91195	114	15	,	,	PUNCT
esrj-91195	114	16	inspired	inspire	VERB
esrj-91195	114	17	by	by	ADP
esrj-91195	114	18	the	the	DET
esrj-91195	114	19	action	action	NOUN
esrj-91195	114	20	-	-	PUNCT
esrj-91195	114	21	response	response	NOUN
esrj-91195	114	22	behavior	behavior	NOUN
esrj-91195	114	23	seen	see	VERB
esrj-91195	114	24	in	in	ADP
esrj-91195	114	25	biological	biological	ADJ
esrj-91195	114	26	nerve	nerve	NOUN
esrj-91195	114	27	cells	cell	NOUN
esrj-91195	114	28	(	(	PUNCT
esrj-91195	114	29	broomhead	broomhead	NOUN
esrj-91195	114	30	&	&	CCONJ
esrj-91195	114	31	lowe	lowe	PROPN
esrj-91195	114	32	,	,	PUNCT
esrj-91195	114	33	1988	1988	NUM
esrj-91195	114	34	)	)	PUNCT
esrj-91195	114	35	.	.	PUNCT
esrj-91195	115	1	the	the	DET
esrj-91195	115	2	rbfnn	rbfnn	PROPN
esrj-91195	115	3	is	be	AUX
esrj-91195	115	4	a	a	DET
esrj-91195	115	5	curve	curve	NOUN
esrj-91195	115	6	-	-	PUNCT
esrj-91195	115	7	fitting	fit	VERB
esrj-91195	115	8	approach	approach	NOUN
esrj-91195	115	9	in	in	ADP
esrj-91195	115	10	multidimensional	multidimensional	ADJ
esrj-91195	115	11	space	space	NOUN
esrj-91195	115	12	and	and	CCONJ
esrj-91195	115	13	is	be	AUX
esrj-91195	115	14	a	a	DET
esrj-91195	115	15	special	special	ADJ
esrj-91195	115	16	version	version	NOUN
esrj-91195	115	17	of	of	ADP
esrj-91195	115	18	the	the	DET
esrj-91195	115	19	multi	multi	ADJ
esrj-91195	115	20	-	-	ADJ
esrj-91195	115	21	layer	layer	ADJ
esrj-91195	115	22	artificial	artificial	ADJ
esrj-91195	115	23	neural	neural	ADJ
esrj-91195	115	24	network	network	NOUN
esrj-91195	115	25	that	that	PRON
esrj-91195	115	26	uses	use	VERB
esrj-91195	115	27	the	the	DET
esrj-91195	115	28	radial	radial	ADJ
esrj-91195	115	29	basis	basis	NOUN
esrj-91195	115	30	function	function	NOUN
esrj-91195	115	31	as	as	ADP
esrj-91195	115	32	its	its	PRON
esrj-91195	115	33	activation	activation	NOUN
esrj-91195	115	34	function	function	NOUN
esrj-91195	115	35	.	.	PUNCT
esrj-91195	116	1	like	like	ADP
esrj-91195	116	2	the	the	DET
esrj-91195	116	3	general	general	ADJ
esrj-91195	116	4	ann	ann	PROPN
esrj-91195	116	5	architecture	architecture	NOUN
esrj-91195	116	6	,	,	PUNCT
esrj-91195	116	7	the	the	DET
esrj-91195	116	8	rbfnn	rbfnn	NOUN
esrj-91195	116	9	method	method	NOUN
esrj-91195	116	10	is	be	AUX
esrj-91195	116	11	defined	define	VERB
esrj-91195	116	12	in	in	ADP
esrj-91195	116	13	three	three	NUM
esrj-91195	116	14	layers	layer	NOUN
esrj-91195	116	15	:	:	PUNCT
esrj-91195	116	16	input	input	NOUN
esrj-91195	116	17	layer	layer	NOUN
esrj-91195	116	18	,	,	PUNCT
esrj-91195	116	19	hidden	hide	VERB
esrj-91195	116	20	layer	layer	NOUN
esrj-91195	116	21	,	,	PUNCT
esrj-91195	116	22	and	and	CCONJ
esrj-91195	116	23	output	output	NOUN
esrj-91195	116	24	layer	layer	NOUN
esrj-91195	116	25	(	(	PUNCT
esrj-91195	116	26	figure	figure	NOUN
esrj-91195	116	27	3	3	NUM
esrj-91195	116	28	)	)	PUNCT
esrj-91195	116	29	.	.	PUNCT
esrj-91195	117	1	figure	figure	VERB
esrj-91195	117	2	3	3	NUM
esrj-91195	117	3	.	.	PUNCT
esrj-91195	117	4	radial	radial	ADJ
esrj-91195	117	5	basis	basis	NOUN
esrj-91195	117	6	function	function	NOUN
esrj-91195	117	7	neural	neural	ADJ
esrj-91195	117	8	network	network	NOUN
esrj-91195	117	9	(	(	PUNCT
esrj-91195	117	10	rbfnn	rbfnn	PROPN
esrj-91195	117	11	)	)	PUNCT
esrj-91195	117	12	structure	structure	NOUN
esrj-91195	117	13	the	the	DET
esrj-91195	117	14	input	input	NOUN
esrj-91195	117	15	layer	layer	NOUN
esrj-91195	117	16	of	of	ADP
esrj-91195	117	17	the	the	DET
esrj-91195	117	18	network	network	NOUN
esrj-91195	117	19	is	be	AUX
esrj-91195	117	20	directly	directly	ADV
esrj-91195	117	21	connected	connect	VERB
esrj-91195	117	22	to	to	ADP
esrj-91195	117	23	the	the	DET
esrj-91195	117	24	hidden	hide	VERB
esrj-91195	117	25	layer	layer	NOUN
esrj-91195	117	26	and	and	CCONJ
esrj-91195	117	27	subsequently	subsequently	ADV
esrj-91195	117	28	,	,	PUNCT
esrj-91195	117	29	weights	weight	NOUN
esrj-91195	117	30	are	be	AUX
esrj-91195	117	31	only	only	ADV
esrj-91195	117	32	present	present	ADJ
esrj-91195	117	33	between	between	ADP
esrj-91195	117	34	the	the	DET
esrj-91195	117	35	hidden	hide	VERB
esrj-91195	117	36	layer	layer	NOUN
esrj-91195	117	37	and	and	CCONJ
esrj-91195	117	38	the	the	DET
esrj-91195	117	39	output	output	NOUN
esrj-91195	117	40	layer	layer	NOUN
esrj-91195	117	41	.	.	PUNCT
esrj-91195	118	1	the	the	DET
esrj-91195	118	2	rbfnn	rbfnn	PROPN
esrj-91195	118	3	has	have	VERB
esrj-91195	118	4	a	a	DET
esrj-91195	118	5	single	single	ADJ
esrj-91195	118	6	hidden	hide	VERB
esrj-91195	118	7	layer	layer	NOUN
esrj-91195	118	8	and	and	CCONJ
esrj-91195	118	9	radial	radial	ADJ
esrj-91195	118	10	basis	basis	NOUN
esrj-91195	118	11	functions	function	NOUN
esrj-91195	118	12	are	be	AUX
esrj-91195	118	13	used	use	VERB
esrj-91195	118	14	as	as	ADP
esrj-91195	118	15	the	the	DET
esrj-91195	118	16	activation	activation	NOUN
esrj-91195	118	17	function	function	NOUN
esrj-91195	118	18	in	in	ADP
esrj-91195	118	19	the	the	DET
esrj-91195	118	20	hidden	hide	VERB
esrj-91195	118	21	layer	layer	NOUN
esrj-91195	118	22	neurons	neuron	NOUN
esrj-91195	118	23	.	.	PUNCT
esrj-91195	119	1	the	the	DET
esrj-91195	119	2	most	most	ADV
esrj-91195	119	3	commonly	commonly	ADV
esrj-91195	119	4	used	use	VERB
esrj-91195	119	5	radial	radial	ADJ
esrj-91195	119	6	basis	basis	NOUN
esrj-91195	119	7	function	function	NOUN
esrj-91195	119	8	is	be	AUX
esrj-91195	119	9	the	the	DET
esrj-91195	119	10	gaussian	gaussian	ADJ
esrj-91195	119	11	function	function	NOUN
esrj-91195	119	12	(	(	PUNCT
esrj-91195	119	13	hartman	hartman	PROPN
esrj-91195	119	14	et	et	PROPN
esrj-91195	119	15	al	al	PROPN
esrj-91195	119	16	.	.	PROPN
esrj-91195	119	17	,	,	PUNCT
esrj-91195	119	18	1990	1990	NUM
esrj-91195	119	19	;	;	PUNCT
esrj-91195	119	20	park	park	NOUN
esrj-91195	119	21	&	&	CCONJ
esrj-91195	119	22	sandberg	sandberg	PROPN
esrj-91195	119	23	,	,	PUNCT
esrj-91195	119	24	1991	1991	NUM
esrj-91195	119	25	)	)	PUNCT
esrj-91195	119	26	.	.	PUNCT
esrj-91195	120	1	the	the	DET
esrj-91195	120	2	output	output	NOUN
esrj-91195	120	3	of	of	ADP
esrj-91195	120	4	an	an	DET
esrj-91195	120	5	rbfnn	rbfnn	NOUN
esrj-91195	120	6	with	with	ADP
esrj-91195	120	7	gaussian	gaussian	NOUN
esrj-91195	120	8	-	-	PUNCT
esrj-91195	120	9	based	base	VERB
esrj-91195	120	10	function	function	NOUN
esrj-91195	120	11	can	can	AUX
esrj-91195	120	12	be	be	AUX
esrj-91195	120	13	calculated	calculate	VERB
esrj-91195	120	14	using	use	VERB
esrj-91195	120	15	the	the	DET
esrj-91195	120	16	following	follow	VERB
esrj-91195	120	17	equations	equation	NOUN
esrj-91195	120	18	(	(	PUNCT
esrj-91195	120	19	haykin	haykin	PROPN
esrj-91195	120	20	,	,	PUNCT
esrj-91195	120	21	1994	1994	NUM
esrj-91195	120	22	)	)	PUNCT
esrj-91195	120	23	.	.	PUNCT
esrj-91195	121	1	(	(	PUNCT
esrj-91195	121	2	3	3	X
esrj-91195	121	3	)	)	PUNCT
esrj-91195	121	4	(	(	PUNCT
esrj-91195	121	5	4	4	NUM
esrj-91195	121	6	)	)	PUNCT
esrj-91195	121	7	(	(	PUNCT
esrj-91195	121	8	5	5	X
esrj-91195	121	9	)	)	PUNCT
esrj-91195	121	10	where	where	SCONJ
esrj-91195	121	11	is	be	AUX
esrj-91195	121	12	the	the	DET
esrj-91195	121	13	input	input	NOUN
esrj-91195	121	14	vector	vector	NOUN
esrj-91195	121	15	,	,	PUNCT
esrj-91195	121	16	ci	ci	PROPN
esrj-91195	121	17	is	be	AUX
esrj-91195	121	18	the	the	DET
esrj-91195	121	19	center	center	NOUN
esrj-91195	121	20	of	of	ADP
esrj-91195	121	21	the	the	DET
esrj-91195	121	22	rbf	rbf	PROPN
esrj-91195	121	23	unit	unit	NOUN
esrj-91195	121	24	,	,	PUNCT
esrj-91195	121	25	is	be	AUX
esrj-91195	121	26	the	the	DET
esrj-91195	121	27	width	width	NOUN
esrj-91195	121	28	of	of	ADP
esrj-91195	121	29	the	the	DET
esrj-91195	121	30	neuron	neuron	NOUN
esrj-91195	121	31	,	,	PUNCT
esrj-91195	121	32	n	n	PRON
esrj-91195	121	33	is	be	AUX
esrj-91195	121	34	the	the	DET
esrj-91195	121	35	number	number	NOUN
esrj-91195	121	36	of	of	ADP
esrj-91195	121	37	cells	cell	NOUN
esrj-91195	121	38	in	in	ADP
esrj-91195	121	39	the	the	DET
esrj-91195	121	40	hidden	hide	VERB
esrj-91195	121	41	layer	layer	NOUN
esrj-91195	121	42	,	,	PUNCT
esrj-91195	121	43	and	and	CCONJ
esrj-91195	121	44	wi	wi	PROPN
esrj-91195	121	45	shows	show	VERB
esrj-91195	121	46	the	the	DET
esrj-91195	121	47	link	link	NOUN
esrj-91195	121	48	weights	weight	NOUN
esrj-91195	121	49	between	between	ADP
esrj-91195	121	50	the	the	DET
esrj-91195	121	51	hidden	hidden	ADJ
esrj-91195	121	52	and	and	CCONJ
esrj-91195	121	53	output	output	NOUN
esrj-91195	121	54	layers	layer	NOUN
esrj-91195	121	55	.	.	PUNCT
esrj-91195	122	1	a	a	DET
esrj-91195	122	2	different	different	ADJ
esrj-91195	122	3	number	number	NOUN
esrj-91195	122	4	of	of	ADP
esrj-91195	122	5	hidden	hide	VERB
esrj-91195	122	6	layer	layer	NOUN
esrj-91195	122	7	neurons	neuron	NOUN
esrj-91195	122	8	and	and	CCONJ
esrj-91195	122	9	spread	spread	ADJ
esrj-91195	122	10	parameters	parameter	NOUN
esrj-91195	122	11	were	be	AUX
esrj-91195	122	12	investigated	investigate	VERB
esrj-91195	122	13	using	use	VERB
esrj-91195	122	14	the	the	DET
esrj-91195	122	15	rmse	rmse	NOUN
esrj-91195	122	16	.	.	PUNCT
esrj-91195	123	1	general	general	ADJ
esrj-91195	123	2	regression	regression	PROPN
esrj-91195	123	3	neural	neural	ADJ
esrj-91195	123	4	network	network	NOUN
esrj-91195	123	5	(	(	PUNCT
esrj-91195	123	6	grnn	grnn	PROPN
esrj-91195	123	7	)	)	PUNCT
esrj-91195	123	8	the	the	DET
esrj-91195	123	9	grnn	grnn	NOUN
esrj-91195	123	10	proposed	propose	VERB
esrj-91195	123	11	by	by	ADP
esrj-91195	123	12	specht	specht	PROPN
esrj-91195	123	13	(	(	PUNCT
esrj-91195	123	14	1993	1993	NUM
esrj-91195	123	15	)	)	PUNCT
esrj-91195	123	16	does	do	AUX
esrj-91195	123	17	not	not	PART
esrj-91195	123	18	require	require	VERB
esrj-91195	123	19	an	an	DET
esrj-91195	123	20	iterative	iterative	NOUN
esrj-91195	123	21	procedure	procedure	NOUN
esrj-91195	123	22	as	as	ADP
esrj-91195	123	23	in	in	ADP
esrj-91195	123	24	the	the	DET
esrj-91195	123	25	mlpnn	mlpnn	PROPN
esrj-91195	123	26	method	method	NOUN
esrj-91195	123	27	.	.	PUNCT
esrj-91195	124	1	it	it	PRON
esrj-91195	124	2	needs	need	VERB
esrj-91195	124	3	only	only	ADV
esrj-91195	124	4	one	one	NUM
esrj-91195	124	5	-	-	PUNCT
esrj-91195	124	6	way	way	NOUN
esrj-91195	124	7	learning	learning	NOUN
esrj-91195	124	8	.	.	PUNCT
esrj-91195	125	1	due	due	ADP
esrj-91195	125	2	to	to	ADP
esrj-91195	125	3	the	the	DET
esrj-91195	125	4	simplicity	simplicity	NOUN
esrj-91195	125	5	of	of	ADP
esrj-91195	125	6	the	the	DET
esrj-91195	125	7	network	network	NOUN
esrj-91195	125	8	structure	structure	NOUN
esrj-91195	125	9	and	and	CCONJ
esrj-91195	125	10	ease	ease	NOUN
esrj-91195	125	11	of	of	ADP
esrj-91195	125	12	implementation	implementation	NOUN
esrj-91195	125	13	,	,	PUNCT
esrj-91195	125	14	this	this	DET
esrj-91195	125	15	functional	functional	ADJ
esrj-91195	125	16	approach	approach	NOUN
esrj-91195	125	17	has	have	AUX
esrj-91195	125	18	been	be	AUX
esrj-91195	125	19	used	use	VERB
esrj-91195	125	20	in	in	ADP
esrj-91195	125	21	different	different	ADJ
esrj-91195	125	22	geodetic	geodetic	ADJ
esrj-91195	125	23	applications	application	NOUN
esrj-91195	125	24	(	(	PUNCT
esrj-91195	125	25	ziggah	ziggah	NOUN
esrj-91195	125	26	et	et	PROPN
esrj-91195	125	27	al	al	PROPN
esrj-91195	125	28	.	.	PROPN
esrj-91195	125	29	,	,	PUNCT
esrj-91195	125	30	2017	2017	NUM
esrj-91195	125	31	;	;	PUNCT
esrj-91195	125	32	cakir	cakir	PROPN
esrj-91195	125	33	&	&	CCONJ
esrj-91195	125	34	konakoglu	konakoglu	PROPN
esrj-91195	125	35	,	,	PUNCT
esrj-91195	125	36	2019	2019	NUM
esrj-91195	125	37	;	;	PUNCT
esrj-91195	125	38	li	li	PROPN
esrj-91195	125	39	et	et	PROPN
esrj-91195	125	40	al	al	PROPN
esrj-91195	125	41	.	.	PROPN
esrj-91195	125	42	,	,	PUNCT
esrj-91195	125	43	2020	2020	NUM
esrj-91195	125	44	)	)	PUNCT
esrj-91195	125	45	.	.	PUNCT
esrj-91195	126	1	the	the	DET
esrj-91195	126	2	grnn	grnn	PROPN
esrj-91195	126	3	consists	consist	VERB
esrj-91195	126	4	of	of	ADP
esrj-91195	126	5	four	four	NUM
esrj-91195	126	6	layers	layer	NOUN
esrj-91195	126	7	:	:	PUNCT
esrj-91195	126	8	the	the	DET
esrj-91195	126	9	input	input	NOUN
esrj-91195	126	10	layer	layer	NOUN
esrj-91195	126	11	,	,	PUNCT
esrj-91195	126	12	the	the	DET
esrj-91195	126	13	pattern	pattern	NOUN
esrj-91195	126	14	layer	layer	NOUN
esrj-91195	126	15	,	,	PUNCT
esrj-91195	126	16	the	the	DET
esrj-91195	126	17	summation	summation	NOUN
esrj-91195	126	18	layer	layer	NOUN
esrj-91195	126	19	,	,	PUNCT
esrj-91195	126	20	and	and	CCONJ
esrj-91195	126	21	the	the	DET
esrj-91195	126	22	output	output	NOUN
esrj-91195	126	23	layer	layer	NOUN
esrj-91195	126	24	(	(	PUNCT
esrj-91195	126	25	specht	specht	NOUN
esrj-91195	126	26	,	,	PUNCT
esrj-91195	126	27	1993	1993	NUM
esrj-91195	126	28	)	)	PUNCT
esrj-91195	126	29	.	.	PUNCT
esrj-91195	127	1	the	the	DET
esrj-91195	127	2	structure	structure	NOUN
esrj-91195	127	3	of	of	ADP
esrj-91195	127	4	the	the	DET
esrj-91195	127	5	grnn	grnn	NOUN
esrj-91195	127	6	is	be	AUX
esrj-91195	127	7	given	give	VERB
esrj-91195	127	8	in	in	ADP
esrj-91195	127	9	figure	figure	NOUN
esrj-91195	127	10	4	4	NUM
esrj-91195	127	11	.	.	PUNCT
esrj-91195	127	12	figure	figure	VERB
esrj-91195	127	13	4	4	NUM
esrj-91195	127	14	.	.	PUNCT
esrj-91195	127	15	general	general	ADJ
esrj-91195	127	16	regression	regression	PROPN
esrj-91195	127	17	neural	neural	ADJ
esrj-91195	127	18	network	network	NOUN
esrj-91195	127	19	(	(	PUNCT
esrj-91195	127	20	grnn	grnn	PROPN
esrj-91195	127	21	)	)	PUNCT
esrj-91195	127	22	structure	structure	NOUN
esrj-91195	127	23	the	the	DET
esrj-91195	127	24	input	input	NOUN
esrj-91195	127	25	layer	layer	NOUN
esrj-91195	127	26	,	,	PUNCT
esrj-91195	127	27	i.e.	i.e.	X
esrj-91195	127	28	,	,	PUNCT
esrj-91195	127	29	the	the	DET
esrj-91195	127	30	first	first	ADJ
esrj-91195	127	31	layer	layer	NOUN
esrj-91195	127	32	where	where	SCONJ
esrj-91195	127	33	inputs	input	NOUN
esrj-91195	127	34	are	be	AUX
esrj-91195	127	35	given	give	VERB
esrj-91195	127	36	,	,	PUNCT
esrj-91195	127	37	depends	depend	VERB
esrj-91195	127	38	on	on	ADP
esrj-91195	127	39	the	the	DET
esrj-91195	127	40	following	follow	VERB
esrj-91195	127	41	pattern	pattern	NOUN
esrj-91195	127	42	layer	layer	NOUN
esrj-91195	127	43	.	.	PUNCT
esrj-91195	128	1	the	the	DET
esrj-91195	128	2	distances	distance	NOUN
esrj-91195	128	3	between	between	ADP
esrj-91195	128	4	training	training	NOUN
esrj-91195	128	5	data	datum	NOUN
esrj-91195	128	6	and	and	CCONJ
esrj-91195	128	7	testing	testing	NOUN
esrj-91195	128	8	data	datum	NOUN
esrj-91195	128	9	are	be	AUX
esrj-91195	128	10	calculated	calculate	VERB
esrj-91195	128	11	in	in	ADP
esrj-91195	128	12	this	this	DET
esrj-91195	128	13	layer	layer	NOUN
esrj-91195	128	14	.	.	PUNCT
esrj-91195	129	1	the	the	DET
esrj-91195	129	2	results	result	NOUN
esrj-91195	129	3	are	be	AUX
esrj-91195	129	4	passed	pass	VERB
esrj-91195	129	5	through	through	ADP
esrj-91195	129	6	the	the	DET
esrj-91195	129	7	radial	radial	ADJ
esrj-91195	129	8	basis	basis	NOUN
esrj-91195	129	9	function	function	NOUN
esrj-91195	129	10	together	together	ADV
esrj-91195	129	11	with	with	ADP
esrj-91195	129	12	the	the	DET
esrj-91195	129	13	selected	select	VERB
esrj-91195	129	14	value	value	NOUN
esrj-91195	129	15	to	to	PART
esrj-91195	129	16	obtain	obtain	VERB
esrj-91195	129	17	the	the	DET
esrj-91195	129	18	weight	weight	NOUN
esrj-91195	129	19	values	value	NOUN
esrj-91195	129	20	.	.	PUNCT
esrj-91195	130	1	these	these	DET
esrj-91195	130	2	weight	weight	NOUN
esrj-91195	130	3	values	value	NOUN
esrj-91195	130	4	are	be	AUX
esrj-91195	130	5	transferred	transfer	VERB
esrj-91195	130	6	to	to	ADP
esrj-91195	130	7	the	the	DET
esrj-91195	130	8	numerator	numerator	NOUN
esrj-91195	130	9	and	and	CCONJ
esrj-91195	130	10	denominator	denominator	NOUN
esrj-91195	130	11	neurons	neuron	NOUN
esrj-91195	130	12	in	in	ADP
esrj-91195	130	13	the	the	DET
esrj-91195	130	14	summation	summation	NOUN
esrj-91195	130	15	layer	layer	NOUN
esrj-91195	130	16	.	.	PUNCT
esrj-91195	131	1	in	in	ADP
esrj-91195	131	2	the	the	DET
esrj-91195	131	3	numerator	numerator	NOUN
esrj-91195	131	4	neuron	neuron	PROPN
esrj-91195	131	5	,	,	PUNCT
esrj-91195	131	6	the	the	DET
esrj-91195	131	7	output	output	NOUN
esrj-91195	131	8	value	value	NOUN
esrj-91195	131	9	of	of	ADP
esrj-91195	131	10	the	the	DET
esrj-91195	131	11	training	training	NOUN
esrj-91195	131	12	data	datum	NOUN
esrj-91195	131	13	whose	whose	DET
esrj-91195	131	14	weight	weight	NOUN
esrj-91195	131	15	values	value	NOUN
esrj-91195	131	16	are	be	AUX
esrj-91195	131	17	in	in	ADP
esrj-91195	131	18	the	the	DET
esrj-91195	131	19	neuron	neuron	NOUN
esrj-91195	131	20	is	be	AUX
esrj-91195	131	21	multiplied	multiply	VERB
esrj-91195	131	22	and	and	CCONJ
esrj-91195	131	23	the	the	DET
esrj-91195	131	24	multiplication	multiplication	NOUN
esrj-91195	131	25	values	value	NOUN
esrj-91195	131	26	are	be	AUX
esrj-91195	131	27	summed	sum	VERB
esrj-91195	131	28	up	up	ADP
esrj-91195	131	29	.	.	PUNCT
esrj-91195	132	1	in	in	ADP
esrj-91195	132	2	the	the	DET
esrj-91195	132	3	denominator	denominator	NOUN
esrj-91195	132	4	neuron	neuron	NOUN
esrj-91195	132	5	,	,	PUNCT
esrj-91195	132	6	weights	weight	NOUN
esrj-91195	132	7	are	be	AUX
esrj-91195	132	8	summed	sum	VERB
esrj-91195	132	9	up	up	ADP
esrj-91195	132	10	directly	directly	ADV
esrj-91195	132	11	.	.	PUNCT
esrj-91195	133	1	the	the	DET
esrj-91195	133	2	output	output	NOUN
esrj-91195	133	3	value	value	NOUN
esrj-91195	133	4	is	be	AUX
esrj-91195	133	5	obtained	obtain	VERB
esrj-91195	133	6	by	by	ADP
esrj-91195	133	7	dividing	divide	VERB
esrj-91195	133	8	the	the	DET
esrj-91195	133	9	numerator	numerator	NOUN
esrj-91195	133	10	value	value	NOUN
esrj-91195	133	11	by	by	ADP
esrj-91195	133	12	the	the	DET
esrj-91195	133	13	denominator	denominator	NOUN
esrj-91195	133	14	value	value	NOUN
esrj-91195	133	15	in	in	ADP
esrj-91195	133	16	the	the	DET
esrj-91195	133	17	output	output	NOUN
esrj-91195	133	18	layer	layer	NOUN
esrj-91195	133	19	,	,	PUNCT
esrj-91195	133	20	as	as	SCONJ
esrj-91195	133	21	shown	show	VERB
esrj-91195	133	22	in	in	ADP
esrj-91195	133	23	equation	equation	NOUN
esrj-91195	133	24	(	(	PUNCT
esrj-91195	133	25	6	6	NUM
esrj-91195	133	26	)	)	PUNCT
esrj-91195	133	27	.	.	PUNCT
esrj-91195	134	1	(	(	PUNCT
esrj-91195	134	2	6	6	NUM
esrj-91195	134	3	)	)	PUNCT
esrj-91195	134	4	where	where	SCONJ
esrj-91195	134	5	n	n	PRON
esrj-91195	134	6	is	be	AUX
esrj-91195	134	7	the	the	DET
esrj-91195	134	8	number	number	NOUN
esrj-91195	134	9	of	of	ADP
esrj-91195	134	10	data	datum	NOUN
esrj-91195	134	11	,	,	PUNCT
esrj-91195	134	12	s	s	VERB
esrj-91195	134	13	is	be	AUX
esrj-91195	134	14	the	the	DET
esrj-91195	134	15	spread	spread	ADJ
esrj-91195	134	16	parameter	parameter	NOUN
esrj-91195	134	17	,	,	PUNCT
esrj-91195	134	18	and	and	CCONJ
esrj-91195	134	19	di	di	X
esrj-91195	134	20	2	2	NUM
esrj-91195	134	21	represents	represent	VERB
esrj-91195	134	22	the	the	DET
esrj-91195	134	23	scalar	scalar	ADJ
esrj-91195	134	24	function	function	NOUN
esrj-91195	134	25	,	,	PUNCT
esrj-91195	134	26	as	as	SCONJ
esrj-91195	134	27	shown	show	VERB
esrj-91195	134	28	in	in	ADP
esrj-91195	134	29	equation	equation	NOUN
esrj-91195	134	30	(	(	PUNCT
esrj-91195	134	31	7	7	NUM
esrj-91195	134	32	)	)	PUNCT
esrj-91195	134	33	.	.	PUNCT
esrj-91195	135	1	(	(	PUNCT
esrj-91195	135	2	7	7	X
esrj-91195	135	3	)	)	PUNCT
esrj-91195	135	4	375geoid	375geoid	PROPN
esrj-91195	135	5	undulation	undulation	NOUN
esrj-91195	135	6	prediction	prediction	NOUN
esrj-91195	135	7	using	use	VERB
esrj-91195	135	8	anns	ann	NOUN
esrj-91195	135	9	(	(	PUNCT
esrj-91195	135	10	rbfnn	rbfnn	NOUN
esrj-91195	135	11	and	and	CCONJ
esrj-91195	135	12	grnn	grnn	NOUN
esrj-91195	135	13	)	)	PUNCT
esrj-91195	135	14	,	,	PUNCT
esrj-91195	135	15	multiple	multiple	ADJ
esrj-91195	135	16	linear	linear	ADJ
esrj-91195	135	17	regression	regression	NOUN
esrj-91195	135	18	(	(	PUNCT
esrj-91195	135	19	mlr	mlr	NOUN
esrj-91195	135	20	)	)	PUNCT
esrj-91195	135	21	,	,	PUNCT
esrj-91195	135	22	and	and	CCONJ
esrj-91195	135	23	interpolation	interpolation	NOUN
esrj-91195	135	24	methods	method	NOUN
esrj-91195	135	25	:	:	PUNCT
esrj-91195	135	26	a	a	DET
esrj-91195	135	27	comparative	comparative	ADJ
esrj-91195	135	28	study	study	NOUN
esrj-91195	135	29	in	in	ADP
esrj-91195	135	30	this	this	DET
esrj-91195	135	31	method	method	NOUN
esrj-91195	135	32	,	,	PUNCT
esrj-91195	135	33	having	have	VERB
esrj-91195	135	34	only	only	ADV
esrj-91195	135	35	the	the	DET
esrj-91195	135	36	spread	spread	ADJ
esrj-91195	135	37	parameter	parameter	NOUN
esrj-91195	135	38	(	(	PUNCT
esrj-91195	135	39	s	s	NOUN
esrj-91195	135	40	)	)	PUNCT
esrj-91195	135	41	,	,	PUNCT
esrj-91195	135	42	there	there	PRON
esrj-91195	135	43	are	be	VERB
esrj-91195	135	44	not	not	PART
esrj-91195	135	45	as	as	ADV
esrj-91195	135	46	many	many	ADJ
esrj-91195	135	47	main	main	ADJ
esrj-91195	135	48	design	design	NOUN
esrj-91195	135	49	parameters	parameter	NOUN
esrj-91195	135	50	as	as	ADP
esrj-91195	135	51	with	with	ADP
esrj-91195	135	52	the	the	DET
esrj-91195	135	53	mlpnn	mlpnn	NOUN
esrj-91195	135	54	(	(	PUNCT
esrj-91195	135	55	e.g.	e.g.	ADV
esrj-91195	135	56	,	,	PUNCT
esrj-91195	135	57	number	number	NOUN
esrj-91195	135	58	of	of	ADP
esrj-91195	135	59	hidden	hidden	ADJ
esrj-91195	135	60	layers	layer	NOUN
esrj-91195	135	61	,	,	PUNCT
esrj-91195	135	62	number	number	NOUN
esrj-91195	135	63	of	of	ADP
esrj-91195	135	64	neurons	neuron	NOUN
esrj-91195	135	65	in	in	ADP
esrj-91195	135	66	each	each	DET
esrj-91195	135	67	hidden	hide	VERB
esrj-91195	135	68	layer	layer	NOUN
esrj-91195	135	69	,	,	PUNCT
esrj-91195	135	70	activation	activation	NOUN
esrj-91195	135	71	function	function	NOUN
esrj-91195	135	72	,	,	PUNCT
esrj-91195	135	73	and	and	CCONJ
esrj-91195	135	74	training	training	NOUN
esrj-91195	135	75	type	type	NOUN
esrj-91195	135	76	)	)	PUNCT
esrj-91195	135	77	.	.	PUNCT
esrj-91195	136	1	the	the	DET
esrj-91195	136	2	only	only	ADJ
esrj-91195	136	3	important	important	ADJ
esrj-91195	136	4	parameter	parameter	NOUN
esrj-91195	136	5	that	that	PRON
esrj-91195	136	6	has	have	VERB
esrj-91195	136	7	to	to	PART
esrj-91195	136	8	be	be	AUX
esrj-91195	136	9	determined	determine	VERB
esrj-91195	136	10	using	use	VERB
esrj-91195	136	11	this	this	DET
esrj-91195	136	12	method	method	NOUN
esrj-91195	136	13	is	be	AUX
esrj-91195	136	14	the	the	DET
esrj-91195	136	15	spread	spread	ADJ
esrj-91195	136	16	parameter	parameter	NOUN
esrj-91195	136	17	(	(	PUNCT
esrj-91195	136	18	s	s	NOUN
esrj-91195	136	19	)	)	PUNCT
esrj-91195	136	20	.	.	PUNCT
esrj-91195	137	1	there	there	PRON
esrj-91195	137	2	is	be	VERB
esrj-91195	137	3	no	no	DET
esrj-91195	137	4	specific	specific	ADJ
esrj-91195	137	5	rule	rule	NOUN
esrj-91195	137	6	about	about	ADP
esrj-91195	137	7	how	how	SCONJ
esrj-91195	137	8	this	this	DET
esrj-91195	137	9	parameter	parameter	NOUN
esrj-91195	137	10	is	be	AUX
esrj-91195	137	11	selected	select	VERB
esrj-91195	137	12	.	.	PUNCT
esrj-91195	138	1	in	in	ADP
esrj-91195	138	2	this	this	DET
esrj-91195	138	3	study	study	NOUN
esrj-91195	138	4	,	,	PUNCT
esrj-91195	138	5	different	different	ADJ
esrj-91195	138	6	spread	spread	NOUN
esrj-91195	138	7	parameters	parameter	NOUN
esrj-91195	138	8	between	between	ADP
esrj-91195	138	9	0	0	NUM
esrj-91195	138	10	and	and	CCONJ
esrj-91195	138	11	1	1	NUM
esrj-91195	138	12	were	be	AUX
esrj-91195	138	13	tested	test	VERB
esrj-91195	138	14	using	use	VERB
esrj-91195	138	15	the	the	DET
esrj-91195	138	16	minimum	minimum	ADJ
esrj-91195	138	17	rmse	rmse	NOUN
esrj-91195	138	18	criterion	criterion	NOUN
esrj-91195	138	19	.	.	PUNCT
esrj-91195	139	1	multiple	multiple	ADJ
esrj-91195	139	2	linear	linear	ADJ
esrj-91195	139	3	regression	regression	NOUN
esrj-91195	139	4	(	(	PUNCT
esrj-91195	139	5	mlr	mlr	NOUN
esrj-91195	139	6	)	)	PUNCT
esrj-91195	139	7	in	in	ADP
esrj-91195	139	8	regression	regression	NOUN
esrj-91195	139	9	methods	method	NOUN
esrj-91195	139	10	,	,	PUNCT
esrj-91195	139	11	the	the	DET
esrj-91195	139	12	influencing	influence	VERB
esrj-91195	139	13	variables	variable	NOUN
esrj-91195	139	14	are	be	AUX
esrj-91195	139	15	called	call	VERB
esrj-91195	139	16	explanatory	explanatory	ADJ
esrj-91195	139	17	variables	variable	NOUN
esrj-91195	139	18	(	(	PUNCT
esrj-91195	139	19	independent	independent	ADJ
esrj-91195	139	20	variables	variable	NOUN
esrj-91195	139	21	)	)	PUNCT
esrj-91195	139	22	,	,	PUNCT
esrj-91195	139	23	and	and	CCONJ
esrj-91195	139	24	the	the	DET
esrj-91195	139	25	affected	affected	ADJ
esrj-91195	139	26	variable	variable	NOUN
esrj-91195	139	27	is	be	AUX
esrj-91195	139	28	called	call	VERB
esrj-91195	139	29	the	the	DET
esrj-91195	139	30	described	describe	VERB
esrj-91195	139	31	variable	variable	NOUN
esrj-91195	139	32	(	(	PUNCT
esrj-91195	139	33	dependent	dependent	ADJ
esrj-91195	139	34	variable	variable	NOUN
esrj-91195	139	35	)	)	PUNCT
esrj-91195	139	36	.	.	PUNCT
esrj-91195	140	1	the	the	DET
esrj-91195	140	2	mlr	mlr	NOUN
esrj-91195	140	3	method	method	NOUN
esrj-91195	140	4	reveals	reveal	VERB
esrj-91195	140	5	the	the	DET
esrj-91195	140	6	causeeffect	causeeffect	NOUN
esrj-91195	140	7	relationship	relationship	NOUN
esrj-91195	140	8	between	between	ADP
esrj-91195	140	9	the	the	DET
esrj-91195	140	10	dependent	dependent	ADJ
esrj-91195	140	11	variable	variable	NOUN
esrj-91195	140	12	(	(	PUNCT
esrj-91195	140	13	y	y	NOUN
esrj-91195	140	14	)	)	PUNCT
esrj-91195	140	15	and	and	CCONJ
esrj-91195	140	16	independent	independent	ADJ
esrj-91195	140	17	variables	variable	NOUN
esrj-91195	140	18	(	(	PUNCT
esrj-91195	140	19	x1	x1	PROPN
esrj-91195	140	20	,	,	PUNCT
esrj-91195	140	21	x2	x2	PROPN
esrj-91195	140	22	,	,	PUNCT
esrj-91195	140	23	…	…	PUNCT
esrj-91195	140	24	,	,	PUNCT
esrj-91195	140	25	xn	xn	PROPN
esrj-91195	140	26	)	)	PUNCT
esrj-91195	140	27	as	as	ADP
esrj-91195	140	28	a	a	DET
esrj-91195	140	29	mathematical	mathematical	ADJ
esrj-91195	140	30	model	model	NOUN
esrj-91195	140	31	which	which	PRON
esrj-91195	140	32	can	can	AUX
esrj-91195	140	33	be	be	AUX
esrj-91195	140	34	written	write	VERB
esrj-91195	140	35	as	as	ADP
esrj-91195	140	36	equation	equation	NOUN
esrj-91195	140	37	(	(	PUNCT
esrj-91195	140	38	8)	8)	NUM
esrj-91195	140	39	(	(	PUNCT
esrj-91195	140	40	şahin	şahin	PROPN
esrj-91195	140	41	et	et	PROPN
esrj-91195	140	42	al	al	PROPN
esrj-91195	140	43	.	.	PROPN
esrj-91195	140	44	,	,	PUNCT
esrj-91195	140	45	2013	2013	NUM
esrj-91195	140	46	)	)	PUNCT
esrj-91195	140	47	.	.	PUNCT
esrj-91195	141	1	(	(	PUNCT
esrj-91195	141	2	8)	8)	NUM
esrj-91195	141	3	where	where	SCONJ
esrj-91195	141	4	a0	a0	NOUN
esrj-91195	141	5	,	,	PUNCT
esrj-91195	141	6	a1	a1	PROPN
esrj-91195	141	7	,	,	PUNCT
esrj-91195	141	8	a2	a2	PROPN
esrj-91195	141	9	,	,	PUNCT
esrj-91195	141	10	…	…	PUNCT
esrj-91195	141	11	,	,	PUNCT
esrj-91195	141	12	an	an	DET
esrj-91195	141	13	show	show	NOUN
esrj-91195	141	14	the	the	DET
esrj-91195	141	15	effect	effect	NOUN
esrj-91195	141	16	of	of	ADP
esrj-91195	141	17	each	each	DET
esrj-91195	141	18	independent	independent	ADJ
esrj-91195	141	19	variable	variable	NOUN
esrj-91195	141	20	on	on	ADP
esrj-91195	141	21	the	the	DET
esrj-91195	141	22	dependent	dependent	ADJ
esrj-91195	141	23	variable	variable	NOUN
esrj-91195	141	24	.	.	PUNCT
esrj-91195	142	1	interpolation	interpolation	NOUN
esrj-91195	142	2	methods	method	NOUN
esrj-91195	142	3	interpolation	interpolation	NOUN
esrj-91195	142	4	is	be	AUX
esrj-91195	142	5	the	the	DET
esrj-91195	142	6	prediction	prediction	NOUN
esrj-91195	142	7	of	of	ADP
esrj-91195	142	8	the	the	DET
esrj-91195	142	9	dimensions	dimension	NOUN
esrj-91195	142	10	of	of	ADP
esrj-91195	142	11	unmeasured	unmeasured	ADJ
esrj-91195	142	12	points	point	NOUN
esrj-91195	142	13	using	use	VERB
esrj-91195	142	14	measured	measured	ADJ
esrj-91195	142	15	values	value	NOUN
esrj-91195	142	16	of	of	ADP
esrj-91195	142	17	sample	sample	NOUN
esrj-91195	142	18	reference	reference	NOUN
esrj-91195	142	19	points	point	NOUN
esrj-91195	142	20	.	.	PUNCT
esrj-91195	143	1	in	in	ADP
esrj-91195	143	2	this	this	DET
esrj-91195	143	3	study	study	NOUN
esrj-91195	143	4	,	,	PUNCT
esrj-91195	143	5	surfer	surfer	NOUN
esrj-91195	143	6	software	software	NOUN
esrj-91195	143	7	v.	v.	ADP
esrj-91195	143	8	17	17	NUM
esrj-91195	143	9	was	be	AUX
esrj-91195	143	10	used	use	VERB
esrj-91195	143	11	for	for	ADP
esrj-91195	143	12	interpolation	interpolation	NOUN
esrj-91195	143	13	calculation	calculation	NOUN
esrj-91195	143	14	(	(	PUNCT
esrj-91195	143	15	golden	golden	ADJ
esrj-91195	143	16	software	software	NOUN
esrj-91195	143	17	,	,	PUNCT
esrj-91195	143	18	2019	2019	NUM
esrj-91195	143	19	)	)	PUNCT
esrj-91195	143	20	.	.	PUNCT
esrj-91195	144	1	the	the	DET
esrj-91195	144	2	software	software	NOUN
esrj-91195	144	3	turns	turn	VERB
esrj-91195	144	4	both	both	DET
esrj-91195	144	5	simple	simple	ADJ
esrj-91195	144	6	and	and	CCONJ
esrj-91195	144	7	complex	complex	ADJ
esrj-91195	144	8	data	datum	NOUN
esrj-91195	144	9	into	into	ADP
esrj-91195	144	10	understandable	understandable	ADJ
esrj-91195	144	11	visual	visual	ADJ
esrj-91195	144	12	tools	tool	NOUN
esrj-91195	144	13	such	such	ADJ
esrj-91195	144	14	as	as	ADP
esrj-91195	144	15	maps	map	NOUN
esrj-91195	144	16	,	,	PUNCT
esrj-91195	144	17	charts	chart	NOUN
esrj-91195	144	18	,	,	PUNCT
esrj-91195	144	19	and	and	CCONJ
esrj-91195	144	20	models	model	NOUN
esrj-91195	144	21	.	.	PUNCT
esrj-91195	145	1	the	the	DET
esrj-91195	145	2	surfer	surfer	NOUN
esrj-91195	145	3	software	software	NOUN
esrj-91195	145	4	v.	v.	ADP
esrj-91195	145	5	17	17	NUM
esrj-91195	145	6	offers	offer	VERB
esrj-91195	145	7	users	user	NOUN
esrj-91195	145	8	the	the	DET
esrj-91195	145	9	opportunity	opportunity	NOUN
esrj-91195	145	10	to	to	PART
esrj-91195	145	11	determine	determine	VERB
esrj-91195	145	12	a	a	DET
esrj-91195	145	13	geoid	geoid	NOUN
esrj-91195	145	14	using	use	VERB
esrj-91195	145	15	12	12	NUM
esrj-91195	145	16	different	different	ADJ
esrj-91195	145	17	interpolation	interpolation	NOUN
esrj-91195	145	18	methods	method	NOUN
esrj-91195	145	19	.	.	PUNCT
esrj-91195	146	1	of	of	ADP
esrj-91195	146	2	these	these	DET
esrj-91195	146	3	12	12	NUM
esrj-91195	146	4	interpolation	interpolation	NOUN
esrj-91195	146	5	methods	method	NOUN
esrj-91195	146	6	,	,	PUNCT
esrj-91195	146	7	those	those	PRON
esrj-91195	146	8	used	use	VERB
esrj-91195	146	9	for	for	ADP
esrj-91195	146	10	this	this	DET
esrj-91195	146	11	study	study	NOUN
esrj-91195	146	12	included	include	VERB
esrj-91195	146	13	the	the	DET
esrj-91195	146	14	kriging	krige	VERB
esrj-91195	146	15	(	(	PUNCT
esrj-91195	146	16	krig	krig	PROPN
esrj-91195	146	17	)	)	PUNCT
esrj-91195	146	18	,	,	PUNCT
esrj-91195	146	19	inverse	inverse	NOUN
esrj-91195	146	20	distance	distance	NOUN
esrj-91195	146	21	to	to	ADP
esrj-91195	146	22	a	a	DET
esrj-91195	146	23	power	power	NOUN
esrj-91195	146	24	(	(	PUNCT
esrj-91195	146	25	idp	idp	PROPN
esrj-91195	146	26	)	)	PUNCT
esrj-91195	146	27	,	,	PUNCT
esrj-91195	146	28	triangulation	triangulation	NOUN
esrj-91195	146	29	with	with	ADP
esrj-91195	146	30	linear	linear	ADJ
esrj-91195	146	31	interpolation	interpolation	NOUN
esrj-91195	146	32	(	(	PUNCT
esrj-91195	146	33	tli	tli	NOUN
esrj-91195	146	34	)	)	PUNCT
esrj-91195	146	35	,	,	PUNCT
esrj-91195	146	36	minimum	minimum	ADJ
esrj-91195	146	37	curvature	curvature	NOUN
esrj-91195	146	38	surface	surface	NOUN
esrj-91195	146	39	(	(	PUNCT
esrj-91195	146	40	mcs	mcs	NOUN
esrj-91195	146	41	)	)	PUNCT
esrj-91195	146	42	,	,	PUNCT
esrj-91195	146	43	natural	natural	ADJ
esrj-91195	146	44	neighbor	neighbor	NOUN
esrj-91195	146	45	(	(	PUNCT
esrj-91195	146	46	nn	nn	PROPN
esrj-91195	146	47	)	)	PUNCT
esrj-91195	146	48	,	,	PUNCT
esrj-91195	146	49	nearest	near	ADJ
esrj-91195	146	50	neighbor	neighbor	NOUN
esrj-91195	146	51	(	(	PUNCT
esrj-91195	146	52	nrn	nrn	PROPN
esrj-91195	146	53	)	)	PUNCT
esrj-91195	146	54	,	,	PUNCT
esrj-91195	146	55	local	local	ADJ
esrj-91195	146	56	polynomial	polynomial	ADJ
esrj-91195	146	57	(	(	PUNCT
esrj-91195	146	58	lp	lp	NOUN
esrj-91195	146	59	)	)	PUNCT
esrj-91195	146	60	,	,	PUNCT
esrj-91195	146	61	radial	radial	ADJ
esrj-91195	146	62	basis	basis	NOUN
esrj-91195	146	63	function	function	NOUN
esrj-91195	146	64	(	(	PUNCT
esrj-91195	146	65	rbf	rbf	PROPN
esrj-91195	146	66	)	)	PUNCT
esrj-91195	146	67	,	,	PUNCT
esrj-91195	146	68	polynomial	polynomial	ADJ
esrj-91195	146	69	regression	regression	NOUN
esrj-91195	146	70	(	(	PUNCT
esrj-91195	146	71	pr	pr	NOUN
esrj-91195	146	72	)	)	PUNCT
esrj-91195	146	73	,	,	PUNCT
esrj-91195	146	74	and	and	CCONJ
esrj-91195	146	75	modified	modify	VERB
esrj-91195	146	76	shepard	shepard	NOUN
esrj-91195	146	77	’s	’s	PART
esrj-91195	146	78	(	(	PUNCT
esrj-91195	146	79	ms	ms	NOUN
esrj-91195	146	80	)	)	PUNCT
esrj-91195	146	81	.	.	PUNCT
esrj-91195	147	1	detailed	detailed	ADJ
esrj-91195	147	2	information	information	NOUN
esrj-91195	147	3	about	about	ADP
esrj-91195	147	4	these	these	DET
esrj-91195	147	5	ten	ten	NUM
esrj-91195	147	6	methods	method	NOUN
esrj-91195	147	7	can	can	AUX
esrj-91195	147	8	be	be	AUX
esrj-91195	147	9	found	find	VERB
esrj-91195	147	10	in	in	ADP
esrj-91195	147	11	the	the	DET
esrj-91195	147	12	work	work	NOUN
esrj-91195	147	13	of	of	ADP
esrj-91195	147	14	keckler	keckler	NOUN
esrj-91195	147	15	(	(	PUNCT
esrj-91195	147	16	1995	1995	NUM
esrj-91195	147	17	)	)	PUNCT
esrj-91195	147	18	.	.	PUNCT
esrj-91195	148	1	k	k	X
esrj-91195	148	2	-	-	ADJ
esrj-91195	148	3	fold	fold	ADJ
esrj-91195	148	4	cross	cross	ADJ
esrj-91195	148	5	-	-	ADJ
esrj-91195	148	6	validation	validation	ADJ
esrj-91195	148	7	method	method	NOUN
esrj-91195	148	8	in	in	ADP
esrj-91195	148	9	classic	classic	ADJ
esrj-91195	148	10	and	and	CCONJ
esrj-91195	148	11	ai	ai	ADJ
esrj-91195	148	12	applications	application	NOUN
esrj-91195	148	13	,	,	PUNCT
esrj-91195	148	14	datasets	dataset	NOUN
esrj-91195	148	15	are	be	AUX
esrj-91195	148	16	divided	divide	VERB
esrj-91195	148	17	into	into	ADP
esrj-91195	148	18	those	those	PRON
esrj-91195	148	19	for	for	ADP
esrj-91195	148	20	the	the	DET
esrj-91195	148	21	training	training	NOUN
esrj-91195	148	22	and	and	CCONJ
esrj-91195	148	23	those	those	PRON
esrj-91195	148	24	for	for	ADP
esrj-91195	148	25	the	the	DET
esrj-91195	148	26	testing	testing	NOUN
esrj-91195	148	27	of	of	ADP
esrj-91195	148	28	the	the	DET
esrj-91195	148	29	established	establish	VERB
esrj-91195	148	30	model	model	NOUN
esrj-91195	148	31	.	.	PUNCT
esrj-91195	149	1	this	this	DET
esrj-91195	149	2	separation	separation	NOUN
esrj-91195	149	3	method	method	NOUN
esrj-91195	149	4	is	be	AUX
esrj-91195	149	5	carried	carry	VERB
esrj-91195	149	6	out	out	ADP
esrj-91195	149	7	in	in	ADP
esrj-91195	149	8	several	several	ADJ
esrj-91195	149	9	ways	way	NOUN
esrj-91195	149	10	.	.	PUNCT
esrj-91195	150	1	the	the	DET
esrj-91195	150	2	hold	hold	VERB
esrj-91195	150	3	-	-	PUNCT
esrj-91195	150	4	out	out	ADP
esrj-91195	150	5	method	method	NOUN
esrj-91195	150	6	is	be	AUX
esrj-91195	150	7	widely	widely	ADV
esrj-91195	150	8	used	use	VERB
esrj-91195	150	9	to	to	PART
esrj-91195	150	10	provide	provide	VERB
esrj-91195	150	11	generalization	generalization	NOUN
esrj-91195	150	12	.	.	PUNCT
esrj-91195	151	1	in	in	ADP
esrj-91195	151	2	classic	classic	ADJ
esrj-91195	151	3	and	and	CCONJ
esrj-91195	151	4	ai	ai	PROPN
esrj-91195	151	5	methods	method	NOUN
esrj-91195	151	6	,	,	PUNCT
esrj-91195	151	7	the	the	DET
esrj-91195	151	8	sampling	sample	VERB
esrj-91195	151	9	methodology	methodology	NOUN
esrj-91195	151	10	used	use	VERB
esrj-91195	151	11	for	for	ADP
esrj-91195	151	12	data	datum	NOUN
esrj-91195	151	13	division	division	NOUN
esrj-91195	151	14	can	can	AUX
esrj-91195	151	15	have	have	VERB
esrj-91195	151	16	a	a	DET
esrj-91195	151	17	significant	significant	ADJ
esrj-91195	151	18	impact	impact	NOUN
esrj-91195	151	19	on	on	ADP
esrj-91195	151	20	the	the	DET
esrj-91195	151	21	quality	quality	NOUN
esrj-91195	151	22	of	of	ADP
esrj-91195	151	23	the	the	DET
esrj-91195	151	24	subsets	subset	NOUN
esrj-91195	151	25	used	use	VERB
esrj-91195	151	26	for	for	ADP
esrj-91195	151	27	training	training	NOUN
esrj-91195	151	28	and	and	CCONJ
esrj-91195	151	29	testing	testing	NOUN
esrj-91195	151	30	.	.	PUNCT
esrj-91195	152	1	the	the	DET
esrj-91195	152	2	k	k	ADJ
esrj-91195	152	3	-	-	ADJ
esrj-91195	152	4	fold	fold	ADJ
esrj-91195	152	5	crossvalidation	crossvalidation	NOUN
esrj-91195	152	6	method	method	NOUN
esrj-91195	152	7	is	be	AUX
esrj-91195	152	8	one	one	NUM
esrj-91195	152	9	of	of	ADP
esrj-91195	152	10	the	the	DET
esrj-91195	152	11	methods	method	NOUN
esrj-91195	152	12	used	use	VERB
esrj-91195	152	13	in	in	ADP
esrj-91195	152	14	data	datum	NOUN
esrj-91195	152	15	partition	partition	NOUN
esrj-91195	152	16	.	.	PUNCT
esrj-91195	153	1	in	in	ADP
esrj-91195	153	2	this	this	DET
esrj-91195	153	3	validation	validation	NOUN
esrj-91195	153	4	method	method	NOUN
esrj-91195	153	5	,	,	PUNCT
esrj-91195	153	6	the	the	DET
esrj-91195	153	7	dataset	dataset	NOUN
esrj-91195	153	8	to	to	PART
esrj-91195	153	9	be	be	AUX
esrj-91195	153	10	used	use	VERB
esrj-91195	153	11	in	in	ADP
esrj-91195	153	12	the	the	DET
esrj-91195	153	13	prediction	prediction	NOUN
esrj-91195	153	14	is	be	AUX
esrj-91195	153	15	randomly	randomly	ADV
esrj-91195	153	16	divided	divide	VERB
esrj-91195	153	17	into	into	ADP
esrj-91195	153	18	a	a	DET
esrj-91195	153	19	k	k	NOUN
esrj-91195	153	20	number	number	NOUN
esrj-91195	153	21	of	of	ADP
esrj-91195	153	22	parts	part	NOUN
esrj-91195	153	23	.	.	PUNCT
esrj-91195	154	1	the	the	DET
esrj-91195	154	2	testing	testing	NOUN
esrj-91195	154	3	datasets	dataset	NOUN
esrj-91195	154	4	consist	consist	VERB
esrj-91195	154	5	of	of	ADP
esrj-91195	154	6	parts	part	NOUN
esrj-91195	154	7	created	create	VERB
esrj-91195	154	8	in	in	ADP
esrj-91195	154	9	the	the	DET
esrj-91195	154	10	order	order	NOUN
esrj-91195	154	11	of	of	ADP
esrj-91195	154	12	each	each	DET
esrj-91195	154	13	separate	separate	ADJ
esrj-91195	154	14	training	training	NOUN
esrj-91195	154	15	part	part	NOUN
esrj-91195	154	16	(	(	PUNCT
esrj-91195	154	17	k1	k1	NOUN
esrj-91195	154	18	…	…	PUNCT
esrj-91195	154	19	)	)	PUNCT
esrj-91195	154	20	.	.	PUNCT
esrj-91195	155	1	in	in	ADP
esrj-91195	155	2	this	this	DET
esrj-91195	155	3	way	way	NOUN
esrj-91195	155	4	,	,	PUNCT
esrj-91195	155	5	every	every	DET
esrj-91195	155	6	part	part	NOUN
esrj-91195	155	7	that	that	PRON
esrj-91195	155	8	is	be	AUX
esrj-91195	155	9	created	create	VERB
esrj-91195	155	10	up	up	ADP
esrj-91195	155	11	to	to	ADP
esrj-91195	155	12	k	k	PROPN
esrj-91195	155	13	is	be	AUX
esrj-91195	155	14	used	use	VERB
esrj-91195	155	15	as	as	ADP
esrj-91195	155	16	a	a	DET
esrj-91195	155	17	testing	testing	NOUN
esrj-91195	155	18	set	set	NOUN
esrj-91195	155	19	.	.	PUNCT
esrj-91195	156	1	the	the	DET
esrj-91195	156	2	accuracy	accuracy	NOUN
esrj-91195	156	3	of	of	ADP
esrj-91195	156	4	the	the	DET
esrj-91195	156	5	method	method	NOUN
esrj-91195	156	6	is	be	AUX
esrj-91195	156	7	determined	determine	VERB
esrj-91195	156	8	by	by	ADP
esrj-91195	156	9	taking	take	VERB
esrj-91195	156	10	the	the	DET
esrj-91195	156	11	average	average	NOUN
esrj-91195	156	12	of	of	ADP
esrj-91195	156	13	the	the	DET
esrj-91195	156	14	accuracy	accuracy	NOUN
esrj-91195	156	15	values	value	NOUN
esrj-91195	156	16	obtained	obtain	VERB
esrj-91195	156	17	from	from	ADP
esrj-91195	156	18	each	each	DET
esrj-91195	156	19	part	part	NOUN
esrj-91195	156	20	(	(	PUNCT
esrj-91195	156	21	stone	stone	NOUN
esrj-91195	156	22	,	,	PUNCT
esrj-91195	156	23	1974	1974	NUM
esrj-91195	156	24	;	;	PUNCT
esrj-91195	156	25	kohavi	kohavi	NOUN
esrj-91195	156	26	,	,	PUNCT
esrj-91195	156	27	1995	1995	NUM
esrj-91195	156	28	;	;	PUNCT
esrj-91195	157	1	rodriguez	rodriguez	PROPN
esrj-91195	157	2	et	et	PROPN
esrj-91195	157	3	al	al	PROPN
esrj-91195	157	4	.	.	PROPN
esrj-91195	157	5	,	,	PUNCT
esrj-91195	157	6	2009	2009	NUM
esrj-91195	157	7	)	)	PUNCT
esrj-91195	157	8	.	.	PUNCT
esrj-91195	158	1	in	in	ADP
esrj-91195	158	2	this	this	DET
esrj-91195	158	3	study	study	NOUN
esrj-91195	158	4	,	,	PUNCT
esrj-91195	158	5	the	the	DET
esrj-91195	158	6	value	value	NOUN
esrj-91195	158	7	of	of	ADP
esrj-91195	158	8	k	k	PROPN
esrj-91195	158	9	was	be	AUX
esrj-91195	158	10	taken	take	VERB
esrj-91195	158	11	as	as	ADP
esrj-91195	158	12	5	5	NUM
esrj-91195	158	13	(	(	PUNCT
esrj-91195	158	14	figure	figure	NOUN
esrj-91195	158	15	5	5	NUM
esrj-91195	158	16	)	)	PUNCT
esrj-91195	158	17	.	.	PUNCT
esrj-91195	159	1	in	in	ADP
esrj-91195	159	2	figure	figure	NOUN
esrj-91195	159	3	5	5	NUM
esrj-91195	159	4	,	,	PUNCT
esrj-91195	159	5	the	the	DET
esrj-91195	159	6	dataset	dataset	NOUN
esrj-91195	159	7	is	be	AUX
esrj-91195	159	8	divided	divide	VERB
esrj-91195	159	9	into	into	ADP
esrj-91195	159	10	five	five	NUM
esrj-91195	159	11	equal	equal	ADJ
esrj-91195	159	12	parts	part	NOUN
esrj-91195	159	13	.	.	PUNCT
esrj-91195	160	1	the	the	DET
esrj-91195	160	2	green	green	ADJ
esrj-91195	160	3	parts	part	NOUN
esrj-91195	160	4	represent	represent	VERB
esrj-91195	160	5	the	the	DET
esrj-91195	160	6	training	training	NOUN
esrj-91195	160	7	data	datum	NOUN
esrj-91195	160	8	and	and	CCONJ
esrj-91195	160	9	the	the	DET
esrj-91195	160	10	orange	orange	PROPN
esrj-91195	160	11	parts	part	NOUN
esrj-91195	160	12	the	the	DET
esrj-91195	160	13	testing	testing	NOUN
esrj-91195	160	14	data	datum	NOUN
esrj-91195	160	15	.	.	PUNCT
esrj-91195	161	1	in	in	ADP
esrj-91195	161	2	other	other	ADJ
esrj-91195	161	3	words	word	NOUN
esrj-91195	161	4	,	,	PUNCT
esrj-91195	161	5	when	when	SCONJ
esrj-91195	161	6	four	four	NUM
esrj-91195	161	7	parts	part	NOUN
esrj-91195	161	8	of	of	ADP
esrj-91195	161	9	five	five	NUM
esrj-91195	161	10	datasets	dataset	NOUN
esrj-91195	161	11	are	be	AUX
esrj-91195	161	12	used	use	VERB
esrj-91195	161	13	for	for	ADP
esrj-91195	161	14	training	training	NOUN
esrj-91195	161	15	,	,	PUNCT
esrj-91195	161	16	one	one	NUM
esrj-91195	161	17	part	part	NOUN
esrj-91195	161	18	(	(	PUNCT
esrj-91195	161	19	the	the	DET
esrj-91195	161	20	fifth	fifth	ADJ
esrj-91195	161	21	)	)	PUNCT
esrj-91195	161	22	is	be	AUX
esrj-91195	161	23	used	use	VERB
esrj-91195	161	24	for	for	ADP
esrj-91195	161	25	testing	testing	NOUN
esrj-91195	161	26	.	.	PUNCT
esrj-91195	162	1	in	in	ADP
esrj-91195	162	2	this	this	DET
esrj-91195	162	3	way	way	NOUN
esrj-91195	162	4	,	,	PUNCT
esrj-91195	162	5	modeling	modeling	NOUN
esrj-91195	162	6	was	be	AUX
esrj-91195	162	7	performed	perform	VERB
esrj-91195	162	8	five	five	NUM
esrj-91195	162	9	times	time	NOUN
esrj-91195	162	10	,	,	PUNCT
esrj-91195	162	11	using	use	VERB
esrj-91195	162	12	each	each	DET
esrj-91195	162	13	part	part	NOUN
esrj-91195	162	14	once	once	ADV
esrj-91195	162	15	for	for	ADP
esrj-91195	162	16	testing	testing	NOUN
esrj-91195	162	17	and	and	CCONJ
esrj-91195	162	18	the	the	DET
esrj-91195	162	19	other	other	ADJ
esrj-91195	162	20	four	four	NUM
esrj-91195	162	21	parts	part	NOUN
esrj-91195	162	22	for	for	ADP
esrj-91195	162	23	training	training	NOUN
esrj-91195	162	24	.	.	PUNCT
esrj-91195	163	1	the	the	DET
esrj-91195	163	2	arithmetic	arithmetic	ADJ
esrj-91195	163	3	averages	average	NOUN
esrj-91195	163	4	of	of	ADP
esrj-91195	163	5	the	the	DET
esrj-91195	163	6	success	success	NOUN
esrj-91195	163	7	rates	rate	NOUN
esrj-91195	163	8	were	be	AUX
esrj-91195	163	9	calculated	calculate	VERB
esrj-91195	163	10	for	for	ADP
esrj-91195	163	11	the	the	DET
esrj-91195	163	12	five	five	NUM
esrj-91195	163	13	models	model	NOUN
esrj-91195	163	14	and	and	CCONJ
esrj-91195	163	15	the	the	DET
esrj-91195	163	16	resulting	result	VERB
esrj-91195	163	17	success	success	NOUN
esrj-91195	163	18	rate	rate	NOUN
esrj-91195	163	19	was	be	AUX
esrj-91195	163	20	determined	determine	VERB
esrj-91195	163	21	.	.	PUNCT
esrj-91195	164	1	ziggah	ziggah	PROPN
esrj-91195	164	2	et	et	PROPN
esrj-91195	164	3	al	al	PROPN
esrj-91195	164	4	.	.	PROPN
esrj-91195	165	1	(	(	PUNCT
esrj-91195	165	2	2019	2019	NUM
esrj-91195	165	3	)	)	PUNCT
esrj-91195	165	4	demonstrated	demonstrate	VERB
esrj-91195	165	5	the	the	DET
esrj-91195	165	6	potential	potential	NOUN
esrj-91195	165	7	and	and	CCONJ
esrj-91195	165	8	feasibility	feasibility	NOUN
esrj-91195	165	9	of	of	ADP
esrj-91195	165	10	using	use	VERB
esrj-91195	165	11	the	the	DET
esrj-91195	165	12	k	k	ADJ
esrj-91195	165	13	-	-	ADJ
esrj-91195	165	14	fold	fold	ADJ
esrj-91195	165	15	cross	cross	ADJ
esrj-91195	165	16	-	-	ADJ
esrj-91195	165	17	validation	validation	ADJ
esrj-91195	165	18	method	method	NOUN
esrj-91195	165	19	on	on	ADP
esrj-91195	165	20	coordinate	coordinate	NOUN
esrj-91195	165	21	transformation	transformation	NOUN
esrj-91195	165	22	from	from	ADP
esrj-91195	165	23	geodetic	geodetic	ADJ
esrj-91195	165	24	applications	application	NOUN
esrj-91195	165	25	.	.	PUNCT
esrj-91195	166	1	the	the	DET
esrj-91195	166	2	dataset	dataset	NOUN
esrj-91195	166	3	for	for	ADP
esrj-91195	166	4	a	a	DET
esrj-91195	166	5	ghana	ghana	PROPN
esrj-91195	166	6	geodetic	geodetic	ADJ
esrj-91195	166	7	reference	reference	NOUN
esrj-91195	166	8	network	network	NOUN
esrj-91195	166	9	was	be	AUX
esrj-91195	166	10	divided	divide	VERB
esrj-91195	166	11	into	into	ADP
esrj-91195	166	12	separate	separate	ADJ
esrj-91195	166	13	training	training	NOUN
esrj-91195	166	14	and	and	CCONJ
esrj-91195	166	15	testing	testing	NOUN
esrj-91195	166	16	data	datum	NOUN
esrj-91195	166	17	according	accord	VERB
esrj-91195	166	18	to	to	ADP
esrj-91195	166	19	the	the	DET
esrj-91195	166	20	hold	hold	VERB
esrj-91195	166	21	-	-	PUNCT
esrj-91195	166	22	out	out	ADP
esrj-91195	166	23	method	method	NOUN
esrj-91195	166	24	.	.	PUNCT
esrj-91195	167	1	the	the	DET
esrj-91195	167	2	findings	finding	NOUN
esrj-91195	167	3	revealed	reveal	VERB
esrj-91195	167	4	that	that	SCONJ
esrj-91195	167	5	false	false	ADJ
esrj-91195	167	6	results	result	NOUN
esrj-91195	167	7	could	could	AUX
esrj-91195	167	8	be	be	AUX
esrj-91195	167	9	generated	generate	VERB
esrj-91195	167	10	,	,	PUNCT
esrj-91195	167	11	and	and	CCONJ
esrj-91195	167	12	it	it	PRON
esrj-91195	167	13	was	be	AUX
esrj-91195	167	14	shown	show	VERB
esrj-91195	167	15	that	that	SCONJ
esrj-91195	167	16	the	the	DET
esrj-91195	167	17	use	use	NOUN
esrj-91195	167	18	of	of	ADP
esrj-91195	167	19	the	the	DET
esrj-91195	167	20	k	k	ADJ
esrj-91195	167	21	-	-	ADJ
esrj-91195	167	22	fold	fold	ADJ
esrj-91195	167	23	cross	cross	ADJ
esrj-91195	167	24	-	-	ADJ
esrj-91195	167	25	validation	validation	ADJ
esrj-91195	167	26	method	method	NOUN
esrj-91195	167	27	provided	provide	VERB
esrj-91195	167	28	a	a	DET
esrj-91195	167	29	better	well	ADJ
esrj-91195	167	30	solution	solution	NOUN
esrj-91195	167	31	for	for	ADP
esrj-91195	167	32	the	the	DET
esrj-91195	167	33	correct	correct	ADJ
esrj-91195	167	34	evaluation	evaluation	NOUN
esrj-91195	167	35	of	of	ADP
esrj-91195	167	36	method	method	NOUN
esrj-91195	167	37	performance	performance	NOUN
esrj-91195	167	38	,	,	PUNCT
esrj-91195	167	39	especially	especially	ADV
esrj-91195	167	40	in	in	ADP
esrj-91195	167	41	the	the	DET
esrj-91195	167	42	case	case	NOUN
esrj-91195	167	43	of	of	ADP
esrj-91195	167	44	a	a	DET
esrj-91195	167	45	sparse	sparse	ADJ
esrj-91195	167	46	dataset	dataset	NOUN
esrj-91195	167	47	.	.	PUNCT
esrj-91195	168	1	figure	figure	NOUN
esrj-91195	168	2	5	5	NUM
esrj-91195	168	3	.	.	NOUN
esrj-91195	168	4	5	5	NUM
esrj-91195	168	5	-	-	PUNCT
esrj-91195	168	6	layer	layer	NOUN
esrj-91195	168	7	cross	cross	ADJ
esrj-91195	168	8	-	-	ADJ
esrj-91195	168	9	validation	validation	ADJ
esrj-91195	168	10	example	example	NOUN
esrj-91195	168	11	performance	performance	NOUN
esrj-91195	168	12	evaluation	evaluation	NOUN
esrj-91195	168	13	criteria	criterion	NOUN
esrj-91195	168	14	to	to	PART
esrj-91195	168	15	measure	measure	VERB
esrj-91195	168	16	the	the	DET
esrj-91195	168	17	prediction	prediction	NOUN
esrj-91195	168	18	success	success	NOUN
esrj-91195	168	19	of	of	ADP
esrj-91195	168	20	the	the	DET
esrj-91195	168	21	developed	develop	VERB
esrj-91195	168	22	models	model	NOUN
esrj-91195	168	23	,	,	PUNCT
esrj-91195	168	24	this	this	DET
esrj-91195	168	25	study	study	NOUN
esrj-91195	168	26	used	use	VERB
esrj-91195	168	27	the	the	DET
esrj-91195	168	28	root	root	NOUN
esrj-91195	168	29	mean	mean	ADJ
esrj-91195	168	30	square	square	ADJ
esrj-91195	168	31	error	error	NOUN
esrj-91195	168	32	(	(	PUNCT
esrj-91195	168	33	rmse	rmse	NOUN
esrj-91195	168	34	)	)	PUNCT
esrj-91195	168	35	,	,	PUNCT
esrj-91195	168	36	mean	mean	VERB
esrj-91195	168	37	absolute	absolute	ADJ
esrj-91195	168	38	error	error	NOUN
esrj-91195	168	39	(	(	PUNCT
esrj-91195	168	40	mae	mae	PROPN
esrj-91195	168	41	)	)	PUNCT
esrj-91195	168	42	,	,	PUNCT
esrj-91195	168	43	nash	nash	NOUN
esrj-91195	168	44	-	-	PUNCT
esrj-91195	168	45	sutcliffe	sutcliffe	PROPN
esrj-91195	168	46	efficiency	efficiency	NOUN
esrj-91195	168	47	(	(	PUNCT
esrj-91195	168	48	nse	nse	NOUN
esrj-91195	168	49	)	)	PUNCT
esrj-91195	168	50	coefficient	coefficient	NOUN
esrj-91195	168	51	,	,	PUNCT
esrj-91195	168	52	and	and	CCONJ
esrj-91195	168	53	the	the	DET
esrj-91195	168	54	coefficient	coefficient	NOUN
esrj-91195	168	55	of	of	ADP
esrj-91195	168	56	determination	determination	NOUN
esrj-91195	168	57	(	(	PUNCT
esrj-91195	168	58	r2	r2	PROPN
esrj-91195	168	59	)	)	PUNCT
esrj-91195	168	60	.	.	PUNCT
esrj-91195	169	1	the	the	DET
esrj-91195	169	2	equations	equation	NOUN
esrj-91195	169	3	used	use	VERB
esrj-91195	169	4	in	in	ADP
esrj-91195	169	5	the	the	DET
esrj-91195	169	6	calculation	calculation	NOUN
esrj-91195	169	7	of	of	ADP
esrj-91195	169	8	the	the	DET
esrj-91195	169	9	selected	select	VERB
esrj-91195	169	10	statistical	statistical	ADJ
esrj-91195	169	11	criteria	criterion	NOUN
esrj-91195	169	12	are	be	AUX
esrj-91195	169	13	given	give	VERB
esrj-91195	169	14	below	below	ADV
esrj-91195	169	15	.	.	PUNCT
esrj-91195	170	1	root	root	NOUN
esrj-91195	170	2	mean	mean	PROPN
esrj-91195	170	3	square	square	NOUN
esrj-91195	170	4	error	error	NOUN
esrj-91195	170	5	:	:	PUNCT
esrj-91195	170	6	(	(	PUNCT
esrj-91195	170	7	9	9	X
esrj-91195	170	8	)	)	PUNCT
esrj-91195	170	9	mean	mean	VERB
esrj-91195	170	10	absolute	absolute	ADJ
esrj-91195	170	11	error	error	NOUN
esrj-91195	170	12	:	:	PUNCT
esrj-91195	170	13	(	(	PUNCT
esrj-91195	170	14	10	10	NUM
esrj-91195	170	15	)	)	PUNCT
esrj-91195	170	16	nash	nash	NOUN
esrj-91195	170	17	–	–	PUNCT
esrj-91195	170	18	sutcliffe	sutcliffe	PROPN
esrj-91195	170	19	efficiency	efficiency	NOUN
esrj-91195	170	20	coefficient	coefficient	NOUN
esrj-91195	170	21	:	:	PUNCT
esrj-91195	170	22	(	(	PUNCT
esrj-91195	170	23	11	11	NUM
esrj-91195	170	24	)	)	PUNCT
esrj-91195	170	25	coefficient	coefficient	NOUN
esrj-91195	170	26	of	of	ADP
esrj-91195	170	27	determination	determination	NOUN
esrj-91195	170	28	:	:	PUNCT
esrj-91195	170	29	(	(	PUNCT
esrj-91195	170	30	12	12	NUM
esrj-91195	170	31	)	)	PUNCT
esrj-91195	170	32	where	where	SCONJ
esrj-91195	170	33	n	n	PRON
esrj-91195	170	34	is	be	AUX
esrj-91195	170	35	the	the	DET
esrj-91195	170	36	number	number	NOUN
esrj-91195	170	37	of	of	ADP
esrj-91195	170	38	datasets	dataset	NOUN
esrj-91195	170	39	,	,	PUNCT
esrj-91195	170	40	oi	oi	ADV
esrj-91195	170	41	is	be	AUX
esrj-91195	170	42	the	the	DET
esrj-91195	170	43	observed	observe	VERB
esrj-91195	170	44	geoid	geoid	ADJ
esrj-91195	170	45	undulation	undulation	NOUN
esrj-91195	170	46	value	value	NOUN
esrj-91195	170	47	,	,	PUNCT
esrj-91195	170	48	is	be	AUX
esrj-91195	170	49	the	the	DET
esrj-91195	170	50	mean	mean	NOUN
esrj-91195	170	51	of	of	ADP
esrj-91195	170	52	the	the	DET
esrj-91195	170	53	observed	observe	VERB
esrj-91195	170	54	geoid	geoid	ADJ
esrj-91195	170	55	undulation	undulation	NOUN
esrj-91195	170	56	values	value	NOUN
esrj-91195	170	57	,	,	PUNCT
esrj-91195	170	58	pi	pi	NOUN
esrj-91195	170	59	is	be	AUX
esrj-91195	170	60	the	the	DET
esrj-91195	170	61	predicted	predict	VERB
esrj-91195	170	62	geoid	geoid	ADJ
esrj-91195	170	63	undulation	undulation	NOUN
esrj-91195	170	64	value	value	NOUN
esrj-91195	170	65	,	,	PUNCT
esrj-91195	170	66	and	and	CCONJ
esrj-91195	170	67	represents	represent	VERB
esrj-91195	170	68	the	the	DET
esrj-91195	170	69	mean	mean	NOUN
esrj-91195	170	70	of	of	ADP
esrj-91195	170	71	the	the	DET
esrj-91195	170	72	predicted	predict	VERB
esrj-91195	170	73	geoid	geoid	ADJ
esrj-91195	170	74	undulation	undulation	NOUN
esrj-91195	170	75	values	value	NOUN
esrj-91195	170	76	.	.	PUNCT
esrj-91195	171	1	the	the	DET
esrj-91195	171	2	rmse	rmse	NOUN
esrj-91195	171	3	is	be	AUX
esrj-91195	171	4	used	use	VERB
esrj-91195	171	5	to	to	PART
esrj-91195	171	6	determine	determine	VERB
esrj-91195	171	7	the	the	DET
esrj-91195	171	8	error	error	NOUN
esrj-91195	171	9	rate	rate	NOUN
esrj-91195	171	10	between	between	ADP
esrj-91195	171	11	the	the	DET
esrj-91195	171	12	prediction	prediction	NOUN
esrj-91195	171	13	and	and	CCONJ
esrj-91195	171	14	the	the	DET
esrj-91195	171	15	corresponding	corresponding	ADJ
esrj-91195	171	16	observation	observation	NOUN
esrj-91195	171	17	.	.	PUNCT
esrj-91195	172	1	the	the	DET
esrj-91195	172	2	prediction	prediction	NOUN
esrj-91195	172	3	ability	ability	NOUN
esrj-91195	172	4	of	of	ADP
esrj-91195	172	5	the	the	DET
esrj-91195	172	6	method	method	NOUN
esrj-91195	172	7	increases	increase	VERB
esrj-91195	172	8	as	as	SCONJ
esrj-91195	172	9	the	the	DET
esrj-91195	172	10	error	error	NOUN
esrj-91195	172	11	value	value	NOUN
esrj-91195	172	12	approaches	approach	VERB
esrj-91195	172	13	zero	zero	NUM
esrj-91195	172	14	.	.	PUNCT
esrj-91195	173	1	the	the	DET
esrj-91195	173	2	mae	mae	PROPN
esrj-91195	173	3	is	be	AUX
esrj-91195	173	4	used	use	VERB
esrj-91195	173	5	to	to	PART
esrj-91195	173	6	determine	determine	VERB
esrj-91195	173	7	the	the	DET
esrj-91195	173	8	absolute	absolute	ADJ
esrj-91195	173	9	error	error	NOUN
esrj-91195	173	10	between	between	ADP
esrj-91195	173	11	the	the	DET
esrj-91195	173	12	prediction	prediction	NOUN
esrj-91195	173	13	and	and	CCONJ
esrj-91195	173	14	the	the	DET
esrj-91195	173	15	corresponding	corresponding	ADJ
esrj-91195	173	16	observation	observation	NOUN
esrj-91195	173	17	.	.	PUNCT
esrj-91195	174	1	the	the	PRON
esrj-91195	174	2	closer	close	ADV
esrj-91195	174	3	the	the	DET
esrj-91195	174	4	error	error	NOUN
esrj-91195	174	5	value	value	NOUN
esrj-91195	174	6	is	be	AUX
esrj-91195	174	7	to	to	ADP
esrj-91195	174	8	zero	zero	NUM
esrj-91195	174	9	,	,	PUNCT
esrj-91195	174	10	the	the	PRON
esrj-91195	174	11	better	well	ADJ
esrj-91195	174	12	the	the	DET
esrj-91195	174	13	method	method	NOUN
esrj-91195	174	14	’s	’s	PART
esrj-91195	174	15	prediction	prediction	NOUN
esrj-91195	174	16	ability	ability	NOUN
esrj-91195	174	17	376	376	NUM
esrj-91195	174	18	berkant	berkant	PROPN
esrj-91195	174	19	konakoglu	konakoglu	PROPN
esrj-91195	174	20	,	,	PUNCT
esrj-91195	174	21	alper	alper	PROPN
esrj-91195	174	22	akar	akar	PROPN
esrj-91195	174	23	is	be	AUX
esrj-91195	174	24	indicated	indicate	VERB
esrj-91195	174	25	.	.	PUNCT
esrj-91195	175	1	the	the	DET
esrj-91195	175	2	nse	nse	NOUN
esrj-91195	175	3	coefficient	coefficient	NOUN
esrj-91195	175	4	ranges	range	VERB
esrj-91195	175	5	from	from	ADP
esrj-91195	175	6	‒∞	‒∞	ADP
esrj-91195	175	7	to	to	ADP
esrj-91195	175	8	1	1	NUM
esrj-91195	175	9	.	.	PUNCT
esrj-91195	176	1	thus	thus	ADV
esrj-91195	176	2	,	,	PUNCT
esrj-91195	176	3	when	when	SCONJ
esrj-91195	176	4	nse	nse	NOUN
esrj-91195	176	5	=	=	NOUN
esrj-91195	176	6	1	1	NUM
esrj-91195	176	7	,	,	PUNCT
esrj-91195	176	8	it	it	PRON
esrj-91195	176	9	means	mean	VERB
esrj-91195	176	10	that	that	SCONJ
esrj-91195	176	11	the	the	DET
esrj-91195	176	12	method	method	NOUN
esrj-91195	176	13	is	be	AUX
esrj-91195	176	14	perfect	perfect	ADJ
esrj-91195	176	15	.	.	PUNCT
esrj-91195	177	1	an	an	DET
esrj-91195	177	2	nse	nse	NOUN
esrj-91195	177	3	value	value	NOUN
esrj-91195	177	4	of	of	ADP
esrj-91195	177	5	between	between	ADP
esrj-91195	177	6	0	0	NUM
esrj-91195	177	7	and	and	CCONJ
esrj-91195	177	8	1	1	NUM
esrj-91195	177	9	generally	generally	ADV
esrj-91195	177	10	means	mean	VERB
esrj-91195	177	11	that	that	SCONJ
esrj-91195	177	12	the	the	DET
esrj-91195	177	13	method	method	NOUN
esrj-91195	177	14	performance	performance	NOUN
esrj-91195	177	15	is	be	AUX
esrj-91195	177	16	acceptable	acceptable	ADJ
esrj-91195	177	17	,	,	PUNCT
esrj-91195	177	18	whereas	whereas	SCONJ
esrj-91195	177	19	a	a	DET
esrj-91195	177	20	value	value	NOUN
esrj-91195	177	21	of	of	ADP
esrj-91195	177	22	less	less	ADJ
esrj-91195	177	23	than	than	ADP
esrj-91195	177	24	0	0	NUM
esrj-91195	177	25	emphasizes	emphasize	VERB
esrj-91195	177	26	that	that	SCONJ
esrj-91195	177	27	the	the	DET
esrj-91195	177	28	average	average	ADJ
esrj-91195	177	29	observation	observation	NOUN
esrj-91195	177	30	value	value	NOUN
esrj-91195	177	31	showing	show	VERB
esrj-91195	177	32	the	the	DET
esrj-91195	177	33	performance	performance	NOUN
esrj-91195	177	34	of	of	ADP
esrj-91195	177	35	the	the	DET
esrj-91195	177	36	method	method	NOUN
esrj-91195	177	37	is	be	AUX
esrj-91195	177	38	insufficient	insufficient	ADJ
esrj-91195	177	39	is	be	AUX
esrj-91195	177	40	a	a	DET
esrj-91195	177	41	better	well	ADJ
esrj-91195	177	42	prediction	prediction	NOUN
esrj-91195	177	43	than	than	ADP
esrj-91195	177	44	the	the	DET
esrj-91195	177	45	calculated	calculate	VERB
esrj-91195	177	46	data	datum	NOUN
esrj-91195	177	47	.	.	PUNCT
esrj-91195	178	1	the	the	DET
esrj-91195	178	2	r2	r2	PROPN
esrj-91195	178	3	is	be	AUX
esrj-91195	178	4	an	an	DET
esrj-91195	178	5	indication	indication	NOUN
esrj-91195	178	6	of	of	ADP
esrj-91195	178	7	whether	whether	SCONJ
esrj-91195	178	8	or	or	CCONJ
esrj-91195	178	9	not	not	PART
esrj-91195	178	10	the	the	DET
esrj-91195	178	11	regression	regression	NOUN
esrj-91195	178	12	equation	equation	NOUN
esrj-91195	178	13	is	be	AUX
esrj-91195	178	14	compatible	compatible	ADJ
esrj-91195	178	15	with	with	ADP
esrj-91195	178	16	the	the	DET
esrj-91195	178	17	data	datum	NOUN
esrj-91195	178	18	.	.	PUNCT
esrj-91195	179	1	it	it	PRON
esrj-91195	179	2	is	be	AUX
esrj-91195	179	3	the	the	DET
esrj-91195	179	4	ratio	ratio	NOUN
esrj-91195	179	5	that	that	PRON
esrj-91195	179	6	explains	explain	VERB
esrj-91195	179	7	one	one	NUM
esrj-91195	179	8	difference	difference	NOUN
esrj-91195	179	9	in	in	ADP
esrj-91195	179	10	terms	term	NOUN
esrj-91195	179	11	of	of	ADP
esrj-91195	179	12	the	the	DET
esrj-91195	179	13	overall	overall	ADJ
esrj-91195	179	14	difference	difference	NOUN
esrj-91195	179	15	.	.	PUNCT
esrj-91195	180	1	this	this	DET
esrj-91195	180	2	ratio	ratio	NOUN
esrj-91195	180	3	is	be	AUX
esrj-91195	180	4	called	call	VERB
esrj-91195	180	5	the	the	DET
esrj-91195	180	6	coefficient	coefficient	NOUN
esrj-91195	180	7	of	of	ADP
esrj-91195	180	8	certainty	certainty	NOUN
esrj-91195	180	9	and	and	CCONJ
esrj-91195	180	10	shows	show	VERB
esrj-91195	180	11	the	the	DET
esrj-91195	180	12	extent	extent	NOUN
esrj-91195	180	13	to	to	PART
esrj-91195	180	14	which	which	PRON
esrj-91195	180	15	the	the	DET
esrj-91195	180	16	difference	difference	NOUN
esrj-91195	180	17	in	in	ADP
esrj-91195	180	18	the	the	DET
esrj-91195	180	19	dependent	dependent	ADJ
esrj-91195	180	20	variable	variable	NOUN
esrj-91195	180	21	can	can	AUX
esrj-91195	180	22	be	be	AUX
esrj-91195	180	23	explained	explain	VERB
esrj-91195	180	24	by	by	ADP
esrj-91195	180	25	the	the	DET
esrj-91195	180	26	independent	independent	ADJ
esrj-91195	180	27	variable	variable	NOUN
esrj-91195	180	28	.	.	PUNCT
esrj-91195	181	1	the	the	DET
esrj-91195	181	2	r2	r2	PROPN
esrj-91195	181	3	is	be	AUX
esrj-91195	181	4	expressed	express	VERB
esrj-91195	181	5	by	by	ADP
esrj-91195	181	6	a	a	DET
esrj-91195	181	7	value	value	NOUN
esrj-91195	181	8	of	of	ADP
esrj-91195	181	9	between	between	ADP
esrj-91195	181	10	0	0	NUM
esrj-91195	181	11	and	and	CCONJ
esrj-91195	181	12	1	1	NUM
esrj-91195	181	13	.	.	X
esrj-91195	182	1	a	a	DET
esrj-91195	182	2	value	value	NOUN
esrj-91195	182	3	close	close	ADV
esrj-91195	182	4	to	to	PART
esrj-91195	182	5	1	1	NUM
esrj-91195	182	6	indicates	indicate	VERB
esrj-91195	182	7	that	that	SCONJ
esrj-91195	182	8	a	a	DET
esrj-91195	182	9	large	large	ADJ
esrj-91195	182	10	part	part	NOUN
esrj-91195	182	11	of	of	ADP
esrj-91195	182	12	the	the	DET
esrj-91195	182	13	variance	variance	NOUN
esrj-91195	182	14	in	in	ADP
esrj-91195	182	15	the	the	DET
esrj-91195	182	16	dependent	dependent	ADJ
esrj-91195	182	17	variable	variable	NOUN
esrj-91195	182	18	explains	explain	VERB
esrj-91195	182	19	the	the	DET
esrj-91195	182	20	independent	independent	ADJ
esrj-91195	182	21	variable	variable	NOUN
esrj-91195	182	22	in	in	ADP
esrj-91195	182	23	the	the	DET
esrj-91195	182	24	method	method	NOUN
esrj-91195	182	25	.	.	PUNCT
esrj-91195	183	1	results	result	NOUN
esrj-91195	183	2	and	and	CCONJ
esrj-91195	183	3	discussion	discussion	VERB
esrj-91195	183	4	the	the	DET
esrj-91195	183	5	results	result	NOUN
esrj-91195	183	6	of	of	ADP
esrj-91195	183	7	the	the	DET
esrj-91195	183	8	developed	develop	VERB
esrj-91195	183	9	models	model	NOUN
esrj-91195	183	10	are	be	AUX
esrj-91195	183	11	given	give	VERB
esrj-91195	183	12	under	under	ADP
esrj-91195	183	13	separate	separate	ADJ
esrj-91195	183	14	headings	heading	NOUN
esrj-91195	183	15	.	.	PUNCT
esrj-91195	184	1	it	it	PRON
esrj-91195	184	2	should	should	AUX
esrj-91195	184	3	be	be	AUX
esrj-91195	184	4	noted	note	VERB
esrj-91195	184	5	that	that	SCONJ
esrj-91195	184	6	in	in	ADP
esrj-91195	184	7	the	the	DET
esrj-91195	184	8	tables	table	NOUN
esrj-91195	184	9	,	,	PUNCT
esrj-91195	184	10	the	the	DET
esrj-91195	184	11	best	good	ADJ
esrj-91195	184	12	metric	metric	ADJ
esrj-91195	184	13	values	value	NOUN
esrj-91195	184	14	are	be	AUX
esrj-91195	184	15	highlighted	highlight	VERB
esrj-91195	184	16	in	in	ADP
esrj-91195	184	17	dark	dark	ADJ
esrj-91195	184	18	gray	gray	NOUN
esrj-91195	184	19	,	,	PUNCT
esrj-91195	184	20	whereas	whereas	SCONJ
esrj-91195	184	21	the	the	DET
esrj-91195	184	22	worst	bad	ADJ
esrj-91195	184	23	are	be	AUX
esrj-91195	184	24	highlighted	highlight	VERB
esrj-91195	184	25	in	in	ADP
esrj-91195	184	26	light	light	ADJ
esrj-91195	184	27	gray	gray	ADJ
esrj-91195	184	28	.	.	PUNCT
esrj-91195	185	1	rbfnn	rbfnn	NOUN
esrj-91195	185	2	results	result	VERB
esrj-91195	185	3	the	the	DET
esrj-91195	185	4	performance	performance	NOUN
esrj-91195	185	5	of	of	ADP
esrj-91195	185	6	the	the	DET
esrj-91195	185	7	rbfnn	rbfnn	NOUN
esrj-91195	185	8	is	be	AUX
esrj-91195	185	9	based	base	VERB
esrj-91195	185	10	on	on	ADP
esrj-91195	185	11	the	the	DET
esrj-91195	185	12	spread	spread	ADJ
esrj-91195	185	13	parameter	parameter	NOUN
esrj-91195	185	14	and	and	CCONJ
esrj-91195	185	15	the	the	DET
esrj-91195	185	16	maximum	maximum	ADJ
esrj-91195	185	17	number	number	NOUN
esrj-91195	185	18	of	of	ADP
esrj-91195	185	19	neurons	neuron	NOUN
esrj-91195	185	20	in	in	ADP
esrj-91195	185	21	the	the	DET
esrj-91195	185	22	hidden	hide	VERB
esrj-91195	185	23	layer	layer	NOUN
esrj-91195	185	24	.	.	PUNCT
esrj-91195	186	1	various	various	ADJ
esrj-91195	186	2	attempts	attempt	NOUN
esrj-91195	186	3	were	be	AUX
esrj-91195	186	4	made	make	VERB
esrj-91195	186	5	to	to	PART
esrj-91195	186	6	achieve	achieve	VERB
esrj-91195	186	7	the	the	DET
esrj-91195	186	8	best	good	ADJ
esrj-91195	186	9	performance	performance	NOUN
esrj-91195	186	10	using	use	VERB
esrj-91195	186	11	a	a	DET
esrj-91195	186	12	different	different	ADJ
esrj-91195	186	13	number	number	NOUN
esrj-91195	186	14	of	of	ADP
esrj-91195	186	15	neurons	neuron	NOUN
esrj-91195	186	16	and	and	CCONJ
esrj-91195	186	17	different	different	ADJ
esrj-91195	186	18	spread	spread	NOUN
esrj-91195	186	19	parameters	parameter	NOUN
esrj-91195	186	20	.	.	PUNCT
esrj-91195	187	1	the	the	DET
esrj-91195	187	2	best	good	ADJ
esrj-91195	187	3	performance	performance	NOUN
esrj-91195	187	4	criteria	criterion	NOUN
esrj-91195	187	5	results	result	NOUN
esrj-91195	187	6	obtained	obtain	VERB
esrj-91195	187	7	during	during	ADP
esrj-91195	187	8	the	the	DET
esrj-91195	187	9	training	training	NOUN
esrj-91195	187	10	and	and	CCONJ
esrj-91195	187	11	testing	testing	NOUN
esrj-91195	187	12	phases	phase	NOUN
esrj-91195	187	13	are	be	AUX
esrj-91195	187	14	given	give	VERB
esrj-91195	187	15	in	in	ADP
esrj-91195	187	16	table	table	NOUN
esrj-91195	187	17	2	2	NUM
esrj-91195	187	18	.	.	PUNCT
esrj-91195	188	1	the	the	DET
esrj-91195	188	2	optimum	optimum	ADJ
esrj-91195	188	3	values	value	NOUN
esrj-91195	188	4	for	for	ADP
esrj-91195	188	5	the	the	DET
esrj-91195	188	6	maximum	maximum	ADJ
esrj-91195	188	7	number	number	NOUN
esrj-91195	188	8	of	of	ADP
esrj-91195	188	9	neurons	neuron	NOUN
esrj-91195	188	10	and	and	CCONJ
esrj-91195	188	11	their	their	PRON
esrj-91195	188	12	spread	spread	NOUN
esrj-91195	188	13	were	be	AUX
esrj-91195	188	14	430/0.025	430/0.025	NUM
esrj-91195	188	15	for	for	ADP
esrj-91195	188	16	ds#1	ds#1	PROPN
esrj-91195	188	17	,	,	PUNCT
esrj-91195	188	18	421/0.056	421/0.056	NOUN
esrj-91195	188	19	for	for	ADP
esrj-91195	188	20	ds#2	ds#2	PRON
esrj-91195	188	21	,	,	PUNCT
esrj-91195	188	22	427/0.076	427/0.076	NUM
esrj-91195	188	23	for	for	ADP
esrj-91195	188	24	ds#3	ds#3	PROPN
esrj-91195	188	25	,	,	PUNCT
esrj-91195	188	26	412/0.034	412/0.034	NOUN
esrj-91195	188	27	for	for	ADP
esrj-91195	188	28	ds#4	ds#4	PROPN
esrj-91195	188	29	,	,	PUNCT
esrj-91195	188	30	and	and	CCONJ
esrj-91195	188	31	410/0.065	410/0.065	NUM
esrj-91195	188	32	for	for	ADP
esrj-91195	188	33	ds#5	ds#5	PROPN
esrj-91195	188	34	.	.	PUNCT
esrj-91195	188	35	table	table	NOUN
esrj-91195	188	36	2	2	NUM
esrj-91195	188	37	.	.	PUNCT
esrj-91195	188	38	rbfnn	rbfnn	VERB
esrj-91195	189	1	training	training	NOUN
esrj-91195	189	2	and	and	CCONJ
esrj-91195	189	3	testing	testing	NOUN
esrj-91195	189	4	phase	phase	NOUN
esrj-91195	189	5	results	result	VERB
esrj-91195	189	6	phase	phase	NOUN
esrj-91195	189	7	dataset	dataset	NOUN
esrj-91195	189	8	rmse	rmse	NOUN
esrj-91195	189	9	(	(	PUNCT
esrj-91195	189	10	m	m	PROPN
esrj-91195	189	11	)	)	PUNCT
esrj-91195	189	12	mae	mae	PROPN
esrj-91195	189	13	(	(	PUNCT
esrj-91195	189	14	m	m	NOUN
esrj-91195	189	15	)	)	PUNCT
esrj-91195	189	16	nse	nse	NOUN
esrj-91195	189	17	training	training	NOUN
esrj-91195	189	18	ds#1	ds#1	PROPN
esrj-91195	189	19	0.112	0.112	NUM
esrj-91195	189	20	0.079	0.079	NUM
esrj-91195	189	21	0.99383	0.99383	NUM
esrj-91195	189	22	ds#2	ds#2	DET
esrj-91195	189	23	0.001	0.001	NUM
esrj-91195	189	24	0.000	0.000	NUM
esrj-91195	189	25	0.99999	0.99999	NUM
esrj-91195	189	26	ds#3	ds#3	VERB
esrj-91195	189	27	0.000	0.000	NUM
esrj-91195	189	28	0.000	0.000	NUM
esrj-91195	189	29	0.99999	0.99999	NUM
esrj-91195	189	30	ds#4	ds#4	PROPN
esrj-91195	189	31	0.034	0.034	NUM
esrj-91195	189	32	0.009	0.009	NUM
esrj-91195	189	33	0.99944	0.99944	NUM
esrj-91195	189	34	ds#5	ds#5	NOUN
esrj-91195	189	35	0.003	0.003	NUM
esrj-91195	189	36	0.001	0.001	NUM
esrj-91195	189	37	0.99999	0.99999	NUM
esrj-91195	189	38	testing	testing	NOUN
esrj-91195	189	39	ds#1	ds#1	PROPN
esrj-91195	189	40	0.197	0.197	NUM
esrj-91195	189	41	0.115	0.115	NUM
esrj-91195	189	42	0.97925	0.97925	NUM
esrj-91195	189	43	ds#2	ds#2	NOUN
esrj-91195	189	44	0.780	0.780	NUM
esrj-91195	189	45	0.542	0.542	NUM
esrj-91195	189	46	0.68478	0.68478	NUM
esrj-91195	189	47	ds#3	ds#3	VERB
esrj-91195	189	48	0.525	0.525	NUM
esrj-91195	189	49	0.364	0.364	NUM
esrj-91195	189	50	0.86442	0.86442	NUM
esrj-91195	189	51	ds#4	ds#4	VERB
esrj-91195	189	52	0.720	0.720	NUM
esrj-91195	189	53	0.590	0.590	NUM
esrj-91195	189	54	0.73029	0.73029	NUM
esrj-91195	190	1	ds#5	ds#5	NOUN
esrj-91195	190	2	0.569	0.569	NUM
esrj-91195	190	3	0.389	0.389	NUM
esrj-91195	190	4	0.83665	0.83665	NUM
esrj-91195	190	5	the	the	DET
esrj-91195	190	6	lowest	low	ADJ
esrj-91195	190	7	rmse	rmse	ADJ
esrj-91195	190	8	value	value	NOUN
esrj-91195	190	9	of	of	ADP
esrj-91195	190	10	the	the	DET
esrj-91195	190	11	prediction	prediction	NOUN
esrj-91195	190	12	models	model	NOUN
esrj-91195	190	13	at	at	ADP
esrj-91195	190	14	the	the	DET
esrj-91195	190	15	training	training	NOUN
esrj-91195	190	16	stage	stage	NOUN
esrj-91195	190	17	was	be	AUX
esrj-91195	190	18	obtained	obtain	VERB
esrj-91195	190	19	with	with	ADP
esrj-91195	190	20	ds#3	ds#3	PROPN
esrj-91195	190	21	and	and	CCONJ
esrj-91195	190	22	the	the	DET
esrj-91195	190	23	highest	high	ADJ
esrj-91195	190	24	with	with	ADP
esrj-91195	190	25	ds#1	ds#1	PROPN
esrj-91195	190	26	.	.	PUNCT
esrj-91195	191	1	moreover	moreover	ADV
esrj-91195	191	2	,	,	PUNCT
esrj-91195	191	3	the	the	DET
esrj-91195	191	4	lowest	low	ADJ
esrj-91195	191	5	mae	mae	PROPN
esrj-91195	191	6	value	value	NOUN
esrj-91195	191	7	was	be	AUX
esrj-91195	191	8	also	also	ADV
esrj-91195	191	9	found	find	VERB
esrj-91195	191	10	in	in	ADP
esrj-91195	191	11	ds#3	ds#3	PROPN
esrj-91195	191	12	.	.	PUNCT
esrj-91195	192	1	according	accord	VERB
esrj-91195	192	2	to	to	ADP
esrj-91195	192	3	table	table	NOUN
esrj-91195	192	4	2	2	NUM
esrj-91195	192	5	,	,	PUNCT
esrj-91195	192	6	the	the	DET
esrj-91195	192	7	highest	high	ADJ
esrj-91195	192	8	nse	nse	NOUN
esrj-91195	192	9	values	value	NOUN
esrj-91195	192	10	during	during	ADP
esrj-91195	192	11	the	the	DET
esrj-91195	192	12	training	training	NOUN
esrj-91195	192	13	stage	stage	NOUN
esrj-91195	192	14	were	be	AUX
esrj-91195	192	15	with	with	ADP
esrj-91195	192	16	ds#2	ds#2	NOUN
esrj-91195	192	17	,	,	PUNCT
esrj-91195	192	18	ds#3	ds#3	X
esrj-91195	192	19	,	,	PUNCT
esrj-91195	192	20	and	and	CCONJ
esrj-91195	192	21	ds#5	ds#5	PROPN
esrj-91195	192	22	.	.	PUNCT
esrj-91195	193	1	at	at	ADP
esrj-91195	193	2	the	the	DET
esrj-91195	193	3	testing	testing	NOUN
esrj-91195	193	4	stage	stage	NOUN
esrj-91195	193	5	,	,	PUNCT
esrj-91195	193	6	the	the	DET
esrj-91195	193	7	lowest	low	ADJ
esrj-91195	193	8	rmse	rmse	NOUN
esrj-91195	193	9	value	value	NOUN
esrj-91195	193	10	was	be	AUX
esrj-91195	193	11	obtained	obtain	VERB
esrj-91195	193	12	with	with	ADP
esrj-91195	193	13	ds#1	ds#1	PROPN
esrj-91195	193	14	and	and	CCONJ
esrj-91195	193	15	the	the	DET
esrj-91195	193	16	highest	high	ADJ
esrj-91195	193	17	with	with	ADP
esrj-91195	193	18	ds#2	ds#2	NOUN
esrj-91195	193	19	,	,	PUNCT
esrj-91195	193	20	whereas	whereas	SCONJ
esrj-91195	193	21	the	the	DET
esrj-91195	193	22	lowest	low	ADJ
esrj-91195	193	23	mae	mae	PROPN
esrj-91195	193	24	and	and	CCONJ
esrj-91195	193	25	the	the	DET
esrj-91195	193	26	highest	high	ADJ
esrj-91195	193	27	nse	nse	NOUN
esrj-91195	193	28	values	value	NOUN
esrj-91195	193	29	were	be	AUX
esrj-91195	193	30	determined	determine	VERB
esrj-91195	193	31	in	in	ADP
esrj-91195	193	32	ds#1	ds#1	PROPN
esrj-91195	193	33	.	.	PUNCT
esrj-91195	194	1	during	during	ADP
esrj-91195	194	2	the	the	DET
esrj-91195	194	3	training	training	NOUN
esrj-91195	194	4	phase	phase	NOUN
esrj-91195	194	5	,	,	PUNCT
esrj-91195	194	6	ds#2	ds#2	PRON
esrj-91195	194	7	,	,	PUNCT
esrj-91195	194	8	ds#3	ds#3	X
esrj-91195	194	9	,	,	PUNCT
esrj-91195	194	10	ds#4	ds#4	PROPN
esrj-91195	194	11	,	,	PUNCT
esrj-91195	194	12	and	and	CCONJ
esrj-91195	194	13	ds#5	ds#5	PROPN
esrj-91195	194	14	performed	perform	VERB
esrj-91195	194	15	well	well	ADV
esrj-91195	194	16	,	,	PUNCT
esrj-91195	194	17	whereas	whereas	SCONJ
esrj-91195	194	18	in	in	ADP
esrj-91195	194	19	the	the	DET
esrj-91195	194	20	testing	testing	NOUN
esrj-91195	194	21	phase	phase	NOUN
esrj-91195	194	22	,	,	PUNCT
esrj-91195	194	23	the	the	DET
esrj-91195	194	24	performance	performance	NOUN
esrj-91195	194	25	results	result	NOUN
esrj-91195	194	26	of	of	ADP
esrj-91195	194	27	ds#1	ds#1	PROPN
esrj-91195	194	28	were	be	AUX
esrj-91195	194	29	better	well	ADJ
esrj-91195	194	30	than	than	ADP
esrj-91195	194	31	in	in	ADP
esrj-91195	194	32	the	the	DET
esrj-91195	194	33	others	other	NOUN
esrj-91195	194	34	.	.	PUNCT
esrj-91195	195	1	in	in	ADP
esrj-91195	195	2	order	order	NOUN
esrj-91195	195	3	to	to	PART
esrj-91195	195	4	demonstrate	demonstrate	VERB
esrj-91195	195	5	the	the	DET
esrj-91195	195	6	performance	performance	NOUN
esrj-91195	195	7	of	of	ADP
esrj-91195	195	8	the	the	DET
esrj-91195	195	9	rbfnn	rbfnn	NOUN
esrj-91195	195	10	,	,	PUNCT
esrj-91195	195	11	the	the	DET
esrj-91195	195	12	predicted	predict	VERB
esrj-91195	195	13	geoid	geoid	ADJ
esrj-91195	195	14	undulation	undulation	NOUN
esrj-91195	195	15	values	value	NOUN
esrj-91195	195	16	and	and	CCONJ
esrj-91195	195	17	those	those	PRON
esrj-91195	195	18	observed	observe	VERB
esrj-91195	195	19	at	at	ADP
esrj-91195	195	20	the	the	DET
esrj-91195	195	21	testing	testing	NOUN
esrj-91195	195	22	stage	stage	NOUN
esrj-91195	195	23	for	for	ADP
esrj-91195	195	24	each	each	DET
esrj-91195	195	25	dataset	dataset	NOUN
esrj-91195	195	26	(	(	PUNCT
esrj-91195	195	27	ds#1	ds#1	PROPN
esrj-91195	195	28	,	,	PUNCT
esrj-91195	195	29	ds#2	ds#2	PRON
esrj-91195	195	30	,	,	PUNCT
esrj-91195	195	31	ds#3	ds#3	X
esrj-91195	195	32	,	,	PUNCT
esrj-91195	195	33	ds#4	ds#4	PROPN
esrj-91195	195	34	,	,	PUNCT
esrj-91195	195	35	and	and	CCONJ
esrj-91195	195	36	ds#5	ds#5	NOUN
esrj-91195	195	37	)	)	PUNCT
esrj-91195	195	38	are	be	AUX
esrj-91195	195	39	shown	show	VERB
esrj-91195	195	40	in	in	ADP
esrj-91195	195	41	figure	figure	NOUN
esrj-91195	195	42	6	6	NUM
esrj-91195	195	43	.	.	PUNCT
esrj-91195	196	1	thus	thus	ADV
esrj-91195	196	2	,	,	PUNCT
esrj-91195	196	3	it	it	PRON
esrj-91195	196	4	is	be	AUX
esrj-91195	196	5	clear	clear	ADJ
esrj-91195	196	6	that	that	SCONJ
esrj-91195	196	7	the	the	DET
esrj-91195	196	8	geoid	geoid	ADJ
esrj-91195	196	9	undulation	undulation	NOUN
esrj-91195	196	10	prediction	prediction	NOUN
esrj-91195	196	11	made	make	VERB
esrj-91195	196	12	with	with	ADP
esrj-91195	196	13	ds#1	ds#1	PROPN
esrj-91195	196	14	was	be	AUX
esrj-91195	196	15	superior	superior	ADJ
esrj-91195	196	16	to	to	ADP
esrj-91195	196	17	that	that	PRON
esrj-91195	196	18	of	of	ADP
esrj-91195	196	19	the	the	DET
esrj-91195	196	20	other	other	ADJ
esrj-91195	196	21	datasets	dataset	NOUN
esrj-91195	196	22	.	.	PUNCT
esrj-91195	197	1	it	it	PRON
esrj-91195	197	2	is	be	AUX
esrj-91195	197	3	also	also	ADV
esrj-91195	197	4	clear	clear	ADJ
esrj-91195	197	5	that	that	SCONJ
esrj-91195	197	6	the	the	DET
esrj-91195	197	7	a	a	DET
esrj-91195	197	8	and	and	CCONJ
esrj-91195	197	9	b	b	NOUN
esrj-91195	197	10	coefficients	coefficient	NOUN
esrj-91195	197	11	of	of	ADP
esrj-91195	197	12	the	the	DET
esrj-91195	197	13	distribution	distribution	NOUN
esrj-91195	197	14	graphs	graph	NOUN
esrj-91195	197	15	from	from	ADP
esrj-91195	197	16	the	the	DET
esrj-91195	197	17	fit	fit	ADJ
esrj-91195	197	18	line	line	NOUN
esrj-91195	197	19	equations	equation	NOUN
esrj-91195	197	20	(	(	PUNCT
esrj-91195	197	21	assuming	assume	VERB
esrj-91195	197	22	that	that	SCONJ
esrj-91195	197	23	the	the	DET
esrj-91195	197	24	equation	equation	NOUN
esrj-91195	197	25	was	be	AUX
esrj-91195	197	26	y	y	NOUN
esrj-91195	197	27	=	=	NOUN
esrj-91195	197	28	ax	ax	NOUN
esrj-91195	197	29	+	+	CCONJ
esrj-91195	197	30	b	b	X
esrj-91195	197	31	)	)	PUNCT
esrj-91195	197	32	were	be	AUX
esrj-91195	197	33	higher	high	ADJ
esrj-91195	197	34	than	than	ADP
esrj-91195	197	35	the	the	DET
esrj-91195	197	36	a	a	PRON
esrj-91195	197	37	and	and	CCONJ
esrj-91195	197	38	b	b	NOUN
esrj-91195	197	39	coefficients	coefficient	NOUN
esrj-91195	197	40	obtained	obtain	VERB
esrj-91195	197	41	with	with	ADP
esrj-91195	197	42	the	the	DET
esrj-91195	197	43	other	other	ADJ
esrj-91195	197	44	datasets	dataset	NOUN
esrj-91195	197	45	;	;	PUNCT
esrj-91195	197	46	a	a	PRON
esrj-91195	197	47	was	be	AUX
esrj-91195	197	48	close	close	ADJ
esrj-91195	197	49	to	to	ADP
esrj-91195	197	50	1	1	NUM
esrj-91195	197	51	and	and	CCONJ
esrj-91195	197	52	b	b	NOUN
esrj-91195	197	53	close	close	ADV
esrj-91195	197	54	to	to	ADP
esrj-91195	197	55	0	0	NUM
esrj-91195	197	56	,	,	PUNCT
esrj-91195	197	57	and	and	CCONJ
esrj-91195	197	58	the	the	DET
esrj-91195	197	59	r2	r2	PROPN
esrj-91195	197	60	value	value	NOUN
esrj-91195	197	61	was	be	AUX
esrj-91195	197	62	higher	high	ADJ
esrj-91195	197	63	.	.	PUNCT
esrj-91195	198	1	grnn	grnn	PROPN
esrj-91195	198	2	results	result	VERB
esrj-91195	198	3	the	the	DET
esrj-91195	198	4	performance	performance	NOUN
esrj-91195	198	5	of	of	ADP
esrj-91195	198	6	the	the	DET
esrj-91195	198	7	grnn	grnn	NOUN
esrj-91195	198	8	largely	largely	ADV
esrj-91195	198	9	depends	depend	VERB
esrj-91195	198	10	on	on	ADP
esrj-91195	198	11	the	the	DET
esrj-91195	198	12	spread	spread	ADJ
esrj-91195	198	13	parameter	parameter	NOUN
esrj-91195	198	14	,	,	PUNCT
esrj-91195	198	15	so	so	ADV
esrj-91195	198	16	determining	determine	VERB
esrj-91195	198	17	this	this	DET
esrj-91195	198	18	parameter	parameter	NOUN
esrj-91195	198	19	is	be	AUX
esrj-91195	198	20	of	of	ADP
esrj-91195	198	21	great	great	ADJ
esrj-91195	198	22	importance	importance	NOUN
esrj-91195	198	23	.	.	PUNCT
esrj-91195	199	1	therefore	therefore	ADV
esrj-91195	199	2	,	,	PUNCT
esrj-91195	199	3	in	in	ADP
esrj-91195	199	4	order	order	NOUN
esrj-91195	199	5	to	to	PART
esrj-91195	199	6	figure	figure	VERB
esrj-91195	199	7	6	6	NUM
esrj-91195	199	8	.	.	PUNCT
esrj-91195	199	9	comparison	comparison	NOUN
esrj-91195	199	10	of	of	ADP
esrj-91195	199	11	the	the	DET
esrj-91195	199	12	geoid	geoid	ADJ
esrj-91195	199	13	undulation	undulation	NOUN
esrj-91195	199	14	values	value	NOUN
esrj-91195	199	15	predicted	predict	VERB
esrj-91195	199	16	by	by	ADP
esrj-91195	199	17	rbfnn	rbfnn	NOUN
esrj-91195	199	18	and	and	CCONJ
esrj-91195	199	19	those	those	PRON
esrj-91195	199	20	observed	observe	VERB
esrj-91195	199	21	at	at	ADP
esrj-91195	199	22	the	the	DET
esrj-91195	199	23	testing	testing	NOUN
esrj-91195	199	24	phase	phase	NOUN
esrj-91195	199	25	optimize	optimize	NOUN
esrj-91195	199	26	prediction	prediction	NOUN
esrj-91195	199	27	performance	performance	NOUN
esrj-91195	199	28	,	,	PUNCT
esrj-91195	199	29	this	this	DET
esrj-91195	199	30	parameter	parameter	NOUN
esrj-91195	199	31	must	must	AUX
esrj-91195	199	32	be	be	AUX
esrj-91195	199	33	accurately	accurately	ADV
esrj-91195	199	34	determined	determine	VERB
esrj-91195	199	35	according	accord	VERB
esrj-91195	199	36	to	to	ADP
esrj-91195	199	37	the	the	DET
esrj-91195	199	38	evaluation	evaluation	NOUN
esrj-91195	199	39	criteria	criterion	NOUN
esrj-91195	199	40	.	.	PUNCT
esrj-91195	200	1	the	the	DET
esrj-91195	200	2	grnn	grnn	NOUN
esrj-91195	200	3	spread	spread	VERB
esrj-91195	200	4	parameter	parameter	NOUN
esrj-91195	200	5	values	value	NOUN
esrj-91195	200	6	that	that	PRON
esrj-91195	200	7	provided	provide	VERB
esrj-91195	200	8	the	the	DET
esrj-91195	200	9	best	good	ADJ
esrj-91195	200	10	testing	testing	NOUN
esrj-91195	200	11	performance	performance	NOUN
esrj-91195	200	12	were	be	AUX
esrj-91195	200	13	determined	determine	VERB
esrj-91195	200	14	as	as	ADP
esrj-91195	200	15	0.015	0.015	NUM
esrj-91195	200	16	,	,	PUNCT
esrj-91195	200	17	0.013	0.013	NUM
esrj-91195	200	18	,	,	PUNCT
esrj-91195	200	19	0.020	0.020	NUM
esrj-91195	200	20	,	,	PUNCT
esrj-91195	200	21	0.022	0.022	NUM
esrj-91195	200	22	,	,	PUNCT
esrj-91195	200	23	and	and	CCONJ
esrj-91195	200	24	0.012	0.012	NUM
esrj-91195	200	25	for	for	ADP
esrj-91195	200	26	ds#1	ds#1	PROPN
esrj-91195	200	27	,	,	PUNCT
esrj-91195	200	28	ds#2	ds#2	PRON
esrj-91195	200	29	,	,	PUNCT
esrj-91195	200	30	ds#3	ds#3	X
esrj-91195	200	31	,	,	PUNCT
esrj-91195	200	32	ds#4	ds#4	PROPN
esrj-91195	200	33	,	,	PUNCT
esrj-91195	200	34	and	and	CCONJ
esrj-91195	200	35	ds#5	ds#5	PROPN
esrj-91195	200	36	,	,	PUNCT
esrj-91195	200	37	respectively	respectively	ADV
esrj-91195	200	38	.	.	PUNCT
esrj-91195	201	1	the	the	DET
esrj-91195	201	2	prediction	prediction	NOUN
esrj-91195	201	3	performance	performance	NOUN
esrj-91195	201	4	of	of	ADP
esrj-91195	201	5	the	the	DET
esrj-91195	201	6	grnn	grnn	NOUN
esrj-91195	201	7	for	for	ADP
esrj-91195	201	8	the	the	DET
esrj-91195	201	9	training	training	NOUN
esrj-91195	201	10	and	and	CCONJ
esrj-91195	201	11	testing	testing	NOUN
esrj-91195	201	12	stages	stage	NOUN
esrj-91195	201	13	in	in	ADP
esrj-91195	201	14	terms	term	NOUN
esrj-91195	201	15	of	of	ADP
esrj-91195	201	16	the	the	DET
esrj-91195	201	17	rmse	rmse	NOUN
esrj-91195	201	18	,	,	PUNCT
esrj-91195	201	19	mae	mae	PROPN
esrj-91195	201	20	,	,	PUNCT
esrj-91195	201	21	and	and	CCONJ
esrj-91195	201	22	nse	nse	NOUN
esrj-91195	201	23	is	be	AUX
esrj-91195	201	24	given	give	VERB
esrj-91195	201	25	in	in	ADP
esrj-91195	201	26	table	table	NOUN
esrj-91195	201	27	3	3	NUM
esrj-91195	201	28	.	.	PUNCT
esrj-91195	202	1	according	accord	VERB
esrj-91195	202	2	to	to	ADP
esrj-91195	202	3	table	table	NOUN
esrj-91195	202	4	3	3	NUM
esrj-91195	202	5	,	,	PUNCT
esrj-91195	202	6	the	the	DET
esrj-91195	202	7	lowest	low	ADJ
esrj-91195	202	8	rmse	rmse	NOUN
esrj-91195	202	9	value	value	NOUN
esrj-91195	202	10	in	in	ADP
esrj-91195	202	11	the	the	DET
esrj-91195	202	12	training	training	NOUN
esrj-91195	202	13	stage	stage	NOUN
esrj-91195	202	14	was	be	AUX
esrj-91195	202	15	obtained	obtain	VERB
esrj-91195	202	16	with	with	ADP
esrj-91195	202	17	ds#4	ds#4	PROPN
esrj-91195	202	18	,	,	PUNCT
esrj-91195	202	19	and	and	CCONJ
esrj-91195	202	20	the	the	DET
esrj-91195	202	21	highest	high	ADJ
esrj-91195	202	22	with	with	ADP
esrj-91195	202	23	ds#2	ds#2	PRON
esrj-91195	202	24	.	.	PUNCT
esrj-91195	203	1	the	the	DET
esrj-91195	203	2	lowest	low	ADJ
esrj-91195	203	3	mae	mae	PROPN
esrj-91195	203	4	value	value	NOUN
esrj-91195	203	5	was	be	AUX
esrj-91195	203	6	determined	determine	VERB
esrj-91195	203	7	with	with	ADP
esrj-91195	203	8	ds#4	ds#4	PROPN
esrj-91195	203	9	,	,	PUNCT
esrj-91195	203	10	whereas	whereas	SCONJ
esrj-91195	203	11	the	the	DET
esrj-91195	203	12	highest	high	ADJ
esrj-91195	203	13	nse	nse	NOUN
esrj-91195	203	14	value	value	NOUN
esrj-91195	203	15	at	at	ADP
esrj-91195	203	16	the	the	DET
esrj-91195	203	17	training	training	NOUN
esrj-91195	203	18	stage	stage	NOUN
esrj-91195	203	19	was	be	AUX
esrj-91195	203	20	determined	determine	VERB
esrj-91195	203	21	again	again	ADV
esrj-91195	203	22	in	in	ADP
esrj-91195	203	23	ds#4	ds#4	PROPN
esrj-91195	203	24	.	.	PUNCT
esrj-91195	204	1	the	the	DET
esrj-91195	204	2	lowest	low	ADJ
esrj-91195	204	3	rmse	rmse	NOUN
esrj-91195	204	4	value	value	NOUN
esrj-91195	204	5	in	in	ADP
esrj-91195	204	6	the	the	DET
esrj-91195	204	7	testing	testing	NOUN
esrj-91195	204	8	phase	phase	NOUN
esrj-91195	204	9	was	be	AUX
esrj-91195	204	10	found	find	VERB
esrj-91195	204	11	with	with	ADP
esrj-91195	204	12	ds#4	ds#4	PROPN
esrj-91195	204	13	and	and	CCONJ
esrj-91195	204	14	the	the	DET
esrj-91195	204	15	highest	high	ADJ
esrj-91195	204	16	with	with	ADP
esrj-91195	204	17	ds#2	ds#2	NOUN
esrj-91195	204	18	,	,	PUNCT
esrj-91195	204	19	whereas	whereas	SCONJ
esrj-91195	204	20	the	the	DET
esrj-91195	204	21	lowest	low	ADJ
esrj-91195	204	22	mae	mae	PROPN
esrj-91195	204	23	and	and	CCONJ
esrj-91195	204	24	the	the	DET
esrj-91195	204	25	highest	high	ADJ
esrj-91195	204	26	nse	nse	NOUN
esrj-91195	204	27	values	value	NOUN
esrj-91195	204	28	were	be	AUX
esrj-91195	204	29	determined	determine	VERB
esrj-91195	204	30	in	in	ADP
esrj-91195	204	31	ds#4	ds#4	PROPN
esrj-91195	204	32	.	.	PUNCT
esrj-91195	205	1	during	during	ADP
esrj-91195	205	2	the	the	DET
esrj-91195	205	3	training	training	NOUN
esrj-91195	205	4	and	and	CCONJ
esrj-91195	205	5	testing	testing	NOUN
esrj-91195	205	6	phases	phase	NOUN
esrj-91195	205	7	,	,	PUNCT
esrj-91195	205	8	the	the	DET
esrj-91195	205	9	performance	performance	NOUN
esrj-91195	205	10	results	result	NOUN
esrj-91195	205	11	of	of	ADP
esrj-91195	205	12	ds#4	ds#4	PROPN
esrj-91195	205	13	were	be	AUX
esrj-91195	205	14	better	well	ADJ
esrj-91195	205	15	than	than	ADP
esrj-91195	205	16	those	those	PRON
esrj-91195	205	17	of	of	ADP
esrj-91195	205	18	the	the	DET
esrj-91195	205	19	others	other	NOUN
esrj-91195	205	20	.	.	PUNCT
esrj-91195	206	1	in	in	ADP
esrj-91195	206	2	order	order	NOUN
esrj-91195	206	3	to	to	PART
esrj-91195	206	4	illustrate	illustrate	VERB
esrj-91195	206	5	the	the	DET
esrj-91195	206	6	success	success	NOUN
esrj-91195	206	7	of	of	ADP
esrj-91195	206	8	the	the	DET
esrj-91195	206	9	grnn	grnn	NOUN
esrj-91195	206	10	,	,	PUNCT
esrj-91195	206	11	the	the	DET
esrj-91195	206	12	predicted	predict	VERB
esrj-91195	206	13	geoid	geoid	ADJ
esrj-91195	206	14	undulation	undulation	NOUN
esrj-91195	206	15	values	value	NOUN
esrj-91195	206	16	and	and	CCONJ
esrj-91195	206	17	those	those	PRON
esrj-91195	206	18	observed	observe	VERB
esrj-91195	206	19	at	at	ADP
esrj-91195	206	20	the	the	DET
esrj-91195	206	21	testing	testing	NOUN
esrj-91195	206	22	stage	stage	NOUN
esrj-91195	206	23	for	for	ADP
esrj-91195	206	24	each	each	DET
esrj-91195	206	25	dataset	dataset	NOUN
esrj-91195	206	26	(	(	PUNCT
esrj-91195	206	27	ds#1	ds#1	PROPN
esrj-91195	206	28	,	,	PUNCT
esrj-91195	206	29	ds#2	ds#2	PRON
esrj-91195	206	30	,	,	PUNCT
esrj-91195	206	31	ds#3	ds#3	X
esrj-91195	206	32	,	,	PUNCT
esrj-91195	206	33	ds#4	ds#4	PROPN
esrj-91195	206	34	,	,	PUNCT
esrj-91195	206	35	and	and	CCONJ
esrj-91195	206	36	ds#5	ds#5	NOUN
esrj-91195	206	37	)	)	PUNCT
esrj-91195	206	38	are	be	AUX
esrj-91195	206	39	shown	show	VERB
esrj-91195	206	40	in	in	ADP
esrj-91195	206	41	figure	figure	NOUN
esrj-91195	206	42	7	7	NUM
esrj-91195	206	43	.	.	PUNCT
esrj-91195	207	1	377geoid	377geoid	NUM
esrj-91195	207	2	undulation	undulation	NOUN
esrj-91195	207	3	prediction	prediction	NOUN
esrj-91195	207	4	using	use	VERB
esrj-91195	207	5	anns	ann	NOUN
esrj-91195	207	6	(	(	PUNCT
esrj-91195	207	7	rbfnn	rbfnn	NOUN
esrj-91195	207	8	and	and	CCONJ
esrj-91195	207	9	grnn	grnn	NOUN
esrj-91195	207	10	)	)	PUNCT
esrj-91195	207	11	,	,	PUNCT
esrj-91195	207	12	multiple	multiple	ADJ
esrj-91195	207	13	linear	linear	ADJ
esrj-91195	207	14	regression	regression	NOUN
esrj-91195	207	15	(	(	PUNCT
esrj-91195	207	16	mlr	mlr	NOUN
esrj-91195	207	17	)	)	PUNCT
esrj-91195	207	18	,	,	PUNCT
esrj-91195	207	19	and	and	CCONJ
esrj-91195	207	20	interpolation	interpolation	NOUN
esrj-91195	207	21	methods	method	NOUN
esrj-91195	207	22	:	:	PUNCT
esrj-91195	207	23	a	a	DET
esrj-91195	207	24	comparative	comparative	ADJ
esrj-91195	207	25	study	study	NOUN
esrj-91195	207	26	table	table	NOUN
esrj-91195	207	27	3	3	NUM
esrj-91195	207	28	.	.	PUNCT
esrj-91195	207	29	grnn	grnn	NOUN
esrj-91195	207	30	training	training	NOUN
esrj-91195	207	31	and	and	CCONJ
esrj-91195	207	32	testing	testing	NOUN
esrj-91195	207	33	phase	phase	NOUN
esrj-91195	207	34	results	result	VERB
esrj-91195	207	35	phase	phase	NOUN
esrj-91195	207	36	dataset	dataset	NOUN
esrj-91195	207	37	rmse	rmse	NOUN
esrj-91195	207	38	(	(	PUNCT
esrj-91195	207	39	m	m	PROPN
esrj-91195	207	40	)	)	PUNCT
esrj-91195	207	41	mae	mae	PROPN
esrj-91195	207	42	(	(	PUNCT
esrj-91195	207	43	m	m	NOUN
esrj-91195	207	44	)	)	PUNCT
esrj-91195	207	45	nse	nse	NOUN
esrj-91195	207	46	training	training	NOUN
esrj-91195	207	47	ds#1	ds#1	PROPN
esrj-91195	207	48	0.024	0.024	NUM
esrj-91195	207	49	0.007	0.007	NUM
esrj-91195	207	50	0.99969	0.99969	NUM
esrj-91195	207	51	ds#2	ds#2	ADV
esrj-91195	207	52	0.048	0.048	NUM
esrj-91195	207	53	0.019	0.019	NUM
esrj-91195	207	54	0.99886	0.99886	NUM
esrj-91195	207	55	ds#3	ds#3	VERB
esrj-91195	207	56	0.019	0.019	NUM
esrj-91195	207	57	0.004	0.004	NUM
esrj-91195	207	58	0.99981	0.99981	NUM
esrj-91195	207	59	ds#4	ds#4	PROPN
esrj-91195	207	60	0.012	0.012	NUM
esrj-91195	207	61	0.003	0.003	NUM
esrj-91195	207	62	0.99993	0.99993	NUM
esrj-91195	207	63	ds#5	ds#5	PROPN
esrj-91195	207	64	0.039	0.039	NUM
esrj-91195	207	65	0.016	0.016	NUM
esrj-91195	207	66	0.99927	0.99927	NUM
esrj-91195	207	67	testing	test	VERB
esrj-91195	207	68	ds#1	ds#1	PROPN
esrj-91195	207	69	0.199	0.199	NUM
esrj-91195	207	70	0.147	0.147	NUM
esrj-91195	207	71	0.97873	0.97873	NUM
esrj-91195	207	72	ds#2	ds#2	NOUN
esrj-91195	207	73	0.201	0.201	NUM
esrj-91195	207	74	0.140	0.140	NUM
esrj-91195	207	75	0.97912	0.97912	NUM
esrj-91195	208	1	ds#3	ds#3	VERB
esrj-91195	208	2	0.173	0.173	NUM
esrj-91195	208	3	0.134	0.134	NUM
esrj-91195	208	4	0.98530	0.98530	NUM
esrj-91195	208	5	ds#4	ds#4	VERB
esrj-91195	208	6	0.167	0.167	NUM
esrj-91195	208	7	0.124	0.124	NUM
esrj-91195	208	8	0.98550	0.98550	NUM
esrj-91195	208	9	ds#5	ds#5	NOUN
esrj-91195	208	10	0.187	0.187	NUM
esrj-91195	208	11	0.140	0.140	NUM
esrj-91195	208	12	0.98230	0.98230	NUM
esrj-91195	208	13	the	the	DET
esrj-91195	208	14	diagrams	diagram	NOUN
esrj-91195	208	15	show	show	VERB
esrj-91195	208	16	that	that	SCONJ
esrj-91195	208	17	the	the	DET
esrj-91195	208	18	results	result	NOUN
esrj-91195	208	19	obtained	obtain	VERB
esrj-91195	208	20	with	with	ADP
esrj-91195	208	21	all	all	DET
esrj-91195	208	22	datasets	dataset	NOUN
esrj-91195	208	23	were	be	AUX
esrj-91195	208	24	quite	quite	ADV
esrj-91195	208	25	close	close	ADJ
esrj-91195	208	26	to	to	ADP
esrj-91195	208	27	each	each	DET
esrj-91195	208	28	other	other	ADJ
esrj-91195	208	29	.	.	PUNCT
esrj-91195	209	1	the	the	DET
esrj-91195	209	2	fact	fact	NOUN
esrj-91195	209	3	that	that	SCONJ
esrj-91195	209	4	in	in	ADP
esrj-91195	209	5	the	the	DET
esrj-91195	209	6	equation	equation	NOUN
esrj-91195	209	7	for	for	ADP
esrj-91195	209	8	determining	determine	VERB
esrj-91195	209	9	linearity	linearity	NOUN
esrj-91195	209	10	(	(	PUNCT
esrj-91195	209	11	y	y	NOUN
esrj-91195	209	12	=	=	NOUN
esrj-91195	209	13	ax	ax	NOUN
esrj-91195	209	14	+	+	CCONJ
esrj-91195	209	15	b	b	NOUN
esrj-91195	209	16	)	)	PUNCT
esrj-91195	209	17	,	,	PUNCT
esrj-91195	209	18	coefficient	coefficient	VERB
esrj-91195	209	19	a	a	PRON
esrj-91195	209	20	was	be	AUX
esrj-91195	209	21	close	close	ADJ
esrj-91195	209	22	to	to	ADP
esrj-91195	209	23	1	1	NUM
esrj-91195	209	24	and	and	CCONJ
esrj-91195	209	25	coefficient	coefficient	VERB
esrj-91195	209	26	b	b	X
esrj-91195	209	27	close	close	ADJ
esrj-91195	209	28	to	to	PART
esrj-91195	209	29	0	0	NUM
esrj-91195	209	30	indicated	indicate	VERB
esrj-91195	209	31	that	that	SCONJ
esrj-91195	209	32	there	there	PRON
esrj-91195	209	33	was	be	VERB
esrj-91195	209	34	a	a	DET
esrj-91195	209	35	good	good	ADJ
esrj-91195	209	36	relationship	relationship	NOUN
esrj-91195	209	37	between	between	ADP
esrj-91195	209	38	the	the	DET
esrj-91195	209	39	observed	observe	VERB
esrj-91195	209	40	and	and	CCONJ
esrj-91195	209	41	the	the	DET
esrj-91195	209	42	predicted	predict	VERB
esrj-91195	209	43	geoid	geoid	ADJ
esrj-91195	209	44	undulation	undulation	NOUN
esrj-91195	209	45	values	value	NOUN
esrj-91195	209	46	.	.	PUNCT
esrj-91195	210	1	in	in	ADP
esrj-91195	210	2	addition	addition	NOUN
esrj-91195	210	3	,	,	PUNCT
esrj-91195	210	4	as	as	SCONJ
esrj-91195	210	5	can	can	AUX
esrj-91195	210	6	be	be	AUX
esrj-91195	210	7	seen	see	VERB
esrj-91195	210	8	from	from	ADP
esrj-91195	210	9	figure	figure	NOUN
esrj-91195	210	10	7	7	NUM
esrj-91195	210	11	,	,	PUNCT
esrj-91195	210	12	the	the	DET
esrj-91195	210	13	determination	determination	NOUN
esrj-91195	210	14	coefficient	coefficient	NOUN
esrj-91195	210	15	(	(	PUNCT
esrj-91195	210	16	r2	r2	PROPN
esrj-91195	210	17	)	)	PUNCT
esrj-91195	210	18	between	between	ADP
esrj-91195	210	19	the	the	DET
esrj-91195	210	20	observed	observe	VERB
esrj-91195	210	21	and	and	CCONJ
esrj-91195	210	22	predicted	predict	VERB
esrj-91195	210	23	values	value	NOUN
esrj-91195	210	24	of	of	ADP
esrj-91195	210	25	ds#4	ds#4	PROPN
esrj-91195	210	26	during	during	ADP
esrj-91195	210	27	the	the	DET
esrj-91195	210	28	testing	testing	NOUN
esrj-91195	210	29	phase	phase	NOUN
esrj-91195	210	30	was	be	AUX
esrj-91195	210	31	0.98575	0.98575	NUM
esrj-91195	210	32	.	.	PUNCT
esrj-91195	211	1	mlr	mlr	NOUN
esrj-91195	211	2	results	result	VERB
esrj-91195	211	3	the	the	DET
esrj-91195	211	4	results	result	NOUN
esrj-91195	211	5	calculated	calculate	VERB
esrj-91195	211	6	according	accord	VERB
esrj-91195	211	7	to	to	ADP
esrj-91195	211	8	the	the	DET
esrj-91195	211	9	regression	regression	NOUN
esrj-91195	211	10	equations	equation	NOUN
esrj-91195	211	11	for	for	ADP
esrj-91195	211	12	geoid	geoid	ADJ
esrj-91195	211	13	undulation	undulation	NOUN
esrj-91195	211	14	are	be	AUX
esrj-91195	211	15	given	give	VERB
esrj-91195	211	16	below	below	ADV
esrj-91195	211	17	for	for	ADP
esrj-91195	211	18	the	the	DET
esrj-91195	211	19	five	five	NUM
esrj-91195	211	20	different	different	ADJ
esrj-91195	211	21	datasets	dataset	NOUN
esrj-91195	211	22	.	.	PUNCT
esrj-91195	212	1	n	n	PROPN
esrj-91195	212	2	(	(	PUNCT
esrj-91195	212	3	ds#1	ds#1	PROPN
esrj-91195	212	4	)	)	PUNCT
esrj-91195	212	5	=	=	SYM
esrj-91195	213	1	594.174	594.174	NUM
esrj-91195	213	2	–	–	PUNCT
esrj-91195	213	3	12.028j	12.028j	NUM
esrj-91195	213	4	–	–	PUNCT
esrj-91195	213	5	1.891l	1.891l	NUM
esrj-91195	213	6	(	(	PUNCT
esrj-91195	213	7	13	13	NUM
esrj-91195	213	8	)	)	PUNCT
esrj-91195	213	9	n	n	CCONJ
esrj-91195	213	10	(	(	PUNCT
esrj-91195	213	11	ds#2	ds#2	NOUN
esrj-91195	213	12	)	)	PUNCT
esrj-91195	213	13	=	=	SYM
esrj-91195	213	14	593.165	593.165	NUM
esrj-91195	213	15	–	–	PUNCT
esrj-91195	213	16	12.013j	12.013j	NUM
esrj-91195	213	17	–	–	PUNCT
esrj-91195	213	18	1.881l	1.881l	NUM
esrj-91195	213	19	(	(	PUNCT
esrj-91195	213	20	14	14	NUM
esrj-91195	213	21	)	)	PUNCT
esrj-91195	213	22	n	n	CCONJ
esrj-91195	213	23	(	(	PUNCT
esrj-91195	213	24	ds#3	ds#3	PROPN
esrj-91195	213	25	)	)	PUNCT
esrj-91195	213	26	=	=	SYM
esrj-91195	213	27	586.129	586.129	NUM
esrj-91195	213	28	–	–	PUNCT
esrj-91195	213	29	11.866j	11.866j	NUM
esrj-91195	213	30	–	–	PUNCT
esrj-91195	213	31	1.855l	1.855l	NUM
esrj-91195	213	32	(	(	PUNCT
esrj-91195	213	33	15	15	NUM
esrj-91195	213	34	)	)	PUNCT
esrj-91195	213	35	n	n	CCONJ
esrj-91195	213	36	(	(	PUNCT
esrj-91195	213	37	ds#4	ds#4	PROPN
esrj-91195	213	38	)	)	PUNCT
esrj-91195	213	39	=	=	SYM
esrj-91195	213	40	642.606	642.606	NUM
esrj-91195	213	41	–	–	PUNCT
esrj-91195	213	42	13.672j	13.672j	NUM
esrj-91195	213	43	–	–	PUNCT
esrj-91195	213	44	1.423l	1.423l	NUM
esrj-91195	213	45	(	(	PUNCT
esrj-91195	213	46	16	16	NUM
esrj-91195	213	47	)	)	PUNCT
esrj-91195	213	48	n	n	CCONJ
esrj-91195	213	49	(	(	PUNCT
esrj-91195	213	50	ds#5	ds#5	PROPN
esrj-91195	213	51	)	)	PUNCT
esrj-91195	213	52	=	=	PUNCT
esrj-91195	213	53	594.498	594.498	NUM
esrj-91195	213	54	–	–	PUNCT
esrj-91195	213	55	11.987j	11.987j	NUM
esrj-91195	213	56	–	–	PUNCT
esrj-91195	213	57	1.941l	1.941l	NUM
esrj-91195	213	58	(	(	PUNCT
esrj-91195	213	59	17	17	NUM
esrj-91195	213	60	)	)	PUNCT
esrj-91195	213	61	the	the	DET
esrj-91195	213	62	geoid	geoid	ADJ
esrj-91195	213	63	undulation	undulation	NOUN
esrj-91195	213	64	(	(	PUNCT
esrj-91195	213	65	n	n	CCONJ
esrj-91195	213	66	)	)	PUNCT
esrj-91195	213	67	was	be	AUX
esrj-91195	213	68	defined	define	VERB
esrj-91195	213	69	as	as	SCONJ
esrj-91195	213	70	the	the	DET
esrj-91195	213	71	dependent	dependent	ADJ
esrj-91195	213	72	variable	variable	NOUN
esrj-91195	213	73	and	and	CCONJ
esrj-91195	213	74	latitude	latitude	NOUN
esrj-91195	213	75	(	(	PUNCT
esrj-91195	213	76	j	j	NOUN
esrj-91195	213	77	)	)	PUNCT
esrj-91195	213	78	and	and	CCONJ
esrj-91195	213	79	longitude	longitude	NOUN
esrj-91195	213	80	(	(	PUNCT
esrj-91195	213	81	l	l	NOUN
esrj-91195	213	82	)	)	PUNCT
esrj-91195	213	83	were	be	AUX
esrj-91195	213	84	regarded	regard	VERB
esrj-91195	213	85	as	as	ADP
esrj-91195	213	86	the	the	DET
esrj-91195	213	87	independent	independent	ADJ
esrj-91195	213	88	variables	variable	NOUN
esrj-91195	213	89	.	.	PUNCT
esrj-91195	214	1	the	the	DET
esrj-91195	214	2	results	result	NOUN
esrj-91195	214	3	of	of	ADP
esrj-91195	214	4	the	the	DET
esrj-91195	214	5	statistical	statistical	ADJ
esrj-91195	214	6	evaluation	evaluation	NOUN
esrj-91195	214	7	criteria	criterion	NOUN
esrj-91195	214	8	for	for	ADP
esrj-91195	214	9	the	the	DET
esrj-91195	214	10	mlr	mlr	PROPN
esrj-91195	214	11	geoid	geoid	PROPN
esrj-91195	214	12	undulation	undulation	NOUN
esrj-91195	214	13	prediction	prediction	NOUN
esrj-91195	214	14	are	be	AUX
esrj-91195	214	15	presented	present	VERB
esrj-91195	214	16	in	in	ADP
esrj-91195	214	17	table	table	NOUN
esrj-91195	214	18	4	4	NUM
esrj-91195	214	19	.	.	PUNCT
esrj-91195	214	20	table	table	NOUN
esrj-91195	214	21	4	4	NUM
esrj-91195	214	22	.	.	PUNCT
esrj-91195	214	23	mlr	mlr	NOUN
esrj-91195	214	24	training	training	NOUN
esrj-91195	214	25	and	and	CCONJ
esrj-91195	214	26	testing	testing	NOUN
esrj-91195	214	27	phase	phase	NOUN
esrj-91195	214	28	results	result	VERB
esrj-91195	214	29	phase	phase	NOUN
esrj-91195	214	30	dataset	dataset	NOUN
esrj-91195	214	31	rmse	rmse	NOUN
esrj-91195	214	32	(	(	PUNCT
esrj-91195	214	33	m	m	PROPN
esrj-91195	214	34	)	)	PUNCT
esrj-91195	214	35	mae	mae	PROPN
esrj-91195	214	36	(	(	PUNCT
esrj-91195	214	37	m	m	NOUN
esrj-91195	214	38	)	)	PUNCT
esrj-91195	214	39	nse	nse	NOUN
esrj-91195	214	40	training	training	NOUN
esrj-91195	214	41	ds#1	ds#1	PROPN
esrj-91195	214	42	0.432	0.432	NUM
esrj-91195	214	43	0.346	0.346	NUM
esrj-91195	214	44	0.90959	0.90959	NUM
esrj-91195	214	45	ds#2	ds#2	ADV
esrj-91195	214	46	0.448	0.448	NUM
esrj-91195	214	47	0.356	0.356	NUM
esrj-91195	214	48	0.90175	0.90175	NUM
esrj-91195	215	1	ds#3	ds#3	VERB
esrj-91195	215	2	0.449	0.449	NUM
esrj-91195	215	3	0.356	0.356	NUM
esrj-91195	215	4	0.90038	0.90038	NUM
esrj-91195	215	5	ds#4	ds#4	PROPN
esrj-91195	215	6	0.436	0.436	NUM
esrj-91195	215	7	0.341	0.341	NUM
esrj-91195	215	8	0.90753	0.90753	NUM
esrj-91195	215	9	ds#5	ds#5	NOUN
esrj-91195	215	10	0.436	0.436	NUM
esrj-91195	215	11	0.346	0.346	NUM
esrj-91195	215	12	0.90685	0.90685	NUM
esrj-91195	215	13	testing	testing	NOUN
esrj-91195	215	14	ds#1	ds#1	PROPN
esrj-91195	215	15	0.466	0.466	NUM
esrj-91195	215	16	0.351	0.351	NUM
esrj-91195	215	17	0.88358	0.88358	NUM
esrj-91195	215	18	ds#2	ds#2	DET
esrj-91195	215	19	0.402	0.402	NUM
esrj-91195	215	20	0.310	0.310	NUM
esrj-91195	215	21	0.91627	0.91627	NUM
esrj-91195	215	22	ds#3	ds#3	VERB
esrj-91195	215	23	0.397	0.397	NUM
esrj-91195	215	24	0.312	0.312	NUM
esrj-91195	215	25	0.92260	0.92260	NUM
esrj-91195	215	26	ds#4	ds#4	PROPN
esrj-91195	215	27	0.557	0.557	NUM
esrj-91195	215	28	0.446	0.446	NUM
esrj-91195	215	29	0.83850	0.83850	NUM
esrj-91195	215	30	ds#5	ds#5	NOUN
esrj-91195	215	31	0.455	0.455	NUM
esrj-91195	215	32	0.340	0.340	NUM
esrj-91195	215	33	0.89569	0.89569	NUM
esrj-91195	215	34	figure	figure	NOUN
esrj-91195	215	35	7	7	NUM
esrj-91195	215	36	.	.	NOUN
esrj-91195	215	37	comparison	comparison	NOUN
esrj-91195	215	38	of	of	ADP
esrj-91195	215	39	the	the	DET
esrj-91195	215	40	geoid	geoid	ADJ
esrj-91195	215	41	undulation	undulation	NOUN
esrj-91195	215	42	values	value	NOUN
esrj-91195	215	43	predicted	predict	VERB
esrj-91195	215	44	by	by	ADP
esrj-91195	215	45	grnn	grnn	NOUN
esrj-91195	215	46	and	and	CCONJ
esrj-91195	215	47	those	those	PRON
esrj-91195	215	48	observed	observe	VERB
esrj-91195	215	49	at	at	ADP
esrj-91195	215	50	the	the	DET
esrj-91195	215	51	testing	testing	NOUN
esrj-91195	215	52	phase	phase	NOUN
esrj-91195	215	53	according	accord	VERB
esrj-91195	215	54	to	to	ADP
esrj-91195	215	55	table	table	NOUN
esrj-91195	215	56	4	4	NUM
esrj-91195	215	57	,	,	PUNCT
esrj-91195	215	58	the	the	DET
esrj-91195	215	59	lowest	low	ADJ
esrj-91195	215	60	and	and	CCONJ
esrj-91195	215	61	highest	high	ADJ
esrj-91195	215	62	rmse	rmse	ADJ
esrj-91195	215	63	values	value	NOUN
esrj-91195	215	64	in	in	ADP
esrj-91195	215	65	the	the	DET
esrj-91195	215	66	training	training	NOUN
esrj-91195	215	67	stage	stage	NOUN
esrj-91195	215	68	were	be	AUX
esrj-91195	215	69	obtained	obtain	VERB
esrj-91195	215	70	with	with	ADP
esrj-91195	215	71	ds#1	ds#1	PROPN
esrj-91195	215	72	and	and	CCONJ
esrj-91195	215	73	ds#3	ds#3	PROPN
esrj-91195	215	74	,	,	PUNCT
esrj-91195	215	75	respectively	respectively	ADV
esrj-91195	215	76	.	.	PUNCT
esrj-91195	216	1	the	the	DET
esrj-91195	216	2	lowest	low	ADJ
esrj-91195	216	3	mae	mae	PROPN
esrj-91195	216	4	value	value	NOUN
esrj-91195	216	5	was	be	AUX
esrj-91195	216	6	determined	determine	VERB
esrj-91195	216	7	with	with	ADP
esrj-91195	216	8	ds#4	ds#4	PROPN
esrj-91195	216	9	and	and	CCONJ
esrj-91195	216	10	the	the	DET
esrj-91195	216	11	highest	high	ADJ
esrj-91195	216	12	with	with	ADP
esrj-91195	216	13	ds#3	ds#3	PROPN
esrj-91195	216	14	.	.	PUNCT
esrj-91195	217	1	the	the	DET
esrj-91195	217	2	highest	high	ADJ
esrj-91195	217	3	nse	nse	NOUN
esrj-91195	217	4	value	value	NOUN
esrj-91195	217	5	at	at	ADP
esrj-91195	217	6	the	the	DET
esrj-91195	217	7	training	training	NOUN
esrj-91195	217	8	stage	stage	NOUN
esrj-91195	217	9	was	be	AUX
esrj-91195	217	10	determined	determine	VERB
esrj-91195	217	11	in	in	ADP
esrj-91195	217	12	ds#1	ds#1	PROPN
esrj-91195	217	13	.	.	PUNCT
esrj-91195	218	1	at	at	ADP
esrj-91195	218	2	the	the	DET
esrj-91195	218	3	testing	testing	NOUN
esrj-91195	218	4	stage	stage	NOUN
esrj-91195	218	5	,	,	PUNCT
esrj-91195	218	6	the	the	DET
esrj-91195	218	7	lowest	low	ADJ
esrj-91195	218	8	and	and	CCONJ
esrj-91195	218	9	highest	high	ADJ
esrj-91195	218	10	rmse	rmse	ADJ
esrj-91195	218	11	values	value	NOUN
esrj-91195	218	12	were	be	AUX
esrj-91195	218	13	determined	determine	VERB
esrj-91195	218	14	with	with	ADP
esrj-91195	218	15	ds#3	ds#3	ADJ
esrj-91195	218	16	and	and	CCONJ
esrj-91195	218	17	ds#4	ds#4	PROPN
esrj-91195	218	18	,	,	PUNCT
esrj-91195	218	19	respectively	respectively	ADV
esrj-91195	218	20	.	.	PUNCT
esrj-91195	219	1	in	in	ADP
esrj-91195	219	2	addition	addition	NOUN
esrj-91195	219	3	,	,	PUNCT
esrj-91195	219	4	the	the	DET
esrj-91195	219	5	lowest	low	ADJ
esrj-91195	219	6	and	and	CCONJ
esrj-91195	219	7	highest	high	ADJ
esrj-91195	219	8	mae	mae	PROPN
esrj-91195	219	9	values	value	NOUN
esrj-91195	219	10	were	be	AUX
esrj-91195	219	11	determined	determine	VERB
esrj-91195	219	12	in	in	ADP
esrj-91195	219	13	ds#2	ds#2	PRON
esrj-91195	219	14	and	and	CCONJ
esrj-91195	219	15	ds#4	ds#4	ADJ
esrj-91195	219	16	,	,	PUNCT
esrj-91195	219	17	respectively	respectively	ADV
esrj-91195	219	18	.	.	PUNCT
esrj-91195	220	1	the	the	DET
esrj-91195	220	2	lowest	low	ADJ
esrj-91195	220	3	and	and	CCONJ
esrj-91195	220	4	highest	high	ADJ
esrj-91195	220	5	nse	nse	NOUN
esrj-91195	220	6	values	value	NOUN
esrj-91195	220	7	were	be	AUX
esrj-91195	220	8	calculated	calculate	VERB
esrj-91195	220	9	for	for	ADP
esrj-91195	220	10	ds#4	ds#4	PROPN
esrj-91195	220	11	and	and	CCONJ
esrj-91195	220	12	ds#3	ds#3	PROPN
esrj-91195	220	13	.	.	PUNCT
esrj-91195	221	1	in	in	ADP
esrj-91195	221	2	order	order	NOUN
esrj-91195	221	3	to	to	PART
esrj-91195	221	4	illustrate	illustrate	VERB
esrj-91195	221	5	the	the	DET
esrj-91195	221	6	success	success	NOUN
esrj-91195	221	7	of	of	ADP
esrj-91195	221	8	the	the	DET
esrj-91195	221	9	mlr	mlr	NOUN
esrj-91195	221	10	,	,	PUNCT
esrj-91195	221	11	the	the	DET
esrj-91195	221	12	predicted	predict	VERB
esrj-91195	221	13	geoid	geoid	ADJ
esrj-91195	221	14	undulation	undulation	NOUN
esrj-91195	221	15	values	value	NOUN
esrj-91195	221	16	and	and	CCONJ
esrj-91195	221	17	those	those	PRON
esrj-91195	221	18	observed	observe	VERB
esrj-91195	221	19	at	at	ADP
esrj-91195	221	20	the	the	DET
esrj-91195	221	21	testing	testing	NOUN
esrj-91195	221	22	stage	stage	NOUN
esrj-91195	221	23	for	for	ADP
esrj-91195	221	24	each	each	DET
esrj-91195	221	25	dataset	dataset	NOUN
esrj-91195	221	26	(	(	PUNCT
esrj-91195	221	27	ds#1	ds#1	PROPN
esrj-91195	221	28	,	,	PUNCT
esrj-91195	221	29	ds#2	ds#2	PRON
esrj-91195	221	30	,	,	PUNCT
esrj-91195	221	31	ds#3	ds#3	X
esrj-91195	221	32	,	,	PUNCT
esrj-91195	221	33	ds#4	ds#4	PROPN
esrj-91195	221	34	,	,	PUNCT
esrj-91195	221	35	and	and	CCONJ
esrj-91195	221	36	ds#5	ds#5	NOUN
esrj-91195	221	37	)	)	PUNCT
esrj-91195	221	38	are	be	AUX
esrj-91195	221	39	shown	show	VERB
esrj-91195	221	40	in	in	ADP
esrj-91195	221	41	figure	figure	NOUN
esrj-91195	221	42	8	8	NUM
esrj-91195	221	43	.	.	PUNCT
esrj-91195	221	44	results	result	NOUN
esrj-91195	221	45	of	of	ADP
esrj-91195	221	46	interpolation	interpolation	NOUN
esrj-91195	221	47	methods	method	NOUN
esrj-91195	221	48	the	the	DET
esrj-91195	221	49	differences	difference	NOUN
esrj-91195	221	50	between	between	ADP
esrj-91195	221	51	the	the	DET
esrj-91195	221	52	predicted	predict	VERB
esrj-91195	221	53	and	and	CCONJ
esrj-91195	221	54	the	the	DET
esrj-91195	221	55	observed	observe	VERB
esrj-91195	221	56	geoid	geoid	ADJ
esrj-91195	221	57	undulation	undulation	NOUN
esrj-91195	221	58	values	value	NOUN
esrj-91195	221	59	at	at	ADP
esrj-91195	221	60	the	the	DET
esrj-91195	221	61	test	test	NOUN
esrj-91195	221	62	points	point	NOUN
esrj-91195	221	63	for	for	ADP
esrj-91195	221	64	the	the	DET
esrj-91195	221	65	interpolation	interpolation	NOUN
esrj-91195	221	66	methods	method	NOUN
esrj-91195	221	67	were	be	AUX
esrj-91195	221	68	computed	compute	VERB
esrj-91195	221	69	to	to	PART
esrj-91195	221	70	determine	determine	VERB
esrj-91195	221	71	the	the	DET
esrj-91195	221	72	rmse	rmse	NOUN
esrj-91195	221	73	,	,	PUNCT
esrj-91195	221	74	mae	mae	PROPN
esrj-91195	221	75	,	,	PUNCT
esrj-91195	221	76	and	and	CCONJ
esrj-91195	221	77	nse	nse	NOUN
esrj-91195	221	78	for	for	ADP
esrj-91195	221	79	the	the	DET
esrj-91195	221	80	five	five	NUM
esrj-91195	221	81	different	different	ADJ
esrj-91195	221	82	datasets	dataset	NOUN
esrj-91195	221	83	(	(	PUNCT
esrj-91195	221	84	ds#1	ds#1	PROPN
esrj-91195	221	85	,	,	PUNCT
esrj-91195	221	86	ds#2	ds#2	PRON
esrj-91195	221	87	,	,	PUNCT
esrj-91195	221	88	ds#3	ds#3	X
esrj-91195	221	89	,	,	PUNCT
esrj-91195	221	90	ds#4	ds#4	PROPN
esrj-91195	221	91	,	,	PUNCT
esrj-91195	221	92	and	and	CCONJ
esrj-91195	221	93	ds#5	ds#5	PROPN
esrj-91195	221	94	)	)	PUNCT
esrj-91195	221	95	,	,	PUNCT
esrj-91195	221	96	and	and	CCONJ
esrj-91195	221	97	are	be	AUX
esrj-91195	221	98	given	give	VERB
esrj-91195	221	99	in	in	ADP
esrj-91195	221	100	table	table	NOUN
esrj-91195	221	101	5	5	NUM
esrj-91195	221	102	.	.	X
esrj-91195	221	103	378	378	NUM
esrj-91195	221	104	berkant	berkant	ADJ
esrj-91195	221	105	konakoglu	konakoglu	PROPN
esrj-91195	221	106	,	,	PUNCT
esrj-91195	221	107	alper	alper	PROPN
esrj-91195	221	108	akar	akar	PROPN
esrj-91195	221	109	table	table	NOUN
esrj-91195	221	110	5	5	NUM
esrj-91195	221	111	.	.	PUNCT
esrj-91195	221	112	statistical	statistical	ADJ
esrj-91195	221	113	findings	finding	NOUN
esrj-91195	221	114	for	for	ADP
esrj-91195	221	115	the	the	DET
esrj-91195	221	116	interpolation	interpolation	NOUN
esrj-91195	221	117	methods	method	NOUN
esrj-91195	221	118	dataset	dataset	VERB
esrj-91195	221	119	(	(	PUNCT
esrj-91195	221	120	ds#1	ds#1	PROPN
esrj-91195	221	121	)	)	PUNCT
esrj-91195	221	122	(	(	PUNCT
esrj-91195	221	123	ds#2	ds#2	NOUN
esrj-91195	221	124	)	)	PUNCT
esrj-91195	221	125	interpolation	interpolation	NOUN
esrj-91195	221	126	methods	method	NOUN
esrj-91195	221	127	rmse	rmse	NOUN
esrj-91195	221	128	(	(	PUNCT
esrj-91195	221	129	m	m	PROPN
esrj-91195	221	130	)	)	PUNCT
esrj-91195	221	131	mae	mae	PROPN
esrj-91195	221	132	(	(	PUNCT
esrj-91195	221	133	m	m	NOUN
esrj-91195	221	134	)	)	PUNCT
esrj-91195	221	135	nse	nse	NOUN
esrj-91195	221	136	rmse	rmse	NOUN
esrj-91195	221	137	(	(	PUNCT
esrj-91195	221	138	m	m	PROPN
esrj-91195	221	139	)	)	PUNCT
esrj-91195	221	140	mae	mae	PROPN
esrj-91195	221	141	(	(	PUNCT
esrj-91195	221	142	m	m	NOUN
esrj-91195	221	143	)	)	PUNCT
esrj-91195	221	144	nse	nse	NOUN
esrj-91195	221	145	krig	krig	NOUN
esrj-91195	221	146	0.298	0.298	NUM
esrj-91195	221	147	0.140	0.140	NUM
esrj-91195	221	148	0.95226	0.95226	NUM
esrj-91195	221	149	0.239	0.239	NUM
esrj-91195	221	150	0.127	0.127	NUM
esrj-91195	221	151	0.97038	0.97038	NUM
esrj-91195	221	152	idp	idp	PROPN
esrj-91195	221	153	0.320	0.320	NUM
esrj-91195	221	154	0.179	0.179	NUM
esrj-91195	221	155	0.94504	0.94504	NUM
esrj-91195	221	156	0.275	0.275	NUM
esrj-91195	221	157	0.180	0.180	NUM
esrj-91195	221	158	0.96082	0.96082	NUM
esrj-91195	221	159	tli	tli	X
esrj-91195	222	1	0.301	0.301	NUM
esrj-91195	222	2	0.149	0.149	NUM
esrj-91195	222	3	0.95130	0.95130	NUM
esrj-91195	222	4	0.248	0.248	NUM
esrj-91195	222	5	0.133	0.133	NUM
esrj-91195	222	6	0.96814	0.96814	NUM
esrj-91195	222	7	mcs	mcs	NOUN
esrj-91195	222	8	0.301	0.301	NUM
esrj-91195	222	9	0.151	0.151	NUM
esrj-91195	222	10	0.95146	0.95146	NUM
esrj-91195	222	11	0.251	0.251	NUM
esrj-91195	222	12	0.140	0.140	NUM
esrj-91195	222	13	0.96740	0.96740	NUM
esrj-91195	222	14	nn	nn	NUM
esrj-91195	222	15	0.298	0.298	NUM
esrj-91195	222	16	0.143	0.143	NUM
esrj-91195	222	17	0.95232	0.95232	NUM
esrj-91195	222	18	0.237	0.237	NUM
esrj-91195	222	19	0.128	0.128	NUM
esrj-91195	222	20	0.97077	0.97077	NUM
esrj-91195	222	21	nrn	nrn	NOUN
esrj-91195	222	22	0.341	0.341	NUM
esrj-91195	222	23	0.235	0.235	NUM
esrj-91195	222	24	0.93746	0.93746	NUM
esrj-91195	222	25	0.291	0.291	NUM
esrj-91195	222	26	0.219	0.219	NUM
esrj-91195	222	27	0.95601	0.95601	NUM
esrj-91195	222	28	lp	lp	NOUN
esrj-91195	222	29	0.296	0.296	NUM
esrj-91195	222	30	0.159	0.159	NUM
esrj-91195	222	31	0.95317	0.95317	NUM
esrj-91195	222	32	0.230	0.230	NUM
esrj-91195	222	33	0.135	0.135	NUM
esrj-91195	222	34	0.97260	0.97260	NUM
esrj-91195	222	35	rbf	rbf	PROPN
esrj-91195	222	36	0.322	0.322	NUM
esrj-91195	222	37	0.167	0.167	NUM
esrj-91195	222	38	0.94438	0.94438	NUM
esrj-91195	222	39	0.268	0.268	NUM
esrj-91195	222	40	0.151	0.151	NUM
esrj-91195	222	41	0.96270	0.96270	NUM
esrj-91195	222	42	pr	pr	VERB
esrj-91195	222	43	0.477	0.477	NUM
esrj-91195	222	44	0.363	0.363	NUM
esrj-91195	222	45	0.87820	0.87820	NUM
esrj-91195	222	46	0.411	0.411	NUM
esrj-91195	222	47	0.311	0.311	NUM
esrj-91195	222	48	0.91242	0.91242	NUM
esrj-91195	223	1	ms	ms	NOUN
esrj-91195	223	2	0.443	0.443	NUM
esrj-91195	223	3	0.223	0.223	NUM
esrj-91195	223	4	0.89477	0.89477	NUM
esrj-91195	223	5	0.306	0.306	NUM
esrj-91195	223	6	0.172	0.172	NUM
esrj-91195	223	7	0.95134	0.95134	NUM
esrj-91195	223	8	dataset	dataset	NOUN
esrj-91195	223	9	(	(	PUNCT
esrj-91195	223	10	ds#3	ds#3	PROPN
esrj-91195	223	11	)	)	PUNCT
esrj-91195	223	12	(	(	PUNCT
esrj-91195	223	13	ds#4	ds#4	PROPN
esrj-91195	223	14	)	)	PUNCT
esrj-91195	223	15	interpolation	interpolation	NOUN
esrj-91195	223	16	methods	method	NOUN
esrj-91195	223	17	rmse	rmse	NOUN
esrj-91195	223	18	(	(	PUNCT
esrj-91195	223	19	m	m	PROPN
esrj-91195	223	20	)	)	PUNCT
esrj-91195	223	21	mae	mae	PROPN
esrj-91195	223	22	(	(	PUNCT
esrj-91195	223	23	m	m	NOUN
esrj-91195	223	24	)	)	PUNCT
esrj-91195	223	25	nse	nse	NOUN
esrj-91195	223	26	rmse	rmse	NOUN
esrj-91195	223	27	(	(	PUNCT
esrj-91195	223	28	m	m	PROPN
esrj-91195	223	29	)	)	PUNCT
esrj-91195	223	30	mae	mae	PROPN
esrj-91195	223	31	(	(	PUNCT
esrj-91195	223	32	m	m	NOUN
esrj-91195	223	33	)	)	PUNCT
esrj-91195	223	34	nse	nse	NOUN
esrj-91195	223	35	krig	krig	NOUN
esrj-91195	223	36	1.406	1.406	NUM
esrj-91195	223	37	1.020	1.020	NUM
esrj-91195	223	38	0.02753	0.02753	NUM
esrj-91195	223	39	0.243	0.243	NUM
esrj-91195	223	40	0.131	0.131	NUM
esrj-91195	223	41	0.96927	0.96927	NUM
esrj-91195	223	42	idp	idp	PROPN
esrj-91195	223	43	1.380	1.380	NUM
esrj-91195	223	44	0.993	0.993	NUM
esrj-91195	223	45	0.06381	0.06381	NUM
esrj-91195	223	46	0.255	0.255	NUM
esrj-91195	223	47	0.166	0.166	NUM
esrj-91195	223	48	0.96604	0.96604	NUM
esrj-91195	223	49	tli	tli	NOUN
esrj-91195	223	50	1.407	1.407	NUM
esrj-91195	223	51	1.012	1.012	NUM
esrj-91195	223	52	0.02653	0.02653	NUM
esrj-91195	223	53	0.240	0.240	NUM
esrj-91195	223	54	0.131	0.131	NUM
esrj-91195	223	55	0.97014	0.97014	NUM
esrj-91195	223	56	mcs	mcs	PROPN
esrj-91195	223	57	1.416	1.416	NUM
esrj-91195	223	58	1.026	1.026	NUM
esrj-91195	223	59	0.01322	0.01322	NUM
esrj-91195	223	60	0.250	0.250	NUM
esrj-91195	223	61	0.138	0.138	NUM
esrj-91195	223	62	0.96759	0.96759	NUM
esrj-91195	223	63	nn	nn	PROPN
esrj-91195	223	64	1.411	1.411	NUM
esrj-91195	223	65	1.024	1.024	NUM
esrj-91195	223	66	0.02063	0.02063	NUM
esrj-91195	223	67	0.240	0.240	NUM
esrj-91195	223	68	0.131	0.131	NUM
esrj-91195	223	69	0.97007	0.97007	NUM
esrj-91195	223	70	nrn	nrn	NOUN
esrj-91195	223	71	1.427	1.427	NUM
esrj-91195	223	72	1.045	1.045	NUM
esrj-91195	223	73	-0.00203	-0.00203	NOUN
esrj-91195	224	1	0.332	0.332	NUM
esrj-91195	224	2	0.230	0.230	NUM
esrj-91195	224	3	0.94248	0.94248	NUM
esrj-91195	224	4	lp	lp	NOUN
esrj-91195	224	5	1.421	1.421	NUM
esrj-91195	224	6	1.043	1.043	NUM
esrj-91195	224	7	0.00655	0.00655	NUM
esrj-91195	224	8	0.228	0.228	NUM
esrj-91195	224	9	0.140	0.140	NUM
esrj-91195	224	10	0.97287	0.97287	NUM
esrj-91195	224	11	rbf	rbf	PROPN
esrj-91195	224	12	1.404	1.404	NUM
esrj-91195	224	13	1.026	1.026	NUM
esrj-91195	224	14	0.03100	0.03100	NUM
esrj-91195	224	15	0.261	0.261	NUM
esrj-91195	224	16	0.146	0.146	NUM
esrj-91195	224	17	0.96455	0.96455	NUM
esrj-91195	224	18	pr	pr	NOUN
esrj-91195	224	19	1.432	1.432	NUM
esrj-91195	224	20	1.039	1.039	NUM
esrj-91195	224	21	-0.00800	-0.00800	NOUN
esrj-91195	224	22	0.461	0.461	NUM
esrj-91195	224	23	0.378	0.378	NUM
esrj-91195	224	24	0.88948	0.88948	NUM
esrj-91195	224	25	ms	ms	NOUN
esrj-91195	224	26	1.421	1.421	NUM
esrj-91195	224	27	1.053	1.053	NUM
esrj-91195	224	28	0.00677	0.00677	NUM
esrj-91195	224	29	0.291	0.291	NUM
esrj-91195	224	30	0.170	0.170	NUM
esrj-91195	224	31	0.95587	0.95587	NUM
esrj-91195	224	32	dataset	dataset	NOUN
esrj-91195	224	33	(	(	PUNCT
esrj-91195	224	34	ds#5	ds#5	NOUN
esrj-91195	224	35	)	)	PUNCT
esrj-91195	224	36	interpolation	interpolation	NOUN
esrj-91195	224	37	methods	method	NOUN
esrj-91195	224	38	rmse	rmse	NOUN
esrj-91195	224	39	(	(	PUNCT
esrj-91195	224	40	m	m	PROPN
esrj-91195	224	41	)	)	PUNCT
esrj-91195	224	42	mae	mae	PROPN
esrj-91195	224	43	(	(	PUNCT
esrj-91195	224	44	m	m	NOUN
esrj-91195	224	45	)	)	PUNCT
esrj-91195	224	46	nse	nse	NOUN
esrj-91195	224	47	krig	krig	NOUN
esrj-91195	224	48	0.143	0.143	NUM
esrj-91195	224	49	0.095	0.095	NUM
esrj-91195	224	50	0.98961	0.98961	NUM
esrj-91195	224	51	idp	idp	PROPN
esrj-91195	225	1	0.207	0.207	NUM
esrj-91195	225	2	0.164	0.164	NUM
esrj-91195	225	3	0.97842	0.97842	NUM
esrj-91195	225	4	tli	tli	NOUN
esrj-91195	225	5	0.159	0.159	NUM
esrj-91195	225	6	0.102	0.102	NUM
esrj-91195	225	7	0.98729	0.98729	NUM
esrj-91195	225	8	mcs	mcs	NOUN
esrj-91195	225	9	0.178	0.178	NUM
esrj-91195	225	10	0.110	0.110	NUM
esrj-91195	225	11	0.98409	0.98409	NUM
esrj-91195	225	12	nn	nn	PROPN
esrj-91195	225	13	0.142	0.142	NUM
esrj-91195	225	14	0.097	0.097	NUM
esrj-91195	225	15	0.98986	0.98986	NUM
esrj-91195	225	16	nrn	nrn	NOUN
esrj-91195	225	17	0.275	0.275	NUM
esrj-91195	225	18	0.217	0.217	NUM
esrj-91195	225	19	0.96196	0.96196	NUM
esrj-91195	225	20	lp	lp	NOUN
esrj-91195	225	21	0.171	0.171	NUM
esrj-91195	225	22	0.130	0.130	NUM
esrj-91195	225	23	0.98532	0.98532	NUM
esrj-91195	225	24	rbf	rbf	PROPN
esrj-91195	225	25	0.188	0.188	NUM
esrj-91195	225	26	0.122	0.122	NUM
esrj-91195	225	27	0.98214	0.98214	NUM
esrj-91195	225	28	pr	pr	NOUN
esrj-91195	225	29	0.462	0.462	NUM
esrj-91195	225	30	0.345	0.345	NUM
esrj-91195	225	31	0.89245	0.89245	NUM
esrj-91195	226	1	ms	ms	NOUN
esrj-91195	226	2	0.404	0.404	NUM
esrj-91195	226	3	0.206	0.206	NUM
esrj-91195	226	4	0.91746	0.91746	NUM
esrj-91195	226	5	379geoid	379geoid	NUM
esrj-91195	226	6	undulation	undulation	NOUN
esrj-91195	226	7	prediction	prediction	NOUN
esrj-91195	226	8	using	use	VERB
esrj-91195	226	9	anns	ann	NOUN
esrj-91195	226	10	(	(	PUNCT
esrj-91195	226	11	rbfnn	rbfnn	NOUN
esrj-91195	226	12	and	and	CCONJ
esrj-91195	226	13	grnn	grnn	NOUN
esrj-91195	226	14	)	)	PUNCT
esrj-91195	226	15	,	,	PUNCT
esrj-91195	226	16	multiple	multiple	ADJ
esrj-91195	226	17	linear	linear	ADJ
esrj-91195	226	18	regression	regression	NOUN
esrj-91195	226	19	(	(	PUNCT
esrj-91195	226	20	mlr	mlr	NOUN
esrj-91195	226	21	)	)	PUNCT
esrj-91195	226	22	,	,	PUNCT
esrj-91195	226	23	and	and	CCONJ
esrj-91195	226	24	interpolation	interpolation	NOUN
esrj-91195	226	25	methods	method	NOUN
esrj-91195	226	26	:	:	PUNCT
esrj-91195	226	27	a	a	DET
esrj-91195	226	28	comparative	comparative	ADJ
esrj-91195	226	29	study	study	NOUN
esrj-91195	226	30	figure	figure	NOUN
esrj-91195	226	31	8	8	NUM
esrj-91195	226	32	.	.	PUNCT
esrj-91195	226	33	comparison	comparison	NOUN
esrj-91195	226	34	of	of	ADP
esrj-91195	226	35	the	the	DET
esrj-91195	226	36	geoid	geoid	ADJ
esrj-91195	226	37	undulation	undulation	NOUN
esrj-91195	226	38	values	value	NOUN
esrj-91195	226	39	predicted	predict	VERB
esrj-91195	226	40	by	by	ADP
esrj-91195	226	41	mlr	mlr	PROPN
esrj-91195	226	42	and	and	CCONJ
esrj-91195	226	43	those	those	PRON
esrj-91195	226	44	observed	observe	VERB
esrj-91195	226	45	at	at	ADP
esrj-91195	226	46	the	the	DET
esrj-91195	226	47	testing	testing	NOUN
esrj-91195	226	48	phase	phase	NOUN
esrj-91195	226	49	it	it	PRON
esrj-91195	226	50	can	can	AUX
esrj-91195	226	51	be	be	AUX
esrj-91195	226	52	seen	see	VERB
esrj-91195	226	53	from	from	ADP
esrj-91195	226	54	figure	figure	NOUN
esrj-91195	226	55	8	8	NUM
esrj-91195	226	56	that	that	SCONJ
esrj-91195	226	57	the	the	DET
esrj-91195	226	58	a	a	PRON
esrj-91195	226	59	and	and	CCONJ
esrj-91195	226	60	b	b	NOUN
esrj-91195	226	61	values	value	NOUN
esrj-91195	226	62	of	of	ADP
esrj-91195	226	63	the	the	DET
esrj-91195	226	64	predictions	prediction	NOUN
esrj-91195	226	65	made	make	VERB
esrj-91195	226	66	using	use	VERB
esrj-91195	226	67	different	different	ADJ
esrj-91195	226	68	datasets	dataset	NOUN
esrj-91195	226	69	yielded	yield	VERB
esrj-91195	226	70	close	close	ADJ
esrj-91195	226	71	results	result	NOUN
esrj-91195	226	72	.	.	PUNCT
esrj-91195	227	1	moreover	moreover	ADV
esrj-91195	227	2	,	,	PUNCT
esrj-91195	227	3	the	the	DET
esrj-91195	227	4	figure	figure	NOUN
esrj-91195	227	5	shows	show	VERB
esrj-91195	227	6	that	that	SCONJ
esrj-91195	227	7	the	the	DET
esrj-91195	227	8	determination	determination	NOUN
esrj-91195	227	9	coefficient	coefficient	NOUN
esrj-91195	227	10	(	(	PUNCT
esrj-91195	227	11	r2	r2	PROPN
esrj-91195	227	12	)	)	PUNCT
esrj-91195	227	13	between	between	ADP
esrj-91195	227	14	the	the	DET
esrj-91195	227	15	observed	observe	VERB
esrj-91195	227	16	and	and	CCONJ
esrj-91195	227	17	predicted	predict	VERB
esrj-91195	227	18	values	value	NOUN
esrj-91195	227	19	of	of	ADP
esrj-91195	227	20	ds#3	ds#3	PROPN
esrj-91195	227	21	during	during	ADP
esrj-91195	227	22	the	the	DET
esrj-91195	227	23	testing	testing	NOUN
esrj-91195	227	24	phase	phase	NOUN
esrj-91195	227	25	was	be	AUX
esrj-91195	227	26	0.92360	0.92360	NUM
esrj-91195	227	27	.	.	PUNCT
esrj-91195	228	1	according	accord	VERB
esrj-91195	228	2	to	to	ADP
esrj-91195	228	3	table	table	NOUN
esrj-91195	228	4	5	5	NUM
esrj-91195	228	5	,	,	PUNCT
esrj-91195	228	6	in	in	ADP
esrj-91195	228	7	terms	term	NOUN
esrj-91195	228	8	of	of	ADP
esrj-91195	228	9	the	the	DET
esrj-91195	228	10	resulting	result	VERB
esrj-91195	228	11	rmse	rmse	NOUN
esrj-91195	228	12	and	and	CCONJ
esrj-91195	228	13	nse	nse	NOUN
esrj-91195	228	14	values	value	NOUN
esrj-91195	228	15	,	,	PUNCT
esrj-91195	228	16	the	the	DET
esrj-91195	228	17	best	good	ADJ
esrj-91195	228	18	performance	performance	NOUN
esrj-91195	228	19	in	in	ADP
esrj-91195	228	20	the	the	DET
esrj-91195	228	21	testing	testing	NOUN
esrj-91195	228	22	phase	phase	NOUN
esrj-91195	228	23	was	be	AUX
esrj-91195	228	24	obtained	obtain	VERB
esrj-91195	228	25	for	for	ADP
esrj-91195	228	26	the	the	DET
esrj-91195	228	27	nn	nn	PROPN
esrj-91195	228	28	in	in	ADP
esrj-91195	228	29	ds#5	ds#5	PROPN
esrj-91195	228	30	.	.	PUNCT
esrj-91195	229	1	however	however	ADV
esrj-91195	229	2	,	,	PUNCT
esrj-91195	229	3	the	the	DET
esrj-91195	229	4	highest	high	ADJ
esrj-91195	229	5	rmse	rmse	ADJ
esrj-91195	229	6	value	value	NOUN
esrj-91195	229	7	was	be	AUX
esrj-91195	229	8	found	find	VERB
esrj-91195	229	9	in	in	ADP
esrj-91195	229	10	ds#3	ds#3	NOUN
esrj-91195	229	11	with	with	ADP
esrj-91195	229	12	the	the	DET
esrj-91195	229	13	pr	pr	NOUN
esrj-91195	229	14	.	.	PUNCT
esrj-91195	230	1	in	in	ADP
esrj-91195	230	2	addition	addition	NOUN
esrj-91195	230	3	,	,	PUNCT
esrj-91195	230	4	the	the	DET
esrj-91195	230	5	lowest	low	ADJ
esrj-91195	230	6	mae	mae	PROPN
esrj-91195	230	7	value	value	NOUN
esrj-91195	230	8	was	be	AUX
esrj-91195	230	9	found	find	VERB
esrj-91195	230	10	in	in	ADP
esrj-91195	230	11	ds#5	ds#5	NOUN
esrj-91195	230	12	using	use	VERB
esrj-91195	230	13	the	the	DET
esrj-91195	230	14	krig	krig	NOUN
esrj-91195	230	15	method	method	NOUN
esrj-91195	230	16	and	and	CCONJ
esrj-91195	230	17	the	the	DET
esrj-91195	230	18	highest	high	ADJ
esrj-91195	230	19	in	in	ADP
esrj-91195	230	20	ds#3	ds#3	PROPN
esrj-91195	230	21	with	with	ADP
esrj-91195	230	22	the	the	DET
esrj-91195	230	23	ms	ms	PROPN
esrj-91195	230	24	.	.	PROPN
esrj-91195	231	1	the	the	DET
esrj-91195	231	2	nse	nse	NOUN
esrj-91195	231	3	value	value	NOUN
esrj-91195	231	4	in	in	ADP
esrj-91195	231	5	ds#3	ds#3	PROPN
esrj-91195	231	6	was	be	AUX
esrj-91195	231	7	very	very	ADV
esrj-91195	231	8	low	low	ADJ
esrj-91195	231	9	with	with	ADP
esrj-91195	231	10	all	all	DET
esrj-91195	231	11	methods	method	NOUN
esrj-91195	231	12	.	.	PUNCT
esrj-91195	232	1	to	to	PART
esrj-91195	232	2	demonstrate	demonstrate	VERB
esrj-91195	232	3	a	a	DET
esrj-91195	232	4	comparison	comparison	NOUN
esrj-91195	232	5	of	of	ADP
esrj-91195	232	6	the	the	DET
esrj-91195	232	7	methods	method	NOUN
esrj-91195	232	8	in	in	ADP
esrj-91195	232	9	terms	term	NOUN
esrj-91195	232	10	of	of	ADP
esrj-91195	232	11	accuracy	accuracy	NOUN
esrj-91195	232	12	,	,	PUNCT
esrj-91195	232	13	figure	figure	NOUN
esrj-91195	232	14	9	9	NUM
esrj-91195	232	15	gives	give	VERB
esrj-91195	232	16	the	the	DET
esrj-91195	232	17	observed	observe	VERB
esrj-91195	232	18	geoid	geoid	ADJ
esrj-91195	232	19	undulation	undulation	NOUN
esrj-91195	232	20	values	value	NOUN
esrj-91195	232	21	with	with	ADP
esrj-91195	232	22	the	the	DET
esrj-91195	232	23	values	value	NOUN
esrj-91195	232	24	calculated	calculate	VERB
esrj-91195	232	25	for	for	ADP
esrj-91195	232	26	ds#5	ds#5	PROPN
esrj-91195	232	27	using	use	VERB
esrj-91195	232	28	the	the	DET
esrj-91195	232	29	interpolation	interpolation	NOUN
esrj-91195	232	30	methods	method	NOUN
esrj-91195	232	31	.	.	PUNCT
esrj-91195	233	1	380	380	NUM
esrj-91195	233	2	berkant	berkant	ADJ
esrj-91195	233	3	konakoglu	konakoglu	PROPN
esrj-91195	233	4	,	,	PUNCT
esrj-91195	233	5	alper	alper	NOUN
esrj-91195	233	6	akar	akar	PROPN
esrj-91195	233	7	as	as	SCONJ
esrj-91195	233	8	can	can	AUX
esrj-91195	233	9	be	be	AUX
esrj-91195	233	10	seen	see	VERB
esrj-91195	233	11	in	in	ADP
esrj-91195	233	12	figure	figure	NOUN
esrj-91195	233	13	9	9	NUM
esrj-91195	233	14	,	,	PUNCT
esrj-91195	233	15	the	the	DET
esrj-91195	233	16	a	a	DET
esrj-91195	233	17	value	value	NOUN
esrj-91195	233	18	obtained	obtain	VERB
esrj-91195	233	19	with	with	ADP
esrj-91195	233	20	the	the	DET
esrj-91195	233	21	ds#5	ds#5	PROPN
esrj-91195	233	22	using	use	VERB
esrj-91195	233	23	the	the	DET
esrj-91195	233	24	nn	nn	PROPN
esrj-91195	233	25	gave	give	VERB
esrj-91195	233	26	a	a	DET
esrj-91195	233	27	result	result	NOUN
esrj-91195	233	28	closer	close	ADV
esrj-91195	233	29	to	to	ADP
esrj-91195	233	30	1	1	NUM
esrj-91195	233	31	than	than	ADP
esrj-91195	233	32	the	the	DET
esrj-91195	233	33	other	other	ADJ
esrj-91195	233	34	datasets	dataset	NOUN
esrj-91195	233	35	.	.	PUNCT
esrj-91195	234	1	likewise	likewise	ADV
esrj-91195	234	2	,	,	PUNCT
esrj-91195	234	3	using	use	VERB
esrj-91195	234	4	the	the	DET
esrj-91195	234	5	nn	nn	PROPN
esrj-91195	234	6	,	,	PUNCT
esrj-91195	234	7	the	the	DET
esrj-91195	234	8	b	b	PROPN
esrj-91195	234	9	value	value	NOUN
esrj-91195	234	10	obtained	obtain	VERB
esrj-91195	234	11	with	with	ADP
esrj-91195	234	12	the	the	DET
esrj-91195	234	13	ds#5	ds#5	NOUN
esrj-91195	234	14	yielded	yield	VERB
esrj-91195	234	15	a	a	DET
esrj-91195	234	16	result	result	NOUN
esrj-91195	234	17	closer	close	ADV
esrj-91195	234	18	to	to	ADP
esrj-91195	234	19	0	0	NUM
esrj-91195	234	20	than	than	ADP
esrj-91195	234	21	the	the	DET
esrj-91195	234	22	other	other	ADJ
esrj-91195	234	23	datasets	dataset	NOUN
esrj-91195	234	24	.	.	PUNCT
esrj-91195	235	1	in	in	ADP
esrj-91195	235	2	the	the	DET
esrj-91195	235	3	testing	testing	NOUN
esrj-91195	235	4	phase	phase	NOUN
esrj-91195	235	5	,	,	PUNCT
esrj-91195	235	6	the	the	DET
esrj-91195	235	7	highest	high	ADJ
esrj-91195	235	8	r2	r2	NOUN
esrj-91195	235	9	among	among	ADP
esrj-91195	235	10	the	the	DET
esrj-91195	235	11	observed	observe	VERB
esrj-91195	235	12	and	and	CCONJ
esrj-91195	235	13	predicted	predict	VERB
esrj-91195	235	14	values	value	NOUN
esrj-91195	235	15	was	be	AUX
esrj-91195	235	16	obtained	obtain	VERB
esrj-91195	235	17	by	by	ADP
esrj-91195	235	18	the	the	DET
esrj-91195	235	19	nn	nn	PROPN
esrj-91195	235	20	interpolation	interpolation	NOUN
esrj-91195	235	21	method	method	NOUN
esrj-91195	235	22	in	in	ADP
esrj-91195	235	23	the	the	DET
esrj-91195	235	24	ds#5	ds#5	PROPN
esrj-91195	235	25	.	.	PUNCT
esrj-91195	236	1	comparison	comparison	NOUN
esrj-91195	236	2	of	of	ADP
esrj-91195	236	3	methods	method	NOUN
esrj-91195	236	4	used	use	VERB
esrj-91195	236	5	in	in	ADP
esrj-91195	236	6	geoid	geoid	ADJ
esrj-91195	236	7	undulation	undulation	NOUN
esrj-91195	236	8	prediction	prediction	NOUN
esrj-91195	236	9	(	(	PUNCT
esrj-91195	236	10	using	use	VERB
esrj-91195	236	11	k	k	ADJ
esrj-91195	236	12	-	-	ADJ
esrj-91195	236	13	fold	fold	ADJ
esrj-91195	236	14	cross	cross	NOUN
esrj-91195	236	15	-	-	NOUN
esrj-91195	236	16	validation	validation	NOUN
esrj-91195	236	17	)	)	PUNCT
esrj-91195	236	18	in	in	ADP
esrj-91195	236	19	order	order	NOUN
esrj-91195	236	20	to	to	PART
esrj-91195	236	21	evaluate	evaluate	VERB
esrj-91195	236	22	the	the	DET
esrj-91195	236	23	general	general	ADJ
esrj-91195	236	24	performance	performance	NOUN
esrj-91195	236	25	of	of	ADP
esrj-91195	236	26	all	all	DET
esrj-91195	236	27	the	the	DET
esrj-91195	236	28	methods	method	NOUN
esrj-91195	236	29	using	use	VERB
esrj-91195	236	30	the	the	DET
esrj-91195	236	31	k	k	ADJ
esrj-91195	236	32	-	-	ADJ
esrj-91195	236	33	fold	fold	ADJ
esrj-91195	236	34	cross	cross	NOUN
esrj-91195	236	35	-	-	NOUN
esrj-91195	236	36	validation	validation	ADJ
esrj-91195	236	37	,	,	PUNCT
esrj-91195	236	38	it	it	PRON
esrj-91195	236	39	was	be	AUX
esrj-91195	236	40	necessary	necessary	ADJ
esrj-91195	236	41	to	to	PART
esrj-91195	236	42	calculate	calculate	VERB
esrj-91195	236	43	the	the	DET
esrj-91195	236	44	averages	average	NOUN
esrj-91195	236	45	of	of	ADP
esrj-91195	236	46	the	the	DET
esrj-91195	236	47	statistical	statistical	ADJ
esrj-91195	236	48	criteria	criterion	NOUN
esrj-91195	236	49	values	value	NOUN
esrj-91195	236	50	(	(	PUNCT
esrj-91195	236	51	rmse	rmse	PROPN
esrj-91195	236	52	,	,	PUNCT
esrj-91195	236	53	mae	mae	PROPN
esrj-91195	236	54	,	,	PUNCT
esrj-91195	236	55	nse	nse	NOUN
esrj-91195	236	56	,	,	PUNCT
esrj-91195	236	57	and	and	CCONJ
esrj-91195	236	58	r2	r2	PROPN
esrj-91195	236	59	)	)	PUNCT
esrj-91195	236	60	.	.	PUNCT
esrj-91195	237	1	in	in	ADP
esrj-91195	237	2	other	other	ADJ
esrj-91195	237	3	words	word	NOUN
esrj-91195	237	4	,	,	PUNCT
esrj-91195	237	5	the	the	DET
esrj-91195	237	6	average	average	ADJ
esrj-91195	237	7	value	value	NOUN
esrj-91195	237	8	was	be	AUX
esrj-91195	237	9	found	find	VERB
esrj-91195	237	10	for	for	ADP
esrj-91195	237	11	each	each	DET
esrj-91195	237	12	performance	performance	NOUN
esrj-91195	237	13	criterion	criterion	NOUN
esrj-91195	237	14	result	result	NOUN
esrj-91195	237	15	given	give	VERB
esrj-91195	237	16	in	in	ADP
esrj-91195	237	17	tables	table	NOUN
esrj-91195	237	18	2	2	NUM
esrj-91195	237	19	,	,	PUNCT
esrj-91195	237	20	3	3	NUM
esrj-91195	237	21	,	,	PUNCT
esrj-91195	237	22	4	4	NUM
esrj-91195	237	23	,	,	PUNCT
esrj-91195	237	24	and	and	CCONJ
esrj-91195	237	25	5	5	NUM
esrj-91195	237	26	.	.	PUNCT
esrj-91195	237	27	according	accord	VERB
esrj-91195	237	28	to	to	ADP
esrj-91195	237	29	the	the	DET
esrj-91195	237	30	5	5	NUM
esrj-91195	237	31	-	-	ADJ
esrj-91195	237	32	fold	fold	ADJ
esrj-91195	237	33	cross	cross	ADJ
esrj-91195	237	34	-	-	ADJ
esrj-91195	237	35	validation	validation	ADJ
esrj-91195	237	36	method	method	NOUN
esrj-91195	237	37	based	base	VERB
esrj-91195	237	38	on	on	ADP
esrj-91195	237	39	the	the	DET
esrj-91195	237	40	average	average	ADJ
esrj-91195	237	41	performance	performance	NOUN
esrj-91195	237	42	values	value	NOUN
esrj-91195	237	43	of	of	ADP
esrj-91195	237	44	the	the	DET
esrj-91195	237	45	statistical	statistical	ADJ
esrj-91195	237	46	criteria	criterion	NOUN
esrj-91195	237	47	,	,	PUNCT
esrj-91195	237	48	in	in	ADP
esrj-91195	237	49	general	general	ADJ
esrj-91195	237	50	,	,	PUNCT
esrj-91195	237	51	all	all	DET
esrj-91195	237	52	interpolation	interpolation	NOUN
esrj-91195	237	53	methods	method	NOUN
esrj-91195	237	54	except	except	SCONJ
esrj-91195	237	55	pr	pr	NOUN
esrj-91195	237	56	gave	give	VERB
esrj-91195	237	57	similar	similar	ADJ
esrj-91195	237	58	results	result	NOUN
esrj-91195	237	59	(	(	PUNCT
esrj-91195	237	60	rmse	rmse	NOUN
esrj-91195	237	61	,	,	PUNCT
esrj-91195	237	62	0.466	0.466	NUM
esrj-91195	237	63	m	m	NOUN
esrj-91195	237	64	0.648	0.648	NUM
esrj-91195	237	65	m	m	PROPN
esrj-91195	237	66	;	;	PUNCT
esrj-91195	237	67	mae	mae	PROPN
esrj-91195	237	68	,	,	PUNCT
esrj-91195	237	69	0.303	0.303	NUM
esrj-91195	237	70	m	m	NOUN
esrj-91195	237	71	0.487	0.487	NUM
esrj-91195	237	72	m	m	NOUN
esrj-91195	237	73	;	;	PUNCT
esrj-91195	237	74	nse	nse	NOUN
esrj-91195	237	75	,	,	PUNCT
esrj-91195	237	76	0.71291	0.71291	NUM
esrj-91195	237	77	0.78283	0.78283	NUM
esrj-91195	237	78	;	;	PUNCT
esrj-91195	237	79	r2	r2	NOUN
esrj-91195	237	80	,	,	PUNCT
esrj-91195	237	81	0.75789	0.75789	NUM
esrj-91195	237	82	0.82706	0.82706	NUM
esrj-91195	237	83	)	)	PUNCT
esrj-91195	237	84	.	.	PUNCT
esrj-91195	238	1	the	the	DET
esrj-91195	238	2	performance	performance	NOUN
esrj-91195	238	3	of	of	ADP
esrj-91195	238	4	the	the	DET
esrj-91195	238	5	pr	pr	NOUN
esrj-91195	238	6	method	method	NOUN
esrj-91195	238	7	was	be	AUX
esrj-91195	238	8	weaker	weak	ADJ
esrj-91195	238	9	than	than	ADP
esrj-91195	238	10	for	for	ADP
esrj-91195	238	11	the	the	DET
esrj-91195	238	12	other	other	ADJ
esrj-91195	238	13	methods	method	NOUN
esrj-91195	238	14	.	.	PUNCT
esrj-91195	239	1	table	table	NOUN
esrj-91195	239	2	6	6	NUM
esrj-91195	239	3	shows	show	VERB
esrj-91195	239	4	the	the	DET
esrj-91195	239	5	average	average	ADJ
esrj-91195	239	6	values	value	NOUN
esrj-91195	239	7	for	for	ADP
esrj-91195	239	8	the	the	DET
esrj-91195	239	9	rmse	rmse	NOUN
esrj-91195	239	10	,	,	PUNCT
esrj-91195	239	11	mae	mae	PROPN
esrj-91195	239	12	,	,	PUNCT
esrj-91195	239	13	nse	nse	NOUN
esrj-91195	239	14	,	,	PUNCT
esrj-91195	239	15	and	and	CCONJ
esrj-91195	239	16	r2	r2	PROPN
esrj-91195	239	17	obtained	obtain	VERB
esrj-91195	239	18	from	from	ADP
esrj-91195	239	19	rbfnn	rbfnn	NOUN
esrj-91195	239	20	,	,	PUNCT
esrj-91195	239	21	grnn	grnn	PROPN
esrj-91195	239	22	,	,	PUNCT
esrj-91195	239	23	mlr	mlr	NOUN
esrj-91195	239	24	,	,	PUNCT
esrj-91195	239	25	and	and	CCONJ
esrj-91195	239	26	pr	pr	NOUN
esrj-91195	239	27	.	.	NOUN
esrj-91195	239	28	table	table	NOUN
esrj-91195	239	29	6	6	NUM
esrj-91195	239	30	.	.	PUNCT
esrj-91195	240	1	average	average	ADJ
esrj-91195	240	2	values	value	NOUN
esrj-91195	240	3	for	for	ADP
esrj-91195	240	4	the	the	DET
esrj-91195	240	5	rmse	rmse	NOUN
esrj-91195	240	6	,	,	PUNCT
esrj-91195	240	7	mae	mae	PROPN
esrj-91195	240	8	,	,	PUNCT
esrj-91195	240	9	nse	nse	NOUN
esrj-91195	240	10	,	,	PUNCT
esrj-91195	240	11	and	and	CCONJ
esrj-91195	240	12	r2	r2	PROPN
esrj-91195	240	13	(	(	PUNCT
esrj-91195	240	14	5	5	NUM
esrj-91195	240	15	-	-	ADJ
esrj-91195	240	16	fold	fold	ADJ
esrj-91195	240	17	)	)	PUNCT
esrj-91195	240	18	method	method	NOUN
esrj-91195	240	19	phase	phase	NOUN
esrj-91195	240	20	rmse	rmse	NOUN
esrj-91195	240	21	(	(	PUNCT
esrj-91195	240	22	m	m	PROPN
esrj-91195	240	23	)	)	PUNCT
esrj-91195	240	24	mae	mae	PROPN
esrj-91195	240	25	(	(	PUNCT
esrj-91195	240	26	m	m	NOUN
esrj-91195	240	27	)	)	PUNCT
esrj-91195	240	28	nse	nse	NOUN
esrj-91195	240	29	r2	r2	PROPN
esrj-91195	240	30	rbfnn	rbfnn	VERB
esrj-91195	240	31	training	train	VERB
esrj-91195	240	32	0.030	0.030	NUM
esrj-91195	240	33	0.018	0.018	NUM
esrj-91195	240	34	0.99598	0.99598	NUM
esrj-91195	240	35	0.99875	0.99875	NUM
esrj-91195	240	36	testing	test	VERB
esrj-91195	240	37	0.558	0.558	NUM
esrj-91195	240	38	0.400	0.400	NUM
esrj-91195	240	39	0.82036	0.82036	NUM
esrj-91195	240	40	0.85108	0.85108	NUM
esrj-91195	240	41	grnn	grnn	NOUN
esrj-91195	240	42	training	train	VERB
esrj-91195	240	43	0.029	0.029	NUM
esrj-91195	240	44	0.010	0.010	NUM
esrj-91195	240	45	0.99881	0.99881	NUM
esrj-91195	240	46	0.99951	0.99951	NUM
esrj-91195	240	47	testing	test	VERB
esrj-91195	240	48	0.185	0.185	NUM
esrj-91195	240	49	0.137	0.137	NUM
esrj-91195	240	50	0.98229	0.98229	NUM
esrj-91195	240	51	0.98249	0.98249	NUM
esrj-91195	240	52	mlr	mlr	NOUN
esrj-91195	240	53	training	train	VERB
esrj-91195	240	54	0.440	0.440	NUM
esrj-91195	240	55	0.307	0.307	NUM
esrj-91195	240	56	0.90522	0.90522	NUM
esrj-91195	240	57	0.90522	0.90522	NUM
esrj-91195	240	58	testing	test	VERB
esrj-91195	240	59	0.455	0.455	NUM
esrj-91195	240	60	0.352	0.352	NUM
esrj-91195	240	61	0.89176	0.89176	NUM
esrj-91195	240	62	0.90080	0.90080	NUM
esrj-91195	240	63	pr	pr	NOUN
esrj-91195	240	64	testing	test	VERB
esrj-91195	240	65	0.648	0.648	NUM
esrj-91195	240	66	0.487	0.487	NUM
esrj-91195	240	67	0.71291	0.71291	NUM
esrj-91195	240	68	0.75789	0.75789	NUM
esrj-91195	240	69	as	as	SCONJ
esrj-91195	240	70	can	can	AUX
esrj-91195	240	71	be	be	AUX
esrj-91195	240	72	seen	see	VERB
esrj-91195	240	73	from	from	ADP
esrj-91195	240	74	table	table	NOUN
esrj-91195	240	75	6	6	NUM
esrj-91195	240	76	,	,	PUNCT
esrj-91195	240	77	on	on	ADP
esrj-91195	240	78	average	average	ADJ
esrj-91195	240	79	,	,	PUNCT
esrj-91195	240	80	the	the	DET
esrj-91195	240	81	grnn	grnn	NOUN
esrj-91195	240	82	outperformed	outperform	VERB
esrj-91195	240	83	all	all	DET
esrj-91195	240	84	methods	method	NOUN
esrj-91195	240	85	investigated	investigate	VERB
esrj-91195	240	86	for	for	ADP
esrj-91195	240	87	training	training	NOUN
esrj-91195	240	88	and	and	CCONJ
esrj-91195	240	89	testing	testing	NOUN
esrj-91195	240	90	phases	phase	NOUN
esrj-91195	240	91	.	.	PUNCT
esrj-91195	241	1	the	the	DET
esrj-91195	241	2	best	good	ADJ
esrj-91195	241	3	nse	nse	NOUN
esrj-91195	241	4	and	and	CCONJ
esrj-91195	241	5	r2	r2	NOUN
esrj-91195	241	6	values	value	NOUN
esrj-91195	241	7	(	(	PUNCT
esrj-91195	241	8	0.99881	0.99881	NUM
esrj-91195	241	9	and	and	CCONJ
esrj-91195	241	10	0.99951	0.99951	NUM
esrj-91195	241	11	)	)	PUNCT
esrj-91195	241	12	and	and	CCONJ
esrj-91195	241	13	the	the	DET
esrj-91195	241	14	lowest	low	ADJ
esrj-91195	241	15	rmse	rmse	NOUN
esrj-91195	241	16	and	and	CCONJ
esrj-91195	241	17	mae	mae	PROPN
esrj-91195	241	18	values	value	NOUN
esrj-91195	241	19	(	(	PUNCT
esrj-91195	241	20	0.029	0.029	NUM
esrj-91195	241	21	m	m	NOUN
esrj-91195	241	22	and	and	CCONJ
esrj-91195	241	23	0.010	0.010	NUM
esrj-91195	241	24	m	m	NOUN
esrj-91195	241	25	)	)	PUNCT
esrj-91195	241	26	were	be	AUX
esrj-91195	241	27	found	find	VERB
esrj-91195	241	28	in	in	ADP
esrj-91195	241	29	the	the	DET
esrj-91195	241	30	grnn	grnn	NOUN
esrj-91195	241	31	training	training	NOUN
esrj-91195	241	32	phase	phase	NOUN
esrj-91195	241	33	.	.	PUNCT
esrj-91195	242	1	the	the	DET
esrj-91195	242	2	grnn	grnn	PROPN
esrj-91195	242	3	also	also	ADV
esrj-91195	242	4	yielded	yield	VERB
esrj-91195	242	5	the	the	DET
esrj-91195	242	6	lowest	low	ADJ
esrj-91195	242	7	rmse	rmse	NOUN
esrj-91195	242	8	and	and	CCONJ
esrj-91195	242	9	mae	mae	PROPN
esrj-91195	242	10	values	value	NOUN
esrj-91195	242	11	(	(	PUNCT
esrj-91195	242	12	0.185	0.185	NUM
esrj-91195	242	13	m	m	NOUN
esrj-91195	242	14	and	and	CCONJ
esrj-91195	242	15	0.137	0.137	NUM
esrj-91195	242	16	m	m	NOUN
esrj-91195	242	17	)	)	PUNCT
esrj-91195	242	18	with	with	ADP
esrj-91195	242	19	the	the	DET
esrj-91195	242	20	highest	high	ADJ
esrj-91195	242	21	nse	nse	NOUN
esrj-91195	242	22	and	and	CCONJ
esrj-91195	242	23	r2	r2	NOUN
esrj-91195	242	24	values	value	NOUN
esrj-91195	242	25	(	(	PUNCT
esrj-91195	242	26	0.98229	0.98229	NUM
esrj-91195	242	27	and	and	CCONJ
esrj-91195	242	28	0.98249	0.98249	NUM
esrj-91195	242	29	)	)	PUNCT
esrj-91195	242	30	for	for	ADP
esrj-91195	242	31	the	the	DET
esrj-91195	242	32	testing	testing	NOUN
esrj-91195	242	33	phase	phase	NOUN
esrj-91195	242	34	.	.	PUNCT
esrj-91195	243	1	it	it	PRON
esrj-91195	243	2	is	be	AUX
esrj-91195	243	3	clear	clear	ADJ
esrj-91195	243	4	that	that	SCONJ
esrj-91195	243	5	the	the	DET
esrj-91195	243	6	highest	high	ADJ
esrj-91195	243	7	rmse	rmse	NOUN
esrj-91195	243	8	and	and	CCONJ
esrj-91195	243	9	mae	mae	PROPN
esrj-91195	243	10	,	,	PUNCT
esrj-91195	243	11	and	and	CCONJ
esrj-91195	243	12	the	the	DET
esrj-91195	243	13	lowest	low	ADJ
esrj-91195	243	14	nse	nse	NOUN
esrj-91195	243	15	and	and	CCONJ
esrj-91195	243	16	r2	r2	NOUN
esrj-91195	243	17	values	value	NOUN
esrj-91195	243	18	were	be	AUX
esrj-91195	243	19	obtained	obtain	VERB
esrj-91195	243	20	with	with	ADP
esrj-91195	243	21	the	the	DET
esrj-91195	243	22	pr	pr	NOUN
esrj-91195	243	23	during	during	ADP
esrj-91195	243	24	the	the	DET
esrj-91195	243	25	testing	testing	NOUN
esrj-91195	243	26	phase	phase	NOUN
esrj-91195	243	27	.	.	PUNCT
esrj-91195	244	1	the	the	DET
esrj-91195	244	2	results	result	NOUN
esrj-91195	244	3	of	of	ADP
esrj-91195	244	4	the	the	DET
esrj-91195	244	5	interpolation	interpolation	NOUN
esrj-91195	244	6	methods	method	NOUN
esrj-91195	244	7	used	use	VERB
esrj-91195	244	8	with	with	ADP
esrj-91195	244	9	ds#3	ds#3	PROPN
esrj-91195	244	10	negatively	negatively	ADV
esrj-91195	244	11	affected	affect	VERB
esrj-91195	244	12	the	the	DET
esrj-91195	244	13	performance	performance	NOUN
esrj-91195	244	14	with	with	ADP
esrj-91195	244	15	the	the	DET
esrj-91195	244	16	other	other	ADJ
esrj-91195	244	17	four	four	NUM
esrj-91195	244	18	datasets	dataset	NOUN
esrj-91195	244	19	and	and	CCONJ
esrj-91195	244	20	reduced	reduce	VERB
esrj-91195	244	21	the	the	DET
esrj-91195	244	22	accuracy	accuracy	NOUN
esrj-91195	244	23	(	(	PUNCT
esrj-91195	244	24	see	see	VERB
esrj-91195	244	25	table	table	NOUN
esrj-91195	244	26	5	5	NUM
esrj-91195	244	27	)	)	PUNCT
esrj-91195	244	28	.	.	PUNCT
esrj-91195	245	1	the	the	DET
esrj-91195	245	2	rbfnn	rbfnn	NOUN
esrj-91195	245	3	yielded	yield	VERB
esrj-91195	245	4	poor	poor	ADJ
esrj-91195	245	5	overall	overall	ADJ
esrj-91195	245	6	results	result	NOUN
esrj-91195	245	7	when	when	SCONJ
esrj-91195	245	8	compared	compare	VERB
esrj-91195	245	9	to	to	ADP
esrj-91195	245	10	the	the	DET
esrj-91195	245	11	interpolation	interpolation	NOUN
esrj-91195	245	12	methods	method	NOUN
esrj-91195	245	13	.	.	PUNCT
esrj-91195	246	1	among	among	ADP
esrj-91195	246	2	the	the	DET
esrj-91195	246	3	methods	method	NOUN
esrj-91195	246	4	used	use	VERB
esrj-91195	246	5	in	in	ADP
esrj-91195	246	6	this	this	DET
esrj-91195	246	7	study	study	NOUN
esrj-91195	246	8	,	,	PUNCT
esrj-91195	246	9	the	the	DET
esrj-91195	246	10	mlr	mlr	NOUN
esrj-91195	246	11	was	be	AUX
esrj-91195	246	12	ranked	rank	VERB
esrj-91195	246	13	second	second	ADV
esrj-91195	246	14	-	-	PUNCT
esrj-91195	246	15	highest	high	ADJ
esrj-91195	246	16	for	for	ADP
esrj-91195	246	17	accuracy	accuracy	NOUN
esrj-91195	246	18	.	.	PUNCT
esrj-91195	247	1	considering	consider	VERB
esrj-91195	247	2	the	the	DET
esrj-91195	247	3	overall	overall	ADJ
esrj-91195	247	4	prediction	prediction	NOUN
esrj-91195	247	5	accuracy	accuracy	NOUN
esrj-91195	247	6	of	of	ADP
esrj-91195	247	7	the	the	DET
esrj-91195	247	8	different	different	ADJ
esrj-91195	247	9	methods	method	NOUN
esrj-91195	247	10	,	,	PUNCT
esrj-91195	247	11	the	the	DET
esrj-91195	247	12	grnn	grnn	NOUN
esrj-91195	247	13	is	be	AUX
esrj-91195	247	14	the	the	DET
esrj-91195	247	15	one	one	NOUN
esrj-91195	247	16	to	to	PART
esrj-91195	247	17	be	be	AUX
esrj-91195	247	18	recommended	recommend	VERB
esrj-91195	247	19	for	for	ADP
esrj-91195	247	20	predicting	predict	VERB
esrj-91195	247	21	geoid	geoid	ADJ
esrj-91195	247	22	undulation	undulation	NOUN
esrj-91195	247	23	.	.	PUNCT
esrj-91195	248	1	conclusion	conclusion	NOUN
esrj-91195	248	2	in	in	ADP
esrj-91195	248	3	this	this	DET
esrj-91195	248	4	study	study	NOUN
esrj-91195	248	5	,	,	PUNCT
esrj-91195	248	6	two	two	NUM
esrj-91195	248	7	different	different	ADJ
esrj-91195	248	8	ann	ann	NOUN
esrj-91195	248	9	methods	method	NOUN
esrj-91195	248	10	(	(	PUNCT
esrj-91195	248	11	rbfnn	rbfnn	NOUN
esrj-91195	248	12	and	and	CCONJ
esrj-91195	248	13	grnn	grnn	NOUN
esrj-91195	248	14	)	)	PUNCT
esrj-91195	248	15	,	,	PUNCT
esrj-91195	248	16	the	the	DET
esrj-91195	248	17	mlr	mlr	NOUN
esrj-91195	248	18	,	,	PUNCT
esrj-91195	248	19	and	and	CCONJ
esrj-91195	248	20	ten	ten	NUM
esrj-91195	248	21	different	different	ADJ
esrj-91195	248	22	interpolation	interpolation	NOUN
esrj-91195	248	23	methods	method	NOUN
esrj-91195	248	24	were	be	AUX
esrj-91195	248	25	comprehensively	comprehensively	ADV
esrj-91195	248	26	investigated	investigate	VERB
esrj-91195	248	27	and	and	CCONJ
esrj-91195	248	28	analyzed	analyze	VERB
esrj-91195	248	29	in	in	ADP
esrj-91195	248	30	order	order	NOUN
esrj-91195	248	31	to	to	PART
esrj-91195	248	32	compare	compare	VERB
esrj-91195	248	33	their	their	PRON
esrj-91195	248	34	performances	performance	NOUN
esrj-91195	248	35	in	in	ADP
esrj-91195	248	36	geoid	geoid	ADJ
esrj-91195	248	37	undulation	undulation	NOUN
esrj-91195	248	38	prediction	prediction	NOUN
esrj-91195	248	39	.	.	PUNCT
esrj-91195	249	1	the	the	DET
esrj-91195	249	2	k	k	ADJ
esrj-91195	249	3	-	-	ADJ
esrj-91195	249	4	fold	fold	ADJ
esrj-91195	249	5	cross	cross	ADJ
esrj-91195	249	6	-	-	ADJ
esrj-91195	249	7	validation	validation	ADJ
esrj-91195	249	8	method	method	NOUN
esrj-91195	249	9	was	be	AUX
esrj-91195	249	10	used	use	VERB
esrj-91195	249	11	to	to	PART
esrj-91195	249	12	obtain	obtain	VERB
esrj-91195	249	13	a	a	DET
esrj-91195	249	14	better	well	ADJ
esrj-91195	249	15	generalization	generalization	NOUN
esrj-91195	249	16	.	.	PUNCT
esrj-91195	250	1	the	the	DET
esrj-91195	250	2	evaluation	evaluation	NOUN
esrj-91195	250	3	of	of	ADP
esrj-91195	250	4	the	the	DET
esrj-91195	250	5	performance	performance	NOUN
esrj-91195	250	6	of	of	ADP
esrj-91195	250	7	the	the	DET
esrj-91195	250	8	datasets	dataset	NOUN
esrj-91195	250	9	using	use	VERB
esrj-91195	250	10	cross	cross	NOUN
esrj-91195	250	11	-	-	ADJ
esrj-91195	250	12	validation	validation	NOUN
esrj-91195	250	13	can	can	AUX
esrj-91195	250	14	be	be	AUX
esrj-91195	250	15	carried	carry	VERB
esrj-91195	250	16	out	out	ADP
esrj-91195	250	17	in	in	ADP
esrj-91195	250	18	two	two	NUM
esrj-91195	250	19	ways	way	NOUN
esrj-91195	250	20	:	:	PUNCT
esrj-91195	250	21	(	(	PUNCT
esrj-91195	250	22	1	1	X
esrj-91195	250	23	)	)	PUNCT
esrj-91195	250	24	by	by	ADP
esrj-91195	250	25	accepting	accept	VERB
esrj-91195	250	26	that	that	SCONJ
esrj-91195	250	27	the	the	DET
esrj-91195	250	28	method	method	NOUN
esrj-91195	250	29	with	with	ADP
esrj-91195	250	30	the	the	DET
esrj-91195	250	31	minimum	minimum	NOUN
esrj-91195	250	32	error	error	NOUN
esrj-91195	250	33	is	be	AUX
esrj-91195	250	34	the	the	DET
esrj-91195	250	35	most	most	ADV
esrj-91195	250	36	appropriate	appropriate	ADJ
esrj-91195	250	37	one	one	NUM
esrj-91195	250	38	or	or	CCONJ
esrj-91195	250	39	(	(	PUNCT
esrj-91195	250	40	2	2	NUM
esrj-91195	250	41	)	)	PUNCT
esrj-91195	250	42	by	by	ADP
esrj-91195	250	43	considering	consider	VERB
esrj-91195	250	44	that	that	SCONJ
esrj-91195	250	45	the	the	DET
esrj-91195	250	46	method	method	NOUN
esrj-91195	250	47	with	with	ADP
esrj-91195	250	48	the	the	DET
esrj-91195	250	49	minimum	minimum	ADJ
esrj-91195	250	50	average	average	NOUN
esrj-91195	250	51	of	of	ADP
esrj-91195	250	52	results	result	NOUN
esrj-91195	250	53	for	for	ADP
esrj-91195	250	54	all	all	DET
esrj-91195	250	55	the	the	DET
esrj-91195	250	56	datasets	dataset	NOUN
esrj-91195	250	57	is	be	AUX
esrj-91195	250	58	the	the	DET
esrj-91195	250	59	most	most	ADV
esrj-91195	250	60	appropriate	appropriate	ADJ
esrj-91195	250	61	one	one	NUM
esrj-91195	250	62	.	.	PUNCT
esrj-91195	251	1	when	when	SCONJ
esrj-91195	251	2	the	the	DET
esrj-91195	251	3	methods	method	NOUN
esrj-91195	251	4	used	use	VERB
esrj-91195	251	5	with	with	ADP
esrj-91195	251	6	each	each	DET
esrj-91195	251	7	dataset	dataset	NOUN
esrj-91195	251	8	were	be	AUX
esrj-91195	251	9	compared	compare	VERB
esrj-91195	251	10	,	,	PUNCT
esrj-91195	251	11	the	the	DET
esrj-91195	251	12	test	test	NOUN
esrj-91195	251	13	results	result	NOUN
esrj-91195	251	14	using	use	VERB
esrj-91195	251	15	the	the	DET
esrj-91195	251	16	nn	nn	PROPN
esrj-91195	251	17	in	in	ADP
esrj-91195	251	18	ds#5	ds#5	PROPN
esrj-91195	251	19	were	be	AUX
esrj-91195	251	20	the	the	DET
esrj-91195	251	21	most	most	ADV
esrj-91195	251	22	successful	successful	ADJ
esrj-91195	251	23	.	.	PUNCT
esrj-91195	252	1	however	however	ADV
esrj-91195	252	2	,	,	PUNCT
esrj-91195	252	3	the	the	DET
esrj-91195	252	4	lowest	low	ADJ
esrj-91195	252	5	rmse	rmse	NOUN
esrj-91195	252	6	,	,	PUNCT
esrj-91195	252	7	mae	mae	PROPN
esrj-91195	252	8	,	,	PUNCT
esrj-91195	252	9	nse	nse	NOUN
esrj-91195	252	10	,	,	PUNCT
esrj-91195	252	11	and	and	CCONJ
esrj-91195	252	12	r2	r2	PROPN
esrj-91195	252	13	values	value	NOUN
esrj-91195	252	14	were	be	AUX
esrj-91195	252	15	obtained	obtain	VERB
esrj-91195	252	16	with	with	ADP
esrj-91195	252	17	the	the	DET
esrj-91195	252	18	ds#3	ds#3	NOUN
esrj-91195	252	19	using	use	VERB
esrj-91195	252	20	the	the	DET
esrj-91195	252	21	ten	ten	NUM
esrj-91195	252	22	different	different	ADJ
esrj-91195	252	23	interpolation	interpolation	NOUN
esrj-91195	252	24	methods	method	NOUN
esrj-91195	252	25	.	.	PUNCT
esrj-91195	253	1	in	in	ADP
esrj-91195	253	2	the	the	DET
esrj-91195	253	3	calculation	calculation	NOUN
esrj-91195	253	4	performed	perform	VERB
esrj-91195	253	5	with	with	ADP
esrj-91195	253	6	the	the	DET
esrj-91195	253	7	same	same	ADJ
esrj-91195	253	8	dataset	dataset	NOUN
esrj-91195	253	9	,	,	PUNCT
esrj-91195	253	10	the	the	DET
esrj-91195	253	11	most	most	ADV
esrj-91195	253	12	successful	successful	ADJ
esrj-91195	253	13	method	method	NOUN
esrj-91195	253	14	after	after	SCONJ
esrj-91195	253	15	the	the	DET
esrj-91195	253	16	nn	nn	PROPN
esrj-91195	253	17	method	method	NOUN
esrj-91195	253	18	was	be	AUX
esrj-91195	253	19	the	the	DET
esrj-91195	253	20	grnn	grnn	NOUN
esrj-91195	253	21	,	,	PUNCT
esrj-91195	253	22	whereas	whereas	SCONJ
esrj-91195	253	23	the	the	DET
esrj-91195	253	24	rbfnn	rbfnn	NOUN
esrj-91195	253	25	and	and	CCONJ
esrj-91195	253	26	mlr	mlr	NOUN
esrj-91195	253	27	gave	give	VERB
esrj-91195	253	28	nearly	nearly	ADV
esrj-91195	253	29	the	the	DET
esrj-91195	253	30	same	same	ADJ
esrj-91195	253	31	results	result	NOUN
esrj-91195	253	32	.	.	PUNCT
esrj-91195	254	1	the	the	DET
esrj-91195	254	2	grnn	grnn	PROPN
esrj-91195	254	3	was	be	AUX
esrj-91195	254	4	the	the	DET
esrj-91195	254	5	most	most	ADV
esrj-91195	254	6	successful	successful	ADJ
esrj-91195	254	7	method	method	NOUN
esrj-91195	254	8	in	in	ADP
esrj-91195	254	9	the	the	DET
esrj-91195	254	10	evaluation	evaluation	NOUN
esrj-91195	254	11	carried	carry	VERB
esrj-91195	254	12	out	out	ADP
esrj-91195	254	13	by	by	ADP
esrj-91195	254	14	averaging	average	VERB
esrj-91195	254	15	the	the	DET
esrj-91195	254	16	rmse	rmse	NOUN
esrj-91195	254	17	,	,	PUNCT
esrj-91195	254	18	mae	mae	PROPN
esrj-91195	254	19	,	,	PUNCT
esrj-91195	254	20	nse	nse	NOUN
esrj-91195	254	21	,	,	PUNCT
esrj-91195	254	22	and	and	CCONJ
esrj-91195	254	23	r2	r2	PROPN
esrj-91195	254	24	values	value	NOUN
esrj-91195	254	25	produced	produce	VERB
esrj-91195	254	26	using	use	VERB
esrj-91195	254	27	the	the	DET
esrj-91195	254	28	five	five	NUM
esrj-91195	254	29	different	different	ADJ
esrj-91195	254	30	datasets	dataset	NOUN
esrj-91195	254	31	.	.	PUNCT
esrj-91195	255	1	in	in	ADP
esrj-91195	255	2	this	this	DET
esrj-91195	255	3	study	study	NOUN
esrj-91195	255	4	,	,	PUNCT
esrj-91195	255	5	the	the	DET
esrj-91195	255	6	analysis	analysis	NOUN
esrj-91195	255	7	of	of	ADP
esrj-91195	255	8	the	the	DET
esrj-91195	255	9	results	result	NOUN
esrj-91195	255	10	of	of	ADP
esrj-91195	255	11	two	two	NUM
esrj-91195	255	12	different	different	ADJ
esrj-91195	255	13	performance	performance	NOUN
esrj-91195	255	14	evaluations	evaluation	NOUN
esrj-91195	255	15	for	for	ADP
esrj-91195	255	16	the	the	DET
esrj-91195	255	17	grnn	grnn	NOUN
esrj-91195	255	18	demonstrated	demonstrate	VERB
esrj-91195	255	19	that	that	SCONJ
esrj-91195	255	20	the	the	DET
esrj-91195	255	21	method	method	NOUN
esrj-91195	255	22	could	could	AUX
esrj-91195	255	23	be	be	AUX
esrj-91195	255	24	successfully	successfully	ADV
esrj-91195	255	25	applied	apply	VERB
esrj-91195	255	26	and	and	CCONJ
esrj-91195	255	27	was	be	AUX
esrj-91195	255	28	capable	capable	ADJ
esrj-91195	255	29	of	of	ADP
esrj-91195	255	30	yielding	yield	VERB
esrj-91195	255	31	results	result	NOUN
esrj-91195	255	32	equally	equally	ADV
esrj-91195	255	33	as	as	ADV
esrj-91195	255	34	accurate	accurate	ADJ
esrj-91195	255	35	as	as	ADP
esrj-91195	255	36	or	or	CCONJ
esrj-91195	255	37	better	well	ADJ
esrj-91195	255	38	than	than	ADP
esrj-91195	255	39	those	those	PRON
esrj-91195	255	40	of	of	ADP
esrj-91195	255	41	the	the	DET
esrj-91195	255	42	rbfnn	rbfnn	NOUN
esrj-91195	255	43	,	,	PUNCT
esrj-91195	255	44	mlr	mlr	NOUN
esrj-91195	255	45	,	,	PUNCT
esrj-91195	255	46	or	or	CCONJ
esrj-91195	255	47	interpolation	interpolation	NOUN
esrj-91195	255	48	methods	method	NOUN
esrj-91195	255	49	used	use	VERB
esrj-91195	255	50	for	for	ADP
esrj-91195	255	51	prediction	prediction	NOUN
esrj-91195	255	52	.	.	PUNCT
esrj-91195	256	1	another	another	DET
esrj-91195	256	2	conclusion	conclusion	NOUN
esrj-91195	256	3	that	that	PRON
esrj-91195	256	4	can	can	AUX
esrj-91195	256	5	be	be	AUX
esrj-91195	256	6	drawn	draw	VERB
esrj-91195	256	7	from	from	ADP
esrj-91195	256	8	this	this	DET
esrj-91195	256	9	study	study	NOUN
esrj-91195	256	10	is	be	AUX
esrj-91195	256	11	that	that	PRON
esrj-91195	256	12	incorrect	incorrect	ADJ
esrj-91195	256	13	point	point	NOUN
esrj-91195	256	14	distribution	distribution	NOUN
esrj-91195	256	15	and	and	CCONJ
esrj-91195	256	16	dataset	dataset	NOUN
esrj-91195	256	17	division	division	NOUN
esrj-91195	256	18	can	can	AUX
esrj-91195	256	19	negatively	negatively	ADV
esrj-91195	256	20	affect	affect	VERB
esrj-91195	256	21	the	the	DET
esrj-91195	256	22	performance	performance	NOUN
esrj-91195	256	23	of	of	ADP
esrj-91195	256	24	interpolation	interpolation	NOUN
esrj-91195	256	25	methods	method	NOUN
esrj-91195	256	26	when	when	SCONJ
esrj-91195	256	27	used	use	VERB
esrj-91195	256	28	in	in	ADP
esrj-91195	256	29	geoid	geoid	ADJ
esrj-91195	256	30	undulation	undulation	NOUN
esrj-91195	256	31	calculation	calculation	NOUN
esrj-91195	256	32	.	.	PUNCT
esrj-91195	257	1	therefore	therefore	ADV
esrj-91195	257	2	,	,	PUNCT
esrj-91195	257	3	it	it	PRON
esrj-91195	257	4	was	be	AUX
esrj-91195	257	5	recognized	recognize	VERB
esrj-91195	257	6	that	that	SCONJ
esrj-91195	257	7	the	the	DET
esrj-91195	257	8	use	use	NOUN
esrj-91195	257	9	of	of	ADP
esrj-91195	257	10	the	the	DET
esrj-91195	257	11	k	k	ADJ
esrj-91195	257	12	-	-	ADJ
esrj-91195	257	13	fold	fold	ADJ
esrj-91195	257	14	cross	cross	NOUN
esrj-91195	257	15	-	-	NOUN
esrj-91195	257	16	validation	validation	NOUN
esrj-91195	257	17	in	in	ADP
esrj-91195	257	18	geoid	geoid	ADJ
esrj-91195	257	19	undulation	undulation	NOUN
esrj-91195	257	20	calculations	calculation	NOUN
esrj-91195	257	21	could	could	AUX
esrj-91195	257	22	expose	expose	VERB
esrj-91195	257	23	incorrect	incorrect	ADJ
esrj-91195	257	24	data	datum	NOUN
esrj-91195	257	25	distribution	distribution	NOUN
esrj-91195	257	26	.	.	PUNCT
esrj-91195	258	1	it	it	PRON
esrj-91195	258	2	was	be	AUX
esrj-91195	258	3	also	also	ADV
esrj-91195	258	4	shown	show	VERB
esrj-91195	258	5	that	that	SCONJ
esrj-91195	258	6	the	the	DET
esrj-91195	258	7	use	use	NOUN
esrj-91195	258	8	of	of	ADP
esrj-91195	258	9	the	the	DET
esrj-91195	258	10	k	k	ADJ
esrj-91195	258	11	-	-	ADJ
esrj-91195	258	12	fold	fold	ADJ
esrj-91195	258	13	cross	cross	ADJ
esrj-91195	258	14	-	-	ADJ
esrj-91195	258	15	validation	validation	ADJ
esrj-91195	258	16	method	method	NOUN
esrj-91195	258	17	may	may	AUX
esrj-91195	258	18	give	give	VERB
esrj-91195	258	19	more	more	ADV
esrj-91195	258	20	accurate	accurate	ADJ
esrj-91195	258	21	results	result	NOUN
esrj-91195	258	22	for	for	ADP
esrj-91195	258	23	large	large	ADJ
esrj-91195	258	24	amounts	amount	NOUN
esrj-91195	258	25	of	of	ADP
esrj-91195	258	26	data	datum	NOUN
esrj-91195	258	27	as	as	ADV
esrj-91195	258	28	well	well	ADV
esrj-91195	258	29	as	as	ADP
esrj-91195	258	30	for	for	ADP
esrj-91195	258	31	a	a	DET
esrj-91195	258	32	sparse	sparse	ADJ
esrj-91195	258	33	number	number	NOUN
esrj-91195	258	34	of	of	ADP
esrj-91195	258	35	data	datum	NOUN
esrj-91195	258	36	.	.	PUNCT
esrj-91195	259	1	using	use	VERB
esrj-91195	259	2	the	the	DET
esrj-91195	259	3	same	same	ADJ
esrj-91195	259	4	dataset	dataset	NOUN
esrj-91195	259	5	,	,	PUNCT
esrj-91195	259	6	the	the	DET
esrj-91195	259	7	k	k	ADJ
esrj-91195	259	8	-	-	ADJ
esrj-91195	259	9	fold	fold	ADJ
esrj-91195	259	10	cross	cross	ADJ
esrj-91195	259	11	-	-	ADJ
esrj-91195	259	12	validation	validation	ADJ
esrj-91195	259	13	method	method	NOUN
esrj-91195	259	14	could	could	AUX
esrj-91195	259	15	also	also	ADV
esrj-91195	259	16	be	be	AUX
esrj-91195	259	17	used	use	VERB
esrj-91195	259	18	to	to	PART
esrj-91195	259	19	test	test	VERB
esrj-91195	259	20	the	the	DET
esrj-91195	259	21	effects	effect	NOUN
esrj-91195	259	22	of	of	ADP
esrj-91195	259	23	other	other	ADJ
esrj-91195	259	24	ann	ann	PROPN
esrj-91195	259	25	methods	method	NOUN
esrj-91195	259	26	and	and	CCONJ
esrj-91195	259	27	compare	compare	VERB
esrj-91195	259	28	them	they	PRON
esrj-91195	259	29	to	to	ADP
esrj-91195	259	30	the	the	DET
esrj-91195	259	31	results	result	NOUN
esrj-91195	259	32	of	of	ADP
esrj-91195	259	33	this	this	DET
esrj-91195	259	34	study	study	NOUN
esrj-91195	259	35	.	.	PUNCT
esrj-91195	260	1	moreover	moreover	ADV
esrj-91195	260	2	,	,	PUNCT
esrj-91195	260	3	additional	additional	ADJ
esrj-91195	260	4	studies	study	NOUN
esrj-91195	260	5	could	could	AUX
esrj-91195	260	6	analyze	analyze	VERB
esrj-91195	260	7	geoid	geoid	ADJ
esrj-91195	260	8	undulation	undulation	NOUN
esrj-91195	260	9	prediction	prediction	NOUN
esrj-91195	260	10	using	use	VERB
esrj-91195	260	11	a	a	DET
esrj-91195	260	12	different	different	ADJ
esrj-91195	260	13	dataset	dataset	NOUN
esrj-91195	260	14	with	with	ADP
esrj-91195	260	15	the	the	DET
esrj-91195	260	16	methods	method	NOUN
esrj-91195	260	17	used	use	VERB
esrj-91195	260	18	in	in	ADP
esrj-91195	260	19	this	this	DET
esrj-91195	260	20	study	study	NOUN
esrj-91195	260	21	.	.	PUNCT
esrj-91195	261	1	it	it	PRON
esrj-91195	261	2	is	be	AUX
esrj-91195	261	3	hoped	hope	VERB
esrj-91195	261	4	that	that	SCONJ
esrj-91195	261	5	the	the	DET
esrj-91195	261	6	present	present	ADJ
esrj-91195	261	7	study	study	NOUN
esrj-91195	261	8	will	will	AUX
esrj-91195	261	9	thus	thus	ADV
esrj-91195	261	10	contribute	contribute	VERB
esrj-91195	261	11	to	to	ADP
esrj-91195	261	12	the	the	DET
esrj-91195	261	13	field	field	NOUN
esrj-91195	261	14	and	and	CCONJ
esrj-91195	261	15	lead	lead	VERB
esrj-91195	261	16	to	to	ADP
esrj-91195	261	17	future	future	ADJ
esrj-91195	261	18	research	research	NOUN
esrj-91195	261	19	on	on	ADP
esrj-91195	261	20	geoid	geoid	ADJ
esrj-91195	261	21	undulation	undulation	NOUN
esrj-91195	261	22	modeling	modeling	NOUN
esrj-91195	261	23	.	.	PUNCT
esrj-91195	262	1	acknowledgments	acknowledgment	NOUN
esrj-91195	262	2	we	we	PRON
esrj-91195	262	3	are	be	AUX
esrj-91195	262	4	grateful	grateful	ADJ
esrj-91195	262	5	to	to	ADP
esrj-91195	262	6	the	the	DET
esrj-91195	262	7	trabzon	trabzon	NOUN
esrj-91195	262	8	ix	ix	ADP
esrj-91195	262	9	regional	regional	ADJ
esrj-91195	262	10	directorate	directorate	NOUN
esrj-91195	262	11	of	of	ADP
esrj-91195	262	12	land	land	NOUN
esrj-91195	262	13	registry	registry	NOUN
esrj-91195	262	14	and	and	CCONJ
esrj-91195	262	15	cadastre	cadastre	NOUN
esrj-91195	262	16	in	in	ADP
esrj-91195	262	17	turkey	turkey	NOUN
esrj-91195	262	18	for	for	ADP
esrj-91195	262	19	providing	provide	VERB
esrj-91195	262	20	valuable	valuable	ADJ
esrj-91195	262	21	datasets	dataset	NOUN
esrj-91195	262	22	for	for	ADP
esrj-91195	262	23	this	this	DET
esrj-91195	262	24	study	study	NOUN
esrj-91195	262	25	.	.	PUNCT
esrj-91195	263	1	additionally	additionally	ADV
esrj-91195	263	2	,	,	PUNCT
esrj-91195	263	3	generic	generic	ADJ
esrj-91195	263	4	mapping	mapping	NOUN
esrj-91195	263	5	tools	tool	NOUN
esrj-91195	263	6	(	(	PUNCT
esrj-91195	263	7	gmt	gmt	PROPN
esrj-91195	263	8	)	)	PUNCT
esrj-91195	263	9	was	be	AUX
esrj-91195	263	10	used	use	VERB
esrj-91195	263	11	to	to	PART
esrj-91195	263	12	generate	generate	VERB
esrj-91195	263	13	figure	figure	NOUN
esrj-91195	263	14	1	1	NUM
esrj-91195	263	15	.	.	PUNCT
esrj-91195	263	16	special	special	ADJ
esrj-91195	263	17	thanks	thank	NOUN
esrj-91195	263	18	are	be	AUX
esrj-91195	263	19	also	also	ADV
esrj-91195	263	20	extended	extend	VERB
esrj-91195	263	21	to	to	ADP
esrj-91195	263	22	the	the	DET
esrj-91195	263	23	four	four	NUM
esrj-91195	263	24	anonymous	anonymous	ADJ
esrj-91195	263	25	reviewers	reviewer	NOUN
esrj-91195	263	26	for	for	ADP
esrj-91195	263	27	their	their	PRON
esrj-91195	263	28	constructive	constructive	ADJ
esrj-91195	263	29	comments	comment	NOUN
esrj-91195	263	30	.	.	PUNCT
esrj-91195	264	1	references	reference	NOUN
esrj-91195	264	2	akcin	akcin	PROPN
esrj-91195	264	3	,	,	PUNCT
esrj-91195	264	4	h.	h.	PROPN
esrj-91195	264	5	&	&	CCONJ
esrj-91195	264	6	celik	celik	PROPN
esrj-91195	264	7	,	,	PUNCT
esrj-91195	264	8	c.	c.	PROPN
esrj-91195	264	9	t.	t.	PROPN
esrj-91195	264	10	(	(	PUNCT
esrj-91195	264	11	2013	2013	NUM
esrj-91195	264	12	)	)	PUNCT
esrj-91195	264	13	.	.	PUNCT
esrj-91195	265	1	performance	performance	NOUN
esrj-91195	265	2	of	of	ADP
esrj-91195	265	3	artificial	artificial	ADJ
esrj-91195	265	4	neural	neural	ADJ
esrj-91195	265	5	networks	network	NOUN
esrj-91195	265	6	on	on	ADP
esrj-91195	265	7	kriging	krige	VERB
esrj-91195	265	8	method	method	NOUN
esrj-91195	265	9	in	in	ADP
esrj-91195	265	10	modeling	model	VERB
esrj-91195	265	11	local	local	ADJ
esrj-91195	265	12	geoid	geoid	NOUN
esrj-91195	265	13	.	.	PUNCT
esrj-91195	266	1	boletim	boletim	PROPN
esrj-91195	266	2	de	de	PROPN
esrj-91195	266	3	ciências	ciências	PROPN
esrj-91195	266	4	geodésicas	geodésicas	NOUN
esrj-91195	266	5	,	,	PUNCT
esrj-91195	266	6	19(1	19(1	NUM
esrj-91195	266	7	)	)	PUNCT
esrj-91195	266	8	,	,	PUNCT
esrj-91195	266	9	84	84	NUM
esrj-91195	266	10	-	-	SYM
esrj-91195	266	11	97	97	NUM
esrj-91195	266	12	.	.	PUNCT
esrj-91195	267	1	doi	doi	NOUN
esrj-91195	267	2	:	:	PUNCT
esrj-91195	267	3	https://doi.org/10.1590/s198221702013000100006	https://doi.org/10.1590/s198221702013000100006	NUM
esrj-91195	267	4	akyilmaz	akyilmaz	NOUN
esrj-91195	267	5	,	,	PUNCT
esrj-91195	267	6	o.	o.	PROPN
esrj-91195	267	7	,	,	PUNCT
esrj-91195	267	8	özlüdemir	özlüdemir	NOUN
esrj-91195	267	9	,	,	PUNCT
esrj-91195	267	10	m.	m.	NOUN
esrj-91195	267	11	t.	t.	PROPN
esrj-91195	267	12	,	,	PUNCT
esrj-91195	267	13	ayan	ayan	PROPN
esrj-91195	267	14	,	,	PUNCT
esrj-91195	267	15	t.	t.	PROPN
esrj-91195	267	16	&	&	CCONJ
esrj-91195	267	17	çelik	çelik	PROPN
esrj-91195	267	18	,	,	PUNCT
esrj-91195	267	19	r.	r.	PROPN
esrj-91195	267	20	n.	n.	PROPN
esrj-91195	267	21	(	(	PUNCT
esrj-91195	267	22	2009	2009	NUM
esrj-91195	267	23	)	)	PUNCT
esrj-91195	267	24	.	.	PUNCT
esrj-91195	268	1	soft	soft	ADJ
esrj-91195	268	2	computing	computing	NOUN
esrj-91195	268	3	methods	method	NOUN
esrj-91195	268	4	for	for	ADP
esrj-91195	268	5	geoidal	geoidal	ADJ
esrj-91195	268	6	height	height	NOUN
esrj-91195	268	7	transformation	transformation	NOUN
esrj-91195	268	8	.	.	PUNCT
esrj-91195	269	1	earth	earth	NOUN
esrj-91195	269	2	,	,	PUNCT
esrj-91195	269	3	planets	planet	NOUN
esrj-91195	269	4	and	and	CCONJ
esrj-91195	269	5	space	space	NOUN
esrj-91195	269	6	,	,	PUNCT
esrj-91195	269	7	61(7	61(7	PROPN
esrj-91195	269	8	)	)	PUNCT
esrj-91195	269	9	,	,	PUNCT
esrj-91195	269	10	825	825	NUM
esrj-91195	269	11	-	-	SYM
esrj-91195	269	12	833	833	NUM
esrj-91195	269	13	.	.	PUNCT
esrj-91195	270	1	doi	doi	NOUN
esrj-91195	270	2	:	:	PUNCT
esrj-91195	270	3	https://doi.org/10.1186/bf03353193	https://doi.org/10.1186/bf03353193	NOUN
esrj-91195	270	4	figure	figure	NOUN
esrj-91195	270	5	9	9	NUM
esrj-91195	270	6	.	.	PUNCT
esrj-91195	270	7	comparison	comparison	NOUN
esrj-91195	270	8	of	of	ADP
esrj-91195	270	9	the	the	DET
esrj-91195	270	10	geoid	geoid	ADJ
esrj-91195	270	11	undulation	undulation	NOUN
esrj-91195	270	12	values	value	NOUN
esrj-91195	270	13	predicted	predict	VERB
esrj-91195	270	14	by	by	ADP
esrj-91195	270	15	interpolation	interpolation	NOUN
esrj-91195	270	16	methods	method	NOUN
esrj-91195	270	17	with	with	ADP
esrj-91195	270	18	the	the	DET
esrj-91195	270	19	observed	observe	VERB
esrj-91195	270	20	values	value	NOUN
esrj-91195	270	21	for	for	ADP
esrj-91195	270	22	ds#5	ds#5	PROPN
esrj-91195	270	23	https://doi.org/10.1590/s1982-21702013000100006	https://doi.org/10.1590/s1982-21702013000100006	PROPN
esrj-91195	270	24	https://doi.org/10.1590/s1982-21702013000100006	https://doi.org/10.1590/s1982-21702013000100006	PROPN
esrj-91195	270	25	https://doi.org/10.1186/bf03353193	https://doi.org/10.1186/bf03353193	PROPN
esrj-91195	271	1	381geoid	381geoid	PROPN
esrj-91195	271	2	undulation	undulation	NOUN
esrj-91195	271	3	prediction	prediction	NOUN
esrj-91195	271	4	using	use	VERB
esrj-91195	271	5	anns	ann	NOUN
esrj-91195	271	6	(	(	PUNCT
esrj-91195	271	7	rbfnn	rbfnn	NOUN
esrj-91195	271	8	and	and	CCONJ
esrj-91195	271	9	grnn	grnn	NOUN
esrj-91195	271	10	)	)	PUNCT
esrj-91195	271	11	,	,	PUNCT
esrj-91195	271	12	multiple	multiple	ADJ
esrj-91195	271	13	linear	linear	ADJ
esrj-91195	271	14	regression	regression	NOUN
esrj-91195	271	15	(	(	PUNCT
esrj-91195	271	16	mlr	mlr	NOUN
esrj-91195	271	17	)	)	PUNCT
esrj-91195	271	18	,	,	PUNCT
esrj-91195	271	19	and	and	CCONJ
esrj-91195	271	20	interpolation	interpolation	NOUN
esrj-91195	271	21	methods	method	NOUN
esrj-91195	271	22	:	:	PUNCT
esrj-91195	271	23	a	a	DET
esrj-91195	271	24	comparative	comparative	ADJ
esrj-91195	271	25	study	study	NOUN
esrj-91195	271	26	albayrak	albayrak	NOUN
esrj-91195	271	27	,	,	PUNCT
esrj-91195	271	28	m.	m.	NOUN
esrj-91195	271	29	,	,	PUNCT
esrj-91195	271	30	özlüdemir	özlüdemir	NOUN
esrj-91195	271	31	,	,	PUNCT
esrj-91195	271	32	m.	m.	NOUN
esrj-91195	271	33	t.	t.	PROPN
esrj-91195	271	34	,	,	PUNCT
esrj-91195	271	35	aref	aref	PROPN
esrj-91195	271	36	,	,	PUNCT
esrj-91195	271	37	m.	m.	NOUN
esrj-91195	271	38	m.	m.	NOUN
esrj-91195	271	39	&	&	CCONJ
esrj-91195	271	40	halicioglu	halicioglu	PROPN
esrj-91195	271	41	,	,	PUNCT
esrj-91195	271	42	k.	k.	PROPN
esrj-91195	271	43	(	(	PUNCT
esrj-91195	271	44	2020	2020	NUM
esrj-91195	271	45	)	)	PUNCT
esrj-91195	271	46	.	.	PUNCT
esrj-91195	272	1	determination	determination	NOUN
esrj-91195	272	2	of	of	ADP
esrj-91195	272	3	istanbul	istanbul	PROPN
esrj-91195	272	4	geoid	geoid	PROPN
esrj-91195	272	5	using	use	VERB
esrj-91195	272	6	gnss	gnss	NOUN
esrj-91195	272	7	/	/	SYM
esrj-91195	272	8	levelling	levelling	NOUN
esrj-91195	272	9	and	and	CCONJ
esrj-91195	272	10	valley	valley	NOUN
esrj-91195	272	11	cross	cross	NOUN
esrj-91195	272	12	levelling	levelling	NOUN
esrj-91195	272	13	data	datum	NOUN
esrj-91195	272	14	.	.	PUNCT
esrj-91195	273	1	geodesy	geodesy	PROPN
esrj-91195	273	2	and	and	CCONJ
esrj-91195	273	3	geodynamics	geodynamic	NOUN
esrj-91195	273	4	,	,	PUNCT
esrj-91195	273	5	11(3	11(3	NUM
esrj-91195	273	6	)	)	PUNCT
esrj-91195	273	7	,	,	PUNCT
esrj-91195	273	8	163	163	NUM
esrj-91195	273	9	-	-	SYM
esrj-91195	273	10	173	173	NUM
esrj-91195	273	11	.	.	PUNCT
esrj-91195	274	1	doi	doi	NOUN
esrj-91195	274	2	:	:	PUNCT
esrj-91195	275	1	https://doi	https://doi	PROPN
esrj-91195	275	2	.	.	PUNCT
esrj-91195	276	1	org/10.1016	org/10.1016	PROPN
esrj-91195	276	2	/	/	SYM
esrj-91195	276	3	j.geog.2020.01.003	j.geog.2020.01.003	PROPN
esrj-91195	276	4	broomhead	broomhead	NOUN
esrj-91195	276	5	,	,	PUNCT
esrj-91195	276	6	d.	d.	PROPN
esrj-91195	276	7	&	&	CCONJ
esrj-91195	276	8	lowe	lowe	PROPN
esrj-91195	276	9	,	,	PUNCT
esrj-91195	276	10	d.	d.	PROPN
esrj-91195	276	11	(	(	PUNCT
esrj-91195	276	12	1988	1988	NUM
esrj-91195	276	13	)	)	PUNCT
esrj-91195	276	14	.	.	PUNCT
esrj-91195	277	1	multivariable	multivariable	ADJ
esrj-91195	277	2	functional	functional	ADJ
esrj-91195	277	3	interpolation	interpolation	NOUN
esrj-91195	277	4	and	and	CCONJ
esrj-91195	277	5	adaptive	adaptive	ADJ
esrj-91195	277	6	networks	network	NOUN
esrj-91195	277	7	.	.	PUNCT
esrj-91195	278	1	complex	complex	ADJ
esrj-91195	278	2	systems	system	NOUN
esrj-91195	278	3	,	,	PUNCT
esrj-91195	278	4	2(6	2(6	NUM
esrj-91195	278	5	)	)	PUNCT
esrj-91195	278	6	,	,	PUNCT
esrj-91195	278	7	568	568	NUM
esrj-91195	278	8	-	-	SYM
esrj-91195	278	9	576	576	NUM
esrj-91195	278	10	.	.	PUNCT
esrj-91195	279	1	cakir	cakir	PROPN
esrj-91195	279	2	,	,	PUNCT
esrj-91195	279	3	l.	l.	PROPN
esrj-91195	279	4	&	&	CCONJ
esrj-91195	279	5	konakoglu	konakoglu	PROPN
esrj-91195	279	6	,	,	PUNCT
esrj-91195	279	7	b.	b.	PROPN
esrj-91195	279	8	(	(	PUNCT
esrj-91195	279	9	2019	2019	NUM
esrj-91195	279	10	)	)	PUNCT
esrj-91195	279	11	.	.	PUNCT
esrj-91195	280	1	the	the	DET
esrj-91195	280	2	impact	impact	NOUN
esrj-91195	280	3	of	of	ADP
esrj-91195	280	4	data	datum	NOUN
esrj-91195	280	5	normalization	normalization	NOUN
esrj-91195	280	6	on	on	ADP
esrj-91195	280	7	2d	2d	NUM
esrj-91195	280	8	coordinate	coordinate	NOUN
esrj-91195	280	9	transformation	transformation	NOUN
esrj-91195	280	10	using	use	VERB
esrj-91195	280	11	grnn	grnn	NOUN
esrj-91195	280	12	.	.	PUNCT
esrj-91195	281	1	geodestki	geodestki	PROPN
esrj-91195	281	2	vestnik	vestnik	PROPN
esrj-91195	281	3	,	,	PUNCT
esrj-91195	281	4	63(4	63(4	NOUN
esrj-91195	281	5	)	)	PUNCT
esrj-91195	281	6	,	,	PUNCT
esrj-91195	281	7	541553	541553	NUM
esrj-91195	281	8	.	.	PUNCT
esrj-91195	282	1	doi	doi	NOUN
esrj-91195	282	2	:	:	PUNCT
esrj-91195	282	3	https://doi.org/10.15292/geodetski-vestnik.2019.04.541-553	https://doi.org/10.15292/geodetski-vestnik.2019.04.541-553	NOUN
esrj-91195	282	4	cakir	cakir	PROPN
esrj-91195	282	5	,	,	PUNCT
esrj-91195	282	6	l.	l.	PROPN
esrj-91195	282	7	&	&	CCONJ
esrj-91195	282	8	yilmaz	yilmaz	PROPN
esrj-91195	282	9	,	,	PUNCT
esrj-91195	282	10	n.	n.	PROPN
esrj-91195	282	11	(	(	PUNCT
esrj-91195	282	12	2014	2014	NUM
esrj-91195	282	13	)	)	PUNCT
esrj-91195	282	14	.	.	PUNCT
esrj-91195	283	1	polynomials	polynomial	NOUN
esrj-91195	283	2	,	,	PUNCT
esrj-91195	283	3	radial	radial	ADJ
esrj-91195	283	4	basis	basis	NOUN
esrj-91195	283	5	functions	function	NOUN
esrj-91195	283	6	and	and	CCONJ
esrj-91195	283	7	multilayer	multilayer	ADJ
esrj-91195	283	8	perceptron	perceptron	PROPN
esrj-91195	283	9	neural	neural	ADJ
esrj-91195	283	10	network	network	NOUN
esrj-91195	283	11	methods	method	NOUN
esrj-91195	283	12	in	in	ADP
esrj-91195	283	13	local	local	ADJ
esrj-91195	283	14	geoid	geoid	ADJ
esrj-91195	283	15	determination	determination	NOUN
esrj-91195	283	16	with	with	ADP
esrj-91195	283	17	gps	gps	PROPN
esrj-91195	283	18	/	/	SYM
esrj-91195	283	19	levelling	levelling	NOUN
esrj-91195	283	20	.	.	PUNCT
esrj-91195	284	1	measurement	measurement	NOUN
esrj-91195	284	2	,	,	PUNCT
esrj-91195	284	3	57	57	NUM
esrj-91195	284	4	,	,	PUNCT
esrj-91195	284	5	148	148	NUM
esrj-91195	284	6	-	-	SYM
esrj-91195	284	7	153	153	NUM
esrj-91195	284	8	.	.	PUNCT
esrj-91195	285	1	doi	doi	NOUN
esrj-91195	285	2	:	:	PUNCT
esrj-91195	286	1	https://doi	https://doi	PROPN
esrj-91195	286	2	.	.	PUNCT
esrj-91195	287	1	org/10.1016	org/10.1016	PROPN
esrj-91195	287	2	/	/	SYM
esrj-91195	287	3	j.measurement.2014.08.003	j.measurement.2014.08.003	CCONJ
esrj-91195	287	4	das	das	PROPN
esrj-91195	287	5	,	,	PUNCT
esrj-91195	287	6	r.	r.	PROPN
esrj-91195	287	7	k.	k.	PROPN
esrj-91195	287	8	,	,	PUNCT
esrj-91195	287	9	samanta	samanta	PROPN
esrj-91195	287	10	,	,	PUNCT
esrj-91195	287	11	s.	s.	PROPN
esrj-91195	287	12	,	,	PUNCT
esrj-91195	287	13	jana	jana	PROPN
esrj-91195	287	14	,	,	PUNCT
esrj-91195	287	15	s.	s.	PROPN
esrj-91195	287	16	k.	k.	PROPN
esrj-91195	287	17	&	&	CCONJ
esrj-91195	287	18	rosa	rosa	PROPN
esrj-91195	287	19	,	,	PUNCT
esrj-91195	287	20	r.	r.	PROPN
esrj-91195	287	21	(	(	PUNCT
esrj-91195	287	22	2018	2018	NUM
esrj-91195	287	23	)	)	PUNCT
esrj-91195	287	24	.	.	PUNCT
esrj-91195	288	1	polynomial	polynomial	ADJ
esrj-91195	288	2	interpolation	interpolation	NOUN
esrj-91195	288	3	methods	method	NOUN
esrj-91195	288	4	in	in	ADP
esrj-91195	288	5	development	development	NOUN
esrj-91195	288	6	of	of	ADP
esrj-91195	288	7	local	local	ADJ
esrj-91195	288	8	geoid	geoid	ADJ
esrj-91195	288	9	model	model	NOUN
esrj-91195	288	10	.	.	PUNCT
esrj-91195	289	1	the	the	DET
esrj-91195	289	2	egyptian	egyptian	ADJ
esrj-91195	289	3	journal	journal	PROPN
esrj-91195	289	4	of	of	ADP
esrj-91195	289	5	remote	remote	ADJ
esrj-91195	289	6	sensing	sensing	NOUN
esrj-91195	289	7	and	and	CCONJ
esrj-91195	289	8	space	space	NOUN
esrj-91195	289	9	science	science	NOUN
esrj-91195	289	10	,	,	PUNCT
esrj-91195	289	11	21(3	21(3	NUM
esrj-91195	289	12	)	)	PUNCT
esrj-91195	289	13	,	,	PUNCT
esrj-91195	289	14	265	265	NUM
esrj-91195	289	15	-	-	SYM
esrj-91195	289	16	271	271	NUM
esrj-91195	289	17	.	.	PUNCT
esrj-91195	290	1	doi	doi	NOUN
esrj-91195	290	2	:	:	PUNCT
esrj-91195	291	1	https://doi	https://doi	PROPN
esrj-91195	291	2	.	.	PUNCT
esrj-91195	291	3	org/10.1016	org/10.1016	PROPN
esrj-91195	291	4	/	/	SYM
esrj-91195	291	5	j.ejrs.2017.03.002	j.ejrs.2017.03.002	NOUN
esrj-91195	291	6	dawod	dawod	NOUN
esrj-91195	291	7	,	,	PUNCT
esrj-91195	291	8	g.	g.	PROPN
esrj-91195	291	9	m.	m.	PROPN
esrj-91195	291	10	&	&	CCONJ
esrj-91195	291	11	abdel	abdel	PROPN
esrj-91195	291	12	-	-	PUNCT
esrj-91195	291	13	aziz	aziz	PROPN
esrj-91195	291	14	,	,	PUNCT
esrj-91195	291	15	t.	t.	NOUN
esrj-91195	291	16	m.	m.	NOUN
esrj-91195	291	17	(	(	PUNCT
esrj-91195	291	18	2020	2020	NUM
esrj-91195	291	19	)	)	PUNCT
esrj-91195	291	20	.	.	PUNCT
esrj-91195	292	1	utilization	utilization	NOUN
esrj-91195	292	2	of	of	ADP
esrj-91195	292	3	geographically	geographically	ADV
esrj-91195	292	4	weighted	weight	VERB
esrj-91195	292	5	regression	regression	NOUN
esrj-91195	292	6	for	for	ADP
esrj-91195	292	7	geoid	geoid	NOUN
esrj-91195	292	8	modelling	modelling	NOUN
esrj-91195	292	9	in	in	ADP
esrj-91195	292	10	egypt	egypt	PROPN
esrj-91195	292	11	.	.	PUNCT
esrj-91195	293	1	journal	journal	PROPN
esrj-91195	293	2	of	of	ADP
esrj-91195	293	3	applied	apply	VERB
esrj-91195	293	4	geodesy	geodesy	PROPN
esrj-91195	293	5	,	,	PUNCT
esrj-91195	293	6	14(1	14(1	NUM
esrj-91195	293	7	)	)	PUNCT
esrj-91195	293	8	,	,	PUNCT
esrj-91195	293	9	1	1	NUM
esrj-91195	293	10	-	-	SYM
esrj-91195	293	11	12	12	NUM
esrj-91195	293	12	.	.	PUNCT
esrj-91195	294	1	doi	doi	NOUN
esrj-91195	294	2	:	:	PUNCT
esrj-91195	294	3	https://doi.org/10.1515/jag-2019-0009	https://doi.org/10.1515/jag-2019-0009	PROPN
esrj-91195	294	4	doganalp	doganalp	PROPN
esrj-91195	294	5	,	,	PUNCT
esrj-91195	294	6	s.	s.	PROPN
esrj-91195	294	7	(	(	PUNCT
esrj-91195	294	8	2016	2016	NUM
esrj-91195	294	9	)	)	PUNCT
esrj-91195	294	10	.	.	PUNCT
esrj-91195	295	1	geoid	geoid	ADJ
esrj-91195	295	2	height	height	NOUN
esrj-91195	295	3	computation	computation	NOUN
esrj-91195	295	4	in	in	ADP
esrj-91195	295	5	strip	strip	NOUN
esrj-91195	295	6	-	-	PUNCT
esrj-91195	295	7	area	area	NOUN
esrj-91195	295	8	project	project	NOUN
esrj-91195	295	9	by	by	ADP
esrj-91195	295	10	using	use	VERB
esrj-91195	295	11	least	least	ADJ
esrj-91195	295	12	-	-	PUNCT
esrj-91195	295	13	squares	square	NOUN
esrj-91195	295	14	collocation	collocation	NOUN
esrj-91195	295	15	.	.	PUNCT
esrj-91195	296	1	acta	acta	PROPN
esrj-91195	296	2	geodynamica	geodynamica	PROPN
esrj-91195	296	3	geomaterialia	geomaterialia	PROPN
esrj-91195	296	4	,	,	PUNCT
esrj-91195	296	5	13(2	13(2	PROPN
esrj-91195	296	6	)	)	PUNCT
esrj-91195	296	7	,	,	PUNCT
esrj-91195	296	8	182	182	NUM
esrj-91195	296	9	.	.	PUNCT
esrj-91195	297	1	doi	doi	NOUN
esrj-91195	297	2	:	:	PUNCT
esrj-91195	297	3	https://doi.org/10.13168/agg.2015.0054	https://doi.org/10.13168/agg.2015.0054	PROPN
esrj-91195	297	4	doganalp	doganalp	PROPN
esrj-91195	297	5	,	,	PUNCT
esrj-91195	297	6	s.	s.	PROPN
esrj-91195	297	7	&	&	CCONJ
esrj-91195	297	8	selvi	selvi	PROPN
esrj-91195	297	9	,	,	PUNCT
esrj-91195	297	10	h.	h.	PROPN
esrj-91195	297	11	z.	z.	PROPN
esrj-91195	297	12	(	(	PUNCT
esrj-91195	297	13	2015	2015	NUM
esrj-91195	297	14	)	)	PUNCT
esrj-91195	297	15	.	.	PUNCT
esrj-91195	298	1	local	local	ADJ
esrj-91195	298	2	geoid	geoid	ADJ
esrj-91195	298	3	determination	determination	NOUN
esrj-91195	298	4	in	in	ADP
esrj-91195	298	5	strip	strip	NOUN
esrj-91195	298	6	area	area	NOUN
esrj-91195	298	7	projects	project	NOUN
esrj-91195	298	8	by	by	ADP
esrj-91195	298	9	using	use	VERB
esrj-91195	298	10	polynomials	polynomial	NOUN
esrj-91195	298	11	,	,	PUNCT
esrj-91195	298	12	least	least	ADJ
esrj-91195	298	13	-	-	PUNCT
esrj-91195	298	14	squares	square	NOUN
esrj-91195	298	15	collocation	collocation	NOUN
esrj-91195	298	16	and	and	CCONJ
esrj-91195	298	17	radial	radial	ADJ
esrj-91195	298	18	basis	basis	NOUN
esrj-91195	298	19	functions	function	NOUN
esrj-91195	298	20	.	.	PUNCT
esrj-91195	299	1	measurement	measurement	NOUN
esrj-91195	299	2	,	,	PUNCT
esrj-91195	299	3	73	73	NUM
esrj-91195	299	4	,	,	PUNCT
esrj-91195	299	5	429	429	NUM
esrj-91195	299	6	-	-	SYM
esrj-91195	299	7	438	438	NUM
esrj-91195	299	8	.	.	PUNCT
esrj-91195	300	1	doi	doi	NOUN
esrj-91195	300	2	:	:	PUNCT
esrj-91195	300	3	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
esrj-91195	300	4	.	.	PUNCT
esrj-91195	301	1	measurement.2015.05.030	measurement.2015.05.030	NOUN
esrj-91195	302	1	elshambaky	elshambaky	PROPN
esrj-91195	302	2	,	,	PUNCT
esrj-91195	302	3	h.	h.	PROPN
esrj-91195	302	4	t.	t.	PROPN
esrj-91195	302	5	(	(	PUNCT
esrj-91195	302	6	2018	2018	NUM
esrj-91195	302	7	)	)	PUNCT
esrj-91195	302	8	.	.	PUNCT
esrj-91195	303	1	application	application	NOUN
esrj-91195	303	2	of	of	ADP
esrj-91195	303	3	neural	neural	ADJ
esrj-91195	303	4	network	network	NOUN
esrj-91195	303	5	technique	technique	NOUN
esrj-91195	303	6	to	to	PART
esrj-91195	303	7	determine	determine	VERB
esrj-91195	303	8	a	a	DET
esrj-91195	303	9	corrector	corrector	NOUN
esrj-91195	303	10	surface	surface	NOUN
esrj-91195	303	11	for	for	ADP
esrj-91195	303	12	global	global	ADJ
esrj-91195	303	13	geopotential	geopotential	ADJ
esrj-91195	303	14	model	model	NOUN
esrj-91195	303	15	using	use	VERB
esrj-91195	303	16	gps	gps	PROPN
esrj-91195	303	17	/	/	SYM
esrj-91195	303	18	levelling	levelling	NOUN
esrj-91195	303	19	measurements	measurement	NOUN
esrj-91195	303	20	in	in	ADP
esrj-91195	303	21	egypt	egypt	PROPN
esrj-91195	303	22	.	.	PUNCT
esrj-91195	304	1	journal	journal	PROPN
esrj-91195	304	2	of	of	ADP
esrj-91195	304	3	applied	apply	VERB
esrj-91195	304	4	geodesy	geodesy	PROPN
esrj-91195	304	5	,	,	PUNCT
esrj-91195	304	6	12(1	12(1	NUM
esrj-91195	304	7	)	)	PUNCT
esrj-91195	304	8	,	,	PUNCT
esrj-91195	304	9	29	29	NUM
esrj-91195	304	10	-	-	SYM
esrj-91195	304	11	43	43	NUM
esrj-91195	304	12	.	.	PUNCT
esrj-91195	305	1	doi	doi	NOUN
esrj-91195	305	2	:	:	PUNCT
esrj-91195	305	3	https://doi.org/10.1515/jag-2017-0017	https://doi.org/10.1515/jag-2017-0017	NOUN
esrj-91195	305	4	erol	erol	PROPN
esrj-91195	305	5	,	,	PUNCT
esrj-91195	305	6	b.	b.	PROPN
esrj-91195	305	7	&	&	CCONJ
esrj-91195	305	8	çelik	çelik	PROPN
esrj-91195	305	9	,	,	PUNCT
esrj-91195	305	10	r.	r.	PROPN
esrj-91195	305	11	n.	n.	PROPN
esrj-91195	305	12	(	(	PUNCT
esrj-91195	305	13	2004	2004	NUM
esrj-91195	305	14	)	)	PUNCT
esrj-91195	305	15	.	.	PUNCT
esrj-91195	306	1	modelling	model	VERB
esrj-91195	306	2	local	local	ADJ
esrj-91195	306	3	gps	gps	NOUN
esrj-91195	306	4	/	/	SYM
esrj-91195	306	5	levelling	levelling	NOUN
esrj-91195	306	6	geoid	geoid	NOUN
esrj-91195	306	7	with	with	ADP
esrj-91195	306	8	the	the	DET
esrj-91195	306	9	assessment	assessment	NOUN
esrj-91195	306	10	of	of	ADP
esrj-91195	306	11	inverse	inverse	NOUN
esrj-91195	306	12	distance	distance	NOUN
esrj-91195	306	13	weighting	weighting	NOUN
esrj-91195	306	14	and	and	CCONJ
esrj-91195	306	15	geostatistical	geostatistical	ADJ
esrj-91195	306	16	kriging	krige	VERB
esrj-91195	306	17	methods	method	NOUN
esrj-91195	306	18	,	,	PUNCT
esrj-91195	306	19	20th	20th	ADJ
esrj-91195	306	20	isprs	isprs	NOUN
esrj-91195	306	21	congress	congress	PROPN
esrj-91195	306	22	,	,	PUNCT
esrj-91195	306	23	technical	technical	PROPN
esrj-91195	306	24	commission	commission	PROPN
esrj-91195	306	25	iv	iv	PROPN
esrj-91195	306	26	,	,	PUNCT
esrj-91195	306	27	istanbul	istanbul	PROPN
esrj-91195	306	28	,	,	PUNCT
esrj-91195	306	29	turkey	turkey	PROPN
esrj-91195	306	30	,	,	PUNCT
esrj-91195	306	31	76	76	NUM
esrj-91195	306	32	.	.	PUNCT
esrj-91195	307	1	erol	erol	PROPN
esrj-91195	307	2	,	,	PUNCT
esrj-91195	307	3	b.	b.	PROPN
esrj-91195	307	4	&	&	CCONJ
esrj-91195	307	5	erol	erol	PROPN
esrj-91195	307	6	,	,	PUNCT
esrj-91195	307	7	s.	s.	PROPN
esrj-91195	307	8	(	(	PUNCT
esrj-91195	307	9	2012	2012	NUM
esrj-91195	307	10	)	)	PUNCT
esrj-91195	307	11	.	.	PUNCT
esrj-91195	308	1	gnss	gnss	NOUN
esrj-91195	308	2	in	in	ADP
esrj-91195	308	3	practical	practical	ADJ
esrj-91195	308	4	determination	determination	NOUN
esrj-91195	308	5	of	of	ADP
esrj-91195	308	6	regional	regional	ADJ
esrj-91195	308	7	heights	height	NOUN
esrj-91195	308	8	.	.	PUNCT
esrj-91195	309	1	in	in	ADP
esrj-91195	309	2	:	:	PUNCT
esrj-91195	309	3	shuanggen	shuanggen	PROPN
esrj-91195	309	4	j.	j.	PROPN
esrj-91195	309	5	(	(	PUNCT
esrj-91195	309	6	editor	editor	NOUN
esrj-91195	309	7	)	)	PUNCT
esrj-91195	309	8	.	.	PUNCT
esrj-91195	310	1	global	global	ADJ
esrj-91195	310	2	navigation	navigation	PROPN
esrj-91195	310	3	satellite	satellite	NOUN
esrj-91195	310	4	systems	system	NOUN
esrj-91195	310	5	:	:	PUNCT
esrj-91195	310	6	signal	signal	NOUN
esrj-91195	310	7	,	,	PUNCT
esrj-91195	310	8	theory	theory	NOUN
esrj-91195	310	9	and	and	CCONJ
esrj-91195	310	10	applications	application	NOUN
esrj-91195	310	11	,	,	PUNCT
esrj-91195	310	12	intech	intech	PROPN
esrj-91195	310	13	,	,	PUNCT
esrj-91195	310	14	shanghai	shanghai	PROPN
esrj-91195	310	15	,	,	PUNCT
esrj-91195	310	16	china	china	PROPN
esrj-91195	310	17	,	,	PUNCT
esrj-91195	310	18	127	127	NUM
esrj-91195	310	19	-	-	SYM
esrj-91195	310	20	160	160	NUM
esrj-91195	310	21	.	.	PUNCT
esrj-91195	311	1	doi	doi	NOUN
esrj-91195	311	2	:	:	PUNCT
esrj-91195	311	3	https://doi.org/10.5772/28820	https://doi.org/10.5772/28820	NOUN
esrj-91195	311	4	erol	erol	NOUN
esrj-91195	311	5	,	,	PUNCT
esrj-91195	311	6	b.	b.	PROPN
esrj-91195	311	7	&	&	CCONJ
esrj-91195	311	8	erol	erol	PROPN
esrj-91195	311	9	,	,	PUNCT
esrj-91195	311	10	s.	s.	PROPN
esrj-91195	311	11	(	(	PUNCT
esrj-91195	311	12	2013	2013	NUM
esrj-91195	311	13	)	)	PUNCT
esrj-91195	311	14	.	.	PUNCT
esrj-91195	312	1	learning	learning	NOUN
esrj-91195	312	2	-	-	PUNCT
esrj-91195	312	3	based	base	VERB
esrj-91195	312	4	computing	computing	NOUN
esrj-91195	312	5	techniques	technique	NOUN
esrj-91195	312	6	in	in	ADP
esrj-91195	312	7	geoid	geoid	NOUN
esrj-91195	312	8	modeling	modeling	NOUN
esrj-91195	312	9	for	for	ADP
esrj-91195	312	10	precise	precise	ADJ
esrj-91195	312	11	height	height	NOUN
esrj-91195	312	12	transformation	transformation	NOUN
esrj-91195	312	13	.	.	PUNCT
esrj-91195	313	1	computers	computer	NOUN
esrj-91195	313	2	and	and	CCONJ
esrj-91195	313	3	geosciences	geoscience	NOUN
esrj-91195	313	4	,	,	PUNCT
esrj-91195	313	5	52	52	NUM
esrj-91195	313	6	,	,	PUNCT
esrj-91195	313	7	95	95	NUM
esrj-91195	313	8	-	-	SYM
esrj-91195	313	9	107	107	NUM
esrj-91195	313	10	.	.	PUNCT
esrj-91195	314	1	doi	doi	NOUN
esrj-91195	314	2	:	:	PUNCT
esrj-91195	314	3	https://doi.org/10.1016/j.cageo.2012.09.010	https://doi.org/10.1016/j.cageo.2012.09.010	NOUN
esrj-91195	314	4	erol	erol	NOUN
esrj-91195	314	5	,	,	PUNCT
esrj-91195	314	6	b.	b.	PROPN
esrj-91195	314	7	,	,	PUNCT
esrj-91195	314	8	erol	erol	PROPN
esrj-91195	314	9	,	,	PUNCT
esrj-91195	314	10	s.	s.	PROPN
esrj-91195	314	11	&	&	CCONJ
esrj-91195	314	12	çelik	çelik	PROPN
esrj-91195	314	13	,	,	PUNCT
esrj-91195	314	14	r.	r.	PROPN
esrj-91195	314	15	n.	n.	PROPN
esrj-91195	314	16	(	(	PUNCT
esrj-91195	314	17	2008	2008	NUM
esrj-91195	314	18	)	)	PUNCT
esrj-91195	314	19	.	.	PUNCT
esrj-91195	315	1	height	height	NOUN
esrj-91195	315	2	transformation	transformation	NOUN
esrj-91195	315	3	using	use	VERB
esrj-91195	315	4	regional	regional	ADJ
esrj-91195	315	5	geoids	geoid	NOUN
esrj-91195	315	6	and	and	CCONJ
esrj-91195	315	7	gps	gps	PROPN
esrj-91195	315	8	/	/	SYM
esrj-91195	315	9	levelling	levelling	NOUN
esrj-91195	315	10	in	in	ADP
esrj-91195	315	11	turkey	turkey	NOUN
esrj-91195	315	12	.	.	PUNCT
esrj-91195	316	1	survey	survey	PROPN
esrj-91195	316	2	review	review	PROPN
esrj-91195	316	3	,	,	PUNCT
esrj-91195	316	4	40(307	40(307	NUM
esrj-91195	316	5	)	)	PUNCT
esrj-91195	316	6	,	,	PUNCT
esrj-91195	316	7	2	2	NUM
esrj-91195	316	8	-	-	SYM
esrj-91195	316	9	18	18	NUM
esrj-91195	316	10	.	.	PUNCT
esrj-91195	317	1	doi	doi	NOUN
esrj-91195	317	2	:	:	PUNCT
esrj-91195	317	3	https://doi.org/10.1179/003962608x253394	https://doi.org/10.1179/003962608x253394	X
esrj-91195	317	4	erol	erol	NOUN
esrj-91195	317	5	,	,	PUNCT
esrj-91195	317	6	s.	s.	PROPN
esrj-91195	317	7	&	&	CCONJ
esrj-91195	317	8	erol	erol	PROPN
esrj-91195	317	9	,	,	PUNCT
esrj-91195	317	10	b.	b.	PROPN
esrj-91195	317	11	(	(	PUNCT
esrj-91195	317	12	2021	2021	NUM
esrj-91195	317	13	)	)	PUNCT
esrj-91195	317	14	.	.	PUNCT
esrj-91195	318	1	a	a	DET
esrj-91195	318	2	comparative	comparative	ADJ
esrj-91195	318	3	assessment	assessment	NOUN
esrj-91195	318	4	of	of	ADP
esrj-91195	318	5	different	different	ADJ
esrj-91195	318	6	interpolation	interpolation	NOUN
esrj-91195	318	7	algorithms	algorithm	NOUN
esrj-91195	318	8	for	for	ADP
esrj-91195	318	9	prediction	prediction	NOUN
esrj-91195	318	10	of	of	ADP
esrj-91195	318	11	gnss	gnss	NOUN
esrj-91195	318	12	/	/	SYM
esrj-91195	318	13	levelling	levelling	NOUN
esrj-91195	318	14	geoid	geoid	ADJ
esrj-91195	318	15	surface	surface	NOUN
esrj-91195	318	16	using	use	VERB
esrj-91195	318	17	scattered	scatter	VERB
esrj-91195	318	18	control	control	NOUN
esrj-91195	318	19	data	datum	NOUN
esrj-91195	318	20	.	.	PUNCT
esrj-91195	319	1	measurement	measurement	PROPN
esrj-91195	319	2	,	,	PUNCT
esrj-91195	319	3	173	173	NUM
esrj-91195	319	4	,	,	PUNCT
esrj-91195	319	5	108623	108623	NUM
esrj-91195	319	6	.	.	PUNCT
esrj-91195	320	1	doi	doi	NOUN
esrj-91195	320	2	:	:	PUNCT
esrj-91195	320	3	https://doi.org/10.1016/j.measurement.2020.108623	https://doi.org/10.1016/j.measurement.2020.108623	PROPN
esrj-91195	320	4	featherstone	featherstone	PROPN
esrj-91195	320	5	,	,	PUNCT
esrj-91195	320	6	w.	w.	PROPN
esrj-91195	320	7	e.	e.	PROPN
esrj-91195	320	8	,	,	PUNCT
esrj-91195	320	9	dentith	dentith	PROPN
esrj-91195	320	10	,	,	PUNCT
esrj-91195	320	11	m.	m.	PROPN
esrj-91195	320	12	c.	c.	PROPN
esrj-91195	320	13	&	&	CCONJ
esrj-91195	320	14	kirby	kirby	PROPN
esrj-91195	320	15	,	,	PUNCT
esrj-91195	320	16	j.	j.	PROPN
esrj-91195	320	17	f.	f.	PROPN
esrj-91195	320	18	(	(	PUNCT
esrj-91195	320	19	1998	1998	NUM
esrj-91195	320	20	)	)	PUNCT
esrj-91195	320	21	.	.	PUNCT
esrj-91195	321	1	strategies	strategy	NOUN
esrj-91195	321	2	for	for	ADP
esrj-91195	321	3	the	the	DET
esrj-91195	321	4	accurate	accurate	ADJ
esrj-91195	321	5	determination	determination	NOUN
esrj-91195	321	6	of	of	ADP
esrj-91195	321	7	orthometric	orthometric	ADJ
esrj-91195	321	8	heights	height	NOUN
esrj-91195	321	9	from	from	ADP
esrj-91195	321	10	gps	gps	PROPN
esrj-91195	321	11	.	.	PUNCT
esrj-91195	322	1	survey	survey	PROPN
esrj-91195	322	2	review	review	PROPN
esrj-91195	322	3	,	,	PUNCT
esrj-91195	322	4	34(267	34(267	NOUN
esrj-91195	322	5	)	)	PUNCT
esrj-91195	322	6	,	,	PUNCT
esrj-91195	322	7	278	278	NUM
esrj-91195	322	8	-	-	SYM
esrj-91195	322	9	296	296	NUM
esrj-91195	322	10	.	.	PUNCT
esrj-91195	323	1	doi	doi	NOUN
esrj-91195	323	2	:	:	PUNCT
esrj-91195	323	3	https://doi.org/10.1179/	https://doi.org/10.1179/	PROPN
esrj-91195	323	4	sre.1998.34.267.278	sre.1998.34.267.278	ADV
esrj-91195	323	5	golden	golden	ADJ
esrj-91195	323	6	software	software	NOUN
esrj-91195	323	7	(	(	PUNCT
esrj-91195	323	8	2019	2019	NUM
esrj-91195	323	9	)	)	PUNCT
esrj-91195	323	10	.	.	PUNCT
esrj-91195	324	1	surfer	surfer	NOUN
esrj-91195	324	2	17	17	NUM
esrj-91195	324	3	user	user	NOUN
esrj-91195	324	4	’s	’s	PART
esrj-91195	324	5	guide	guide	NOUN
esrj-91195	324	6	,	,	PUNCT
esrj-91195	324	7	technical	technical	ADJ
esrj-91195	324	8	manual	manual	NOUN
esrj-91195	324	9	,	,	PUNCT
esrj-91195	324	10	inc	inc	PROPN
esrj-91195	324	11	.	.	PROPN
esrj-91195	324	12	colorado	colorado	PROPN
esrj-91195	324	13	,	,	PUNCT
esrj-91195	324	14	usa	usa	PROPN
esrj-91195	324	15	.	.	PROPN
esrj-91195	324	16	hartman	hartman	PROPN
esrj-91195	324	17	,	,	PUNCT
esrj-91195	324	18	e.	e.	PROPN
esrj-91195	324	19	j.	j.	PROPN
esrj-91195	324	20	,	,	PUNCT
esrj-91195	324	21	keeler	keeler	PROPN
esrj-91195	324	22	,	,	PUNCT
esrj-91195	324	23	j.	j.	PROPN
esrj-91195	324	24	d.	d.	PROPN
esrj-91195	324	25	&	&	CCONJ
esrj-91195	324	26	kowalski	kowalski	PROPN
esrj-91195	324	27	,	,	PUNCT
esrj-91195	324	28	j.	j.	PROPN
esrj-91195	324	29	m.	m.	PROPN
esrj-91195	324	30	(	(	PUNCT
esrj-91195	324	31	1990	1990	NUM
esrj-91195	324	32	)	)	PUNCT
esrj-91195	324	33	.	.	PUNCT
esrj-91195	325	1	layered	layer	VERB
esrj-91195	325	2	neural	neural	ADJ
esrj-91195	325	3	networks	network	NOUN
esrj-91195	325	4	with	with	ADP
esrj-91195	325	5	gaussian	gaussian	ADJ
esrj-91195	325	6	hidden	hide	VERB
esrj-91195	325	7	units	unit	NOUN
esrj-91195	325	8	as	as	ADP
esrj-91195	325	9	universal	universal	ADJ
esrj-91195	325	10	approximations	approximation	NOUN
esrj-91195	325	11	.	.	PUNCT
esrj-91195	326	1	neural	neural	ADJ
esrj-91195	326	2	computation	computation	NOUN
esrj-91195	326	3	,	,	PUNCT
esrj-91195	326	4	2(2	2(2	NUM
esrj-91195	326	5	)	)	PUNCT
esrj-91195	326	6	,	,	PUNCT
esrj-91195	326	7	210	210	NUM
esrj-91195	326	8	-	-	SYM
esrj-91195	326	9	215	215	NUM
esrj-91195	326	10	.	.	PUNCT
esrj-91195	327	1	doi	doi	NOUN
esrj-91195	327	2	:	:	PUNCT
esrj-91195	327	3	https://doi.org/10.1162/neco.1990.2.2.210	https://doi.org/10.1162/neco.1990.2.2.210	NOUN
esrj-91195	327	4	haykin	haykin	PROPN
esrj-91195	327	5	,	,	PUNCT
esrj-91195	327	6	s.	s.	PROPN
esrj-91195	327	7	(	(	PUNCT
esrj-91195	327	8	1994	1994	NUM
esrj-91195	327	9	)	)	PUNCT
esrj-91195	327	10	.	.	PUNCT
esrj-91195	328	1	neural	neural	ADJ
esrj-91195	328	2	networks	network	NOUN
esrj-91195	328	3	:	:	PUNCT
esrj-91195	328	4	a	a	DET
esrj-91195	328	5	comprehensive	comprehensive	ADJ
esrj-91195	328	6	foundation	foundation	NOUN
esrj-91195	328	7	.	.	PUNCT
esrj-91195	329	1	prentice	prentice	PROPN
esrj-91195	329	2	hall	hall	PROPN
esrj-91195	329	3	,	,	PUNCT
esrj-91195	329	4	upper	upper	ADJ
esrj-91195	329	5	saddle	saddle	NOUN
esrj-91195	329	6	river	river	NOUN
esrj-91195	329	7	,	,	PUNCT
esrj-91195	329	8	new	new	PROPN
esrj-91195	329	9	jersey	jersey	PROPN
esrj-91195	329	10	.	.	PUNCT
esrj-91195	330	1	heiskanen	heiskanen	PROPN
esrj-91195	330	2	,	,	PUNCT
esrj-91195	330	3	w.	w.	PROPN
esrj-91195	330	4	a.	a.	PROPN
esrj-91195	330	5	&	&	CCONJ
esrj-91195	330	6	moritz	moritz	PROPN
esrj-91195	330	7	,	,	PUNCT
esrj-91195	330	8	h.	h.	PROPN
esrj-91195	330	9	(	(	PUNCT
esrj-91195	330	10	1967	1967	NUM
esrj-91195	330	11	)	)	PUNCT
esrj-91195	330	12	.	.	PUNCT
esrj-91195	331	1	physical	physical	PROPN
esrj-91195	331	2	geodesy	geodesy	PROPN
esrj-91195	331	3	.	.	PUNCT
esrj-91195	332	1	wh	wh	PROPN
esrj-91195	332	2	freeman	freeman	PROPN
esrj-91195	332	3	,	,	PUNCT
esrj-91195	332	4	san	san	PROPN
esrj-91195	332	5	francisco	francisco	PROPN
esrj-91195	332	6	.	.	PUNCT
esrj-91195	333	1	kaloop	kaloop	PROPN
esrj-91195	333	2	,	,	PUNCT
esrj-91195	333	3	m.	m.	PROPN
esrj-91195	333	4	r.	r.	PROPN
esrj-91195	333	5	,	,	PUNCT
esrj-91195	333	6	rabah	rabah	PROPN
esrj-91195	333	7	,	,	PUNCT
esrj-91195	333	8	m.	m.	NOUN
esrj-91195	333	9	,	,	PUNCT
esrj-91195	333	10	hu	hu	PROPN
esrj-91195	333	11	,	,	PUNCT
esrj-91195	333	12	j.w	j.w	PROPN
esrj-91195	333	13	.	.	PROPN
esrj-91195	333	14	&	&	CCONJ
esrj-91195	333	15	zaki	zaki	PROPN
esrj-91195	333	16	,	,	PUNCT
esrj-91195	333	17	a.	a.	NOUN
esrj-91195	333	18	(	(	PUNCT
esrj-91195	333	19	2018	2018	NUM
esrj-91195	333	20	)	)	PUNCT
esrj-91195	333	21	.	.	PUNCT
esrj-91195	334	1	using	use	VERB
esrj-91195	334	2	advanced	advanced	ADJ
esrj-91195	334	3	soft	soft	ADJ
esrj-91195	334	4	computing	computing	NOUN
esrj-91195	334	5	techniques	technique	NOUN
esrj-91195	334	6	for	for	ADP
esrj-91195	334	7	regional	regional	ADJ
esrj-91195	334	8	shoreline	shoreline	NOUN
esrj-91195	334	9	geoid	geoid	NOUN
esrj-91195	334	10	model	model	NOUN
esrj-91195	334	11	estimation	estimation	NOUN
esrj-91195	334	12	and	and	CCONJ
esrj-91195	334	13	evaluation	evaluation	NOUN
esrj-91195	334	14	.	.	PUNCT
esrj-91195	335	1	marine	marine	PROPN
esrj-91195	335	2	georesources	georesource	NOUN
esrj-91195	335	3	and	and	CCONJ
esrj-91195	335	4	geotechnology	geotechnology	NOUN
esrj-91195	335	5	,	,	PUNCT
esrj-91195	335	6	36(6	36(6	NUM
esrj-91195	335	7	)	)	PUNCT
esrj-91195	335	8	,	,	PUNCT
esrj-91195	335	9	688697	688697	NUM
esrj-91195	335	10	.	.	PUNCT
esrj-91195	336	1	doi	doi	NOUN
esrj-91195	336	2	:	:	PUNCT
esrj-91195	336	3	https://doi.org/10.1080/1064119x.2017.1370622	https://doi.org/10.1080/1064119x.2017.1370622	PROPN
esrj-91195	336	4	kaloop	kaloop	NOUN
esrj-91195	336	5	,	,	PUNCT
esrj-91195	336	6	m.	m.	PROPN
esrj-91195	336	7	r.	r.	PROPN
esrj-91195	336	8	,	,	PUNCT
esrj-91195	336	9	zaki	zaki	PROPN
esrj-91195	336	10	,	,	PUNCT
esrj-91195	336	11	a.	a.	PROPN
esrj-91195	336	12	,	,	PUNCT
esrj-91195	336	13	al	al	PROPN
esrj-91195	336	14	-	-	PUNCT
esrj-91195	336	15	ajami	ajami	PROPN
esrj-91195	336	16	,	,	PUNCT
esrj-91195	336	17	h.	h.	PROPN
esrj-91195	336	18	&	&	CCONJ
esrj-91195	336	19	rabah	rabah	PROPN
esrj-91195	336	20	,	,	PUNCT
esrj-91195	336	21	m.	m.	NOUN
esrj-91195	336	22	(	(	PUNCT
esrj-91195	336	23	2020	2020	NUM
esrj-91195	336	24	)	)	PUNCT
esrj-91195	336	25	.	.	PUNCT
esrj-91195	337	1	optimizing	optimize	VERB
esrj-91195	337	2	local	local	ADJ
esrj-91195	337	3	geoid	geoid	ADJ
esrj-91195	337	4	undulation	undulation	NOUN
esrj-91195	337	5	model	model	NOUN
esrj-91195	337	6	using	use	VERB
esrj-91195	337	7	gps	gps	PROPN
esrj-91195	337	8	/	/	SYM
esrj-91195	337	9	levelling	levelling	NOUN
esrj-91195	337	10	measurements	measurement	NOUN
esrj-91195	337	11	and	and	CCONJ
esrj-91195	337	12	heuristic	heuristic	ADJ
esrj-91195	337	13	regression	regression	NOUN
esrj-91195	337	14	approaches	approach	NOUN
esrj-91195	337	15	.	.	PUNCT
esrj-91195	338	1	survey	survey	NOUN
esrj-91195	338	2	review	review	PROPN
esrj-91195	338	3	,	,	PUNCT
esrj-91195	338	4	52(375	52(375	NOUN
esrj-91195	338	5	)	)	PUNCT
esrj-91195	338	6	,	,	PUNCT
esrj-91195	338	7	544	544	NUM
esrj-91195	338	8	-	-	SYM
esrj-91195	338	9	554	554	NUM
esrj-91195	338	10	.	.	PUNCT
esrj-91195	339	1	doi	doi	NOUN
esrj-91195	339	2	:	:	PUNCT
esrj-91195	339	3	https://doi.org/10.1080/00396265.2019.1665615	https://doi.org/10.1080/00396265.2019.1665615	PROPN
esrj-91195	339	4	karaaslan	karaaslan	PROPN
esrj-91195	339	5	,	,	PUNCT
esrj-91195	339	6	ö.	ö.	PROPN
esrj-91195	339	7	,	,	PUNCT
esrj-91195	339	8	tanır	tanır	PROPN
esrj-91195	339	9	kayıkçı	kayıkçı	PROPN
esrj-91195	339	10	,	,	PUNCT
esrj-91195	339	11	e.	e.	PROPN
esrj-91195	339	12	&	&	CCONJ
esrj-91195	339	13	aşık	aşık	PROPN
esrj-91195	339	14	,	,	PUNCT
esrj-91195	339	15	y.	y.	PROPN
esrj-91195	339	16	(	(	PUNCT
esrj-91195	339	17	2016	2016	NUM
esrj-91195	339	18	)	)	PUNCT
esrj-91195	339	19	.	.	PUNCT
esrj-91195	340	1	comparison	comparison	NOUN
esrj-91195	340	2	of	of	ADP
esrj-91195	340	3	local	local	ADJ
esrj-91195	340	4	geoid	geoid	ADJ
esrj-91195	340	5	height	height	NOUN
esrj-91195	340	6	surfaces	surface	NOUN
esrj-91195	340	7	,	,	PUNCT
esrj-91195	340	8	in	in	ADP
esrj-91195	340	9	the	the	DET
esrj-91195	340	10	province	province	NOUN
esrj-91195	340	11	of	of	ADP
esrj-91195	340	12	trabzon	trabzon	PROPN
esrj-91195	340	13	.	.	PUNCT
esrj-91195	341	1	arabian	arabian	ADJ
esrj-91195	341	2	journal	journal	PROPN
esrj-91195	341	3	of	of	ADP
esrj-91195	341	4	geosciences	geoscience	NOUN
esrj-91195	341	5	,	,	PUNCT
esrj-91195	341	6	9(431	9(431	NUM
esrj-91195	341	7	)	)	PUNCT
esrj-91195	341	8	,	,	PUNCT
esrj-91195	341	9	1	1	NUM
esrj-91195	341	10	-	-	SYM
esrj-91195	341	11	12	12	NUM
esrj-91195	341	12	.	.	PUNCT
esrj-91195	342	1	doi	doi	NOUN
esrj-91195	342	2	:	:	PUNCT
esrj-91195	342	3	https://doi.org/10.1007/s12517-016-2470-2	https://doi.org/10.1007/s12517-016-2470-2	PROPN
esrj-91195	342	4	kavzoglu	kavzoglu	PROPN
esrj-91195	342	5	,	,	PUNCT
esrj-91195	342	6	t.	t.	PROPN
esrj-91195	342	7	&	&	CCONJ
esrj-91195	342	8	saka	saka	PROPN
esrj-91195	342	9	,	,	PUNCT
esrj-91195	342	10	m.	m.	PROPN
esrj-91195	342	11	h.	h.	PROPN
esrj-91195	342	12	(	(	PUNCT
esrj-91195	342	13	2005	2005	NUM
esrj-91195	342	14	)	)	PUNCT
esrj-91195	342	15	.	.	PUNCT
esrj-91195	343	1	modelling	model	VERB
esrj-91195	343	2	local	local	ADJ
esrj-91195	343	3	gps	gps	NOUN
esrj-91195	343	4	/	/	SYM
esrj-91195	343	5	levelling	levelling	NOUN
esrj-91195	343	6	geoid	geoid	NOUN
esrj-91195	343	7	undulations	undulation	NOUN
esrj-91195	343	8	using	use	VERB
esrj-91195	343	9	artificial	artificial	ADJ
esrj-91195	343	10	neural	neural	ADJ
esrj-91195	343	11	networks	network	NOUN
esrj-91195	343	12	.	.	PUNCT
esrj-91195	344	1	journal	journal	PROPN
esrj-91195	344	2	of	of	ADP
esrj-91195	344	3	geodesy	geodesy	PROPN
esrj-91195	344	4	,	,	PUNCT
esrj-91195	344	5	78(9	78(9	NOUN
esrj-91195	344	6	)	)	PUNCT
esrj-91195	344	7	,	,	PUNCT
esrj-91195	344	8	520	520	NUM
esrj-91195	344	9	-	-	SYM
esrj-91195	344	10	527	527	NUM
esrj-91195	344	11	.	.	PUNCT
esrj-91195	345	1	doi	doi	NOUN
esrj-91195	345	2	:	:	PUNCT
esrj-91195	345	3	https://doi.org/10.1007/s00190-004-0420-3	https://doi.org/10.1007/s00190-004-0420-3	NUM
esrj-91195	345	4	keckler	keckler	NOUN
esrj-91195	345	5	,	,	PUNCT
esrj-91195	345	6	d.	d.	PROPN
esrj-91195	345	7	(	(	PUNCT
esrj-91195	345	8	1995	1995	NUM
esrj-91195	345	9	)	)	PUNCT
esrj-91195	345	10	.	.	PUNCT
esrj-91195	346	1	surfer	surfer	NOUN
esrj-91195	346	2	for	for	ADP
esrj-91195	346	3	windows	window	NOUN
esrj-91195	346	4	,	,	PUNCT
esrj-91195	346	5	version	version	NOUN
esrj-91195	346	6	6	6	NUM
esrj-91195	346	7	user	user	NOUN
esrj-91195	346	8	’s	’s	PART
esrj-91195	346	9	guide	guide	NOUN
esrj-91195	346	10	.	.	PUNCT
esrj-91195	347	1	golden	golden	ADJ
esrj-91195	347	2	software	software	NOUN
esrj-91195	347	3	,	,	PUNCT
esrj-91195	347	4	golden	golden	ADJ
esrj-91195	347	5	,	,	PUNCT
esrj-91195	347	6	co	co	NOUN
esrj-91195	347	7	,	,	PUNCT
esrj-91195	347	8	511	511	NUM
esrj-91195	347	9	pp	pp	NOUN
esrj-91195	347	10	.	.	PUNCT
esrj-91195	348	1	kohavi	kohavi	PROPN
esrj-91195	348	2	,	,	PUNCT
esrj-91195	348	3	r.	r.	PROPN
esrj-91195	348	4	(	(	PUNCT
esrj-91195	348	5	1995	1995	NUM
esrj-91195	348	6	)	)	PUNCT
esrj-91195	348	7	.	.	PUNCT
esrj-91195	349	1	a	a	DET
esrj-91195	349	2	study	study	NOUN
esrj-91195	349	3	of	of	ADP
esrj-91195	349	4	cross‐validation	cross‐validation	NOUN
esrj-91195	349	5	and	and	CCONJ
esrj-91195	349	6	bootstrap	bootstrap	NOUN
esrj-91195	349	7	for	for	ADP
esrj-91195	349	8	accuracy	accuracy	NOUN
esrj-91195	349	9	estimation	estimation	NOUN
esrj-91195	349	10	and	and	CCONJ
esrj-91195	349	11	model	model	NOUN
esrj-91195	349	12	selection	selection	NOUN
esrj-91195	349	13	,	,	PUNCT
esrj-91195	349	14	14th	14th	ADJ
esrj-91195	349	15	international	international	ADJ
esrj-91195	349	16	joint	joint	ADJ
esrj-91195	349	17	conference	conference	NOUN
esrj-91195	349	18	on	on	ADP
esrj-91195	349	19	artificial	artificial	ADJ
esrj-91195	349	20	intelligence	intelligence	NOUN
esrj-91195	349	21	,	,	PUNCT
esrj-91195	349	22	montreal	montreal	PROPN
esrj-91195	349	23	,	,	PUNCT
esrj-91195	349	24	quebec	quebec	PROPN
esrj-91195	349	25	,	,	PUNCT
esrj-91195	349	26	canada	canada	PROPN
esrj-91195	349	27	,	,	PUNCT
esrj-91195	349	28	1137	1137	NUM
esrj-91195	349	29	-	-	SYM
esrj-91195	349	30	1143	1143	NUM
esrj-91195	349	31	.	.	PUNCT
esrj-91195	350	1	konakoglu	konakoglu	PROPN
esrj-91195	350	2	,	,	PUNCT
esrj-91195	350	3	b.	b.	PROPN
esrj-91195	350	4	&	&	CCONJ
esrj-91195	350	5	akar	akar	PROPN
esrj-91195	350	6	,	,	PUNCT
esrj-91195	350	7	a.	a.	NOUN
esrj-91195	350	8	(	(	PUNCT
esrj-91195	350	9	2021	2021	NUM
esrj-91195	350	10	)	)	PUNCT
esrj-91195	350	11	.	.	PUNCT
esrj-91195	351	1	geoid	geoid	ADJ
esrj-91195	351	2	undulation	undulation	NOUN
esrj-91195	351	3	prediction	prediction	NOUN
esrj-91195	351	4	using	use	VERB
esrj-91195	351	5	gaussian	gaussian	ADJ
esrj-91195	351	6	process	process	NOUN
esrj-91195	351	7	regression	regression	NOUN
esrj-91195	351	8	:	:	PUNCT
esrj-91195	351	9	a	a	DET
esrj-91195	351	10	case	case	NOUN
esrj-91195	351	11	study	study	NOUN
esrj-91195	351	12	in	in	ADP
esrj-91195	351	13	a	a	DET
esrj-91195	351	14	local	local	ADJ
esrj-91195	351	15	region	region	NOUN
esrj-91195	351	16	in	in	ADP
esrj-91195	351	17	turkey	turkey	PROPN
esrj-91195	351	18	.	.	PUNCT
esrj-91195	352	1	acta	acta	PROPN
esrj-91195	352	2	geodynamica	geodynamica	PROPN
esrj-91195	352	3	et	et	PROPN
esrj-91195	352	4	geomaterialia	geomaterialia	PROPN
esrj-91195	352	5	,	,	PUNCT
esrj-91195	352	6	18	18	NUM
esrj-91195	352	7	,	,	PUNCT
esrj-91195	352	8	1(201	1(201	NUM
esrj-91195	352	9	)	)	PUNCT
esrj-91195	352	10	,	,	PUNCT
esrj-91195	352	11	15	15	NUM
esrj-91195	352	12	-	-	SYM
esrj-91195	352	13	28	28	NUM
esrj-91195	352	14	.	.	PUNCT
esrj-91195	353	1	doi	doi	NOUN
esrj-91195	353	2	:	:	PUNCT
esrj-91195	353	3	https://doi	https://doi	PROPN
esrj-91195	353	4	.	.	PUNCT
esrj-91195	353	5	org/10.13168	org/10.13168	PROPN
esrj-91195	353	6	/	/	SYM
esrj-91195	353	7	agg.2021.0001	agg.2021.0001	PROPN
esrj-91195	353	8	kotsakis	kotsakis	PROPN
esrj-91195	353	9	,	,	PUNCT
esrj-91195	353	10	c.	c.	PROPN
esrj-91195	353	11	&	&	CCONJ
esrj-91195	353	12	sideris	sideris	PROPN
esrj-91195	353	13	,	,	PUNCT
esrj-91195	353	14	m.	m.	NOUN
esrj-91195	353	15	g.	g.	PROPN
esrj-91195	353	16	(	(	PUNCT
esrj-91195	353	17	1999	1999	NUM
esrj-91195	353	18	)	)	PUNCT
esrj-91195	353	19	.	.	PUNCT
esrj-91195	354	1	on	on	ADP
esrj-91195	354	2	the	the	DET
esrj-91195	354	3	adjustment	adjustment	NOUN
esrj-91195	354	4	of	of	ADP
esrj-91195	354	5	combined	combined	ADJ
esrj-91195	354	6	gps	gps	PROPN
esrj-91195	354	7	/	/	SYM
esrj-91195	354	8	levelling	levelling	NOUN
esrj-91195	354	9	/	/	SYM
esrj-91195	354	10	geoid	geoid	ADJ
esrj-91195	354	11	networks	network	NOUN
esrj-91195	354	12	.	.	PUNCT
esrj-91195	355	1	journal	journal	PROPN
esrj-91195	355	2	of	of	ADP
esrj-91195	355	3	geodesy	geodesy	PROPN
esrj-91195	355	4	,	,	PUNCT
esrj-91195	355	5	73(8	73(8	NOUN
esrj-91195	355	6	)	)	PUNCT
esrj-91195	355	7	,	,	PUNCT
esrj-91195	355	8	412	412	NUM
esrj-91195	355	9	-	-	SYM
esrj-91195	355	10	421	421	NUM
esrj-91195	355	11	.	.	PUNCT
esrj-91195	356	1	doi	doi	NOUN
esrj-91195	356	2	:	:	PUNCT
esrj-91195	356	3	https://	https://	PROPN
esrj-91195	356	4	doi.org/10.1007/s001900050261	doi.org/10.1007/s001900050261	PROPN
esrj-91195	356	5	li	li	PROPN
esrj-91195	356	6	,	,	PUNCT
esrj-91195	356	7	l.	l.	PROPN
esrj-91195	356	8	,	,	PUNCT
esrj-91195	356	9	xu	xu	PROPN
esrj-91195	356	10	,	,	PUNCT
esrj-91195	356	11	y.	y.	PROPN
esrj-91195	356	12	,	,	PUNCT
esrj-91195	356	13	yan	yan	PROPN
esrj-91195	356	14	,	,	PUNCT
esrj-91195	356	15	l.	l.	PROPN
esrj-91195	356	16	,	,	PUNCT
esrj-91195	356	17	wang	wang	PROPN
esrj-91195	356	18	,	,	PUNCT
esrj-91195	356	19	s.	s.	PROPN
esrj-91195	356	20	,	,	PUNCT
esrj-91195	356	21	liu	liu	PROPN
esrj-91195	356	22	,	,	PUNCT
esrj-91195	356	23	g.	g.	PROPN
esrj-91195	356	24	&	&	CCONJ
esrj-91195	356	25	liu	liu	PROPN
esrj-91195	356	26	,	,	PUNCT
esrj-91195	356	27	f.	f.	PROPN
esrj-91195	356	28	(	(	PUNCT
esrj-91195	356	29	2020	2020	NUM
esrj-91195	356	30	)	)	PUNCT
esrj-91195	356	31	.	.	PUNCT
esrj-91195	357	1	a	a	DET
esrj-91195	357	2	regional	regional	ADJ
esrj-91195	357	3	nwp	nwp	NOUN
esrj-91195	357	4	tropospheric	tropospheric	ADJ
esrj-91195	357	5	delay	delay	NOUN
esrj-91195	357	6	inversion	inversion	NOUN
esrj-91195	357	7	method	method	NOUN
esrj-91195	357	8	based	base	VERB
esrj-91195	357	9	on	on	ADP
esrj-91195	357	10	a	a	DET
esrj-91195	357	11	general	general	ADJ
esrj-91195	357	12	regression	regression	NOUN
esrj-91195	357	13	neural	neural	ADJ
esrj-91195	357	14	network	network	NOUN
esrj-91195	357	15	model	model	NOUN
esrj-91195	357	16	.	.	PUNCT
esrj-91195	358	1	sensors	sensor	NOUN
esrj-91195	358	2	,	,	PUNCT
esrj-91195	358	3	20(11	20(11	NUM
esrj-91195	358	4	)	)	PUNCT
esrj-91195	358	5	,	,	PUNCT
esrj-91195	358	6	3167	3167	NUM
esrj-91195	358	7	.	.	PUNCT
esrj-91195	359	1	doi	doi	NOUN
esrj-91195	359	2	:	:	PUNCT
esrj-91195	359	3	https://doi.org/10.3390/	https://doi.org/10.3390/	PROPN
esrj-91195	359	4	s20113167	s20113167	ADV
esrj-91195	359	5	ligas	ligas	PROPN
esrj-91195	359	6	,	,	PUNCT
esrj-91195	359	7	m.	m.	NOUN
esrj-91195	359	8	&	&	CCONJ
esrj-91195	359	9	kulczycki	kulczycki	PROPN
esrj-91195	359	10	,	,	PUNCT
esrj-91195	359	11	m.	m.	NOUN
esrj-91195	359	12	(	(	PUNCT
esrj-91195	359	13	2018	2018	NUM
esrj-91195	359	14	)	)	PUNCT
esrj-91195	359	15	.	.	PUNCT
esrj-91195	360	1	kriging	krige	VERB
esrj-91195	360	2	and	and	CCONJ
esrj-91195	360	3	moving	move	VERB
esrj-91195	360	4	window	window	NOUN
esrj-91195	360	5	kriging	krige	VERB
esrj-91195	360	6	on	on	ADP
esrj-91195	360	7	a	a	DET
esrj-91195	360	8	sphere	sphere	NOUN
esrj-91195	360	9	in	in	ADP
esrj-91195	360	10	geometric	geometric	ADJ
esrj-91195	360	11	(	(	PUNCT
esrj-91195	360	12	gnss	gnss	NOUN
esrj-91195	360	13	/	/	SYM
esrj-91195	360	14	levelling	levelling	NOUN
esrj-91195	360	15	)	)	PUNCT
esrj-91195	360	16	geoid	geoid	NOUN
esrj-91195	360	17	modelling	modelling	NOUN
esrj-91195	360	18	.	.	PUNCT
esrj-91195	361	1	survey	survey	NOUN
esrj-91195	361	2	review	review	PROPN
esrj-91195	361	3	,	,	PUNCT
esrj-91195	361	4	50(359	50(359	NOUN
esrj-91195	361	5	)	)	PUNCT
esrj-91195	361	6	,	,	PUNCT
esrj-91195	361	7	155	155	NUM
esrj-91195	361	8	-	-	SYM
esrj-91195	361	9	162	162	NUM
esrj-91195	361	10	.	.	PUNCT
esrj-91195	362	1	doi	doi	NOUN
esrj-91195	362	2	:	:	PUNCT
esrj-91195	362	3	https://doi.org/10.1080/00396265.2016	https://doi.org/10.1080/00396265.2016	PROPN
esrj-91195	362	4	.1247131	.1247131	PUNCT
esrj-91195	363	1	lin	lin	PROPN
esrj-91195	363	2	,	,	PUNCT
esrj-91195	363	3	l.	l.	PROPN
esrj-91195	363	4	s.	s.	PROPN
esrj-91195	363	5	(	(	PUNCT
esrj-91195	363	6	2007	2007	NUM
esrj-91195	363	7	)	)	PUNCT
esrj-91195	363	8	.	.	PUNCT
esrj-91195	364	1	application	application	NOUN
esrj-91195	364	2	of	of	ADP
esrj-91195	364	3	a	a	DET
esrj-91195	364	4	back	back	ADJ
esrj-91195	364	5	-	-	PUNCT
esrj-91195	364	6	propagation	propagation	NOUN
esrj-91195	364	7	artificial	artificial	ADJ
esrj-91195	364	8	neural	neural	ADJ
esrj-91195	364	9	network	network	NOUN
esrj-91195	364	10	to	to	ADP
esrj-91195	364	11	regional	regional	ADJ
esrj-91195	364	12	grid	grid	NOUN
esrj-91195	364	13	-	-	PUNCT
esrj-91195	364	14	based	base	VERB
esrj-91195	364	15	geoid	geoid	ADJ
esrj-91195	364	16	model	model	NOUN
esrj-91195	364	17	generation	generation	NOUN
esrj-91195	364	18	using	use	VERB
esrj-91195	364	19	gps	gps	PROPN
esrj-91195	364	20	and	and	CCONJ
esrj-91195	364	21	leveling	leveling	NOUN
esrj-91195	364	22	data	datum	NOUN
esrj-91195	364	23	.	.	PUNCT
esrj-91195	365	1	journal	journal	NOUN
esrj-91195	365	2	of	of	ADP
esrj-91195	365	3	surveying	surveying	NOUN
esrj-91195	365	4	engineering	engineering	NOUN
esrj-91195	365	5	,	,	PUNCT
esrj-91195	365	6	133(2	133(2	NUM
esrj-91195	365	7	)	)	PUNCT
esrj-91195	365	8	,	,	PUNCT
esrj-91195	365	9	81	81	NUM
esrj-91195	365	10	-	-	SYM
esrj-91195	365	11	89	89	NUM
esrj-91195	365	12	.	.	PUNCT
esrj-91195	366	1	doi	doi	NOUN
esrj-91195	366	2	:	:	PUNCT
esrj-91195	366	3	https://	https://	PROPN
esrj-91195	366	4	doi.org/10.1061/(asce)0733-9453(2007)133:2(81	doi.org/10.1061/(asce)0733-9453(2007)133:2(81	PROPN
esrj-91195	366	5	)	)	PUNCT
esrj-91195	366	6	park	park	NOUN
esrj-91195	366	7	,	,	PUNCT
esrj-91195	366	8	j.	j.	PROPN
esrj-91195	366	9	&	&	CCONJ
esrj-91195	366	10	sandberg	sandberg	PROPN
esrj-91195	366	11	,	,	PUNCT
esrj-91195	366	12	i.	i.	PROPN
esrj-91195	366	13	w.	w.	PROPN
esrj-91195	366	14	(	(	PUNCT
esrj-91195	366	15	1991	1991	NUM
esrj-91195	366	16	)	)	PUNCT
esrj-91195	366	17	.	.	PUNCT
esrj-91195	367	1	universal	universal	ADJ
esrj-91195	367	2	approximation	approximation	NOUN
esrj-91195	367	3	using	use	VERB
esrj-91195	367	4	radial	radial	ADJ
esrj-91195	367	5	-	-	PUNCT
esrj-91195	367	6	basis	basis	NOUN
esrj-91195	367	7	-	-	PUNCT
esrj-91195	367	8	function	function	NOUN
esrj-91195	367	9	networks	network	NOUN
esrj-91195	367	10	.	.	PUNCT
esrj-91195	368	1	neural	neural	ADJ
esrj-91195	368	2	computation	computation	NOUN
esrj-91195	368	3	,	,	PUNCT
esrj-91195	368	4	3(2	3(2	NUM
esrj-91195	368	5	)	)	PUNCT
esrj-91195	368	6	,	,	PUNCT
esrj-91195	368	7	246	246	NUM
esrj-91195	368	8	-	-	SYM
esrj-91195	368	9	257	257	NUM
esrj-91195	368	10	.	.	PUNCT
esrj-91195	369	1	doi	doi	NOUN
esrj-91195	369	2	:	:	PUNCT
esrj-91195	369	3	https://doi.org/10.1162/neco.1991.3.2.246	https://doi.org/10.1162/neco.1991.3.2.246	PROPN
esrj-91195	369	4	pikridas	pikridas	PROPN
esrj-91195	369	5	,	,	PUNCT
esrj-91195	369	6	c.	c.	NOUN
esrj-91195	369	7	,	,	PUNCT
esrj-91195	369	8	fotiou	fotiou	NOUN
esrj-91195	369	9	,	,	PUNCT
esrj-91195	369	10	a.	a.	NOUN
esrj-91195	369	11	,	,	PUNCT
esrj-91195	369	12	katsougiannopoulos	katsougiannopoulos	PROPN
esrj-91195	369	13	,	,	PUNCT
esrj-91195	369	14	s.	s.	PROPN
esrj-91195	369	15	&	&	CCONJ
esrj-91195	369	16	rossikopoulos	rossikopoulo	NOUN
esrj-91195	369	17	,	,	PUNCT
esrj-91195	369	18	d.	d.	PROPN
esrj-91195	369	19	(	(	PUNCT
esrj-91195	369	20	2011	2011	NUM
esrj-91195	369	21	)	)	PUNCT
esrj-91195	369	22	.	.	PUNCT
esrj-91195	370	1	estimation	estimation	NOUN
esrj-91195	370	2	and	and	CCONJ
esrj-91195	370	3	evaluation	evaluation	NOUN
esrj-91195	370	4	of	of	ADP
esrj-91195	370	5	gps	gps	PROPN
esrj-91195	370	6	geoid	geoid	NOUN
esrj-91195	370	7	heights	height	NOUN
esrj-91195	370	8	using	use	VERB
esrj-91195	370	9	an	an	DET
esrj-91195	370	10	artificial	artificial	ADJ
esrj-91195	370	11	neural	neural	ADJ
esrj-91195	370	12	network	network	NOUN
esrj-91195	370	13	model	model	NOUN
esrj-91195	370	14	.	.	PUNCT
esrj-91195	371	1	applied	apply	VERB
esrj-91195	371	2	geomatics	geomatic	NOUN
esrj-91195	371	3	,	,	PUNCT
esrj-91195	371	4	(	(	PUNCT
esrj-91195	371	5	3	3	NUM
esrj-91195	371	6	)	)	PUNCT
esrj-91195	371	7	,	,	PUNCT
esrj-91195	371	8	183	183	NUM
esrj-91195	371	9	-	-	SYM
esrj-91195	371	10	187	187	NUM
esrj-91195	371	11	.	.	PUNCT
esrj-91195	372	1	doi	doi	NOUN
esrj-91195	372	2	:	:	PUNCT
esrj-91195	372	3	https://doi	https://doi	PROPN
esrj-91195	372	4	.	.	PUNCT
esrj-91195	372	5	org/10.1007	org/10.1007	PROPN
esrj-91195	372	6	/	/	SYM
esrj-91195	372	7	s12518	s12518	NOUN
esrj-91195	372	8	-	-	PUNCT
esrj-91195	372	9	011	011	NUM
esrj-91195	372	10	-	-	PUNCT
esrj-91195	372	11	0052	0052	NUM
esrj-91195	372	12	-	-	PUNCT
esrj-91195	372	13	2	2	NUM
esrj-91195	372	14	rabah	rabah	NOUN
esrj-91195	372	15	,	,	PUNCT
esrj-91195	372	16	m.	m.	NOUN
esrj-91195	372	17	&	&	CCONJ
esrj-91195	372	18	kaloop	kaloop	PROPN
esrj-91195	372	19	,	,	PUNCT
esrj-91195	372	20	m.	m.	NOUN
esrj-91195	372	21	(	(	PUNCT
esrj-91195	372	22	2013	2013	NUM
esrj-91195	372	23	)	)	PUNCT
esrj-91195	372	24	.	.	PUNCT
esrj-91195	373	1	the	the	DET
esrj-91195	373	2	use	use	NOUN
esrj-91195	373	3	of	of	ADP
esrj-91195	373	4	minimum	minimum	ADJ
esrj-91195	373	5	curvature	curvature	NOUN
esrj-91195	373	6	surface	surface	NOUN
esrj-91195	373	7	technique	technique	NOUN
esrj-91195	373	8	in	in	ADP
esrj-91195	373	9	geoid	geoid	ADJ
esrj-91195	373	10	computation	computation	NOUN
esrj-91195	373	11	processing	processing	NOUN
esrj-91195	373	12	of	of	ADP
esrj-91195	373	13	egypt	egypt	PROPN
esrj-91195	373	14	.	.	PUNCT
esrj-91195	374	1	arabian	arabian	ADJ
esrj-91195	374	2	journal	journal	PROPN
esrj-91195	374	3	of	of	ADP
esrj-91195	374	4	geosciences	geoscience	NOUN
esrj-91195	374	5	,	,	PUNCT
esrj-91195	374	6	6(4	6(4	NUM
esrj-91195	374	7	)	)	PUNCT
esrj-91195	374	8	,	,	PUNCT
esrj-91195	374	9	1263	1263	NUM
esrj-91195	374	10	-	-	SYM
esrj-91195	374	11	1272	1272	NUM
esrj-91195	374	12	.	.	PUNCT
esrj-91195	375	1	doi	doi	NOUN
esrj-91195	375	2	:	:	PUNCT
esrj-91195	375	3	https://doi.org/10.1007/s12517011-0418-0	https://doi.org/10.1007/s12517011-0418-0	PROPN
esrj-91195	375	4	rodriguez	rodriguez	PROPN
esrj-91195	375	5	,	,	PUNCT
esrj-91195	375	6	j.	j.	PROPN
esrj-91195	375	7	d.	d.	PROPN
esrj-91195	375	8	,	,	PUNCT
esrj-91195	375	9	perez	perez	PROPN
esrj-91195	375	10	,	,	PUNCT
esrj-91195	375	11	a.	a.	PROPN
esrj-91195	375	12	&	&	CCONJ
esrj-91195	375	13	lozano	lozano	PROPN
esrj-91195	375	14	,	,	PUNCT
esrj-91195	375	15	j.	j.	PROPN
esrj-91195	375	16	a.	a.	PROPN
esrj-91195	375	17	(	(	PUNCT
esrj-91195	375	18	2009	2009	NUM
esrj-91195	375	19	)	)	PUNCT
esrj-91195	375	20	.	.	PUNCT
esrj-91195	376	1	sensitivity	sensitivity	NOUN
esrj-91195	376	2	analysis	analysis	NOUN
esrj-91195	376	3	of	of	ADP
esrj-91195	376	4	k	k	ADJ
esrj-91195	376	5	-	-	ADJ
esrj-91195	376	6	fold	fold	ADJ
esrj-91195	376	7	cross	cross	NOUN
esrj-91195	376	8	validation	validation	NOUN
esrj-91195	376	9	in	in	ADP
esrj-91195	376	10	prediction	prediction	NOUN
esrj-91195	376	11	error	error	NOUN
esrj-91195	376	12	estimation	estimation	NOUN
esrj-91195	376	13	.	.	PUNCT
esrj-91195	377	1	ieee	ieee	NOUN
esrj-91195	377	2	transactions	transaction	NOUN
esrj-91195	377	3	on	on	ADP
esrj-91195	377	4	pattern	pattern	NOUN
esrj-91195	377	5	analysis	analysis	NOUN
esrj-91195	377	6	and	and	CCONJ
esrj-91195	377	7	machine	machine	NOUN
esrj-91195	377	8	intelligence	intelligence	NOUN
esrj-91195	377	9	,	,	PUNCT
esrj-91195	377	10	32(3	32(3	NUM
esrj-91195	377	11	)	)	PUNCT
esrj-91195	377	12	,	,	PUNCT
esrj-91195	377	13	569	569	NUM
esrj-91195	377	14	-	-	SYM
esrj-91195	377	15	575	575	NUM
esrj-91195	377	16	.	.	PUNCT
esrj-91195	378	1	doi	doi	NOUN
esrj-91195	378	2	:	:	PUNCT
esrj-91195	378	3	https://doi.org/10.1109/tpami.2009.187	https://doi.org/10.1109/tpami.2009.187	PROPN
esrj-91195	378	4	şahin	şahin	PROPN
esrj-91195	378	5	,	,	PUNCT
esrj-91195	378	6	m.	m.	NOUN
esrj-91195	378	7	,	,	PUNCT
esrj-91195	378	8	kaya	kaya	PROPN
esrj-91195	378	9	,	,	PUNCT
esrj-91195	378	10	y.	y.	PROPN
esrj-91195	378	11	&	&	CCONJ
esrj-91195	378	12	uyar	uyar	PROPN
esrj-91195	378	13	,	,	PUNCT
esrj-91195	378	14	m.	m.	NOUN
esrj-91195	378	15	(	(	PUNCT
esrj-91195	378	16	2013	2013	NUM
esrj-91195	378	17	)	)	PUNCT
esrj-91195	378	18	.	.	PUNCT
esrj-91195	379	1	comparison	comparison	NOUN
esrj-91195	379	2	of	of	ADP
esrj-91195	379	3	ann	ann	PROPN
esrj-91195	379	4	and	and	CCONJ
esrj-91195	379	5	mlr	mlr	NOUN
esrj-91195	379	6	models	model	NOUN
esrj-91195	379	7	for	for	ADP
esrj-91195	379	8	estimating	estimate	VERB
esrj-91195	379	9	solar	solar	ADJ
esrj-91195	379	10	radiation	radiation	NOUN
esrj-91195	379	11	in	in	ADP
esrj-91195	379	12	turkey	turkey	NOUN
esrj-91195	379	13	using	use	VERB
esrj-91195	379	14	noaa	noaa	NOUN
esrj-91195	379	15	/	/	SYM
esrj-91195	379	16	avhrr	avhrr	NOUN
esrj-91195	379	17	data	datum	NOUN
esrj-91195	379	18	.	.	PUNCT
esrj-91195	380	1	advances	advance	NOUN
esrj-91195	380	2	in	in	ADP
esrj-91195	380	3	space	space	NOUN
esrj-91195	380	4	research	research	NOUN
esrj-91195	380	5	,	,	PUNCT
esrj-91195	380	6	51(5	51(5	NUM
esrj-91195	380	7	)	)	PUNCT
esrj-91195	380	8	,	,	PUNCT
esrj-91195	380	9	891	891	NUM
esrj-91195	380	10	-	-	SYM
esrj-91195	380	11	904	904	NUM
esrj-91195	380	12	.	.	PUNCT
esrj-91195	381	1	doi	doi	NOUN
esrj-91195	381	2	:	:	PUNCT
esrj-91195	382	1	https://doi	https://doi	PROPN
esrj-91195	382	2	.	.	PUNCT
esrj-91195	382	3	org/10.1016	org/10.1016	PROPN
esrj-91195	382	4	/	/	SYM
esrj-91195	382	5	j.asr.2012.10.010	j.asr.2012.10.010	PROPN
esrj-91195	382	6	seager	seager	NOUN
esrj-91195	382	7	,	,	PUNCT
esrj-91195	382	8	j.	j.	PROPN
esrj-91195	382	9	,	,	PUNCT
esrj-91195	382	10	collier	collier	NOUN
esrj-91195	382	11	,	,	PUNCT
esrj-91195	382	12	p.	p.	PROPN
esrj-91195	382	13	&	&	CCONJ
esrj-91195	382	14	kirby	kirby	PROPN
esrj-91195	382	15	,	,	PUNCT
esrj-91195	382	16	j.	j.	PROPN
esrj-91195	382	17	(	(	PUNCT
esrj-91195	382	18	1999	1999	NUM
esrj-91195	382	19	)	)	PUNCT
esrj-91195	382	20	.	.	PUNCT
esrj-91195	383	1	modelling	model	VERB
esrj-91195	383	2	geoid	geoid	NOUN
esrj-91195	383	3	undulations	undulation	NOUN
esrj-91195	383	4	with	with	ADP
esrj-91195	383	5	an	an	DET
esrj-91195	383	6	artificial	artificial	ADJ
esrj-91195	383	7	neural	neural	ADJ
esrj-91195	383	8	network	network	NOUN
esrj-91195	383	9	,	,	PUNCT
esrj-91195	383	10	international	international	ADJ
esrj-91195	383	11	joint	joint	ADJ
esrj-91195	383	12	conference	conference	NOUN
esrj-91195	383	13	on	on	ADP
esrj-91195	383	14	neural	neural	ADJ
esrj-91195	383	15	networks	network	NOUN
esrj-91195	383	16	,	,	PUNCT
esrj-91195	383	17	washington	washington	PROPN
esrj-91195	383	18	,	,	PUNCT
esrj-91195	383	19	dc	dc	PROPN
esrj-91195	383	20	,	,	PUNCT
esrj-91195	383	21	usa	usa	PROPN
esrj-91195	383	22	,	,	PUNCT
esrj-91195	383	23	3332	3332	NUM
esrj-91195	383	24	-	-	SYM
esrj-91195	383	25	3335	3335	NUM
esrj-91195	383	26	.	.	PUNCT
esrj-91195	384	1	seeber	seeber	PROPN
esrj-91195	384	2	,	,	PUNCT
esrj-91195	384	3	g.	g.	PROPN
esrj-91195	384	4	(	(	PUNCT
esrj-91195	384	5	2003	2003	NUM
esrj-91195	384	6	)	)	PUNCT
esrj-91195	384	7	.	.	PUNCT
esrj-91195	385	1	satellite	satellite	PROPN
esrj-91195	385	2	geodesy	geodesy	PROPN
esrj-91195	385	3	:	:	PUNCT
esrj-91195	385	4	foundations	foundation	NOUN
esrj-91195	385	5	,	,	PUNCT
esrj-91195	385	6	methods	method	NOUN
esrj-91195	385	7	,	,	PUNCT
esrj-91195	385	8	and	and	CCONJ
esrj-91195	385	9	applications	application	NOUN
esrj-91195	385	10	.	.	PUNCT
esrj-91195	386	1	walter	walter	PROPN
esrj-91195	386	2	de	de	PROPN
esrj-91195	386	3	gruyter	gruyter	PROPN
esrj-91195	386	4	,	,	PUNCT
esrj-91195	386	5	berlin	berlin	PROPN
esrj-91195	386	6	,	,	PUNCT
esrj-91195	386	7	new	new	PROPN
esrj-91195	386	8	york	york	PROPN
esrj-91195	386	9	,	,	PUNCT
esrj-91195	386	10	575	575	NUM
esrj-91195	386	11	pp	pp	NOUN
esrj-91195	386	12	.	.	PUNCT
esrj-91195	387	1	https://doi.org/10.1016/j.geog.2020.01.003	https://doi.org/10.1016/j.geog.2020.01.003	NUM
esrj-91195	387	2	https://doi.org/10.1016/j.geog.2020.01.003	https://doi.org/10.1016/j.geog.2020.01.003	NUM
esrj-91195	387	3	https://doi.org/10.15292/geodetski-vestnik.2019.04.541-553	https://doi.org/10.15292/geodetski-vestnik.2019.04.541-553	NOUN
esrj-91195	387	4	https://doi.org/10.1016/j.measurement.2014.08.003	https://doi.org/10.1016/j.measurement.2014.08.003	PROPN
esrj-91195	387	5	https://doi.org/10.1016/j.measurement.2014.08.003	https://doi.org/10.1016/j.measurement.2014.08.003	PROPN
esrj-91195	387	6	https://doi.org/10.1016/j.ejrs.2017.03.002	https://doi.org/10.1016/j.ejrs.2017.03.002	PROPN
esrj-91195	387	7	https://doi.org/10.1016/j.ejrs.2017.03.002	https://doi.org/10.1016/j.ejrs.2017.03.002	PROPN
esrj-91195	387	8	https://doi.org/10.1515/jag-2019-0009	https://doi.org/10.1515/jag-2019-0009	PROPN
esrj-91195	387	9	https://doi.org/10.13168/agg.2015.0054	https://doi.org/10.13168/agg.2015.0054	PROPN
esrj-91195	387	10	https://doi.org/10.1016/j.measurement.2015.05.030	https://doi.org/10.1016/j.measurement.2015.05.030	PROPN
esrj-91195	387	11	https://doi.org/10.1016/j.measurement.2015.05.030	https://doi.org/10.1016/j.measurement.2015.05.030	PROPN
esrj-91195	387	12	https://doi.org/10.1515/jag-2017-0017	https://doi.org/10.1515/jag-2017-0017	NOUN
esrj-91195	387	13	https://doi.org/10.5772/28820	https://doi.org/10.5772/28820	NOUN
esrj-91195	387	14	https://doi.org/10.5772/28820	https://doi.org/10.5772/28820	NOUN
esrj-91195	388	1	https://doi.org/10.1016/j.cageo.2012.09.010	https://doi.org/10.1016/j.cageo.2012.09.010	NOUN
esrj-91195	388	2	https://doi.org/10.1179/003962608x253394	https://doi.org/10.1179/003962608x253394	PROPN
esrj-91195	388	3	https://doi.org/10.1016/j.measurement.2020.108623	https://doi.org/10.1016/j.measurement.2020.108623	PROPN
esrj-91195	388	4	https://doi.org/10.1016/j.measurement.2020.108623	https://doi.org/10.1016/j.measurement.2020.108623	PROPN
esrj-91195	388	5	https://doi.org/10.1179/sre.1998.34.267.278	https://doi.org/10.1179/sre.1998.34.267.278	NOUN
esrj-91195	388	6	https://doi.org/10.1179/sre.1998.34.267.278	https://doi.org/10.1179/sre.1998.34.267.278	NOUN
esrj-91195	388	7	https://doi.org/10.1162/neco.1990.2.2.210	https://doi.org/10.1162/neco.1990.2.2.210	NOUN
esrj-91195	388	8	https://doi.org/10.1080/1064119x.2017.1370622	https://doi.org/10.1080/1064119x.2017.1370622	PRON
esrj-91195	388	9	https://doi.org/10.1080/00396265.2019.1665615	https://doi.org/10.1080/00396265.2019.1665615	PROPN
esrj-91195	388	10	https://doi.org/10.1007/s12517-016-2470-2	https://doi.org/10.1007/s12517-016-2470-2	NUM
esrj-91195	388	11	https://doi.org/10.1007/s00190-004-0420-3	https://doi.org/10.1007/s00190-004-0420-3	NUM
esrj-91195	388	12	https://doi.org/10.13168/agg.2021.0001	https://doi.org/10.13168/agg.2021.0001	NOUN
esrj-91195	388	13	https://doi.org/10.13168/agg.2021.0001	https://doi.org/10.13168/agg.2021.0001	NOUN
esrj-91195	388	14	https://doi.org/10.1007/s001900050261	https://doi.org/10.1007/s001900050261	NUM
esrj-91195	388	15	https://doi.org/10.1007/s001900050261	https://doi.org/10.1007/s001900050261	NUM
esrj-91195	388	16	https://doi.org/10.3390/s20113167	https://doi.org/10.3390/s20113167	PROPN
esrj-91195	388	17	https://doi.org/10.3390/s20113167	https://doi.org/10.3390/s20113167	PROPN
esrj-91195	388	18	https://doi.org/10.1080/00396265.2016.1247131	https://doi.org/10.1080/00396265.2016.1247131	PUNCT
esrj-91195	389	1	https://doi.org/10.1080/00396265.2016.1247131	https://doi.org/10.1080/00396265.2016.1247131	PROPN
esrj-91195	390	1	https://doi.org/10.1061/(asce)0733-9453(2007)133:2(81	https://doi.org/10.1061/(asce)0733-9453(2007)133:2(81	X
esrj-91195	390	2	)	)	PUNCT
esrj-91195	390	3	https://doi.org/10.1061/(asce)0733-9453(2007)133:2(81	https://doi.org/10.1061/(asce)0733-9453(2007)133:2(81	NOUN
esrj-91195	390	4	)	)	PUNCT
esrj-91195	390	5	https://doi.org/10.1162/neco.1991.3.2.246	https://doi.org/10.1162/neco.1991.3.2.246	PROPN
esrj-91195	390	6	https://doi.org/10.1162/neco.1991.3.2.246	https://doi.org/10.1162/neco.1991.3.2.246	PROPN
esrj-91195	390	7	https://doi.org/10.1007/s12518-011-0052-2	https://doi.org/10.1007/s12518-011-0052-2	PROPN
esrj-91195	390	8	https://doi.org/10.1007/s12518-011-0052-2	https://doi.org/10.1007/s12518-011-0052-2	NUM
esrj-91195	390	9	https://doi.org/10.1007/s12517-011-0418-0	https://doi.org/10.1007/s12517-011-0418-0	NUM
esrj-91195	390	10	https://doi.org/10.1007/s12517-011-0418-0	https://doi.org/10.1007/s12517-011-0418-0	NUM
esrj-91195	391	1	https://doi.org/10.1109/tpami.2009.187	https://doi.org/10.1109/tpami.2009.187	PROPN
esrj-91195	391	2	https://doi.org/10.1109/tpami.2009.187	https://doi.org/10.1109/tpami.2009.187	PROPN
esrj-91195	391	3	https://doi.org/10.1016/j.asr.2012.10.010	https://doi.org/10.1016/j.asr.2012.10.010	NOUN
esrj-91195	391	4	https://doi.org/10.1016/j.asr.2012.10.010	https://doi.org/10.1016/j.asr.2012.10.010	NOUN
esrj-91195	391	5	382	382	NUM
esrj-91195	391	6	berkant	berkant	ADJ
esrj-91195	391	7	konakoglu	konakoglu	PROPN
esrj-91195	391	8	,	,	PUNCT
esrj-91195	391	9	alper	alper	PROPN
esrj-91195	391	10	akar	akar	PROPN
esrj-91195	391	11	singh	singh	PROPN
esrj-91195	391	12	,	,	PUNCT
esrj-91195	391	13	k.	k.	PROPN
esrj-91195	391	14	p.	p.	PROPN
esrj-91195	391	15	,	,	PUNCT
esrj-91195	391	16	basant	basant	PROPN
esrj-91195	391	17	,	,	PUNCT
esrj-91195	391	18	a.	a.	PROPN
esrj-91195	391	19	,	,	PUNCT
esrj-91195	391	20	malik	malik	PROPN
esrj-91195	391	21	,	,	PUNCT
esrj-91195	391	22	a.	a.	PROPN
esrj-91195	391	23	&	&	CCONJ
esrj-91195	391	24	jain	jain	PROPN
esrj-91195	391	25	,	,	PUNCT
esrj-91195	391	26	g.	g.	PROPN
esrj-91195	391	27	(	(	PUNCT
esrj-91195	391	28	2009	2009	NUM
esrj-91195	391	29	)	)	PUNCT
esrj-91195	391	30	.	.	PUNCT
esrj-91195	392	1	artificial	artificial	ADJ
esrj-91195	392	2	neural	neural	ADJ
esrj-91195	392	3	network	network	NOUN
esrj-91195	392	4	modeling	modeling	NOUN
esrj-91195	392	5	of	of	ADP
esrj-91195	392	6	the	the	DET
esrj-91195	392	7	river	river	NOUN
esrj-91195	392	8	water	water	NOUN
esrj-91195	392	9	quality	quality	NOUN
esrj-91195	392	10	-	-	PUNCT
esrj-91195	392	11	a	a	DET
esrj-91195	392	12	case	case	NOUN
esrj-91195	392	13	study	study	NOUN
esrj-91195	392	14	.	.	PUNCT
esrj-91195	393	1	ecological	ecological	ADJ
esrj-91195	393	2	modelling	modelling	NOUN
esrj-91195	393	3	,	,	PUNCT
esrj-91195	393	4	220(6	220(6	NUM
esrj-91195	393	5	)	)	PUNCT
esrj-91195	393	6	,	,	PUNCT
esrj-91195	393	7	888	888	NUM
esrj-91195	393	8	-	-	SYM
esrj-91195	393	9	895	895	NUM
esrj-91195	393	10	.	.	PUNCT
esrj-91195	394	1	doi	doi	NOUN
esrj-91195	394	2	:	:	PUNCT
esrj-91195	394	3	https://doi.org/10.1016/j.ecolmodel.2009.01.004	https://doi.org/10.1016/j.ecolmodel.2009.01.004	PROPN
esrj-91195	394	4	specht	specht	PROPN
esrj-91195	394	5	,	,	PUNCT
esrj-91195	394	6	d.	d.	PROPN
esrj-91195	394	7	f.	f.	PROPN
esrj-91195	394	8	(	(	PUNCT
esrj-91195	394	9	1993	1993	NUM
esrj-91195	394	10	)	)	PUNCT
esrj-91195	394	11	.	.	PUNCT
esrj-91195	395	1	the	the	DET
esrj-91195	395	2	general	general	ADJ
esrj-91195	395	3	regression	regression	NOUN
esrj-91195	395	4	neural	neural	ADJ
esrj-91195	395	5	network	network	NOUN
esrj-91195	395	6	-	-	PUNCT
esrj-91195	395	7	rediscovered	rediscovered	ADJ
esrj-91195	395	8	.	.	PUNCT
esrj-91195	396	1	neural	neural	ADJ
esrj-91195	396	2	networks	network	NOUN
esrj-91195	396	3	,	,	PUNCT
esrj-91195	396	4	6(7	6(7	NUM
esrj-91195	396	5	)	)	PUNCT
esrj-91195	396	6	,	,	PUNCT
esrj-91195	396	7	1033	1033	NUM
esrj-91195	396	8	-	-	SYM
esrj-91195	396	9	1034	1034	NUM
esrj-91195	396	10	.	.	PUNCT
esrj-91195	397	1	doi	doi	NOUN
esrj-91195	397	2	:	:	PUNCT
esrj-91195	397	3	https://doi.org/10.1016/s08936080(09)80013-0	https://doi.org/10.1016/s08936080(09)80013-0	NOUN
esrj-91195	397	4	stone	stone	NOUN
esrj-91195	397	5	,	,	PUNCT
esrj-91195	397	6	m.	m.	NOUN
esrj-91195	397	7	(	(	PUNCT
esrj-91195	397	8	1974	1974	NUM
esrj-91195	397	9	)	)	PUNCT
esrj-91195	397	10	.	.	PUNCT
esrj-91195	398	1	cross	cross	ADJ
esrj-91195	398	2	-	-	ADJ
esrj-91195	398	3	validatory	validatory	ADJ
esrj-91195	398	4	choice	choice	NOUN
esrj-91195	398	5	and	and	CCONJ
esrj-91195	398	6	assessment	assessment	NOUN
esrj-91195	398	7	of	of	ADP
esrj-91195	398	8	statistical	statistical	ADJ
esrj-91195	398	9	predictions	prediction	NOUN
esrj-91195	398	10	.	.	PUNCT
esrj-91195	399	1	journal	journal	NOUN
esrj-91195	399	2	of	of	ADP
esrj-91195	399	3	the	the	DET
esrj-91195	399	4	royal	royal	ADJ
esrj-91195	399	5	statistical	statistical	ADJ
esrj-91195	399	6	society	society	NOUN
esrj-91195	399	7	:	:	PUNCT
esrj-91195	399	8	series	series	PROPN
esrj-91195	399	9	b	b	PROPN
esrj-91195	399	10	(	(	PUNCT
esrj-91195	399	11	methodological	methodological	ADJ
esrj-91195	399	12	)	)	PUNCT
esrj-91195	399	13	,	,	PUNCT
esrj-91195	399	14	36(2	36(2	NUM
esrj-91195	399	15	)	)	PUNCT
esrj-91195	399	16	,	,	PUNCT
esrj-91195	399	17	111	111	NUM
esrj-91195	399	18	-	-	SYM
esrj-91195	399	19	133	133	NUM
esrj-91195	399	20	.	.	PUNCT
esrj-91195	400	1	stopar	stopar	PROPN
esrj-91195	400	2	,	,	PUNCT
esrj-91195	400	3	b.	b.	PROPN
esrj-91195	400	4	,	,	PUNCT
esrj-91195	400	5	ambrožič	ambrožič	NOUN
esrj-91195	400	6	,	,	PUNCT
esrj-91195	400	7	t.	t.	PROPN
esrj-91195	400	8	,	,	PUNCT
esrj-91195	400	9	kuhar	kuhar	PROPN
esrj-91195	400	10	,	,	PUNCT
esrj-91195	400	11	m.	m.	NOUN
esrj-91195	400	12	&	&	CCONJ
esrj-91195	400	13	turk	turk	PROPN
esrj-91195	400	14	,	,	PUNCT
esrj-91195	400	15	g.	g.	PROPN
esrj-91195	400	16	(	(	PUNCT
esrj-91195	400	17	2006	2006	NUM
esrj-91195	400	18	)	)	PUNCT
esrj-91195	400	19	.	.	PUNCT
esrj-91195	401	1	gps	gps	NOUN
esrj-91195	401	2	-	-	PUNCT
esrj-91195	401	3	derived	derive	VERB
esrj-91195	401	4	geoid	geoid	NOUN
esrj-91195	401	5	using	use	VERB
esrj-91195	401	6	artificial	artificial	ADJ
esrj-91195	401	7	neural	neural	ADJ
esrj-91195	401	8	network	network	NOUN
esrj-91195	401	9	and	and	CCONJ
esrj-91195	401	10	least	least	ADJ
esrj-91195	401	11	squares	square	NOUN
esrj-91195	401	12	collocation	collocation	NOUN
esrj-91195	401	13	.	.	PUNCT
esrj-91195	402	1	survey	survey	NOUN
esrj-91195	402	2	review	review	PROPN
esrj-91195	402	3	,	,	PUNCT
esrj-91195	402	4	38(300	38(300	NUM
esrj-91195	402	5	)	)	PUNCT
esrj-91195	402	6	,	,	PUNCT
esrj-91195	402	7	513	513	NUM
esrj-91195	402	8	-	-	SYM
esrj-91195	402	9	524	524	NUM
esrj-91195	402	10	.	.	PUNCT
esrj-91195	403	1	doi	doi	NOUN
esrj-91195	403	2	:	:	PUNCT
esrj-91195	403	3	https://doi.org/10.1179/	https://doi.org/10.1179/	PROPN
esrj-91195	403	4	sre.2006.38.300.513	sre.2006.38.300.513	NUM
esrj-91195	403	5	tusat	tusat	ADJ
esrj-91195	403	6	,	,	PUNCT
esrj-91195	403	7	e.	e.	PROPN
esrj-91195	403	8	(	(	PUNCT
esrj-91195	403	9	2011	2011	NUM
esrj-91195	403	10	)	)	PUNCT
esrj-91195	403	11	.	.	PUNCT
esrj-91195	404	1	a	a	DET
esrj-91195	404	2	comparison	comparison	NOUN
esrj-91195	404	3	of	of	ADP
esrj-91195	404	4	geoid	geoid	ADJ
esrj-91195	404	5	height	height	NOUN
esrj-91195	404	6	obtained	obtain	VERB
esrj-91195	404	7	with	with	ADP
esrj-91195	404	8	adaptive	adaptive	ADJ
esrj-91195	404	9	neural	neural	ADJ
esrj-91195	404	10	fuzzy	fuzzy	ADJ
esrj-91195	404	11	inference	inference	NOUN
esrj-91195	404	12	systems	system	NOUN
esrj-91195	404	13	and	and	CCONJ
esrj-91195	404	14	polynomial	polynomial	ADJ
esrj-91195	404	15	coefficients	coefficient	NOUN
esrj-91195	404	16	methods	method	NOUN
esrj-91195	404	17	.	.	PUNCT
esrj-91195	405	1	international	international	ADJ
esrj-91195	405	2	journal	journal	NOUN
esrj-91195	405	3	of	of	ADP
esrj-91195	405	4	the	the	DET
esrj-91195	405	5	physical	physical	ADJ
esrj-91195	405	6	sciences	sciences	PROPN
esrj-91195	405	7	,	,	PUNCT
esrj-91195	405	8	6(4	6(4	NOUN
esrj-91195	405	9	)	)	PUNCT
esrj-91195	405	10	,	,	PUNCT
esrj-91195	405	11	789	789	NUM
esrj-91195	405	12	-	-	SYM
esrj-91195	405	13	795	795	NUM
esrj-91195	405	14	.	.	PUNCT
esrj-91195	406	1	doi	doi	NOUN
esrj-91195	406	2	:	:	PUNCT
esrj-91195	407	1	https://doi	https://doi	PROPN
esrj-91195	407	2	.	.	PUNCT
esrj-91195	407	3	org/10.5897	org/10.5897	PROPN
esrj-91195	407	4	/	/	SYM
esrj-91195	407	5	ijps11.027	ijps11.027	NOUN
esrj-91195	407	6	tusat	tusat	PROPN
esrj-91195	407	7	,	,	PUNCT
esrj-91195	407	8	e.	e.	PROPN
esrj-91195	407	9	&	&	CCONJ
esrj-91195	407	10	mikailsoy	mikailsoy	PROPN
esrj-91195	407	11	,	,	PUNCT
esrj-91195	407	12	f.	f.	PROPN
esrj-91195	407	13	(	(	PUNCT
esrj-91195	407	14	2018	2018	NUM
esrj-91195	407	15	)	)	PUNCT
esrj-91195	407	16	.	.	PUNCT
esrj-91195	408	1	an	an	DET
esrj-91195	408	2	investigation	investigation	NOUN
esrj-91195	408	3	of	of	ADP
esrj-91195	408	4	the	the	DET
esrj-91195	408	5	criteria	criterion	NOUN
esrj-91195	408	6	used	use	VERB
esrj-91195	408	7	to	to	PART
esrj-91195	408	8	select	select	VERB
esrj-91195	408	9	the	the	DET
esrj-91195	408	10	polynomial	polynomial	ADJ
esrj-91195	408	11	models	model	NOUN
esrj-91195	408	12	employed	employ	VERB
esrj-91195	408	13	in	in	ADP
esrj-91195	408	14	local	local	ADJ
esrj-91195	408	15	gnss	gnss	NOUN
esrj-91195	408	16	/	/	SYM
esrj-91195	408	17	leveling	leveling	NOUN
esrj-91195	408	18	geoid	geoid	ADJ
esrj-91195	408	19	determination	determination	NOUN
esrj-91195	408	20	studies	study	NOUN
esrj-91195	408	21	.	.	PUNCT
esrj-91195	409	1	arabian	arabian	ADJ
esrj-91195	409	2	journal	journal	PROPN
esrj-91195	409	3	of	of	ADP
esrj-91195	409	4	geosciences	geoscience	NOUN
esrj-91195	409	5	,	,	PUNCT
esrj-91195	409	6	11(24	11(24	NUM
esrj-91195	409	7	)	)	PUNCT
esrj-91195	409	8	,	,	PUNCT
esrj-91195	409	9	801	801	NUM
esrj-91195	409	10	.	.	PUNCT
esrj-91195	410	1	doi	doi	NOUN
esrj-91195	410	2	:	:	PUNCT
esrj-91195	410	3	https://doi.org/10.1007/s12517-018-4176-0	https://doi.org/10.1007/s12517-018-4176-0	NUM
esrj-91195	410	4	veronez	veronez	NOUN
esrj-91195	410	5	,	,	PUNCT
esrj-91195	410	6	m.	m.	PROPN
esrj-91195	410	7	r.	r.	PROPN
esrj-91195	410	8	,	,	PUNCT
esrj-91195	410	9	florêncio	florêncio	PROPN
esrj-91195	410	10	de	de	PROPN
esrj-91195	410	11	souza	souza	PROPN
esrj-91195	410	12	,	,	PUNCT
esrj-91195	410	13	s.	s.	PROPN
esrj-91195	410	14	,	,	PUNCT
esrj-91195	410	15	matsuoka	matsuoka	NOUN
esrj-91195	410	16	,	,	PUNCT
esrj-91195	410	17	m.	m.	NOUN
esrj-91195	410	18	t.	t.	PROPN
esrj-91195	410	19	,	,	PUNCT
esrj-91195	410	20	reinhardt	reinhardt	PROPN
esrj-91195	410	21	,	,	PUNCT
esrj-91195	410	22	a.	a.	PROPN
esrj-91195	410	23	&	&	CCONJ
esrj-91195	410	24	macedônio	macedônio	PROPN
esrj-91195	410	25	da	da	PROPN
esrj-91195	410	26	silva	silva	PROPN
esrj-91195	410	27	,	,	PUNCT
esrj-91195	410	28	r.	r.	PROPN
esrj-91195	410	29	(	(	PUNCT
esrj-91195	410	30	2011	2011	NUM
esrj-91195	410	31	)	)	PUNCT
esrj-91195	410	32	.	.	PUNCT
esrj-91195	411	1	regional	regional	ADJ
esrj-91195	411	2	mapping	mapping	NOUN
esrj-91195	411	3	of	of	ADP
esrj-91195	411	4	the	the	DET
esrj-91195	411	5	geoid	geoid	NOUN
esrj-91195	411	6	using	use	VERB
esrj-91195	411	7	gnss	gnss	NOUN
esrj-91195	411	8	(	(	PUNCT
esrj-91195	411	9	gps	gps	PROPN
esrj-91195	411	10	)	)	PUNCT
esrj-91195	411	11	measurements	measurement	NOUN
esrj-91195	411	12	and	and	CCONJ
esrj-91195	411	13	an	an	DET
esrj-91195	411	14	artificial	artificial	ADJ
esrj-91195	411	15	neural	neural	ADJ
esrj-91195	411	16	network	network	NOUN
esrj-91195	411	17	.	.	PUNCT
esrj-91195	412	1	remote	remote	ADJ
esrj-91195	412	2	sensing	sensing	NOUN
esrj-91195	412	3	,	,	PUNCT
esrj-91195	412	4	3(4	3(4	NUM
esrj-91195	412	5	)	)	PUNCT
esrj-91195	412	6	,	,	PUNCT
esrj-91195	412	7	668	668	NUM
esrj-91195	412	8	-	-	SYM
esrj-91195	412	9	683	683	NUM
esrj-91195	412	10	.	.	PUNCT
esrj-91195	413	1	doi	doi	NOUN
esrj-91195	413	2	:	:	PUNCT
esrj-91195	413	3	https://doi.org/10.3390/rs3040668	https://doi.org/10.3390/rs3040668	X
esrj-91195	413	4	yanalak	yanalak	NOUN
esrj-91195	413	5	,	,	PUNCT
esrj-91195	413	6	m.	m.	NOUN
esrj-91195	413	7	&	&	CCONJ
esrj-91195	413	8	baykal	baykal	PROPN
esrj-91195	413	9	,	,	PUNCT
esrj-91195	413	10	o.	o.	PROPN
esrj-91195	413	11	(	(	PUNCT
esrj-91195	413	12	2001	2001	NUM
esrj-91195	413	13	)	)	PUNCT
esrj-91195	413	14	.	.	PUNCT
esrj-91195	414	1	transformation	transformation	NOUN
esrj-91195	414	2	of	of	ADP
esrj-91195	414	3	ellipsoidal	ellipsoidal	ADJ
esrj-91195	414	4	heights	height	NOUN
esrj-91195	414	5	to	to	ADP
esrj-91195	414	6	local	local	ADJ
esrj-91195	414	7	leveling	leveling	NOUN
esrj-91195	414	8	heights	height	NOUN
esrj-91195	414	9	.	.	PUNCT
esrj-91195	415	1	journal	journal	NOUN
esrj-91195	415	2	of	of	ADP
esrj-91195	415	3	surveying	surveying	NOUN
esrj-91195	415	4	engineering	engineering	NOUN
esrj-91195	415	5	,	,	PUNCT
esrj-91195	415	6	127(3	127(3	NUM
esrj-91195	415	7	)	)	PUNCT
esrj-91195	415	8	,	,	PUNCT
esrj-91195	415	9	90	90	NUM
esrj-91195	415	10	-	-	SYM
esrj-91195	415	11	103	103	NUM
esrj-91195	415	12	.	.	PUNCT
esrj-91195	416	1	doi	doi	NOUN
esrj-91195	416	2	:	:	PUNCT
esrj-91195	416	3	https://doi.org/10.1061/(asce)0733-9453(2001)127:3(90	https://doi.org/10.1061/(asce)0733-9453(2001)127:3(90	NUM
esrj-91195	416	4	)	)	PUNCT
esrj-91195	416	5	yılmaz	yılmaz	NOUN
esrj-91195	416	6	,	,	PUNCT
esrj-91195	416	7	m.	m.	NOUN
esrj-91195	416	8	(	(	PUNCT
esrj-91195	416	9	2010	2010	NUM
esrj-91195	416	10	)	)	PUNCT
esrj-91195	416	11	.	.	PUNCT
esrj-91195	417	1	adaptive	adaptive	ADJ
esrj-91195	417	2	network	network	NOUN
esrj-91195	417	3	based	base	VERB
esrj-91195	417	4	on	on	ADP
esrj-91195	417	5	fuzzy	fuzzy	ADJ
esrj-91195	417	6	inference	inference	NOUN
esrj-91195	417	7	system	system	NOUN
esrj-91195	417	8	estimates	estimate	NOUN
esrj-91195	417	9	of	of	ADP
esrj-91195	417	10	geoid	geoid	ADJ
esrj-91195	417	11	heights	height	NOUN
esrj-91195	417	12	interpolation	interpolation	NOUN
esrj-91195	417	13	.	.	PUNCT
esrj-91195	418	1	scientific	scientific	ADJ
esrj-91195	418	2	research	research	NOUN
esrj-91195	418	3	and	and	CCONJ
esrj-91195	418	4	essays	essay	NOUN
esrj-91195	418	5	,	,	PUNCT
esrj-91195	418	6	5(16	5(16	NUM
esrj-91195	418	7	)	)	PUNCT
esrj-91195	418	8	,	,	PUNCT
esrj-91195	418	9	2148	2148	NUM
esrj-91195	418	10	-	-	SYM
esrj-91195	418	11	2154	2154	NUM
esrj-91195	418	12	.	.	PUNCT
esrj-91195	419	1	doi	doi	NOUN
esrj-91195	419	2	:	:	PUNCT
esrj-91195	419	3	https://doi.org/10.5897/sre.9000130	https://doi.org/10.5897/sre.9000130	PROPN
esrj-91195	419	4	yιlmaz	yιlmaz	PROPN
esrj-91195	419	5	,	,	PUNCT
esrj-91195	419	6	m.	m.	NOUN
esrj-91195	419	7	&	&	CCONJ
esrj-91195	419	8	arslan	arslan	PROPN
esrj-91195	419	9	,	,	PUNCT
esrj-91195	419	10	e.	e.	PROPN
esrj-91195	419	11	(	(	PUNCT
esrj-91195	419	12	2008	2008	NUM
esrj-91195	419	13	)	)	PUNCT
esrj-91195	419	14	.	.	PUNCT
esrj-91195	420	1	effect	effect	NOUN
esrj-91195	420	2	of	of	ADP
esrj-91195	420	3	the	the	DET
esrj-91195	420	4	type	type	NOUN
esrj-91195	420	5	of	of	ADP
esrj-91195	420	6	membership	membership	NOUN
esrj-91195	420	7	function	function	NOUN
esrj-91195	420	8	on	on	ADP
esrj-91195	420	9	geoid	geoid	ADJ
esrj-91195	420	10	height	height	NOUN
esrj-91195	420	11	modelling	modelling	NOUN
esrj-91195	420	12	with	with	ADP
esrj-91195	420	13	fuzzy	fuzzy	ADJ
esrj-91195	420	14	logic	logic	NOUN
esrj-91195	420	15	.	.	PUNCT
esrj-91195	421	1	survey	survey	NOUN
esrj-91195	421	2	review	review	NOUN
esrj-91195	421	3	,	,	PUNCT
esrj-91195	421	4	40(310	40(310	NOUN
esrj-91195	421	5	)	)	PUNCT
esrj-91195	421	6	,	,	PUNCT
esrj-91195	421	7	379391	379391	NUM
esrj-91195	421	8	.	.	PUNCT
esrj-91195	422	1	doi	doi	NOUN
esrj-91195	422	2	:	:	PUNCT
esrj-91195	422	3	https://doi.org/10.1179/003962608x325439	https://doi.org/10.1179/003962608x325439	ADJ
esrj-91195	422	4	zaletnyik	zaletnyik	NOUN
esrj-91195	422	5	,	,	PUNCT
esrj-91195	422	6	p.	p.	NOUN
esrj-91195	422	7	,	,	PUNCT
esrj-91195	422	8	völgyesi	völgyesi	PROPN
esrj-91195	422	9	,	,	PUNCT
esrj-91195	422	10	l.	l.	PROPN
esrj-91195	422	11	&	&	CCONJ
esrj-91195	422	12	paláncz	paláncz	PROPN
esrj-91195	422	13	,	,	PUNCT
esrj-91195	422	14	b.	b.	PROPN
esrj-91195	422	15	(	(	PUNCT
esrj-91195	422	16	2004	2004	NUM
esrj-91195	422	17	)	)	PUNCT
esrj-91195	422	18	.	.	PUNCT
esrj-91195	423	1	approach	approach	NOUN
esrj-91195	423	2	of	of	ADP
esrj-91195	423	3	the	the	DET
esrj-91195	423	4	hungarian	hungarian	ADJ
esrj-91195	423	5	geoid	geoid	ADJ
esrj-91195	423	6	surface	surface	NOUN
esrj-91195	423	7	with	with	ADP
esrj-91195	423	8	sequence	sequence	NOUN
esrj-91195	423	9	of	of	ADP
esrj-91195	423	10	neural	neural	ADJ
esrj-91195	423	11	networks	network	NOUN
esrj-91195	423	12	,	,	PUNCT
esrj-91195	423	13	20th	20th	ADJ
esrj-91195	423	14	isprs	isprs	NOUN
esrj-91195	423	15	congress	congress	PROPN
esrj-91195	423	16	,	,	PUNCT
esrj-91195	423	17	istanbul	istanbul	PROPN
esrj-91195	423	18	,	,	PUNCT
esrj-91195	423	19	turkey	turkey	PROPN
esrj-91195	423	20	,	,	PUNCT
esrj-91195	423	21	119–122	119–122	NUM
esrj-91195	423	22	.	.	PUNCT
esrj-91195	424	1	zhan	zhan	PROPN
esrj-91195	424	2	-	-	PUNCT
esrj-91195	424	3	ji	ji	PROPN
esrj-91195	424	4	,	,	PUNCT
esrj-91195	424	5	y.	y.	PROPN
esrj-91195	424	6	&	&	CCONJ
esrj-91195	424	7	yong	yong	PROPN
esrj-91195	424	8	-	-	PUNCT
esrj-91195	424	9	qi	qi	PROPN
esrj-91195	424	10	,	,	PUNCT
esrj-91195	424	11	c.	c.	PROPN
esrj-91195	424	12	(	(	PUNCT
esrj-91195	424	13	1999	1999	NUM
esrj-91195	424	14	)	)	PUNCT
esrj-91195	424	15	.	.	PUNCT
esrj-91195	425	1	determination	determination	NOUN
esrj-91195	425	2	of	of	ADP
esrj-91195	425	3	local	local	ADJ
esrj-91195	425	4	geoid	geoid	NOUN
esrj-91195	425	5	with	with	ADP
esrj-91195	425	6	geometric	geometric	ADJ
esrj-91195	425	7	method	method	NOUN
esrj-91195	425	8	:	:	PUNCT
esrj-91195	425	9	case	case	NOUN
esrj-91195	425	10	study	study	NOUN
esrj-91195	425	11	.	.	PUNCT
esrj-91195	426	1	journal	journal	NOUN
esrj-91195	426	2	of	of	ADP
esrj-91195	426	3	surveying	surveying	NOUN
esrj-91195	426	4	engineering	engineering	NOUN
esrj-91195	426	5	,	,	PUNCT
esrj-91195	426	6	125(3	125(3	NUM
esrj-91195	426	7	)	)	PUNCT
esrj-91195	426	8	,	,	PUNCT
esrj-91195	426	9	136146	136146	NUM
esrj-91195	426	10	.	.	PUNCT
esrj-91195	427	1	doi	doi	NOUN
esrj-91195	427	2	:	:	PUNCT
esrj-91195	427	3	https://doi.org/10.1061/(asce)0733-9453(1999)125:3(136	https://doi.org/10.1061/(asce)0733-9453(1999)125:3(136	PROPN
esrj-91195	427	4	)	)	PUNCT
esrj-91195	427	5	zhong	zhong	PROPN
esrj-91195	427	6	,	,	PUNCT
esrj-91195	427	7	d.	d.	PROPN
esrj-91195	427	8	(	(	PUNCT
esrj-91195	427	9	1997	1997	NUM
esrj-91195	427	10	)	)	PUNCT
esrj-91195	427	11	.	.	PUNCT
esrj-91195	428	1	robust	robust	ADJ
esrj-91195	428	2	estimation	estimation	NOUN
esrj-91195	428	3	and	and	CCONJ
esrj-91195	428	4	optimal	optimal	ADJ
esrj-91195	428	5	selection	selection	NOUN
esrj-91195	428	6	of	of	ADP
esrj-91195	428	7	polynomial	polynomial	ADJ
esrj-91195	428	8	parameters	parameter	NOUN
esrj-91195	428	9	for	for	ADP
esrj-91195	428	10	the	the	DET
esrj-91195	428	11	interpolation	interpolation	NOUN
esrj-91195	428	12	of	of	ADP
esrj-91195	428	13	gps	gps	PROPN
esrj-91195	428	14	geoid	geoid	ADJ
esrj-91195	428	15	heights	height	NOUN
esrj-91195	428	16	.	.	PUNCT
esrj-91195	429	1	journal	journal	PROPN
esrj-91195	429	2	of	of	ADP
esrj-91195	429	3	geodesy	geodesy	PROPN
esrj-91195	429	4	,	,	PUNCT
esrj-91195	429	5	71(9	71(9	NOUN
esrj-91195	429	6	)	)	PUNCT
esrj-91195	429	7	,	,	PUNCT
esrj-91195	429	8	552	552	NUM
esrj-91195	429	9	-	-	SYM
esrj-91195	429	10	561	561	NUM
esrj-91195	429	11	.	.	PUNCT
esrj-91195	430	1	doi	doi	NOUN
esrj-91195	430	2	:	:	PUNCT
esrj-91195	430	3	https://doi.org/10.1007/s001900050123	https://doi.org/10.1007/s001900050123	PROPN
esrj-91195	430	4	ziggah	ziggah	NOUN
esrj-91195	430	5	,	,	PUNCT
esrj-91195	430	6	y.	y.	PROPN
esrj-91195	430	7	y.	y.	PROPN
esrj-91195	430	8	,	,	PUNCT
esrj-91195	430	9	youjian	youjian	PROPN
esrj-91195	430	10	,	,	PUNCT
esrj-91195	430	11	h.	h.	PROPN
esrj-91195	430	12	,	,	PUNCT
esrj-91195	430	13	laari	laari	PROPN
esrj-91195	430	14	,	,	PUNCT
esrj-91195	430	15	p.	p.	PROPN
esrj-91195	430	16	b.	b.	PROPN
esrj-91195	430	17	&	&	CCONJ
esrj-91195	430	18	hui	hui	PROPN
esrj-91195	430	19	,	,	PUNCT
esrj-91195	430	20	z.	z.	PROPN
esrj-91195	430	21	(	(	PUNCT
esrj-91195	430	22	2017	2017	NUM
esrj-91195	430	23	)	)	PUNCT
esrj-91195	430	24	.	.	PUNCT
esrj-91195	431	1	novel	novel	ADJ
esrj-91195	431	2	approach	approach	NOUN
esrj-91195	431	3	to	to	PART
esrj-91195	431	4	improve	improve	VERB
esrj-91195	431	5	geocentric	geocentric	ADJ
esrj-91195	431	6	translation	translation	NOUN
esrj-91195	431	7	model	model	NOUN
esrj-91195	431	8	performance	performance	NOUN
esrj-91195	431	9	using	use	VERB
esrj-91195	431	10	artificial	artificial	ADJ
esrj-91195	431	11	neural	neural	ADJ
esrj-91195	431	12	network	network	NOUN
esrj-91195	431	13	technology	technology	NOUN
esrj-91195	431	14	.	.	PUNCT
esrj-91195	432	1	boletim	boletim	PROPN
esrj-91195	432	2	de	de	PROPN
esrj-91195	432	3	ciências	ciências	PROPN
esrj-91195	432	4	geodésicas	geodésicas	NOUN
esrj-91195	432	5	,	,	PUNCT
esrj-91195	432	6	23(1	23(1	NUM
esrj-91195	432	7	)	)	PUNCT
esrj-91195	432	8	,	,	PUNCT
esrj-91195	432	9	213	213	NUM
esrj-91195	432	10	-	-	SYM
esrj-91195	432	11	233	233	NUM
esrj-91195	432	12	.	.	PUNCT
esrj-91195	433	1	doi	doi	NOUN
esrj-91195	433	2	:	:	PUNCT
esrj-91195	433	3	http://dx.doi.org/10.1590/s1982-21702017000100014	http://dx.doi.org/10.1590/s1982-21702017000100014	NOUN
esrj-91195	433	4	ziggah	ziggah	NOUN
esrj-91195	433	5	,	,	PUNCT
esrj-91195	433	6	y.	y.	PROPN
esrj-91195	433	7	y.	y.	PROPN
esrj-91195	433	8	,	,	PUNCT
esrj-91195	433	9	youjian	youjian	PROPN
esrj-91195	433	10	,	,	PUNCT
esrj-91195	433	11	h.	h.	PROPN
esrj-91195	433	12	,	,	PUNCT
esrj-91195	433	13	tierra	tierra	PROPN
esrj-91195	433	14	,	,	PUNCT
esrj-91195	433	15	a.	a.	PROPN
esrj-91195	433	16	r.	r.	PROPN
esrj-91195	433	17	&	&	CCONJ
esrj-91195	433	18	laari	laari	PROPN
esrj-91195	433	19	,	,	PUNCT
esrj-91195	433	20	p.	p.	PROPN
esrj-91195	433	21	b.	b.	PROPN
esrj-91195	434	1	(	(	PUNCT
esrj-91195	434	2	2019	2019	NUM
esrj-91195	434	3	)	)	PUNCT
esrj-91195	434	4	.	.	PUNCT
esrj-91195	435	1	coordinate	coordinate	NOUN
esrj-91195	435	2	transformation	transformation	NOUN
esrj-91195	435	3	between	between	ADP
esrj-91195	435	4	global	global	ADJ
esrj-91195	435	5	and	and	CCONJ
esrj-91195	435	6	local	local	ADJ
esrj-91195	435	7	data	datum	NOUN
esrj-91195	435	8	based	base	VERB
esrj-91195	435	9	on	on	ADP
esrj-91195	435	10	artificial	artificial	ADJ
esrj-91195	435	11	neural	neural	ADJ
esrj-91195	435	12	network	network	NOUN
esrj-91195	435	13	with	with	ADP
esrj-91195	435	14	k	k	ADJ
esrj-91195	435	15	-	-	ADJ
esrj-91195	435	16	fold	fold	ADJ
esrj-91195	435	17	cross	cross	NOUN
esrj-91195	435	18	-	-	NOUN
esrj-91195	435	19	validation	validation	NOUN
esrj-91195	435	20	in	in	ADP
esrj-91195	435	21	ghana	ghana	PROPN
esrj-91195	435	22	.	.	PUNCT
esrj-91195	436	1	earth	earth	PROPN
esrj-91195	436	2	sciences	sciences	PROPN
esrj-91195	436	3	research	research	PROPN
esrj-91195	436	4	journal	journal	PROPN
esrj-91195	436	5	,	,	PUNCT
esrj-91195	436	6	23(1	23(1	NUM
esrj-91195	436	7	)	)	PUNCT
esrj-91195	436	8	,	,	PUNCT
esrj-91195	436	9	67	67	NUM
esrj-91195	436	10	-	-	SYM
esrj-91195	436	11	77	77	NUM
esrj-91195	436	12	.	.	PUNCT
esrj-91195	437	1	doi	doi	NOUN
esrj-91195	437	2	:	:	PUNCT
esrj-91195	437	3	https://doi.org/10.15446/esrj.v23n1.63860	https://doi.org/10.15446/esrj.v23n1.63860	X
esrj-91195	437	4	https://doi.org/10.1016/j.ecolmodel.2009.01.004	https://doi.org/10.1016/j.ecolmodel.2009.01.004	X
esrj-91195	437	5	https://doi.org/10.1016/j.ecolmodel.2009.01.004	https://doi.org/10.1016/j.ecolmodel.2009.01.004	PROPN
esrj-91195	437	6	https://doi.org/10.1016/s0893-6080(09)80013-0	https://doi.org/10.1016/s0893-6080(09)80013-0	NOUN
esrj-91195	437	7	https://doi.org/10.1016/s0893-6080(09)80013-0	https://doi.org/10.1016/s0893-6080(09)80013-0	NOUN
esrj-91195	437	8	https://doi.org/10.1179/sre.2006.38.300.513	https://doi.org/10.1179/sre.2006.38.300.513	ADJ
esrj-91195	437	9	https://doi.org/10.1179/sre.2006.38.300.513	https://doi.org/10.1179/sre.2006.38.300.513	NOUN
esrj-91195	437	10	https://doi.org/10.5897/ijps11.027	https://doi.org/10.5897/ijps11.027	NUM
esrj-91195	437	11	https://doi.org/10.5897/ijps11.027	https://doi.org/10.5897/ijps11.027	NUM
esrj-91195	437	12	https://doi.org/10.1007/s12517-018-4176-0	https://doi.org/10.1007/s12517-018-4176-0	NUM
esrj-91195	437	13	https://doi.org/10.3390/rs3040668	https://doi.org/10.3390/rs3040668	NOUN
esrj-91195	437	14	https://doi.org/10.1061/(asce)0733-9453(2001)127:3(90	https://doi.org/10.1061/(asce)0733-9453(2001)127:3(90	NOUN
esrj-91195	437	15	)	)	PUNCT
esrj-91195	437	16	https://doi.org/10.5897/sre.9000130	https://doi.org/10.5897/sre.9000130	PROPN
esrj-91195	437	17	https://doi.org/10.1179/003962608x325439	https://doi.org/10.1179/003962608x325439	ADJ
esrj-91195	437	18	https://doi.org/10.1061/(asce)0733-9453(1999)125:3(136	https://doi.org/10.1061/(asce)0733-9453(1999)125:3(136	NOUN
esrj-91195	437	19	)	)	PUNCT
esrj-91195	437	20	https://doi.org/10.1007/s001900050123	https://doi.org/10.1007/s001900050123	VERB
esrj-91195	437	21	http://dx.doi.org/10.1590/s1982-21702017000100014	http://dx.doi.org/10.1590/s1982-21702017000100014	NOUN
esrj-91195	437	22	https://doi.org/10.15446/esrj.v23n1.63860	https://doi.org/10.15446/esrj.v23n1.63860	NOUN