id	sid	tid	token	lemma	pos
cuesj-22	1	1	42	42	NUM
cuesj-22	1	2	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	1	3	cuesj	cuesj	NOUN
cuesj-22	1	4	2019	2019	NUM
cuesj-22	1	5	,	,	PUNCT
cuesj-22	1	6	3	3	NUM
cuesj-22	1	7	(	(	PUNCT
cuesj-22	1	8	1	1	NUM
cuesj-22	1	9	):	):	PUNCT
cuesj-22	1	10	42	42	NUM
cuesj-22	1	11	-	-	SYM
cuesj-22	1	12	49	49	NUM
cuesj-22	1	13	research	research	NOUN
cuesj-22	1	14	article	article	NOUN
cuesj-22	1	15	prediction	prediction	VERB
cuesj-22	1	16	the	the	DET
cuesj-22	1	17	groundwater	groundwater	NOUN
cuesj-22	1	18	depth	depth	NOUN
cuesj-22	1	19	using	use	VERB
cuesj-22	1	20	kriging	krige	VERB
cuesj-22	1	21	method	method	NOUN
cuesj-22	1	22	and	and	CCONJ
cuesj-22	1	23	bayesian	bayesian	NOUN
cuesj-22	1	24	kalman	kalman	NOUN
cuesj-22	1	25	filter	filter	NOUN
cuesj-22	1	26	approach	approach	NOUN
cuesj-22	1	27	in	in	ADP
cuesj-22	1	28	erbil	erbil	PROPN
cuesj-22	1	29	governorate	governorate	PROPN
cuesj-22	1	30	kurdistan	kurdistan	PROPN
cuesj-22	1	31	ibrahim	ibrahim	PROPN
cuesj-22	1	32	mawlood	mawlood	PROPN
cuesj-22	1	33	,	,	PUNCT
cuesj-22	1	34	paree	paree	PROPN
cuesj-22	1	35	khan	khan	PROPN
cuesj-22	1	36	aabdulla	aabdulla	PROPN
cuesj-22	1	37	omer	omer	PROPN
cuesj-22	1	38	department	department	PROPN
cuesj-22	1	39	of	of	ADP
cuesj-22	1	40	statistics	statistic	NOUN
cuesj-22	1	41	,	,	PUNCT
cuesj-22	1	42	college	college	NOUN
cuesj-22	1	43	of	of	ADP
cuesj-22	1	44	administration	administration	NOUN
cuesj-22	1	45	and	and	CCONJ
cuesj-22	1	46	economics	economic	NOUN
cuesj-22	1	47	,	,	PUNCT
cuesj-22	1	48	salahaddin	salahaddin	VERB
cuesj-22	1	49	university	university	NOUN
cuesj-22	1	50	-	-	PUNCT
cuesj-22	1	51	erbil	erbil	PROPN
cuesj-22	1	52	,	,	PUNCT
cuesj-22	1	53	kurdistan	kurdistan	PROPN
cuesj-22	1	54	region	region	PROPN
cuesj-22	1	55	f.r	f.r	PROPN
cuesj-22	1	56	.	.	PROPN
cuesj-22	1	57	iraq	iraq	PROPN
cuesj-22	1	58	abstract	abstract	ADJ
cuesj-22	1	59	the	the	DET
cuesj-22	1	60	aim	aim	NOUN
cuesj-22	1	61	of	of	ADP
cuesj-22	1	62	this	this	DET
cuesj-22	1	63	research	research	NOUN
cuesj-22	1	64	is	be	AUX
cuesj-22	1	65	using	use	VERB
cuesj-22	1	66	the	the	DET
cuesj-22	1	67	kriging	krige	VERB
cuesj-22	1	68	method	method	NOUN
cuesj-22	1	69	as	as	ADP
cuesj-22	1	70	one	one	NUM
cuesj-22	1	71	of	of	ADP
cuesj-22	1	72	geostatistics	geostatistic	NOUN
cuesj-22	1	73	interpolation	interpolation	NOUN
cuesj-22	1	74	methods	method	NOUN
cuesj-22	1	75	on	on	ADP
cuesj-22	1	76	the	the	DET
cuesj-22	1	77	measured	measure	VERB
cuesj-22	1	78	value	value	NOUN
cuesj-22	1	79	of	of	ADP
cuesj-22	1	80	the	the	DET
cuesj-22	1	81	specific	specific	ADJ
cuesj-22	1	82	part	part	NOUN
cuesj-22	1	83	and	and	CCONJ
cuesj-22	1	84	bayesian	bayesian	NOUN
cuesj-22	1	85	kalman	kalman	NOUN
cuesj-22	1	86	filter	filter	NOUN
cuesj-22	1	87	to	to	ADP
cuesj-22	1	88	identifying	identify	VERB
cuesj-22	1	89	the	the	DET
cuesj-22	1	90	depth	depth	NOUN
cuesj-22	1	91	of	of	ADP
cuesj-22	1	92	groundwater	groundwater	NOUN
cuesj-22	1	93	in	in	ADP
cuesj-22	1	94	erbil	erbil	PROPN
cuesj-22	1	95	.	.	PUNCT
cuesj-22	2	1	geostatistics	geostatistics	PROPN
cuesj-22	2	2	is	be	AUX
cuesj-22	2	3	a	a	DET
cuesj-22	2	4	tool	tool	NOUN
cuesj-22	2	5	which	which	PRON
cuesj-22	2	6	is	be	AUX
cuesj-22	2	7	developed	develop	VERB
cuesj-22	2	8	for	for	ADP
cuesj-22	2	9	statistical	statistical	ADJ
cuesj-22	2	10	analysis	analysis	NOUN
cuesj-22	2	11	of	of	ADP
cuesj-22	2	12	any	any	DET
cuesj-22	2	13	continuous	continuous	ADJ
cuesj-22	2	14	data	datum	NOUN
cuesj-22	2	15	that	that	PRON
cuesj-22	2	16	can	can	AUX
cuesj-22	2	17	be	be	AUX
cuesj-22	2	18	measured	measure	VERB
cuesj-22	2	19	at	at	ADP
cuesj-22	2	20	any	any	DET
cuesj-22	2	21	location	location	NOUN
cuesj-22	2	22	in	in	ADP
cuesj-22	2	23	the	the	DET
cuesj-22	2	24	space	space	NOUN
cuesj-22	2	25	.	.	PUNCT
cuesj-22	3	1	the	the	DET
cuesj-22	3	2	kalman	kalman	PROPN
cuesj-22	3	3	filter	filter	NOUN
cuesj-22	3	4	is	be	AUX
cuesj-22	3	5	the	the	DET
cuesj-22	3	6	bayesian	bayesian	NOUN
cuesj-22	3	7	optimum	optimum	ADJ
cuesj-22	3	8	solution	solution	NOUN
cuesj-22	3	9	to	to	ADP
cuesj-22	3	10	the	the	DET
cuesj-22	3	11	problem	problem	NOUN
cuesj-22	3	12	of	of	ADP
cuesj-22	3	13	estimating	estimate	VERB
cuesj-22	3	14	the	the	DET
cuesj-22	3	15	unknown	unknown	ADJ
cuesj-22	3	16	state	state	NOUN
cuesj-22	3	17	of	of	ADP
cuesj-22	3	18	a	a	DET
cuesj-22	3	19	dynamic	dynamic	ADJ
cuesj-22	3	20	system	system	NOUN
cuesj-22	3	21	from	from	ADP
cuesj-22	3	22	noisy	noisy	ADJ
cuesj-22	3	23	data	datum	NOUN
cuesj-22	3	24	and	and	CCONJ
cuesj-22	3	25	is	be	AUX
cuesj-22	3	26	more	more	ADV
cuesj-22	3	27	efficient	efficient	ADJ
cuesj-22	3	28	than	than	ADP
cuesj-22	3	29	computing	compute	VERB
cuesj-22	3	30	the	the	DET
cuesj-22	3	31	estimate	estimate	NOUN
cuesj-22	3	32	directly	directly	ADV
cuesj-22	3	33	from	from	ADP
cuesj-22	3	34	the	the	DET
cuesj-22	3	35	entire	entire	ADJ
cuesj-22	3	36	past	past	ADJ
cuesj-22	3	37	observed	observe	VERB
cuesj-22	3	38	data	datum	NOUN
cuesj-22	3	39	.	.	PUNCT
cuesj-22	4	1	the	the	DET
cuesj-22	4	2	main	main	ADJ
cuesj-22	4	3	goal	goal	NOUN
cuesj-22	4	4	of	of	ADP
cuesj-22	4	5	this	this	DET
cuesj-22	4	6	work	work	NOUN
cuesj-22	4	7	is	be	AUX
cuesj-22	4	8	to	to	PART
cuesj-22	4	9	predict	predict	VERB
cuesj-22	4	10	anew	anew	ADJ
cuesj-22	4	11	value	value	NOUN
cuesj-22	4	12	at	at	ADP
cuesj-22	4	13	the	the	DET
cuesj-22	4	14	unmeasured	unmeasured	ADJ
cuesj-22	4	15	location	location	NOUN
cuesj-22	4	16	by	by	ADP
cuesj-22	4	17	kriging	krige	VERB
cuesj-22	4	18	method	method	NOUN
cuesj-22	4	19	and	and	CCONJ
cuesj-22	4	20	bayesian	bayesian	NOUN
cuesj-22	4	21	kalman	kalman	NOUN
cuesj-22	4	22	filter	filter	NOUN
cuesj-22	4	23	and	and	CCONJ
cuesj-22	4	24	compare	compare	VERB
cuesj-22	4	25	these	these	DET
cuesj-22	4	26	two	two	NUM
cuesj-22	4	27	methods	method	NOUN
cuesj-22	4	28	.	.	PUNCT
cuesj-22	5	1	the	the	DET
cuesj-22	5	2	dataset	dataset	NOUN
cuesj-22	5	3	is	be	AUX
cuesj-22	5	4	the	the	DET
cuesj-22	5	5	observed	observe	VERB
cuesj-22	5	6	values	value	NOUN
cuesj-22	5	7	of	of	ADP
cuesj-22	5	8	the	the	DET
cuesj-22	5	9	(	(	PUNCT
cuesj-22	5	10	295	295	NUM
cuesj-22	5	11	)	)	PUNCT
cuesj-22	5	12	wells	well	NOUN
cuesj-22	5	13	that	that	PRON
cuesj-22	5	14	had	have	AUX
cuesj-22	5	15	been	be	AUX
cuesj-22	5	16	taken	take	VERB
cuesj-22	5	17	from	from	ADP
cuesj-22	5	18	a	a	DET
cuesj-22	5	19	known	know	VERB
cuesj-22	5	20	specific	specific	ADJ
cuesj-22	5	21	place	place	NOUN
cuesj-22	5	22	which	which	PRON
cuesj-22	5	23	called	call	VERB
cuesj-22	5	24	shaqlawa	shaqlawa	NOUN
cuesj-22	5	25	–	–	PUNCT
cuesj-22	5	26	in	in	ADP
cuesj-22	5	27	erbil	erbil	PROPN
cuesj-22	5	28	governorate	governorate	PROPN
cuesj-22	5	29	.	.	PUNCT
cuesj-22	6	1	the	the	DET
cuesj-22	6	2	comparison	comparison	NOUN
cuesj-22	6	3	was	be	AUX
cuesj-22	6	4	done	do	VERB
cuesj-22	6	5	by	by	ADP
cuesj-22	6	6	calculating	calculate	VERB
cuesj-22	6	7	mean	mean	NOUN
cuesj-22	6	8	absolute	absolute	ADJ
cuesj-22	6	9	error	error	NOUN
cuesj-22	6	10	(	(	PUNCT
cuesj-22	6	11	mae	mae	PROPN
cuesj-22	6	12	)	)	PUNCT
cuesj-22	6	13	and	and	CCONJ
cuesj-22	6	14	root	root	NOUN
cuesj-22	6	15	mean	mean	ADJ
cuesj-22	6	16	square	square	ADJ
cuesj-22	6	17	error	error	NOUN
cuesj-22	6	18	(	(	PUNCT
cuesj-22	6	19	rmse	rmse	NOUN
cuesj-22	6	20	)	)	PUNCT
cuesj-22	6	21	for	for	ADP
cuesj-22	6	22	the	the	DET
cuesj-22	6	23	value	value	NOUN
cuesj-22	6	24	of	of	ADP
cuesj-22	6	25	the	the	DET
cuesj-22	6	26	depth	depth	NOUN
cuesj-22	6	27	of	of	ADP
cuesj-22	6	28	groundwater	groundwater	NOUN
cuesj-22	6	29	in	in	ADP
cuesj-22	6	30	the	the	DET
cuesj-22	6	31	eara	eara	NOUN
cuesj-22	6	32	of	of	ADP
cuesj-22	6	33	the	the	DET
cuesj-22	6	34	study	study	NOUN
cuesj-22	6	35	.	.	PUNCT
cuesj-22	7	1	the	the	DET
cuesj-22	7	2	values	value	NOUN
cuesj-22	7	3	of	of	ADP
cuesj-22	7	4	(	(	PUNCT
cuesj-22	7	5	mae	mae	PROPN
cuesj-22	7	6	and	and	CCONJ
cuesj-22	7	7	rmse	rmse	NOUN
cuesj-22	7	8	)	)	PUNCT
cuesj-22	7	9	of	of	ADP
cuesj-22	7	10	each	each	DET
cuesj-22	7	11	models	model	NOUN
cuesj-22	7	12	are	be	AUX
cuesj-22	7	13	compared	compare	VERB
cuesj-22	7	14	and	and	CCONJ
cuesj-22	7	15	the	the	DET
cuesj-22	7	16	smaller	small	ADJ
cuesj-22	7	17	values	value	NOUN
cuesj-22	7	18	of	of	ADP
cuesj-22	7	19	them	they	PRON
cuesj-22	7	20	are	be	AUX
cuesj-22	7	21	the	the	DET
cuesj-22	7	22	better	well	ADJ
cuesj-22	7	23	interpolation	interpolation	NOUN
cuesj-22	7	24	as	as	SCONJ
cuesj-22	7	25	it	it	PRON
cuesj-22	7	26	shown	show	VERB
cuesj-22	7	27	in	in	ADP
cuesj-22	7	28	analyzing	analyze	VERB
cuesj-22	7	29	to	to	PART
cuesj-22	7	30	evaluate	evaluate	VERB
cuesj-22	7	31	the	the	DET
cuesj-22	7	32	precision	precision	NOUN
cuesj-22	7	33	of	of	ADP
cuesj-22	7	34	the	the	DET
cuesj-22	7	35	prediction	prediction	NOUN
cuesj-22	7	36	.	.	PUNCT
cuesj-22	8	1	keywords	keyword	NOUN
cuesj-22	8	2	:	:	PUNCT
cuesj-22	8	3	bayesian	bayesian	NOUN
cuesj-22	8	4	estimation	estimation	NOUN
cuesj-22	8	5	,	,	PUNCT
cuesj-22	8	6	covariance	covariance	NOUN
cuesj-22	8	7	function	function	NOUN
cuesj-22	8	8	,	,	PUNCT
cuesj-22	8	9	gaussian	gaussian	ADJ
cuesj-22	8	10	random	random	ADJ
cuesj-22	8	11	field	field	NOUN
cuesj-22	8	12	,	,	PUNCT
cuesj-22	8	13	groundwater	groundwater	NOUN
cuesj-22	8	14	-	-	PUNCT
cuesj-22	8	15	surface	surface	NOUN
cuesj-22	8	16	interpolation	interpolation	NOUN
cuesj-22	8	17	,	,	PUNCT
cuesj-22	8	18	kalman	kalman	NOUN
cuesj-22	8	19	filter	filter	PROPN
cuesj-22	8	20	,	,	PUNCT
cuesj-22	8	21	kriging	krige	VERB
cuesj-22	8	22	interpolation	interpolation	NOUN
cuesj-22	8	23	method	method	NOUN
cuesj-22	8	24	introduction	introduction	NOUN
cuesj-22	8	25	the	the	DET
cuesj-22	8	26	kriging	kriging	NOUN
cuesj-22	8	27	is	be	AUX
cuesj-22	8	28	a	a	DET
cuesj-22	8	29	spatial	spatial	ADJ
cuesj-22	8	30	interpolation	interpolation	NOUN
cuesj-22	8	31	geostatistical	geostatistical	ADJ
cuesj-22	8	32	method	method	NOUN
cuesj-22	8	33	used	use	VERB
cuesj-22	8	34	for	for	ADP
cuesj-22	8	35	the	the	DET
cuesj-22	8	36	first	first	ADJ
cuesj-22	8	37	time	time	NOUN
cuesj-22	8	38	in	in	ADP
cuesj-22	8	39	meteorology	meteorology	NOUN
cuesj-22	8	40	,	,	PUNCT
cuesj-22	8	41	geology	geology	NOUN
cuesj-22	8	42	,	,	PUNCT
cuesj-22	8	43	environmental	environmental	ADJ
cuesj-22	8	44	sciences	science	NOUN
cuesj-22	8	45	,	,	PUNCT
cuesj-22	8	46	agriculture	agriculture	NOUN
cuesj-22	8	47	,	,	PUNCT
cuesj-22	8	48	and	and	CCONJ
cuesj-22	8	49	others	other	NOUN
cuesj-22	8	50	fields	field	NOUN
cuesj-22	8	51	.	.	PUNCT
cuesj-22	9	1	this	this	DET
cuesj-22	9	2	method	method	NOUN
cuesj-22	9	3	is	be	AUX
cuesj-22	9	4	used	use	VERB
cuesj-22	9	5	to	to	PART
cuesj-22	9	6	find	find	VERB
cuesj-22	9	7	the	the	DET
cuesj-22	9	8	best	good	ADJ
cuesj-22	9	9	estimator	estimator	NOUN
cuesj-22	9	10	under	under	ADP
cuesj-22	9	11	the	the	DET
cuesj-22	9	12	assumption	assumption	NOUN
cuesj-22	9	13	of	of	ADP
cuesj-22	9	14	the	the	DET
cuesj-22	9	15	second	second	ADJ
cuesj-22	9	16	order	order	NOUN
cuesj-22	9	17	stationarity	stationarity	NOUN
cuesj-22	9	18	.	.	PUNCT
cuesj-22	10	1	geostatistics	geostatistics	PROPN
cuesj-22	10	2	is	be	AUX
cuesj-22	10	3	a	a	DET
cuesj-22	10	4	set	set	NOUN
cuesj-22	10	5	of	of	ADP
cuesj-22	10	6	tools	tool	NOUN
cuesj-22	10	7	and	and	CCONJ
cuesj-22	10	8	models	model	NOUN
cuesj-22	10	9	that	that	PRON
cuesj-22	10	10	are	be	AUX
cuesj-22	10	11	developed	develop	VERB
cuesj-22	10	12	for	for	ADP
cuesj-22	10	13	statistical	statistical	ADJ
cuesj-22	10	14	analysis	analysis	NOUN
cuesj-22	10	15	of	of	ADP
cuesj-22	10	16	any	any	DET
cuesj-22	10	17	continuous	continuous	ADJ
cuesj-22	10	18	data	datum	NOUN
cuesj-22	10	19	that	that	PRON
cuesj-22	10	20	can	can	AUX
cuesj-22	10	21	be	be	AUX
cuesj-22	10	22	measured	measure	VERB
cuesj-22	10	23	at	at	ADP
cuesj-22	10	24	any	any	DET
cuesj-22	10	25	location	location	NOUN
cuesj-22	10	26	in	in	ADP
cuesj-22	10	27	the	the	DET
cuesj-22	10	28	space	space	NOUN
cuesj-22	10	29	.	.	PUNCT
cuesj-22	11	1	verify	verify	VERB
cuesj-22	11	2	three	three	NUM
cuesj-22	11	3	data	datum	NOUN
cuesj-22	11	4	feature	feature	NOUN
cuesj-22	11	5	in	in	ADP
cuesj-22	11	6	statistical	statistical	ADJ
cuesj-22	11	7	continuous	continuous	ADJ
cuesj-22	11	8	data	datum	NOUN
cuesj-22	11	9	analysis	analysis	NOUN
cuesj-22	11	10	:	:	PUNCT
cuesj-22	11	11	dependency	dependency	NOUN
cuesj-22	11	12	,	,	PUNCT
cuesj-22	11	13	stationery	stationery	NOUN
cuesj-22	11	14	,	,	PUNCT
cuesj-22	11	15	and	and	CCONJ
cuesj-22	11	16	distribution	distribution	NOUN
cuesj-22	11	17	.	.	PUNCT
cuesj-22	12	1	with	with	ADP
cuesj-22	12	2	these	these	DET
cuesj-22	12	3	features	feature	NOUN
cuesj-22	12	4	,	,	PUNCT
cuesj-22	12	5	you	you	PRON
cuesj-22	12	6	can	can	AUX
cuesj-22	12	7	proceed	proceed	VERB
cuesj-22	12	8	to	to	ADP
cuesj-22	12	9	the	the	DET
cuesj-22	12	10	modeling	modeling	NOUN
cuesj-22	12	11	of	of	ADP
cuesj-22	12	12	the	the	DET
cuesj-22	12	13	geostatistical	geostatistical	ADJ
cuesj-22	12	14	data	datum	NOUN
cuesj-22	12	15	analysis	analysis	NOUN
cuesj-22	12	16	like	like	ADP
cuesj-22	12	17	simple	simple	ADJ
cuesj-22	12	18	kriging	kriging	NOUN
cuesj-22	12	19	.	.	PUNCT
cuesj-22	13	1	in	in	ADP
cuesj-22	13	2	addition	addition	NOUN
cuesj-22	13	3	,	,	PUNCT
cuesj-22	13	4	the	the	DET
cuesj-22	13	5	goal	goal	NOUN
cuesj-22	13	6	of	of	ADP
cuesj-22	13	7	this	this	DET
cuesj-22	13	8	work	work	NOUN
cuesj-22	13	9	is	be	AUX
cuesj-22	13	10	to	to	PART
cuesj-22	13	11	predict	predict	VERB
cuesj-22	13	12	a	a	DET
cuesj-22	13	13	new	new	ADJ
cuesj-22	13	14	value	value	NOUN
cuesj-22	13	15	at	at	ADP
cuesj-22	13	16	the	the	DET
cuesj-22	13	17	unmeasured	unmeasured	ADJ
cuesj-22	13	18	location	location	NOUN
cuesj-22	13	19	by	by	ADP
cuesj-22	13	20	gaussian	gaussian	ADJ
cuesj-22	13	21	semivariogram	semivariogram	NOUN
cuesj-22	13	22	function	function	NOUN
cuesj-22	13	23	(	(	PUNCT
cuesj-22	13	24	model	model	NOUN
cuesj-22	13	25	)	)	PUNCT
cuesj-22	13	26	and	and	CCONJ
cuesj-22	13	27	compare	compare	VERB
cuesj-22	13	28	the	the	DET
cuesj-22	13	29	results	result	NOUN
cuesj-22	13	30	of	of	ADP
cuesj-22	13	31	this	this	DET
cuesj-22	13	32	model	model	NOUN
cuesj-22	13	33	based	base	VERB
cuesj-22	13	34	on	on	ADP
cuesj-22	13	35	the	the	DET
cuesj-22	13	36	simple	simple	ADJ
cuesj-22	13	37	kriging	kriging	NOUN
cuesj-22	13	38	method	method	NOUN
cuesj-22	13	39	and	and	CCONJ
cuesj-22	13	40	understanding	understand	VERB
cuesj-22	13	41	their	their	PRON
cuesj-22	13	42	spatial	spatial	ADJ
cuesj-22	13	43	variability	variability	NOUN
cuesj-22	13	44	with	with	ADP
cuesj-22	13	45	another	another	DET
cuesj-22	13	46	approach	approach	NOUN
cuesj-22	13	47	called	call	VERB
cuesj-22	13	48	bayesian	bayesian	NOUN
cuesj-22	13	49	kalman	kalman	PROPN
cuesj-22	13	50	filter	filter	PROPN
cuesj-22	13	51	.	.	PUNCT
cuesj-22	14	1	kalman	kalman	PROPN
cuesj-22	14	2	filter	filter	PROPN
cuesj-22	14	3	is	be	AUX
cuesj-22	14	4	an	an	DET
cuesj-22	14	5	optimal	optimal	ADJ
cuesj-22	14	6	linear	linear	ADJ
cuesj-22	14	7	estimator	estimator	NOUN
cuesj-22	14	8	which	which	PRON
cuesj-22	14	9	provides	provide	VERB
cuesj-22	14	10	the	the	DET
cuesj-22	14	11	estimation	estimation	NOUN
cuesj-22	14	12	of	of	ADP
cuesj-22	14	13	signals	signal	NOUN
cuesj-22	14	14	in	in	ADP
cuesj-22	14	15	noise	noise	NOUN
cuesj-22	14	16	.	.	PUNCT
cuesj-22	15	1	kalman	kalman	NOUN
cuesj-22	15	2	used	use	VERB
cuesj-22	15	3	the	the	DET
cuesj-22	15	4	state	state	NOUN
cuesj-22	15	5	transition	transition	NOUN
cuesj-22	15	6	models	model	NOUN
cuesj-22	15	7	for	for	ADP
cuesj-22	15	8	the	the	DET
cuesj-22	15	9	dynamic	dynamic	ADJ
cuesj-22	15	10	system	system	NOUN
cuesj-22	15	11	in	in	ADP
cuesj-22	15	12	the	the	DET
cuesj-22	15	13	estimation	estimation	NOUN
cuesj-22	15	14	process	process	NOUN
cuesj-22	15	15	.	.	PUNCT
cuesj-22	16	1	groundwater	groundwater	NOUN
cuesj-22	16	2	-	-	PUNCT
cuesj-22	16	3	surface	surface	NOUN
cuesj-22	16	4	interpolation	interpolation	NOUN
cuesj-22	16	5	in	in	ADP
cuesj-22	16	6	general	general	ADJ
cuesj-22	16	7	,	,	PUNCT
cuesj-22	16	8	spatial	spatial	ADJ
cuesj-22	16	9	statistics	statistic	NOUN
cuesj-22	16	10	and	and	CCONJ
cuesj-22	16	11	geographic	geographic	ADJ
cuesj-22	16	12	information	information	NOUN
cuesj-22	16	13	system	system	NOUN
cuesj-22	16	14	(	(	PUNCT
cuesj-22	16	15	gis	gis	NOUN
cuesj-22	16	16	)	)	PUNCT
cuesj-22	16	17	rely	rely	VERB
cuesj-22	16	18	on	on	ADP
cuesj-22	16	19	each	each	DET
cuesj-22	16	20	other	other	ADJ
cuesj-22	16	21	in	in	ADP
cuesj-22	16	22	many	many	ADJ
cuesj-22	16	23	ways	way	NOUN
cuesj-22	16	24	.	.	PUNCT
cuesj-22	17	1	arc	arc	NOUN
cuesj-22	17	2	gis	gis	PROPN
cuesj-22	17	3	is	be	AUX
cuesj-22	17	4	software	software	NOUN
cuesj-22	17	5	which	which	PRON
cuesj-22	17	6	can	can	AUX
cuesj-22	17	7	be	be	AUX
cuesj-22	17	8	used	use	VERB
cuesj-22	17	9	to	to	PART
cuesj-22	17	10	create	create	VERB
cuesj-22	17	11	covariates	covariate	NOUN
cuesj-22	17	12	for	for	ADP
cuesj-22	17	13	inclusion	inclusion	NOUN
cuesj-22	17	14	in	in	ADP
cuesj-22	17	15	all	all	DET
cuesj-22	17	16	statistical	statistical	ADJ
cuesj-22	17	17	models	model	NOUN
cuesj-22	17	18	and	and	CCONJ
cuesj-22	17	19	to	to	PART
cuesj-22	17	20	bring	bring	VERB
cuesj-22	17	21	out	out	ADP
cuesj-22	17	22	the	the	DET
cuesj-22	17	23	results	result	NOUN
cuesj-22	17	24	from	from	ADP
cuesj-22	17	25	statistical	statistical	ADJ
cuesj-22	17	26	models	model	NOUN
cuesj-22	17	27	.	.	PUNCT
cuesj-22	18	1	the	the	DET
cuesj-22	18	2	work	work	NOUN
cuesj-22	18	3	in	in	ADP
cuesj-22	18	4	this	this	DET
cuesj-22	18	5	study	study	NOUN
cuesj-22	18	6	might	might	AUX
cuesj-22	18	7	be	be	AUX
cuesj-22	18	8	very	very	ADV
cuesj-22	18	9	important	important	ADJ
cuesj-22	18	10	to	to	PART
cuesj-22	18	11	evaluate	evaluate	VERB
cuesj-22	18	12	the	the	DET
cuesj-22	18	13	results	result	NOUN
cuesj-22	18	14	from	from	ADP
cuesj-22	18	15	different	different	ADJ
cuesj-22	18	16	models	model	NOUN
cuesj-22	18	17	in	in	ADP
cuesj-22	18	18	simple	simple	ADJ
cuesj-22	18	19	kriging	krige	VERB
cuesj-22	18	20	interpolation	interpolation	NOUN
cuesj-22	18	21	approaches	approach	NOUN
cuesj-22	18	22	.	.	PUNCT
cuesj-22	19	1	this	this	DET
cuesj-22	19	2	kind	kind	NOUN
cuesj-22	19	3	of	of	ADP
cuesj-22	19	4	comparison	comparison	NOUN
cuesj-22	19	5	presents	present	VERB
cuesj-22	19	6	a	a	DET
cuesj-22	19	7	relevant	relevant	ADJ
cuesj-22	19	8	meaning	meaning	NOUN
cuesj-22	19	9	for	for	ADP
cuesj-22	19	10	the	the	DET
cuesj-22	19	11	variability	variability	NOUN
cuesj-22	19	12	of	of	ADP
cuesj-22	19	13	a	a	DET
cuesj-22	19	14	physical	physical	ADJ
cuesj-22	19	15	model	model	NOUN
cuesj-22	19	16	which	which	PRON
cuesj-22	19	17	used	use	VERB
cuesj-22	19	18	as	as	ADP
cuesj-22	19	19	a	a	DET
cuesj-22	19	20	reference	reference	NOUN
cuesj-22	19	21	to	to	PART
cuesj-22	19	22	validate	validate	VERB
cuesj-22	19	23	the	the	DET
cuesj-22	19	24	interpolation	interpolation	NOUN
cuesj-22	19	25	results	result	NOUN
cuesj-22	19	26	.	.	PUNCT
cuesj-22	20	1	groundwater	groundwater	NOUN
cuesj-22	20	2	depth	depth	NOUN
cuesj-22	20	3	of	of	ADP
cuesj-22	20	4	the	the	DET
cuesj-22	20	5	wells	well	NOUN
cuesj-22	20	6	of	of	ADP
cuesj-22	20	7	any	any	DET
cuesj-22	20	8	sample	sample	NOUN
cuesj-22	20	9	point	point	NOUN
cuesj-22	20	10	data	datum	NOUN
cuesj-22	20	11	for	for	ADP
cuesj-22	20	12	generates	generate	NOUN
cuesj-22	20	13	surface	surface	NOUN
cuesj-22	20	14	need	need	VERB
cuesj-22	20	15	to	to	PART
cuesj-22	20	16	be	be	AUX
cuesj-22	20	17	evaluated	evaluate	VERB
cuesj-22	20	18	and	and	CCONJ
cuesj-22	20	19	preprocessed	preprocesse	VERB
cuesj-22	20	20	before	before	ADP
cuesj-22	20	21	interpolation	interpolation	NOUN
cuesj-22	20	22	.	.	PUNCT
cuesj-22	21	1	the	the	DET
cuesj-22	21	2	locations	location	NOUN
cuesj-22	21	3	and	and	CCONJ
cuesj-22	21	4	values	value	NOUN
cuesj-22	21	5	of	of	ADP
cuesj-22	21	6	sample	sample	NOUN
cuesj-22	21	7	point	point	NOUN
cuesj-22	21	8	data	datum	NOUN
cuesj-22	21	9	will	will	AUX
cuesj-22	21	10	impact	impact	VERB
cuesj-22	21	11	the	the	DET
cuesj-22	21	12	interpolation	interpolation	NOUN
cuesj-22	21	13	result	result	NOUN
cuesj-22	21	14	.	.	PUNCT
cuesj-22	22	1	first	first	ADV
cuesj-22	22	2	,	,	PUNCT
cuesj-22	22	3	all	all	DET
cuesj-22	22	4	the	the	DET
cuesj-22	22	5	collected	collect	VERB
cuesj-22	22	6	data	datum	NOUN
cuesj-22	22	7	should	should	AUX
cuesj-22	22	8	come	come	VERB
cuesj-22	22	9	from	from	ADP
cuesj-22	22	10	the	the	DET
cuesj-22	22	11	same	same	ADJ
cuesj-22	22	12	type	type	NOUN
cuesj-22	22	13	of	of	ADP
cuesj-22	22	14	wells	well	NOUN
cuesj-22	22	15	in	in	ADP
cuesj-22	22	16	the	the	DET
cuesj-22	22	17	same	same	ADJ
cuesj-22	22	18	aquifer	aquifer	NOUN
cuesj-22	22	19	.	.	PUNCT
cuesj-22	23	1	the	the	DET
cuesj-22	23	2	well	well	ADJ
cuesj-22	23	3	information	information	NOUN
cuesj-22	23	4	should	should	AUX
cuesj-22	23	5	be	be	AUX
cuesj-22	23	6	carefully	carefully	ADV
cuesj-22	23	7	evaluated	evaluate	VERB
cuesj-22	23	8	to	to	PART
cuesj-22	23	9	make	make	VERB
cuesj-22	23	10	sure	sure	ADJ
cuesj-22	23	11	the	the	DET
cuesj-22	23	12	data	datum	NOUN
cuesj-22	23	13	reflect	reflect	VERB
cuesj-22	23	14	the	the	DET
cuesj-22	23	15	dynamics	dynamic	NOUN
cuesj-22	23	16	of	of	ADP
cuesj-22	23	17	groundwater	groundwater	NOUN
cuesj-22	23	18	in	in	ADP
cuesj-22	23	19	the	the	DET
cuesj-22	23	20	target	target	NOUN
cuesj-22	23	21	aquifer	aquifer	NOUN
cuesj-22	23	22	,	,	PUNCT
cuesj-22	23	23	not	not	PART
cuesj-22	23	24	other	other	ADJ
cuesj-22	23	25	aquifers	aquifer	NOUN
cuesj-22	23	26	.	.	PUNCT
cuesj-22	24	1	second	second	ADJ
cuesj-22	24	2	,	,	PUNCT
cuesj-22	24	3	the	the	DET
cuesj-22	24	4	spatial	spatial	ADJ
cuesj-22	24	5	distribution	distribution	NOUN
cuesj-22	24	6	of	of	ADP
cuesj-22	24	7	sample	sample	NOUN
cuesj-22	24	8	point	point	NOUN
cuesj-22	24	9	data	datum	NOUN
cuesj-22	24	10	should	should	AUX
cuesj-22	24	11	be	be	AUX
cuesj-22	24	12	carefully	carefully	ADV
cuesj-22	24	13	considered	consider	VERB
cuesj-22	24	14	.	.	PUNCT
cuesj-22	25	1	the	the	DET
cuesj-22	25	2	clustered	clustered	ADJ
cuesj-22	25	3	data	datum	NOUN
cuesj-22	25	4	and	and	CCONJ
cuesj-22	25	5	sparse	sparse	ADJ
cuesj-22	25	6	data	datum	NOUN
cuesj-22	25	7	in	in	ADP
cuesj-22	25	8	one	one	NUM
cuesj-22	25	9	area	area	NOUN
cuesj-22	25	10	corresponding	corresponding	ADJ
cuesj-22	25	11	author	author	NOUN
cuesj-22	25	12	:	:	PUNCT
cuesj-22	25	13	kurdistan	kurdistan	PROPN
cuesj-22	25	14	ibrahim	ibrahim	PROPN
cuesj-22	25	15	mawlood	mawlood	PROPN
cuesj-22	25	16	,	,	PUNCT
cuesj-22	25	17	department	department	NOUN
cuesj-22	25	18	of	of	ADP
cuesj-22	25	19	statistics	statistic	NOUN
cuesj-22	25	20	,	,	PUNCT
cuesj-22	25	21	college	college	NOUN
cuesj-22	25	22	of	of	ADP
cuesj-22	25	23	administration	administration	NOUN
cuesj-22	25	24	and	and	CCONJ
cuesj-22	25	25	economics	economic	NOUN
cuesj-22	25	26	,	,	PUNCT
cuesj-22	25	27	salahaddin	salahaddin	VERB
cuesj-22	25	28	university	university	NOUN
cuesj-22	25	29	-	-	PUNCT
cuesj-22	25	30	erbil	erbil	PROPN
cuesj-22	25	31	,	,	PUNCT
cuesj-22	25	32	kurdistan	kurdistan	PROPN
cuesj-22	25	33	region	region	PROPN
cuesj-22	25	34	f.r	f.r	PROPN
cuesj-22	25	35	.	.	PUNCT
cuesj-22	25	36	iraq	iraq	PROPN
cuesj-22	25	37	.	.	PUNCT
cuesj-22	26	1	e	e	X
cuesj-22	26	2	-	-	NOUN
cuesj-22	26	3	mail	mail	NOUN
cuesj-22	26	4	:	:	PUNCT
cuesj-22	26	5	kurdistan.mawlood@su.edu.krd	kurdistan.mawlood@su.edu.krd	NOUN
cuesj-22	26	6	doi	doi	PROPN
cuesj-22	26	7	:	:	PUNCT
cuesj-22	26	8	10.24086	10.24086	NUM
cuesj-22	26	9	/	/	SYM
cuesj-22	26	10	cuesj.v3n1y2019.pp42	cuesj.v3n1y2019.pp42	NUM
cuesj-22	26	11	-	-	SYM
cuesj-22	26	12	49	49	NUM
cuesj-22	26	13	copyright	copyright	NOUN
cuesj-22	26	14	©	©	PROPN
cuesj-22	26	15	2019	2019	NUM
cuesj-22	26	16	kurdistan	kurdistan	PROPN
cuesj-22	26	17	ibrahim	ibrahim	PROPN
cuesj-22	26	18	mawlood	mawlood	PROPN
cuesj-22	26	19	,	,	PUNCT
cuesj-22	26	20	paree	paree	PROPN
cuesj-22	26	21	khan	khan	PROPN
cuesj-22	26	22	aabdulla	aabdulla	PROPN
cuesj-22	26	23	omer	omer	PROPN
cuesj-22	26	24	.	.	PUNCT
cuesj-22	27	1	this	this	PRON
cuesj-22	27	2	is	be	AUX
cuesj-22	27	3	an	an	DET
cuesj-22	27	4	open	open	ADJ
cuesj-22	27	5	-	-	PUNCT
cuesj-22	27	6	access	access	NOUN
cuesj-22	27	7	article	article	NOUN
cuesj-22	27	8	distributed	distribute	VERB
cuesj-22	27	9	under	under	ADP
cuesj-22	27	10	the	the	DET
cuesj-22	27	11	creative	creative	ADJ
cuesj-22	27	12	commons	common	NOUN
cuesj-22	27	13	attribution	attribution	NOUN
cuesj-22	27	14	license	license	NOUN
cuesj-22	27	15	.	.	PUNCT
cuesj-22	28	1	cihan	cihan	VERB
cuesj-22	28	2	university	university	NOUN
cuesj-22	28	3	-	-	PUNCT
cuesj-22	28	4	erbil	erbil	PROPN
cuesj-22	28	5	scientific	scientific	ADJ
cuesj-22	28	6	journal	journal	NOUN
cuesj-22	28	7	(	(	PUNCT
cuesj-22	28	8	cuesj	cuesj	PROPN
cuesj-22	28	9	)	)	PUNCT
cuesj-22	28	10	received	receive	VERB
cuesj-22	28	11	:	:	PUNCT
cuesj-22	28	12	apr	apr	NOUN
cuesj-22	28	13	27	27	NUM
cuesj-22	28	14	,	,	PUNCT
cuesj-22	28	15	2019	2019	NUM
cuesj-22	28	16	accepted	accept	VERB
cuesj-22	28	17	:	:	PUNCT
cuesj-22	28	18	may	may	AUX
cuesj-22	28	19	29	29	NUM
cuesj-22	28	20	,	,	PUNCT
cuesj-22	28	21	2019	2019	NUM
cuesj-22	28	22	published	publish	VERB
cuesj-22	28	23	:	:	PUNCT
cuesj-22	28	24	jun	jun	PROPN
cuesj-22	28	25	30	30	NUM
cuesj-22	28	26	,	,	PUNCT
cuesj-22	28	27	2019	2019	NUM
cuesj-22	28	28	https://creativecommons.org/licenses/by-nc-nd/4.0/	https://creativecommons.org/licenses/by-nc-nd/4.0/	PROPN
cuesj-22	28	29	mawlood	mawlood	NOUN
cuesj-22	28	30	and	and	CCONJ
cuesj-22	28	31	omer	omer	PROPN
cuesj-22	28	32	:	:	PUNCT
cuesj-22	28	33	prediction	prediction	NOUN
cuesj-22	28	34	the	the	DET
cuesj-22	28	35	groundwater	groundwater	NOUN
cuesj-22	28	36	depth	depth	NOUN
cuesj-22	29	1	43	43	NUM
cuesj-22	29	2	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	29	3	cuesj	cuesj	NOUN
cuesj-22	29	4	2019	2019	NUM
cuesj-22	29	5	,	,	PUNCT
cuesj-22	29	6	3	3	NUM
cuesj-22	29	7	(	(	PUNCT
cuesj-22	29	8	1	1	NUM
cuesj-22	29	9	):	):	PUNCT
cuesj-22	29	10	42	42	NUM
cuesj-22	29	11	-	-	SYM
cuesj-22	29	12	49	49	NUM
cuesj-22	29	13	will	will	AUX
cuesj-22	29	14	cause	cause	VERB
cuesj-22	29	15	different	different	ADJ
cuesj-22	29	16	interpolation	interpolation	NOUN
cuesj-22	29	17	results	result	NOUN
cuesj-22	29	18	.	.	PUNCT
cuesj-22	30	1	after	after	ADP
cuesj-22	30	2	collecting	collect	VERB
cuesj-22	30	3	suitable	suitable	ADJ
cuesj-22	30	4	data	datum	NOUN
cuesj-22	30	5	for	for	ADP
cuesj-22	30	6	interpolation	interpolation	NOUN
cuesj-22	30	7	,	,	PUNCT
cuesj-22	30	8	raster	raster	NOUN
cuesj-22	30	9	calculator	calculator	NOUN
cuesj-22	30	10	in	in	ADP
cuesj-22	30	11	gis	gis	PROPN
cuesj-22	30	12	is	be	AUX
cuesj-22	30	13	used	use	VERB
cuesj-22	30	14	to	to	PART
cuesj-22	30	15	calculate	calculate	VERB
cuesj-22	30	16	the	the	DET
cuesj-22	30	17	elevation	elevation	NOUN
cuesj-22	30	18	of	of	ADP
cuesj-22	30	19	groundwater	groundwater	NOUN
cuesj-22	30	20	table	table	NOUN
cuesj-22	30	21	in	in	ADP
cuesj-22	30	22	each	each	DET
cuesj-22	30	23	well	well	NOUN
cuesj-22	30	24	by	by	ADP
cuesj-22	30	25	dividing	divide	VERB
cuesj-22	30	26	the	the	DET
cuesj-22	30	27	surface	surface	NOUN
cuesj-22	30	28	elevation	elevation	NOUN
cuesj-22	30	29	(	(	PUNCT
cuesj-22	30	30	digital	digital	ADJ
cuesj-22	30	31	elevation	elevation	NOUN
cuesj-22	30	32	model	model	NOUN
cuesj-22	30	33	)	)	PUNCT
cuesj-22	30	34	with	with	ADP
cuesj-22	30	35	the	the	DET
cuesj-22	30	36	groundwater	groundwater	NOUN
cuesj-22	30	37	depth.[1	depth.[1	NOUN
cuesj-22	30	38	]	]	PUNCT
cuesj-22	30	39	gaussian	gaussian	ADJ
cuesj-22	30	40	random	random	ADJ
cuesj-22	30	41	field	field	NOUN
cuesj-22	30	42	used	use	VERB
cuesj-22	30	43	gaussian	gaussian	ADJ
cuesj-22	30	44	random	random	ADJ
cuesj-22	30	45	field	field	NOUN
cuesj-22	30	46	z(s	z(s	NOUN
cuesj-22	30	47	)	)	PUNCT
cuesj-22	30	48	to	to	PART
cuesj-22	30	49	identify	identify	VERB
cuesj-22	30	50	the	the	DET
cuesj-22	30	51	spatial	spatial	ADJ
cuesj-22	30	52	correlation	correlation	NOUN
cuesj-22	30	53	structure	structure	NOUN
cuesj-22	30	54	,	,	PUNCT
cuesj-22	30	55	any	any	DET
cuesj-22	30	56	field	field	NOUN
cuesj-22	30	57	of	of	ADP
cuesj-22	30	58	spatial	spatial	ADJ
cuesj-22	30	59	is	be	AUX
cuesj-22	30	60	a	a	DET
cuesj-22	30	61	set	set	NOUN
cuesj-22	30	62	of	of	ADP
cuesj-22	30	63	random	random	ADJ
cuesj-22	30	64	variables	variable	NOUN
cuesj-22	30	65	which	which	PRON
cuesj-22	30	66	parameterized	parameterized	ADJ
cuesj-22	30	67	by	by	ADP
cuesj-22	30	68	some	some	DET
cuesj-22	30	69	set	set	NOUN
cuesj-22	30	70	(	(	PUNCT
cuesj-22	30	71	d⊂rd	d⊂rd	NOUN
cuesj-22	30	72	)	)	PUNCT
cuesj-22	30	73	.	.	PUNCT
cuesj-22	31	1	the	the	DET
cuesj-22	31	2	simplest	simple	ADJ
cuesj-22	31	3	stochastic	stochastic	ADJ
cuesj-22	31	4	process	process	NOUN
cuesj-22	31	5	form	form	NOUN
cuesj-22	31	6	is	be	AUX
cuesj-22	31	7	as	as	SCONJ
cuesj-22	31	8	follows	follow	VERB
cuesj-22	31	9	:	:	PUNCT
cuesj-22	31	10	z	z	PROPN
cuesj-22	31	11	s	s	X
cuesj-22	31	12	s	s	PROPN
cuesj-22	31	13	d	d	X
cuesj-22	31	14	rd	rd	PROPN
cuesj-22	31	15	(	(	PUNCT
cuesj-22	31	16	)	)	PUNCT
cuesj-22	31	17	:	:	PUNCT
cuesj-22	31	18	{	{	PUNCT
cuesj-22	31	19	∈	∈	PROPN
cuesj-22	31	20	⊂	⊂	X
cuesj-22	31	21	}	}	PUNCT
cuesj-22	31	22	(	(	PUNCT
cuesj-22	31	23	1	1	X
cuesj-22	31	24	)	)	PUNCT
cuesj-22	32	1	where	where	SCONJ
cuesj-22	32	2	:	:	PUNCT
cuesj-22	33	1	z(s	z(s	NOUN
cuesj-22	33	2	):	):	PUNCT
cuesj-22	33	3	field	field	NOUN
cuesj-22	33	4	of	of	ADP
cuesj-22	33	5	random	random	ADJ
cuesj-22	33	6	spatial	spatial	NOUN
cuesj-22	33	7	.	.	PUNCT
cuesj-22	34	1	s	s	X
cuesj-22	34	2	:	:	PUNCT
cuesj-22	34	3	coordinates	coordinate	NOUN
cuesj-22	34	4	spatial	spatial	ADJ
cuesj-22	34	5	random	random	ADJ
cuesj-22	34	6	variable	variable	NOUN
cuesj-22	34	7	.	.	PUNCT
cuesj-22	35	1	d	d	X
cuesj-22	35	2	:	:	PUNCT
cuesj-22	35	3	domain	domain	NOUN
cuesj-22	35	4	of	of	ADP
cuesj-22	35	5	spatial	spatial	ADJ
cuesj-22	35	6	random	random	ADJ
cuesj-22	35	7	variable	variable	NOUN
cuesj-22	35	8	.	.	PUNCT
cuesj-22	36	1	rd	rd	NOUN
cuesj-22	36	2	:	:	PUNCT
cuesj-22	37	1	d	d	ADJ
cuesj-22	37	2	-	-	ADJ
cuesj-22	37	3	dimensional	dimensional	ADJ
cuesj-22	37	4	euclidean	euclidean	ADJ
cuesj-22	37	5	space	space	NOUN
cuesj-22	37	6	.	.	PUNCT
cuesj-22	38	1	any	any	DET
cuesj-22	38	2	finite	finite	ADJ
cuesj-22	38	3	collection{z(s1),z(s2).,z(sk	collection{z(s1),z(s2).,z(sk	NOUN
cuesj-22	38	4	)	)	PUNCT
cuesj-22	38	5	.	.	PUNCT
cuesj-22	38	6	,	,	PUNCT
cuesj-22	38	7	}	}	PUNCT
cuesj-22	38	8	is	be	AUX
cuesj-22	38	9	multivariate	multivariate	NOUN
cuesj-22	38	10	normal	normal	ADJ
cuesj-22	38	11	:	:	PUNCT
cuesj-22	38	12	1	1	NUM
cuesj-22	38	13	1	1	NUM
cuesj-22	38	14	(	(	PUNCT
cuesj-22	38	15	)	)	PUNCT
cuesj-22	38	16	(	(	PUNCT
cuesj-22	38	17	)	)	PUNCT
cuesj-22	38	18	.	.	PUNCT
cuesj-22	38	19	.	.	PUNCT
cuesj-22	39	1	~	~	PUNCT
cuesj-22	39	2	,	,	PUNCT
cuesj-22	39	3	(	(	PUNCT
cuesj-22	39	4	(	(	PUNCT
cuesj-22	39	5	)	)	PUNCT
cuesj-22	39	6	,	,	PUNCT
cuesj-22	39	7	(	(	PUNCT
cuesj-22	39	8	)	)	PUNCT
cuesj-22	39	9	)	)	PUNCT
cuesj-22	39	10	.	.	PUNCT
cuesj-22	40	1	.	.	PUNCT
cuesj-22	41	1	(	(	PUNCT
cuesj-22	41	2	)	)	PUNCT
cuesj-22	41	3	(	(	PUNCT
cuesj-22	41	4	)	)	PUNCT
cuesj-22	41	5	i	i	PRON
cuesj-22	41	6	j	j	PROPN
cuesj-22	42	1	k	k	PROPN
cuesj-22	42	2	k	k	PROPN
cuesj-22	42	3	z	z	PROPN
cuesj-22	42	4	s	s	PROPN
cuesj-22	42	5	s	s	X
cuesj-22	42	6	n	n	NUM
cuesj-22	42	7	cov	cov	PROPN
cuesj-22	42	8	z	z	PROPN
cuesj-22	42	9	s	s	PROPN
cuesj-22	42	10	z	z	PROPN
cuesj-22	42	11	s	s	PROPN
cuesj-22	42	12	z	z	NOUN
cuesj-22	42	13	s	s	PROPN
cuesj-22	42	14	s	s	PROPN
cuesj-22	42	15	µ	µ	X
cuesj-22	42	16	µ	µ	X
cuesj-22	42	17			NOUN
cuesj-22	42	18			PUNCT
cuesj-22	43	1			PROPN
cuesj-22	43	2			NOUN
cuesj-22	43	3			PROPN
cuesj-22	43	4			NOUN
cuesj-22	43	5			VERB
cuesj-22	43	6			PROPN
cuesj-22	43	7			ADJ
cuesj-22	43	8			PROPN
cuesj-22	43	9			NOUN
cuesj-22	43	10			VERB
cuesj-22	43	11			PROPN
cuesj-22	43	12			ADJ
cuesj-22	43	13			PROPN
cuesj-22	43	14			NOUN
cuesj-22	43	15			NOUN
cuesj-22	43	16			NOUN
cuesj-22	43	17			VERB
cuesj-22	44	1			PROPN
cuesj-22	44	2			ADJ
cuesj-22	44	3			PROPN
cuesj-22	44	4			NOUN
cuesj-22	44	5			VERB
cuesj-22	44	6			PROPN
cuesj-22	44	7			NOUN
cuesj-22	44	8			ADV
cuesj-22	44	9			VERB
cuesj-22	44	10			PROPN
cuesj-22	44	11			ADJ
cuesj-22	44	12			ADJ
cuesj-22	44	13			NOUN
cuesj-22	44	14			X
cuesj-22	44	15			NOUN
cuesj-22	44	16			PUNCT
cuesj-22	45	1	if	if	SCONJ
cuesj-22	45	2	the	the	DET
cuesj-22	45	3	following	follow	VERB
cuesj-22	45	4	assumptions	assumption	NOUN
cuesj-22	45	5	hold	hold	VERB
cuesj-22	45	6	,	,	PUNCT
cuesj-22	45	7	then	then	ADV
cuesj-22	45	8	a	a	DET
cuesj-22	45	9	spatial	spatial	ADJ
cuesj-22	45	10	random	random	ADJ
cuesj-22	45	11	field	field	NOUN
cuesj-22	45	12	is	be	AUX
cuesj-22	45	13	called	call	VERB
cuesj-22	45	14	second	second	ADJ
cuesj-22	45	15	-	-	PUNCT
cuesj-22	45	16	order	order	NOUN
cuesj-22	45	17	stationary	stationary	NOUN
cuesj-22	45	18	:	:	PUNCT
cuesj-22	46	1	[	[	X
cuesj-22	46	2	2	2	NUM
cuesj-22	46	3	]	]	PUNCT
cuesj-22	46	4	(	(	PUNCT
cuesj-22	46	5	(	(	PUNCT
cuesj-22	46	6	)	)	PUNCT
cuesj-22	46	7	)	)	PUNCT
cuesj-22	47	1	e	e	X
cuesj-22	47	2	z	z	NOUN
cuesj-22	47	3	s	s	PART
cuesj-22	47	4	s	s	PART
cuesj-22	47	5	dµ=	dµ=	PROPN
cuesj-22	47	6	∀	∀	X
cuesj-22	47	7	∈	∈	PROPN
cuesj-22	47	8	(	(	PUNCT
cuesj-22	47	9	2	2	NUM
cuesj-22	47	10	)	)	PUNCT
cuesj-22	47	11	(	(	PUNCT
cuesj-22	47	12	3	3	X
cuesj-22	47	13	)	)	PUNCT
cuesj-22	48	1	where	where	SCONJ
cuesj-22	48	2	:	:	PUNCT
cuesj-22	49	1	e	e	X
cuesj-22	49	2	:	:	PUNCT
cuesj-22	49	3	expected	expect	VERB
cuesj-22	49	4	value	value	NOUN
cuesj-22	49	5	.	.	PUNCT
cuesj-22	50	1	cov	cov	NOUN
cuesj-22	50	2	:	:	PUNCT
cuesj-22	50	3	covariance	covariance	NOUN
cuesj-22	50	4	function	function	NOUN
cuesj-22	50	5	of	of	ADP
cuesj-22	50	6	two	two	NUM
cuesj-22	50	7	locations	location	NOUN
cuesj-22	50	8	.	.	PUNCT
cuesj-22	51	1	h	h	X
cuesj-22	51	2	=	=	NOUN
cuesj-22	51	3	s2−s1	s2−s1	NOUN
cuesj-22	51	4	:	:	PUNCT
cuesj-22	51	5	vector	vector	NOUN
cuesj-22	51	6	distance	distance	NOUN
cuesj-22	51	7	between	between	ADP
cuesj-22	51	8	z(s1	z(s1	NOUN
cuesj-22	51	9	)	)	PUNCT
cuesj-22	51	10	and	and	CCONJ
cuesj-22	51	11	z(s2	z(s2	NUM
cuesj-22	51	12	)	)	PUNCT
cuesj-22	51	13	.	.	PUNCT
cuesj-22	52	1	however	however	ADV
cuesj-22	52	2	,	,	PUNCT
cuesj-22	52	3	we	we	PRON
cuesj-22	52	4	know	know	VERB
cuesj-22	52	5	that	that	SCONJ
cuesj-22	52	6	the	the	DET
cuesj-22	52	7	covariance	covariance	NOUN
cuesj-22	52	8	function	function	NOUN
cuesj-22	52	9	can	can	AUX
cuesj-22	52	10	be	be	AUX
cuesj-22	52	11	expressed	express	VERB
cuesj-22	52	12	as	as	SCONJ
cuesj-22	52	13	follows	follow	VERB
cuesj-22	52	14	:	:	PUNCT
cuesj-22	52	15	(	(	PUNCT
cuesj-22	52	16	)	)	PUNCT
cuesj-22	52	17	(	(	PUNCT
cuesj-22	52	18	)	)	PUNCT
cuesj-22	52	19	(	(	PUNCT
cuesj-22	52	20	)	)	PUNCT
cuesj-22	52	21	(	(	PUNCT
cuesj-22	52	22	)	)	PUNCT
cuesj-22	53	1	[	[	PUNCT
cuesj-22	53	2	]	]	X
cuesj-22	53	3	1	1	NUM
cuesj-22	53	4	2	2	NUM
cuesj-22	53	5	1	1	NUM
cuesj-22	53	6	1	1	NUM
cuesj-22	53	7	1	1	NUM
cuesj-22	53	8	1	1	NUM
cuesj-22	53	9	1	1	NUM
cuesj-22	53	10	1	1	NUM
cuesj-22	53	11	2	2	NUM
cuesj-22	53	12	1	1	NUM
cuesj-22	53	13	1	1	NUM
cuesj-22	53	14	(	(	PUNCT
cuesj-22	53	15	)	)	PUNCT
cuesj-22	53	16	,	,	PUNCT
cuesj-22	53	17	(	(	PUNCT
cuesj-22	53	18	)	)	PUNCT
cuesj-22	53	19	(	(	PUNCT
cuesj-22	53	20	)	)	PUNCT
cuesj-22	53	21	,	,	PUNCT
cuesj-22	53	22	(	(	PUNCT
cuesj-22	53	23	)	)	PUNCT
cuesj-22	53	24	(	(	PUNCT
cuesj-22	53	25	)	)	PUNCT
cuesj-22	53	26	(	(	PUNCT
cuesj-22	53	27	)	)	PUNCT
cuesj-22	53	28	(	(	PUNCT
cuesj-22	53	29	)	)	PUNCT
cuesj-22	53	30	(	(	PUNCT
cuesj-22	53	31	)	)	PUNCT
cuesj-22	53	32	(	(	PUNCT
cuesj-22	53	33	)	)	PUNCT
cuesj-22	54	1	s	s	NOUN
cuesj-22	54	2	s	s	NOUN
cuesj-22	54	3	h	h	NOUN
cuesj-22	54	4	cov	cov	PROPN
cuesj-22	54	5	z	z	PROPN
cuesj-22	54	6	s	s	PROPN
cuesj-22	54	7	z	z	PROPN
cuesj-22	54	8	s	s	PROPN
cuesj-22	54	9	cov	cov	PROPN
cuesj-22	54	10	z	z	PROPN
cuesj-22	54	11	s	s	PROPN
cuesj-22	54	12	z	z	NOUN
cuesj-22	54	13	s	s	NOUN
cuesj-22	55	1	h	h	NOUN
cuesj-22	56	1	c	c	NOUN
cuesj-22	56	2	h	h	NOUN
cuesj-22	57	1	e	e	PROPN
cuesj-22	57	2	z	z	PROPN
cuesj-22	57	3	s	s	PROPN
cuesj-22	57	4	z	z	NOUN
cuesj-22	57	5	s	s	NOUN
cuesj-22	57	6	h	h	NOUN
cuesj-22	58	1	e	e	PROPN
cuesj-22	58	2	z	z	PROPN
cuesj-22	58	3	s	s	PROPN
cuesj-22	58	4	z	z	NOUN
cuesj-22	58	5	s	s	NOUN
cuesj-22	58	6	h	h	NOUN
cuesj-22	58	7			NOUN
cuesj-22	58	8			NOUN
cuesj-22	58	9			NOUN
cuesj-22	58	10	+	+	CCONJ
cuesj-22	58	11			NOUN
cuesj-22	58	12	=	=	X
cuesj-22	58	13	+	+	PUNCT
cuesj-22	58	14	=	=	NOUN
cuesj-22	58	15			NOUN
cuesj-22	58	16			NOUN
cuesj-22	58	17			NOUN
cuesj-22	58	18	=	=	PUNCT
cuesj-22	58	19	−	−	PROPN
cuesj-22	59	1	+	+	CCONJ
cuesj-22	60	1	−	−	PROPN
cuesj-22	60	2			PROPN
cuesj-22	60	3	=	=	SYM
cuesj-22	61	1	+	+	NUM
cuesj-22	61	2	−	−	PROPN
cuesj-22	61	3	(	(	PUNCT
cuesj-22	61	4	4	4	NUM
cuesj-22	61	5	)	)	PUNCT
cuesj-22	61	6	when	when	SCONJ
cuesj-22	61	7	the	the	DET
cuesj-22	61	8	expected	expect	VERB
cuesj-22	61	9	value	value	NOUN
cuesj-22	61	10	is	be	AUX
cuesj-22	61	11	equal	equal	ADJ
cuesj-22	61	12	to	to	ADP
cuesj-22	61	13	zero	zero	NUM
cuesj-22	61	14	.	.	PUNCT
cuesj-22	62	1	(	(	PUNCT
cuesj-22	62	2	(	(	PUNCT
cuesj-22	62	3	)	)	PUNCT
cuesj-22	62	4	)	)	PUNCT
cuesj-22	62	5	0,e	0,e	X
cuesj-22	63	1	z	z	X
cuesj-22	63	2	s	s	X
cuesj-22	63	3	s	s	X
cuesj-22	63	4	d	d	NOUN
cuesj-22	63	5	=	=	SYM
cuesj-22	63	6	=	=	SYM
cuesj-22	63	7	∀	∀	X
cuesj-22	63	8	∈	∈	NOUN
cuesj-22	63	9	(	(	PUNCT
cuesj-22	63	10	5	5	NUM
cuesj-22	63	11	)	)	PUNCT
cuesj-22	63	12	then	then	ADV
cuesj-22	63	13	,	,	PUNCT
cuesj-22	63	14	cov	cov	PROPN
cuesj-22	63	15	z	z	PROPN
cuesj-22	63	16	s	s	PROPN
cuesj-22	63	17	z	z	PROPN
cuesj-22	63	18	s	s	PROPN
cuesj-22	63	19	cov	cov	PROPN
cuesj-22	63	20	z	z	PROPN
cuesj-22	63	21	s	s	PROPN
cuesj-22	63	22	z	z	NOUN
cuesj-22	63	23	s	s	NOUN
cuesj-22	63	24	h	h	NOUN
cuesj-22	63	25	c	c	NOUN
cuesj-22	63	26	h	h	NOUN
cuesj-22	64	1	e	e	PROPN
cuesj-22	64	2	z	z	PROPN
cuesj-22	64	3	s	s	PROPN
cuesj-22	64	4	z	z	PROPN
cuesj-22	64	5	s	s	PROPN
cuesj-22	64	6	h	h	NOUN
cuesj-22	64	7	(	(	PUNCT
cuesj-22	64	8	)	)	PUNCT
cuesj-22	64	9	,	,	PUNCT
cuesj-22	64	10	(	(	PUNCT
cuesj-22	64	11	)	)	PUNCT
cuesj-22	64	12	(	(	PUNCT
cuesj-22	64	13	)	)	PUNCT
cuesj-22	64	14	,	,	PUNCT
cuesj-22	64	15	(	(	PUNCT
cuesj-22	64	16	)	)	PUNCT
cuesj-22	64	17	(	(	PUNCT
cuesj-22	64	18	)	)	PUNCT
cuesj-22	64	19	(	(	PUNCT
cuesj-22	64	20	)	)	PUNCT
cuesj-22	64	21	(	(	PUNCT
cuesj-22	64	22	)	)	PUNCT
cuesj-22	64	23	1	1	NUM
cuesj-22	64	24	2	2	NUM
cuesj-22	64	25	1	1	NUM
cuesj-22	64	26	1	1	NUM
cuesj-22	64	27	1	1	NUM
cuesj-22	64	28	1	1	NUM
cuesj-22	64	29	(	(	PUNCT
cuesj-22	64	30	)	)	PUNCT
cuesj-22	64	31	=	=	PUNCT
cuesj-22	65	1	+	+	ADJ
cuesj-22	65	2	(	(	PUNCT
cuesj-22	65	3	)	)	PUNCT
cuesj-22	65	4			NOUN
cuesj-22	65	5			NOUN
cuesj-22	65	6	=	=	PUNCT
cuesj-22	66	1	=	=	PUNCT
cuesj-22	67	1	+	+	PUNCT
cuesj-22	67	2	[	[	X
cuesj-22	67	3	[	[	X
cuesj-22	67	4	]	]	X
cuesj-22	67	5	(	(	PUNCT
cuesj-22	67	6	6	6	NUM
cuesj-22	67	7	)	)	PUNCT
cuesj-22	67	8	when	when	SCONJ
cuesj-22	67	9	the	the	DET
cuesj-22	67	10	mean	mean	NOUN
cuesj-22	67	11	of	of	ADP
cuesj-22	67	12	a	a	DET
cuesj-22	67	13	second	second	ADJ
cuesj-22	67	14	-	-	PUNCT
cuesj-22	67	15	order	order	NOUN
cuesj-22	67	16	stationary	stationary	NOUN
cuesj-22	67	17	of	of	ADP
cuesj-22	67	18	spatial	spatial	ADJ
cuesj-22	67	19	random	random	ADJ
cuesj-22	67	20	field	field	NOUN
cuesj-22	67	21	is	be	AUX
cuesj-22	67	22	equal	equal	ADJ
cuesj-22	67	23	to	to	ADP
cuesj-22	67	24	zero	zero	NUM
cuesj-22	67	25	over	over	ADP
cuesj-22	67	26	the	the	DET
cuesj-22	67	27	d	d	NOUN
cuesj-22	67	28	and	and	CCONJ
cuesj-22	67	29	the	the	DET
cuesj-22	67	30	covariance	covariance	NOUN
cuesj-22	67	31	function	function	NOUN
cuesj-22	67	32	of	of	ADP
cuesj-22	67	33	the	the	DET
cuesj-22	67	34	locations	location	NOUN
cuesj-22	67	35	does	do	AUX
cuesj-22	67	36	not	not	PART
cuesj-22	67	37	depend	depend	VERB
cuesj-22	67	38	on	on	ADP
cuesj-22	67	39	s1	s1	NOUN
cuesj-22	67	40	and	and	CCONJ
cuesj-22	67	41	s2	s2	NOUN
cuesj-22	67	42	but	but	CCONJ
cuesj-22	67	43	the	the	DET
cuesj-22	67	44	vector	vector	NOUN
cuesj-22	67	45	h.	h.	PROPN
cuesj-22	67	46	and	and	CCONJ
cuesj-22	67	47	for	for	ADP
cuesj-22	67	48	cov	cov	PROPN
cuesj-22	67	49	h	h	PROPN
cuesj-22	67	50	cov	cov	PROPN
cuesj-22	67	51	z	z	PROPN
cuesj-22	67	52	s	s	PROPN
cuesj-22	67	53	z	z	NOUN
cuesj-22	67	54	s	s	NOUN
cuesj-22	67	55	h	h	NOUN
cuesj-22	67	56	v	v	ADP
cuesj-22	67	57	z	z	PROPN
cuesj-22	67	58	s	s	PROPN
cuesj-22	67	59	(	(	PUNCT
cuesj-22	67	60	)	)	PUNCT
cuesj-22	67	61	(	(	PUNCT
cuesj-22	67	62	)	)	PUNCT
cuesj-22	67	63	,	,	PUNCT
cuesj-22	67	64	(	(	PUNCT
cuesj-22	67	65	)	)	PUNCT
cuesj-22	67	66	(	(	PUNCT
cuesj-22	67	67	)	)	PUNCT
cuesj-22	67	68	=	=	SYM
cuesj-22	68	1	=	=	PUNCT
cuesj-22	68	2	+	+	ADJ
cuesj-22	68	3	(	(	PUNCT
cuesj-22	68	4	)	)	PUNCT
cuesj-22	68	5			NOUN
cuesj-22	68	6			NOUN
cuesj-22	68	7	=	=	PUNCT
cuesj-22	68	8	[	[	PUNCT
cuesj-22	68	9	]	]	X
cuesj-22	68	10	0	0	PUNCT
cuesj-22	68	11	if	if	SCONJ
cuesj-22	68	12	variance	variance	NOUN
cuesj-22	68	13	or	or	CCONJ
cuesj-22	68	14	covariance	covariance	NOUN
cuesj-22	68	15	function	function	NOUN
cuesj-22	68	16	does	do	AUX
cuesj-22	68	17	not	not	PART
cuesj-22	68	18	exist	exist	VERB
cuesj-22	68	19	,	,	PUNCT
cuesj-22	68	20	the	the	DET
cuesj-22	68	21	intrinsic	intrinsic	ADJ
cuesj-22	68	22	hypothesis	hypothesis	NOUN
cuesj-22	68	23	and	and	CCONJ
cuesj-22	68	24	the	the	DET
cuesj-22	68	25	spatial	spatial	ADJ
cuesj-22	68	26	random	random	ADJ
cuesj-22	68	27	field	field	NOUN
cuesj-22	68	28	are	be	AUX
cuesj-22	68	29	called	call	VERB
cuesj-22	68	30	(	(	PUNCT
cuesj-22	68	31	intrinsic	intrinsic	ADJ
cuesj-22	68	32	stationery	stationery	NOUN
cuesj-22	68	33	)	)	PUNCT
cuesj-22	68	34	if	if	SCONJ
cuesj-22	68	35	the	the	DET
cuesj-22	68	36	following	follow	VERB
cuesj-22	68	37	assumption	assumption	NOUN
cuesj-22	68	38	holds	hold	VERB
cuesj-22	68	39	as	as	ADP
cuesj-22	68	40	:	:	PUNCT
cuesj-22	68	41	e	e	PROPN
cuesj-22	68	42	z	z	NOUN
cuesj-22	68	43	s	s	PART
cuesj-22	68	44	or	or	CCONJ
cuesj-22	68	45	e	e	NOUN
cuesj-22	68	46	z	z	PROPN
cuesj-22	68	47	s	s	PROPN
cuesj-22	69	1	z	z	NOUN
cuesj-22	69	2	s	s	X
cuesj-22	69	3	s	s	PROPN
cuesj-22	69	4	d	d	X
cuesj-22	69	5	v	v	NOUN
cuesj-22	69	6	z	z	PROPN
cuesj-22	69	7	s	s	NOUN
cuesj-22	69	8	z	z	NOUN
cuesj-22	69	9	s	s	X
cuesj-22	69	10	s	s	NOUN
cuesj-22	69	11	s	s	X
cuesj-22	69	12	s	s	X
cuesj-22	69	13	(	(	PUNCT
cuesj-22	69	14	(	(	PUNCT
cuesj-22	69	15	)	)	PUNCT
cuesj-22	69	16	)	)	PUNCT
cuesj-22	70	1	(	(	PUNCT
cuesj-22	70	2	)	)	PUNCT
cuesj-22	70	3	(	(	PUNCT
cuesj-22	70	4	)	)	PUNCT
cuesj-22	70	5	(	(	PUNCT
cuesj-22	70	6	)	)	PUNCT
cuesj-22	70	7	(	(	PUNCT
cuesj-22	70	8	)	)	PUNCT
cuesj-22	70	9	(	(	PUNCT
cuesj-22	70	10	)	)	PUNCT
cuesj-22	70	11	=	=	SYM
cuesj-22	70	12	−	−	PROPN
cuesj-22	70	13	[	[	PUNCT
cuesj-22	70	14	]	]	X
cuesj-22	70	15	=	=	SYM
cuesj-22	70	16	∀	∀	X
cuesj-22	70	17	∈	∈	NOUN
cuesj-22	71	1	−	−	PROPN
cuesj-22	71	2	[	[	X
cuesj-22	71	3	]	]	X
cuesj-22	71	4	=	=	SYM
cuesj-22	71	5	−	−	NOUN
cuesj-22	71	6	∀	∀	X
cuesj-22	71	7	µ	µ	X
cuesj-22	71	8	γ	γ	X
cuesj-22	71	9	1	1	NUM
cuesj-22	71	10	2	2	NUM
cuesj-22	71	11	1	1	NUM
cuesj-22	71	12	2	2	NUM
cuesj-22	71	13	1	1	NUM
cuesj-22	71	14	2	2	NUM
cuesj-22	71	15	1	1	NUM
cuesj-22	71	16	0	0	NUM
cuesj-22	71	17	2	2	NUM
cuesj-22	71	18	,	,	PUNCT
cuesj-22	71	19	,	,	PUNCT
cuesj-22	71	20	s	s	PROPN
cuesj-22	71	21	d2	d2	PROPN
cuesj-22	71	22	∈	∈	PROPN
cuesj-22	71	23	(	(	PUNCT
cuesj-22	71	24	7	7	NUM
cuesj-22	71	25	)	)	PUNCT
cuesj-22	71	26	where	where	SCONJ
cuesj-22	71	27	,	,	PUNCT
cuesj-22	71	28	v	v	NOUN
cuesj-22	71	29	:	:	PUNCT
cuesj-22	71	30	variance	variance	NOUN
cuesj-22	71	31	,	,	PUNCT
cuesj-22	71	32	γ	γ	NOUN
cuesj-22	71	33	:	:	PUNCT
cuesj-22	71	34	semivariogram	semivariogram	NOUN
cuesj-22	71	35	,	,	PUNCT
cuesj-22	71	36	2γ	2γ	NOUN
cuesj-22	71	37	:	:	PUNCT
cuesj-22	71	38	variogram	variogram	NOUN
cuesj-22	71	39	2	2	NUM
cuesj-22	71	40	1	1	NUM
cuesj-22	71	41	2	2	NUM
cuesj-22	71	42	1	1	NUM
cuesj-22	71	43	1	1	NUM
cuesj-22	71	44	1	1	NUM
cuesj-22	71	45	1	1	NUM
cuesj-22	71	46	2	2	NUM
cuesj-22	71	47			NUM
cuesj-22	71	48	=	=	PUNCT
cuesj-22	72	1	−	−	PROPN
cuesj-22	72	2	[	[	PUNCT
cuesj-22	72	3	]	]	X
cuesj-22	72	4	=	=	PUNCT
cuesj-22	73	1	−	−	PUNCT
cuesj-22	73	2	+	+	NOUN
cuesj-22	73	3	[	[	X
cuesj-22	73	4	]	]	X
cuesj-22	73	5	=	=	PUNCT
cuesj-22	74	1	+	+	NUM
cuesj-22	74	2	−	−	PROPN
cuesj-22	75	1	[	[	PUNCT
cuesj-22	75	2	]	]	X
cuesj-22	75	3	(	(	PUNCT
cuesj-22	75	4	)	)	PUNCT
cuesj-22	75	5	−	−	NOUN
cuesj-22	75	6	v	v	ADP
cuesj-22	75	7	z	z	PROPN
cuesj-22	75	8	s	s	PROPN
cuesj-22	75	9	z	z	NOUN
cuesj-22	75	10	s	s	PROPN
cuesj-22	75	11	v	v	NOUN
cuesj-22	75	12	z	z	PROPN
cuesj-22	75	13	s	s	PROPN
cuesj-22	75	14	z	z	NOUN
cuesj-22	75	15	s	s	NOUN
cuesj-22	75	16	h	h	NOUN
cuesj-22	75	17	e	e	PROPN
cuesj-22	75	18	z	z	PROPN
cuesj-22	75	19	s	s	PART
cuesj-22	75	20	h	h	NOUN
cuesj-22	75	21	z	z	NOUN
cuesj-22	75	22	s	s	PROPN
cuesj-22	75	23	e	e	X
cuesj-22	75	24	z	z	X
cuesj-22	75	25	(	(	PUNCT
cuesj-22	75	26	)	)	PUNCT
cuesj-22	75	27	(	(	PUNCT
cuesj-22	75	28	)	)	PUNCT
cuesj-22	75	29	(	(	PUNCT
cuesj-22	75	30	)	)	PUNCT
cuesj-22	75	31	(	(	PUNCT
cuesj-22	75	32	)	)	PUNCT
cuesj-22	75	33	(	(	PUNCT
cuesj-22	75	34	)	)	PUNCT
cuesj-22	75	35	(	(	PUNCT
cuesj-22	75	36	)	)	PUNCT
cuesj-22	75	37	(	(	PUNCT
cuesj-22	75	38	ss	ss	NOUN
cuesj-22	75	39	h	h	NOUN
cuesj-22	75	40	z	z	NOUN
cuesj-22	75	41	s	s	PROPN
cuesj-22	75	42	e	e	X
cuesj-22	75	43	z	z	NOUN
cuesj-22	75	44	s	s	PART
cuesj-22	75	45	h	h	NOUN
cuesj-22	75	46	z	z	PROPN
cuesj-22	75	47	s	s	NOUN
cuesj-22	75	48	1	1	NUM
cuesj-22	75	49	1	1	NUM
cuesj-22	75	50	2	2	NUM
cuesj-22	75	51	1	1	NUM
cuesj-22	75	52	1	1	NUM
cuesj-22	75	53	2	2	NUM
cuesj-22	75	54	+	+	CCONJ
cuesj-22	75	55	−	−	PROPN
cuesj-22	75	56	[	[	PUNCT
cuesj-22	75	57	]	]	X
cuesj-22	75	58	(	(	PUNCT
cuesj-22	75	59	)	)	PUNCT
cuesj-22	75	60	=	=	PUNCT
cuesj-22	76	1	+	+	NUM
cuesj-22	77	1	−	−	PROPN
cuesj-22	77	2	[	[	PUNCT
cuesj-22	77	3	]	]	X
cuesj-22	77	4	(	(	PUNCT
cuesj-22	77	5	)	)	PUNCT
cuesj-22	77	6	)	)	PUNCT
cuesj-22	77	7	(	(	PUNCT
cuesj-22	77	8	)	)	PUNCT
cuesj-22	77	9	(	(	PUNCT
cuesj-22	77	10	)	)	PUNCT
cuesj-22	77	11	(	(	PUNCT
cuesj-22	77	12	)	)	PUNCT
cuesj-22	77	13	(	(	PUNCT
cuesj-22	77	14	8)	8)	NUM
cuesj-22	77	15	in	in	ADP
cuesj-22	77	16	addition	addition	NOUN
cuesj-22	77	17	,	,	PUNCT
cuesj-22	77	18	if	if	SCONJ
cuesj-22	77	19	the	the	DET
cuesj-22	77	20	covariance	covariance	NOUN
cuesj-22	77	21	function	function	VERB
cuesj-22	77	22	c(s1−s2)=c(h	c(s1−s2)=c(h	PROPN
cuesj-22	77	23	)	)	PUNCT
cuesj-22	77	24	or	or	CCONJ
cuesj-22	77	25	semivariogram	semivariogram	NOUN
cuesj-22	77	26	γ(s1−s2)=γ(h	γ(s1−s2)=γ(h	PUNCT
cuesj-22	77	27	)	)	PUNCT
cuesj-22	77	28	depends	depend	VERB
cuesj-22	77	29	only	only	ADV
cuesj-22	77	30	on	on	ADP
cuesj-22	77	31	separation	separation	NOUN
cuesj-22	77	32	distance	distance	NOUN
cuesj-22	77	33	between	between	ADP
cuesj-22	77	34	s1	s1	NOUN
cuesj-22	77	35	and	and	CCONJ
cuesj-22	77	36	s2	s2	PROPN
cuesj-22	77	37	,	,	PUNCT
cuesj-22	77	38	h=||s1−s2||	h=||s1−s2||	NOUN
cuesj-22	77	39	,	,	PUNCT
cuesj-22	77	40	then	then	ADV
cuesj-22	77	41	the	the	DET
cuesj-22	77	42	spatial	spatial	ADJ
cuesj-22	77	43	random	random	ADJ
cuesj-22	77	44	field	field	NOUN
cuesj-22	77	45	is	be	AUX
cuesj-22	77	46	called	call	VERB
cuesj-22	77	47	isotropic.[3	isotropic.[3	NOUN
cuesj-22	77	48	]	]	PUNCT
cuesj-22	77	49	the	the	DET
cuesj-22	77	50	kriging	krige	VERB
cuesj-22	77	51	kriging	krige	VERB
cuesj-22	77	52	is	be	AUX
cuesj-22	77	53	a	a	DET
cuesj-22	77	54	spatial	spatial	ADJ
cuesj-22	77	55	interpolation	interpolation	NOUN
cuesj-22	77	56	geostatistical	geostatistical	ADJ
cuesj-22	77	57	method	method	NOUN
cuesj-22	77	58	used	use	VERB
cuesj-22	77	59	for	for	ADP
cuesj-22	77	60	the	the	DET
cuesj-22	77	61	1st	1st	ADJ
cuesj-22	77	62	time	time	NOUN
cuesj-22	77	63	in	in	ADP
cuesj-22	77	64	meteorology	meteorology	NOUN
cuesj-22	77	65	,	,	PUNCT
cuesj-22	77	66	geology	geology	NOUN
cuesj-22	77	67	,	,	PUNCT
cuesj-22	77	68	environmental	environmental	ADJ
cuesj-22	77	69	sciences	science	NOUN
cuesj-22	77	70	,	,	PUNCT
cuesj-22	77	71	agriculture	agriculture	NOUN
cuesj-22	77	72	,	,	PUNCT
cuesj-22	77	73	and	and	CCONJ
cuesj-22	77	74	other	other	ADJ
cuesj-22	77	75	fields	field	NOUN
cuesj-22	77	76	.	.	PUNCT
cuesj-22	78	1	this	this	DET
cuesj-22	78	2	method	method	NOUN
cuesj-22	78	3	is	be	AUX
cuesj-22	78	4	used	use	VERB
cuesj-22	78	5	to	to	PART
cuesj-22	78	6	find	find	VERB
cuesj-22	78	7	the	the	DET
cuesj-22	78	8	best	good	ADJ
cuesj-22	78	9	estimator	estimator	NOUN
cuesj-22	78	10	under	under	ADP
cuesj-22	78	11	the	the	DET
cuesj-22	78	12	assumption	assumption	NOUN
cuesj-22	78	13	of	of	ADP
cuesj-22	78	14	the	the	DET
cuesj-22	78	15	second	second	ADJ
cuesj-22	78	16	-	-	PUNCT
cuesj-22	78	17	order	order	NOUN
cuesj-22	78	18	stationarity	stationarity	NOUN
cuesj-22	78	19	.	.	PUNCT
cuesj-22	79	1	a	a	DET
cuesj-22	79	2	geological	geological	ADJ
cuesj-22	79	3	process	process	NOUN
cuesj-22	79	4	may	may	AUX
cuesj-22	79	5	not	not	PART
cuesj-22	79	6	be	be	AUX
cuesj-22	79	7	stationary	stationary	ADJ
cuesj-22	79	8	in	in	ADP
cuesj-22	79	9	reality	reality	NOUN
cuesj-22	79	10	.	.	PUNCT
cuesj-22	80	1	in	in	ADP
cuesj-22	80	2	case	case	NOUN
cuesj-22	80	3	,	,	PUNCT
cuesj-22	80	4	where	where	SCONJ
cuesj-22	80	5	the	the	DET
cuesj-22	80	6	process	process	NOUN
cuesj-22	80	7	is	be	AUX
cuesj-22	80	8	non	non	ADJ
cuesj-22	80	9	-	-	ADJ
cuesj-22	80	10	stationary	stationary	ADJ
cuesj-22	80	11	,	,	PUNCT
cuesj-22	80	12	we	we	PRON
cuesj-22	80	13	could	could	AUX
cuesj-22	80	14	use	use	VERB
cuesj-22	80	15	non	non	ADJ
cuesj-22	80	16	-	-	ADJ
cuesj-22	80	17	linear	linear	ADJ
cuesj-22	80	18	functions.[4	functions.[4	NOUN
cuesj-22	80	19	]	]	PUNCT
cuesj-22	80	20	here	here	ADV
cuesj-22	80	21	,	,	PUNCT
cuesj-22	80	22	the	the	DET
cuesj-22	80	23	classifying	classify	VERB
cuesj-22	80	24	geostatistical	geostatistical	ADJ
cuesj-22	80	25	techniques	technique	NOUN
cuesj-22	80	26	are	be	AUX
cuesj-22	80	27	as	as	SCONJ
cuesj-22	80	28	follows	follow	VERB
cuesj-22	80	29	[	[	NOUN
cuesj-22	80	30	table	table	NOUN
cuesj-22	80	31	1	1	NUM
cuesj-22	80	32	]	]	PUNCT
cuesj-22	80	33	.	.	PUNCT
cuesj-22	81	1	assumptions	assumption	NOUN
cuesj-22	81	2	in	in	ADP
cuesj-22	81	3	some	some	PRON
cuesj-22	81	4	of	of	ADP
cuesj-22	81	5	the	the	DET
cuesj-22	81	6	interpolation	interpolation	NOUN
cuesj-22	81	7	methods	method	NOUN
cuesj-22	81	8	especially	especially	ADV
cuesj-22	81	9	geostatistical	geostatistical	ADJ
cuesj-22	81	10	methods	method	NOUN
cuesj-22	81	11	,	,	PUNCT
cuesj-22	81	12	they	they	PRON
cuesj-22	81	13	have	have	VERB
cuesj-22	81	14	their	their	PRON
cuesj-22	81	15	own	own	ADJ
cuesj-22	81	16	assumptions	assumption	NOUN
cuesj-22	81	17	as	as	ADP
cuesj-22	81	18	follow:[5	follow:[5	PROPN
cuesj-22	81	19	]	]	PUNCT
cuesj-22	81	20	1	1	NUM
cuesj-22	81	21	.	.	PUNCT
cuesj-22	81	22	stationarity	stationarity	NOUN
cuesj-22	81	23	2	2	NUM
cuesj-22	81	24	.	.	PUNCT
cuesj-22	81	25	intrinsic	intrinsic	ADJ
cuesj-22	81	26	hypothesis	hypothesis	NOUN
cuesj-22	81	27	3	3	NUM
cuesj-22	81	28	.	.	PUNCT
cuesj-22	81	29	isotropy	isotropy	VERB
cuesj-22	81	30	and	and	CCONJ
cuesj-22	81	31	anisotropy	anisotropy	VERB
cuesj-22	81	32	4	4	NUM
cuesj-22	81	33	.	.	PUNCT
cuesj-22	81	34	unbiased	unbiased	ADJ
cuesj-22	81	35	.	.	PUNCT
cuesj-22	82	1	semivariogram	semivariogram	NOUN
cuesj-22	82	2	function	function	NOUN
cuesj-22	82	3	and	and	CCONJ
cuesj-22	82	4	covariance	covariance	NOUN
cuesj-22	82	5	function	function	VERB
cuesj-22	82	6	the	the	DET
cuesj-22	82	7	semivariogram	semivariogram	NOUN
cuesj-22	82	8	function	function	NOUN
cuesj-22	82	9	is	be	AUX
cuesj-22	82	10	a	a	DET
cuesj-22	82	11	structure	structure	NOUN
cuesj-22	82	12	of	of	ADP
cuesj-22	82	13	intrinsically	intrinsically	ADV
cuesj-22	82	14	stationary	stationary	ADJ
cuesj-22	82	15	spatial	spatial	ADJ
cuesj-22	82	16	random	random	ADJ
cuesj-22	82	17	field	field	NOUN
cuesj-22	82	18	which	which	PRON
cuesj-22	82	19	describes	describe	VERB
cuesj-22	82	20	a	a	DET
cuesj-22	82	21	broader	broad	ADJ
cuesj-22	82	22	class	class	NOUN
cuesj-22	82	23	of	of	ADP
cuesj-22	82	24	erath	erath	PROPN
cuesj-22	82	25	phenomena	phenomena	PROPN
cuesj-22	82	26	.	.	PUNCT
cuesj-22	83	1	in	in	ADP
cuesj-22	83	2	the	the	DET
cuesj-22	83	3	case	case	NOUN
cuesj-22	83	4	of	of	ADP
cuesj-22	83	5	the	the	DET
cuesj-22	83	6	second	second	ADJ
cuesj-22	83	7	-	-	PUNCT
cuesj-22	83	8	order	order	NOUN
cuesj-22	83	9	stationary	stationary	ADJ
cuesj-22	83	10	spatial	spatial	ADJ
cuesj-22	83	11	random	random	ADJ
cuesj-22	83	12	processes	process	NOUN
cuesj-22	83	13	,	,	PUNCT
cuesj-22	83	14	there	there	PRON
cuesj-22	83	15	is	be	VERB
cuesj-22	83	16	an	an	DET
cuesj-22	83	17	equivalence	equivalence	NOUN
cuesj-22	83	18	between	between	ADP
cuesj-22	83	19	covariance	covariance	NOUN
cuesj-22	83	20	function	function	NOUN
cuesj-22	83	21	and	and	CCONJ
cuesj-22	83	22	semivariogram	semivariogram	NOUN
cuesj-22	83	23	function	function	NOUN
cuesj-22	83	24	as	as	SCONJ
cuesj-22	83	25	follows	follow	VERB
cuesj-22	83	26	:	:	PUNCT
cuesj-22	83	27	table	table	NOUN
cuesj-22	83	28	1	1	NUM
cuesj-22	83	29	:	:	PUNCT
cuesj-22	83	30	classification	classification	NOUN
cuesj-22	83	31	of	of	ADP
cuesj-22	83	32	geostatistical	geostatistical	ADJ
cuesj-22	83	33	techniques	technique	NOUN
cuesj-22	83	34	model	model	NOUN
cuesj-22	83	35	stationary	stationary	ADJ
cuesj-22	83	36	nonstationary	nonstationary	ADJ
cuesj-22	83	37	linear	linear	ADJ
cuesj-22	83	38	ordinary	ordinary	ADJ
cuesj-22	83	39	/	/	SYM
cuesj-22	83	40	simple	simple	ADJ
cuesj-22	83	41	kriging	krige	VERB
cuesj-22	83	42	universal	universal	ADJ
cuesj-22	83	43	kriging	kriging	NOUN
cuesj-22	83	44	;	;	PUNCT
cuesj-22	83	45	kriging	krige	VERB
cuesj-22	83	46	using	use	VERB
cuesj-22	83	47	irf	irf	PROPN
cuesj-22	83	48	-	-	PUNCT
cuesj-22	83	49	k	k	PROPN
cuesj-22	83	50	nonlinear	nonlinear	ADJ
cuesj-22	83	51	disjunctive	disjunctive	ADJ
cuesj-22	83	52	kriging	krige	VERB
cuesj-22	83	53	simulation	simulation	NOUN
cuesj-22	83	54	simulation	simulation	NOUN
cuesj-22	83	55	of	of	ADP
cuesj-22	83	56	irf	irf	PROPN
cuesj-22	83	57	-	-	PUNCT
cuesj-22	83	58	k	k	PROPN
cuesj-22	83	59	irf	irf	PROPN
cuesj-22	83	60	-	-	PUNCT
cuesj-22	83	61	k	k	PROPN
cuesj-22	83	62	:	:	PUNCT
cuesj-22	83	63	intrinsic	intrinsic	ADJ
cuesj-22	83	64	random	random	ADJ
cuesj-22	83	65	functions	function	NOUN
cuesj-22	83	66	of	of	ADP
cuesj-22	83	67	order	order	NOUN
cuesj-22	83	68	k	k	X
cuesj-22	83	69	(	(	PUNCT
cuesj-22	83	70	)	)	PUNCT
cuesj-22	83	71	(	(	PUNCT
cuesj-22	83	72	)	)	PUNCT
cuesj-22	83	73	(	(	PUNCT
cuesj-22	83	74	)	)	SYM
cuesj-22	83	75	1	1	NUM
cuesj-22	83	76	2	2	NUM
cuesj-22	83	77	1	1	NUM
cuesj-22	83	78	1	1	NUM
cuesj-22	83	79	2	2	NUM
cuesj-22	83	80	2	2	NUM
cuesj-22	83	81	1	1	NUM
cuesj-22	83	82	2	2	NUM
cuesj-22	83	83	1	1	NUM
cuesj-22	83	84	2	2	NUM
cuesj-22	83	85	(	(	PUNCT
cuesj-22	83	86	)	)	PUNCT
cuesj-22	83	87	,	,	PUNCT
cuesj-22	83	88	(	(	PUNCT
cuesj-22	83	89	)	)	PUNCT
cuesj-22	83	90	(	(	PUNCT
cuesj-22	83	91	)	)	PUNCT
cuesj-22	83	92	(	(	PUNCT
cuesj-22	83	93	)	)	PUNCT
cuesj-22	83	94	(	(	PUNCT
cuesj-22	83	95	)	)	PUNCT
cuesj-22	83	96	,	,	PUNCT
cuesj-22	83	97	s	s	VERB
cuesj-22	83	98	scov	scov	PROPN
cuesj-22	83	99	z	z	PROPN
cuesj-22	83	100	s	s	PROPN
cuesj-22	83	101	z	z	NOUN
cuesj-22	83	102	s	s	PART
cuesj-22	83	103	e	e	NOUN
cuesj-22	83	104	z	z	PROPN
cuesj-22	83	105	s	s	PROPN
cuesj-22	83	106	z	z	NOUN
cuesj-22	83	107	s	s	PART
cuesj-22	83	108	c	c	NOUN
cuesj-22	83	109	s	s	X
cuesj-22	83	110	s	s	NOUN
cuesj-22	83	111	s	s	X
cuesj-22	83	112	s	s	X
cuesj-22	83	113	d	d	X
cuesj-22	83	114	µ	µ	X
cuesj-22	83	115	µ	µ	X
cuesj-22	83	116	=	=	PUNCT
cuesj-22	83	117	−	−	NOUN
cuesj-22	83	118	−	−	NOUN
cuesj-22	84	1	=	=	NOUN
cuesj-22	84	2			NOUN
cuesj-22	84	3			NOUN
cuesj-22	84	4	−	−	PROPN
cuesj-22	84	5	∈	∈	PROPN
cuesj-22	84	6	mawlood	mawlood	NOUN
cuesj-22	84	7	and	and	CCONJ
cuesj-22	84	8	omer	omer	PROPN
cuesj-22	84	9	:	:	PUNCT
cuesj-22	84	10	prediction	prediction	NOUN
cuesj-22	84	11	the	the	DET
cuesj-22	84	12	groundwater	groundwater	NOUN
cuesj-22	84	13	depth	depth	NOUN
cuesj-22	84	14	44	44	NUM
cuesj-22	84	15	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	84	16	cuesj	cuesj	NOUN
cuesj-22	84	17	2019	2019	NUM
cuesj-22	84	18	,	,	PUNCT
cuesj-22	84	19	3	3	NUM
cuesj-22	84	20	(	(	PUNCT
cuesj-22	84	21	1	1	NUM
cuesj-22	84	22	):	):	PUNCT
cuesj-22	84	23	42	42	NUM
cuesj-22	84	24	-	-	SYM
cuesj-22	84	25	49	49	NUM
cuesj-22	84	26	v	v	NOUN
cuesj-22	84	27	z	z	NOUN
cuesj-22	84	28	s	s	NOUN
cuesj-22	84	29	h	h	NOUN
cuesj-22	84	30	z	z	PROPN
cuesj-22	84	31	s	s	PROPN
cuesj-22	84	32	v	v	ADP
cuesj-22	84	33	z	z	NOUN
cuesj-22	84	34	s	s	NOUN
cuesj-22	84	35	h	h	NOUN
cuesj-22	84	36	v	v	ADP
cuesj-22	84	37	z	z	PROPN
cuesj-22	84	38	s	s	PART
cuesj-22	84	39	cov	cov	PROPN
cuesj-22	84	40	z	z	PROPN
cuesj-22	84	41	s	s	PROPN
cuesj-22	84	42	h	h	NOUN
cuesj-22	84	43	z	z	PROPN
cuesj-22	84	44	s	s	PROPN
cuesj-22	84	45	v	v	ADP
cuesj-22	84	46	z	z	NOUN
cuesj-22	84	47	s	s	PART
cuesj-22	84	48	(	(	PUNCT
cuesj-22	84	49	)	)	PUNCT
cuesj-22	84	50	(	(	PUNCT
cuesj-22	84	51	)	)	PUNCT
cuesj-22	84	52	(	(	PUNCT
cuesj-22	84	53	)	)	PUNCT
cuesj-22	84	54	(	(	PUNCT
cuesj-22	84	55	)	)	PUNCT
cuesj-22	84	56	(	(	PUNCT
cuesj-22	84	57	)	)	PUNCT
cuesj-22	84	58	,	,	PUNCT
cuesj-22	84	59	(	(	PUNCT
cuesj-22	84	60	)	)	PUNCT
cuesj-22	84	61	(	(	PUNCT
cuesj-22	84	62	)	)	PUNCT
cuesj-22	85	1	+	+	CCONJ
cuesj-22	85	2	−	−	PROPN
cuesj-22	85	3	[	[	X
cuesj-22	85	4	]	]	X
cuesj-22	85	5	=	=	PUNCT
cuesj-22	86	1	+	+	PROPN
cuesj-22	86	2	[	[	X
cuesj-22	86	3	]	]	X
cuesj-22	86	4	+	+	PUNCT
cuesj-22	86	5	[	[	PUNCT
cuesj-22	86	6	]	]	X
cuesj-22	86	7	−	−	PUNCT
cuesj-22	87	1	+	+	NOUN
cuesj-22	87	2	[	[	X
cuesj-22	87	3	]	]	X
cuesj-22	87	4	=	=	PUNCT
cuesj-22	87	5	[	[	PUNCT
cuesj-22	87	6	2	2	NUM
cuesj-22	87	7	2	2	NUM
cuesj-22	87	8	]	]	PUNCT
cuesj-22	87	9	]	]	X
cuesj-22	87	10	−	−	PUNCT
cuesj-22	88	1	+	+	NOUN
cuesj-22	88	2	[	[	X
cuesj-22	88	3	]	]	X
cuesj-22	88	4	=	=	PUNCT
cuesj-22	88	5	−	−	PROPN
cuesj-22	88	6	[	[	X
cuesj-22	88	7	]	]	X
cuesj-22	88	8	=	=	PUNCT
cuesj-22	88	9	÷	÷	PUNCT
cuesj-22	88	10	=	=	PUNCT
cuesj-22	89	1	−	−	PROPN
cuesj-22	89	2	2	2	NUM
cuesj-22	89	3	2	2	NUM
cuesj-22	89	4	0	0	NUM
cuesj-22	89	5	2	2	NUM
cuesj-22	89	6	2	2	NUM
cuesj-22	89	7	0	0	NUM
cuesj-22	90	1	cov	cov	PROPN
cuesj-22	90	2	z	z	PROPN
cuesj-22	90	3	s	s	PROPN
cuesj-22	90	4	h	h	NOUN
cuesj-22	90	5	z	z	NOUN
cuesj-22	90	6	s	s	PART
cuesj-22	90	7	c	c	NOUN
cuesj-22	90	8	c	c	NOUN
cuesj-22	90	9	h	h	NOUN
cuesj-22	90	10	h	h	NOUN
cuesj-22	91	1	h	h	NOUN
cuesj-22	92	1	c	c	NOUN
cuesj-22	92	2	c	c	NOUN
cuesj-22	92	3	h	h	PROPN
cuesj-22	92	4	(	(	PUNCT
cuesj-22	92	5	)	)	PUNCT
cuesj-22	92	6	,	,	PUNCT
cuesj-22	92	7	(	(	PUNCT
cuesj-22	92	8	)	)	PUNCT
cuesj-22	92	9	(	(	PUNCT
cuesj-22	92	10	)	)	PUNCT
cuesj-22	92	11	(	(	PUNCT
cuesj-22	92	12	)	)	PUNCT
cuesj-22	92	13	(	(	PUNCT
cuesj-22	92	14	)	)	PUNCT
cuesj-22	92	15	(	(	PUNCT
cuesj-22	92	16	)	)	PUNCT
cuesj-22	92	17	(	(	PUNCT
cuesj-22	92	18	)	)	PUNCT
cuesj-22	92	19	(	(	PUNCT
cuesj-22	92	20	)	)	PUNCT
cuesj-22	93	1			NUM
cuesj-22	93	2			NUM
cuesj-22	93	3	(	(	PUNCT
cuesj-22	93	4	10	10	NUM
cuesj-22	93	5	)	)	PUNCT
cuesj-22	93	6	the	the	DET
cuesj-22	93	7	semivariogram	semivariogram	NOUN
cuesj-22	93	8	function	function	NOUN
cuesj-22	93	9	is	be	AUX
cuesj-22	93	10	a	a	DET
cuesj-22	93	11	measure	measure	NOUN
cuesj-22	93	12	of	of	ADP
cuesj-22	93	13	dissimilarity	dissimilarity	NOUN
cuesj-22	93	14	between	between	ADP
cuesj-22	93	15	pairs	pair	NOUN
cuesj-22	93	16	of	of	ADP
cuesj-22	93	17	locations	location	NOUN
cuesj-22	93	18	or	or	CCONJ
cuesj-22	93	19	observed	observe	VERB
cuesj-22	93	20	value	value	NOUN
cuesj-22	93	21	z(s+h	z(s+h	NOUN
cuesj-22	93	22	)	)	PUNCT
cuesj-22	93	23	and	and	CCONJ
cuesj-22	93	24	z(s	z(s	PROPN
cuesj-22	93	25	)	)	PUNCT
cuesj-22	93	26	.	.	PUNCT
cuesj-22	94	1	there	there	PRON
cuesj-22	94	2	are	be	VERB
cuesj-22	94	3	three	three	NUM
cuesj-22	94	4	parameters	parameter	NOUN
cuesj-22	94	5	of	of	ADP
cuesj-22	94	6	semivariogram	semivariogram	NOUN
cuesj-22	94	7	function	function	NOUN
cuesj-22	94	8	for	for	ADP
cuesj-22	94	9	the	the	DET
cuesj-22	94	10	spatial	spatial	ADJ
cuesj-22	94	11	second	second	ADJ
cuesj-22	94	12	-	-	PUNCT
cuesj-22	94	13	order	order	NOUN
cuesj-22	94	14	stationary	stationary	ADJ
cuesj-22	94	15	processes	process	NOUN
cuesj-22	94	16	:	:	PUNCT
cuesj-22	94	17	nugget	nugget	ADJ
cuesj-22	94	18	effect	effect	NOUN
cuesj-22	94	19	(	(	PUNCT
cuesj-22	94	20	c0	c0	NOUN
cuesj-22	94	21	)	)	PUNCT
cuesj-22	94	22	,	,	PUNCT
cuesj-22	94	23	range	range	NOUN
cuesj-22	94	24	(	(	PUNCT
cuesj-22	94	25	a	a	NOUN
cuesj-22	94	26	)	)	PUNCT
cuesj-22	94	27	,	,	PUNCT
cuesj-22	94	28	and	and	CCONJ
cuesj-22	94	29	partial	partial	ADJ
cuesj-22	94	30	sill	sill	NOUN
cuesj-22	94	31	(	(	PUNCT
cuesj-22	94	32	c	c	NOUN
cuesj-22	94	33	)	)	PUNCT
cuesj-22	94	34	which	which	PRON
cuesj-22	94	35	are	be	AUX
cuesj-22	94	36	shown	show	VERB
cuesj-22	94	37	in	in	ADP
cuesj-22	94	38	figure	figure	NOUN
cuesj-22	94	39	1	1	NUM
cuesj-22	94	40	.	.	PUNCT
cuesj-22	95	1	the	the	DET
cuesj-22	95	2	sum	sum	NOUN
cuesj-22	95	3	(	(	PUNCT
cuesj-22	95	4	c0+c	c0+c	NOUN
cuesj-22	95	5	)	)	PUNCT
cuesj-22	95	6	is	be	AUX
cuesj-22	95	7	called	call	VERB
cuesj-22	95	8	the	the	DET
cuesj-22	95	9	sill	sill	ADJ
cuesj-22	95	10	(	(	PUNCT
cuesj-22	95	11	marcin	marcin	NOUN
cuesj-22	95	12	and	and	CCONJ
cuesj-22	95	13	marek	marek	PROPN
cuesj-22	95	14	,	,	PUNCT
cuesj-22	95	15	2010	2010	NUM
cuesj-22	95	16	)	)	PUNCT
cuesj-22	95	17	,	,	PUNCT
cuesj-22	95	18	(	(	PUNCT
cuesj-22	95	19	sluiter	sluiter	NOUN
cuesj-22	95	20	,	,	PUNCT
cuesj-22	95	21	2009	2009	NUM
cuesj-22	95	22	)	)	PUNCT
cuesj-22	95	23	.	.	PUNCT
cuesj-22	96	1	the	the	DET
cuesj-22	96	2	gaussian	gaussian	ADJ
cuesj-22	96	3	semivariogram	semivariogram	NOUN
cuesj-22	96	4	model	model	NOUN
cuesj-22	96	5	the	the	DET
cuesj-22	96	6	gaussian	gaussian	ADJ
cuesj-22	96	7	model	model	NOUN
cuesj-22	96	8	is	be	AUX
cuesj-22	96	9	commonly	commonly	ADV
cuesj-22	96	10	used	use	VERB
cuesj-22	96	11	to	to	PART
cuesj-22	96	12	represent	represent	VERB
cuesj-22	96	13	events	event	NOUN
cuesj-22	96	14	with	with	ADP
cuesj-22	96	15	a	a	DET
cuesj-22	96	16	small	small	ADJ
cuesj-22	96	17	scale	scale	NOUN
cuesj-22	96	18	spatial	spatial	ADJ
cuesj-22	96	19	structure,[6	structure,[6	PROPN
cuesj-22	96	20	]	]	PUNCT
cuesj-22	96	21	the	the	DET
cuesj-22	96	22	equation	equation	NOUN
cuesj-22	96	23	for	for	ADP
cuesj-22	96	24	this	this	DET
cuesj-22	96	25	model	model	NOUN
cuesj-22	96	26	is	be	AUX
cuesj-22	96	27	similar	similar	ADJ
cuesj-22	96	28	to	to	ADP
cuesj-22	96	29	the	the	DET
cuesj-22	96	30	normal	normal	ADJ
cuesj-22	96	31	cumulative	cumulative	ADJ
cuesj-22	96	32	distribution	distribution	NOUN
cuesj-22	96	33	function	function	NOUN
cuesj-22	96	34	,	,	PUNCT
cuesj-22	96	35	and	and	CCONJ
cuesj-22	96	36	it	it	PRON
cuesj-22	96	37	is	be	AUX
cuesj-22	96	38	given	give	VERB
cuesj-22	96	39	by	by	ADP
cuesj-22	96	40	[	[	PUNCT
cuesj-22	96	41	figure	figure	NOUN
cuesj-22	96	42	2	2	NUM
cuesj-22	96	43	]	]	PUNCT
cuesj-22	96	44	:	:	PUNCT
cuesj-22	97	1			NUM
cuesj-22	97	2	z	z	NOUN
cuesj-22	97	3	h	h	NOUN
cuesj-22	98	1	c	c	NOUN
cuesj-22	98	2	c	c	NOUN
cuesj-22	98	3	h	h	PROPN
cuesj-22	99	1	a	a	DET
cuesj-22	99	2	h	h	NOUN
cuesj-22	99	3	(	(	PUNCT
cuesj-22	99	4	)	)	PUNCT
cuesj-22	99	5	exp	exp	NOUN
cuesj-22	99	6	,	,	PUNCT
cuesj-22	99	7	=	=	PUNCT
cuesj-22	100	1	+	+	NUM
cuesj-22	101	1	−	−	PROPN
cuesj-22	101	2	−	−	VERB
cuesj-22	101	3			PROPN
cuesj-22	101	4			PROPN
cuesj-22	101	5			ADP
cuesj-22	101	6			PROPN
cuesj-22	101	7			NOUN
cuesj-22	101	8			ADJ
cuesj-22	101	9			ADJ
cuesj-22	101	10			PROPN
cuesj-22	101	11			PROPN
cuesj-22	101	12			PROPN
cuesj-22	101	13			PROPN
cuesj-22	101	14	>	>	X
cuesj-22	101	15	0	0	NUM
cuesj-22	101	16	2	2	NUM
cuesj-22	101	17	0	0	NUM
cuesj-22	101	18	21	21	NUM
cuesj-22	101	19	0	0	NUM
cuesj-22	101	20	(	(	PUNCT
cuesj-22	101	21	11	11	NUM
cuesj-22	101	22	)	)	PUNCT
cuesj-22	101	23	the	the	DET
cuesj-22	101	24	simple	simple	ADJ
cuesj-22	101	25	kriging	krige	VERB
cuesj-22	101	26	interpolation	interpolation	NOUN
cuesj-22	101	27	method	method	NOUN
cuesj-22	101	28	simple	simple	ADJ
cuesj-22	101	29	kriging	kriging	NOUN
cuesj-22	101	30	can	can	AUX
cuesj-22	101	31	deliver	deliver	VERB
cuesj-22	101	32	the	the	DET
cuesj-22	101	33	value	value	NOUN
cuesj-22	101	34	at	at	ADP
cuesj-22	101	35	any	any	DET
cuesj-22	101	36	unmeasured	unmeasured	ADJ
cuesj-22	101	37	location	location	NOUN
cuesj-22	101	38	(	(	PUNCT
cuesj-22	101	39	s0	s0	PROPN
cuesj-22	101	40	)	)	PUNCT
cuesj-22	101	41	using	use	VERB
cuesj-22	101	42	a	a	DET
cuesj-22	101	43	linear	linear	ADJ
cuesj-22	101	44	estimator	estimator	NOUN
cuesj-22	101	45	λi	λi	INTJ
cuesj-22	101	46	for	for	ADP
cuesj-22	101	47	the	the	DET
cuesj-22	101	48	measured	measured	ADJ
cuesj-22	101	49	values	value	NOUN
cuesj-22	101	50	at	at	ADP
cuesj-22	101	51	locations	location	NOUN
cuesj-22	101	52	(	(	PUNCT
cuesj-22	101	53	s1,s2.,sn	s1,s2.,sn	NOUN
cuesj-22	101	54	):	):	PUNCT
cuesj-22	101	55	z	z	PROPN
cuesj-22	101	56	s	s	NOUN
cuesj-22	101	57	z	z	NOUN
cuesj-22	102	1	si	si	INTJ
cuesj-22	103	1	i	i	PRON
cuesj-22	104	1	i	i	PRON
cuesj-22	105	1	n	n	VERB
cuesj-22	106	1	i	i	PRON
cuesj-22	106	2	i	i	PRON
cuesj-22	106	3	n	n	PROPN
cuesj-22	106	4	(	(	PUNCT
cuesj-22	106	5	)	)	PUNCT
cuesj-22	106	6	(	(	PUNCT
cuesj-22	106	7	)	)	PUNCT
cuesj-22	106	8	,	,	PUNCT
cuesj-22	106	9	0	0	NUM
cuesj-22	106	10	1	1	NUM
cuesj-22	106	11	1	1	NUM
cuesj-22	106	12	1=	1=	NUM
cuesj-22	106	13	=	=	SYM
cuesj-22	107	1	=	=	PUNCT
cuesj-22	107	2	=	=	SYM
cuesj-22	107	3	∑	∑	PUNCT
cuesj-22	107	4	∑	∑	X
cuesj-22	107	5			X
cuesj-22	107	6	(	(	PUNCT
cuesj-22	107	7	12	12	NUM
cuesj-22	107	8	)	)	PUNCT
cuesj-22	107	9	as	as	SCONJ
cuesj-22	107	10	the	the	DET
cuesj-22	107	11	sum	sum	NOUN
cuesj-22	107	12	of	of	ADP
cuesj-22	107	13	all	all	DET
cuesj-22	107	14	linear	linear	ADJ
cuesj-22	107	15	estimators	estimator	NOUN
cuesj-22	107	16	equals	equal	VERB
cuesj-22	107	17	one	one	NUM
cuesj-22	107	18	,	,	PUNCT
cuesj-22	107	19	the	the	DET
cuesj-22	107	20	unbiased	unbiased	ADJ
cuesj-22	107	21	prediction	prediction	NOUN
cuesj-22	107	22	is	be	AUX
cuesj-22	107	23	ensured	ensure	VERB
cuesj-22	107	24	.	.	PUNCT
cuesj-22	108	1	using	use	VERB
cuesj-22	108	2	the	the	DET
cuesj-22	108	3	intrinsic	intrinsic	ADJ
cuesj-22	108	4	hypothesis	hypothesis	NOUN
cuesj-22	108	5	,	,	PUNCT
cuesj-22	108	6	the	the	DET
cuesj-22	108	7	estimation	estimation	NOUN
cuesj-22	108	8	variance	variance	NOUN
cuesj-22	108	9	is	be	AUX
cuesj-22	108	10	calculated	calculate	VERB
cuesj-22	108	11	by	by	ADP
cuesj-22	108	12	the	the	DET
cuesj-22	108	13	formula	formula	NOUN
cuesj-22	108	14	:	:	PUNCT
cuesj-22	108	15	(	(	PUNCT
cuesj-22	108	16	)	)	PUNCT
cuesj-22	108	17	2	2	NUM
cuesj-22	108	18	2	2	NUM
cuesj-22	108	19	0	0	NUM
cuesj-22	108	20	0	0	NUM
cuesj-22	108	21	0	0	NUM
cuesj-22	108	22	0	0	NUM
cuesj-22	108	23	0	0	NUM
cuesj-22	108	24	1	1	NUM
cuesj-22	108	25	1	1	NUM
cuesj-22	108	26	1	1	NUM
cuesj-22	108	27	ˆ	ˆ	NOUN
cuesj-22	108	28	(	(	PUNCT
cuesj-22	108	29	)	)	PUNCT
cuesj-22	108	30	(	(	PUNCT
cuesj-22	108	31	(	(	PUNCT
cuesj-22	108	32	)	)	PUNCT
cuesj-22	108	33	(	(	PUNCT
cuesj-22	108	34	(	(	PUNCT
cuesj-22	108	35	)	)	PUNCT
cuesj-22	108	36	(	(	PUNCT
cuesj-22	108	37	)	)	PUNCT
cuesj-22	108	38	)	)	PUNCT
cuesj-22	108	39	2	2	NUM
cuesj-22	108	40	(	(	PUNCT
cuesj-22	108	41	)	)	PUNCT
cuesj-22	108	42	(	(	PUNCT
cuesj-22	108	43	)	)	PUNCT
cuesj-22	108	44	e	e	NOUN
cuesj-22	108	45	n	n	CCONJ
cuesj-22	108	46	n	n	CCONJ
cuesj-22	108	47	n	n	NOUN
cuesj-22	109	1	i	i	PRON
cuesj-22	109	2	i	i	PRON
cuesj-22	110	1	i	i	INTJ
cuesj-22	110	2	j	j	VERB
cuesj-22	111	1	i	i	PRON
cuesj-22	111	2	j	j	VERB
cuesj-22	112	1	i	i	PRON
cuesj-22	112	2	i	i	PRON
cuesj-22	113	1	j	j	PROPN
cuesj-22	113	2	s	s	VERB
cuesj-22	113	3	var	var	NOUN
cuesj-22	113	4	z	z	PROPN
cuesj-22	113	5	s	s	PROPN
cuesj-22	113	6	e	e	X
cuesj-22	113	7	z	z	PROPN
cuesj-22	113	8	s	s	PROPN
cuesj-22	113	9	z	z	NOUN
cuesj-22	113	10	s	s	X
cuesj-22	113	11	s	s	NOUN
cuesj-22	113	12	s	s	X
cuesj-22	113	13	s	s	X
cuesj-22	113	14	s	s	X
cuesj-22	113	15			PROPN
cuesj-22	113	16			ADJ
cuesj-22	113	17			NUM
cuesj-22	113	18			X
cuesj-22	113	19			X
cuesj-22	113	20			NUM
cuesj-22	113	21	=	=	PUNCT
cuesj-22	114	1	=	=	PUNCT
cuesj-22	114	2	=	=	PUNCT
cuesj-22	114	3	=	=	PUNCT
cuesj-22	114	4	=	=	PUNCT
cuesj-22	115	1	−	−	PROPN
cuesj-22	115	2	=	=	PUNCT
cuesj-22	116	1	−	−	PROPN
cuesj-22	116	2	−	−	PROPN
cuesj-22	116	3	−∑	−∑	PROPN
cuesj-22	117	1	∑∑	∑∑	PROPN
cuesj-22	117	2	(	(	PUNCT
cuesj-22	117	3	13	13	NUM
cuesj-22	117	4	)	)	PUNCT
cuesj-22	117	5	the	the	DET
cuesj-22	117	6	semivariance	semivariance	NOUN
cuesj-22	117	7	(	(	PUNCT
cuesj-22	117	8	sj−s0	sj−s0	NOUN
cuesj-22	117	9	)	)	PUNCT
cuesj-22	117	10	and(si−s0	and(si−s0	NOUN
cuesj-22	117	11	)	)	PUNCT
cuesj-22	117	12	are	be	AUX
cuesj-22	117	13	taken	take	VERB
cuesj-22	117	14	from	from	ADP
cuesj-22	117	15	the	the	DET
cuesj-22	117	16	variogram	variogram	NOUN
cuesj-22	117	17	model	model	NOUN
cuesj-22	117	18	.	.	PUNCT
cuesj-22	118	1	the	the	DET
cuesj-22	118	2	goal	goal	NOUN
cuesj-22	118	3	is	be	AUX
cuesj-22	118	4	to	to	PART
cuesj-22	118	5	minimize	minimize	VERB
cuesj-22	118	6	σ2(s	σ2(s	NOUN
cuesj-22	118	7	)	)	PUNCT
cuesj-22	118	8	under	under	ADP
cuesj-22	118	9	the	the	DET
cuesj-22	118	10	unbiased	unbiased	ADJ
cuesj-22	118	11	conditions	condition	NOUN
cuesj-22	118	12	and	and	CCONJ
cuesj-22	118	13	to	to	PART
cuesj-22	118	14	find	find	VERB
cuesj-22	118	15	the	the	DET
cuesj-22	118	16	corresponding	corresponding	ADJ
cuesj-22	118	17	weight	weight	NOUN
cuesj-22	118	18	.	.	PUNCT
cuesj-22	119	1	the	the	DET
cuesj-22	119	2	weights	weight	NOUN
cuesj-22	119	3	are	be	AUX
cuesj-22	119	4	chosen	choose	VERB
cuesj-22	119	5	to	to	PART
cuesj-22	119	6	fulfill	fulfill	VERB
cuesj-22	119	7	:	:	PUNCT
cuesj-22	120	1	0	0	NUM
cuesj-22	120	2	0	0	NUM
cuesj-22	120	3	ˆ	ˆ	NOUN
cuesj-22	120	4	(	(	PUNCT
cuesj-22	120	5	(	(	PUNCT
cuesj-22	120	6	)	)	PUNCT
cuesj-22	120	7	(	(	PUNCT
cuesj-22	120	8	)	)	PUNCT
cuesj-22	120	9	)	)	PUNCT
cuesj-22	120	10	0e	0e	PROPN
cuesj-22	121	1	z	z	PROPN
cuesj-22	121	2	s	s	PROPN
cuesj-22	121	3	z	z	NOUN
cuesj-22	121	4	s−	s−	NOUN
cuesj-22	121	5	=	=	PUNCT
cuesj-22	121	6	according	accord	VERB
cuesj-22	121	7	to	to	ADP
cuesj-22	121	8	the	the	DET
cuesj-22	121	9	assumption	assumption	NOUN
cuesj-22	121	10	that	that	PRON
cuesj-22	121	11	e[z(s1)−z(s2)]=0	e[z(s1)−z(s2)]=0	PUNCT
cuesj-22	121	12	which	which	PRON
cuesj-22	121	13	called	call	VERB
cuesj-22	121	14	intrinsic	intrinsic	ADJ
cuesj-22	121	15	stationery	stationery	NOUN
cuesj-22	121	16	and	and	CCONJ
cuesj-22	121	17	to	to	PART
cuesj-22	121	18	solve	solve	VERB
cuesj-22	121	19	this	this	PRON
cuesj-22	121	20	the	the	DET
cuesj-22	121	21	lagrange	lagrange	NOUN
cuesj-22	121	22	multiplier	multiplier	ADV
cuesj-22	121	23	(	(	PUNCT
cuesj-22	121	24	μ	μ	NOUN
cuesj-22	121	25	)	)	PUNCT
cuesj-22	121	26	is	be	AUX
cuesj-22	121	27	introduced	introduce	VERB
cuesj-22	121	28	,	,	PUNCT
cuesj-22	121	29	not	not	PART
cuesj-22	121	30	easy	easy	ADJ
cuesj-22	121	31	solving	solve	VERB
cuesj-22	121	32	this	this	DET
cuesj-22	121	33	model	model	NOUN
cuesj-22	121	34	by	by	ADP
cuesj-22	121	35	any	any	DET
cuesj-22	121	36	other	other	ADJ
cuesj-22	121	37	ways	way	NOUN
cuesj-22	121	38	:	:	PUNCT
cuesj-22	121	39	z	z	PROPN
cuesj-22	121	40	s	s	X
cuesj-22	121	41	s	s	X
cuesj-22	121	42	s	s	X
cuesj-22	121	43	s	s	X
cuesj-22	121	44	s	s	X
cuesj-22	122	1	so	so	ADV
cuesj-22	123	1	i	i	PRON
cuesj-22	124	1	i	i	PRON
cuesj-22	125	1	j	j	VERB
cuesj-22	126	1	i	i	PRON
cuesj-22	126	2	n	n	PROPN
cuesj-22	126	3	j	j	PROPN
cuesj-22	126	4	(	(	PUNCT
cuesj-22	126	5	)	)	PUNCT
cuesj-22	126	6	(	(	PUNCT
cuesj-22	126	7	)	)	PUNCT
cuesj-22	126	8	(	(	PUNCT
cuesj-22	126	9	)	)	PUNCT
cuesj-22	126	10	(	(	PUNCT
cuesj-22	126	11	)	)	PUNCT
cuesj-22	126	12	=	=	SYM
cuesj-22	127	1	−	−	PROPN
cuesj-22	128	1	+	+	NUM
cuesj-22	128	2	=	=	SYM
cuesj-22	128	3	−	−	PROPN
cuesj-22	128	4	=	=	PUNCT
cuesj-22	128	5	∑	∑	PUNCT
cuesj-22	128	6	λ	λ	PROPN
cuesj-22	128	7	γ	γ	X
cuesj-22	128	8	µ	µ	X
cuesj-22	128	9	γ	γ	X
cuesj-22	128	10	1	1	NUM
cuesj-22	128	11	0	0	NUM
cuesj-22	128	12	0	0	NUM
cuesj-22	128	13	(	(	PUNCT
cuesj-22	128	14	14	14	NUM
cuesj-22	128	15	)	)	PUNCT
cuesj-22	128	16	the	the	DET
cuesj-22	128	17	solutions	solution	NOUN
cuesj-22	128	18	to	to	ADP
cuesj-22	128	19	this	this	DET
cuesj-22	128	20	linear	linear	ADJ
cuesj-22	128	21	equation	equation	NOUN
cuesj-22	128	22	system	system	NOUN
cuesj-22	128	23	are	be	AUX
cuesj-22	128	24	the	the	DET
cuesj-22	128	25	values	value	NOUN
cuesj-22	128	26	of	of	ADP
cuesj-22	128	27	the	the	DET
cuesj-22	128	28	linear	linear	ADJ
cuesj-22	128	29	estimators	estimator	NOUN
cuesj-22	128	30	.	.	PUNCT
cuesj-22	129	1	this	this	DET
cuesj-22	129	2	system	system	NOUN
cuesj-22	129	3	is	be	AUX
cuesj-22	129	4	called	call	VERB
cuesj-22	129	5	kriging	krige	VERB
cuesj-22	129	6	system.[7	system.[7	NOUN
cuesj-22	129	7	]	]	PUNCT
cuesj-22	129	8	the	the	DET
cuesj-22	129	9	kriging	krige	VERB
cuesj-22	129	10	variance	variance	NOUN
cuesj-22	129	11	is	be	AUX
cuesj-22	129	12	determined	determine	VERB
cuesj-22	129	13	by	by	ADP
cuesj-22	129	14	:	:	PUNCT
cuesj-22	129	15	σ	σ	PROPN
cuesj-22	129	16	λ	λ	PROPN
cuesj-22	130	1	γ	γ	NOUN
cuesj-22	130	2	µk	µk	INTJ
cuesj-22	131	1	i	i	PRON
cuesj-22	131	2	i	i	PRON
cuesj-22	132	1	i	i	PRON
cuesj-22	132	2	n	n	VERB
cuesj-22	132	3	s	s	NOUN
cuesj-22	132	4	s	s	PROPN
cuesj-22	132	5	s2	s2	NOUN
cuesj-22	132	6	0	0	NUM
cuesj-22	132	7	1	1	NUM
cuesj-22	132	8	0=	0=	NOUN
cuesj-22	133	1	−	−	PROPN
cuesj-22	134	1	+	+	CCONJ
cuesj-22	134	2	=	=	SYM
cuesj-22	134	3	∑	∑	PUNCT
cuesj-22	134	4	(	(	PUNCT
cuesj-22	134	5	)	)	PUNCT
cuesj-22	134	6	(	(	PUNCT
cuesj-22	134	7	)	)	PUNCT
cuesj-22	134	8	(	(	PUNCT
cuesj-22	134	9	15	15	X
cuesj-22	134	10	)	)	PUNCT
cuesj-22	134	11	kalman	kalman	NOUN
cuesj-22	134	12	filtering	filter	VERB
cuesj-22	134	13	the	the	DET
cuesj-22	134	14	kalman	kalman	PROPN
cuesj-22	134	15	filter	filter	NOUN
cuesj-22	134	16	gives	give	VERB
cuesj-22	134	17	unbiased	unbiased	ADJ
cuesj-22	134	18	,	,	PUNCT
cuesj-22	134	19	linear	linear	ADJ
cuesj-22	134	20	,	,	PUNCT
cuesj-22	134	21	and	and	CCONJ
cuesj-22	134	22	minimum	minimum	ADJ
cuesj-22	134	23	variance	variance	NOUN
cuesj-22	134	24	recursive	recursive	ADJ
cuesj-22	134	25	estimate	estimate	NOUN
cuesj-22	134	26	to	to	ADP
cuesj-22	134	27	the	the	DET
cuesj-22	134	28	state	state	NOUN
cuesj-22	134	29	of	of	ADP
cuesj-22	134	30	a	a	DET
cuesj-22	134	31	dynamic	dynamic	ADJ
cuesj-22	134	32	system	system	NOUN
cuesj-22	134	33	from	from	ADP
cuesj-22	134	34	noisy	noisy	ADJ
cuesj-22	134	35	data	datum	NOUN
cuesj-22	134	36	taken	take	VERB
cuesj-22	134	37	in	in	ADP
cuesj-22	134	38	separate	separate	ADJ
cuesj-22	134	39	real	real	ADJ
cuesj-22	134	40	-	-	PUNCT
cuesj-22	134	41	time.[10	time.[10	NOUN
cuesj-22	134	42	]	]	PUNCT
cuesj-22	135	1	kf	kf	PROPN
cuesj-22	135	2	used	use	VERB
cuesj-22	135	3	in	in	ADP
cuesj-22	135	4	the	the	DET
cuesj-22	135	5	formulation	formulation	NOUN
cuesj-22	135	6	of	of	ADP
cuesj-22	135	7	a	a	DET
cuesj-22	135	8	dynamic	dynamic	ADJ
cuesj-22	135	9	model	model	NOUN
cuesj-22	135	10	for	for	ADP
cuesj-22	135	11	linear	linear	PROPN
cuesj-22	135	12	dynamical	dynamical	ADJ
cuesj-22	135	13	systems	system	NOUN
cuesj-22	135	14	provides	provide	VERB
cuesj-22	135	15	a	a	DET
cuesj-22	135	16	recursive	recursive	ADJ
cuesj-22	135	17	solution	solution	NOUN
cuesj-22	135	18	to	to	ADP
cuesj-22	135	19	the	the	DET
cuesj-22	135	20	problem	problem	NOUN
cuesj-22	135	21	of	of	ADP
cuesj-22	135	22	the	the	DET
cuesj-22	135	23	optimal	optimal	ADJ
cuesj-22	135	24	linear	linear	ADJ
cuesj-22	135	25	filter	filter	NOUN
cuesj-22	135	26	.	.	PUNCT
cuesj-22	136	1	the	the	DET
cuesj-22	136	2	recursive	recursive	ADJ
cuesj-22	136	3	solution	solution	NOUN
cuesj-22	136	4	in	in	ADP
cuesj-22	136	5	every	every	DET
cuesj-22	136	6	state	state	NOUN
cuesj-22	136	7	update	update	NOUN
cuesj-22	136	8	estimate	estimate	NOUN
cuesj-22	136	9	is	be	AUX
cuesj-22	136	10	calculated	calculate	VERB
cuesj-22	136	11	from	from	ADP
cuesj-22	136	12	previous	previous	ADJ
cuesj-22	136	13	estimates	estimate	NOUN
cuesj-22	136	14	and	and	CCONJ
cuesj-22	136	15	data	datum	NOUN
cuesj-22	136	16	entry	entry	NOUN
cuesj-22	136	17	;	;	PUNCT
cuesj-22	136	18	only	only	ADV
cuesj-22	136	19	the	the	DET
cuesj-22	136	20	previous	previous	ADJ
cuesj-22	136	21	estimate	estimate	NOUN
cuesj-22	136	22	requires	require	VERB
cuesj-22	136	23	storage	storage	NOUN
cuesj-22	136	24	.	.	PUNCT
cuesj-22	137	1	kf	kf	PROPN
cuesj-22	137	2	has	have	AUX
cuesj-22	137	3	been	be	AUX
cuesj-22	137	4	widely	widely	ADV
cuesj-22	137	5	used	use	VERB
cuesj-22	137	6	in	in	ADP
cuesj-22	137	7	the	the	DET
cuesj-22	137	8	fields	field	NOUN
cuesj-22	137	9	of	of	ADP
cuesj-22	137	10	signal	signal	NOUN
cuesj-22	137	11	processing	processing	NOUN
cuesj-22	137	12	and	and	CCONJ
cuesj-22	137	13	modern	modern	ADJ
cuesj-22	137	14	control	control	NOUN
cuesj-22	137	15	and	and	CCONJ
cuesj-22	137	16	airborne	airborne	ADJ
cuesj-22	137	17	surveillance	surveillance	NOUN
cuesj-22	137	18	systems	system	NOUN
cuesj-22	137	19	,	,	PUNCT
cuesj-22	137	20	radar	radar	NOUN
cuesj-22	137	21	signal	signal	NOUN
cuesj-22	137	22	processing	processing	NOUN
cuesj-22	137	23	and	and	CCONJ
cuesj-22	137	24	control	control	NOUN
cuesj-22	137	25	,	,	PUNCT
cuesj-22	137	26	and	and	CCONJ
cuesj-22	137	27	adaptive	adaptive	ADJ
cuesj-22	137	28	controls	control	NOUN
cuesj-22	137	29	.	.	PUNCT
cuesj-22	138	1	here	here	ADV
cuesj-22	138	2	,	,	PUNCT
cuesj-22	138	3	we	we	PRON
cuesj-22	138	4	will	will	AUX
cuesj-22	138	5	provide	provide	VERB
cuesj-22	138	6	only	only	ADV
cuesj-22	138	7	the	the	DET
cuesj-22	138	8	equations	equation	NOUN
cuesj-22	138	9	needed	need	VERB
cuesj-22	138	10	to	to	PART
cuesj-22	138	11	develop	develop	VERB
cuesj-22	138	12	discrete	discrete	ADJ
cuesj-22	138	13	recursion	recursion	NOUN
cuesj-22	138	14	kf	kf	NOUN
cuesj-22	138	15	,	,	PUNCT
cuesj-22	138	16	discrete	discrete	ADJ
cuesj-22	138	17	state	state	NOUN
cuesj-22	138	18	equations	equation	NOUN
cuesj-22	138	19	are	be	AUX
cuesj-22	138	20	given	give	VERB
cuesj-22	138	21	as	as	ADP
cuesj-22	138	22	linear	linear	PROPN
cuesj-22	138	23	dynamic	dynamic	NOUN
cuesj-22	138	24	for	for	ADP
cuesj-22	138	25	signal	signal	NOUN
cuesj-22	138	26	θt	θt	PROPN
cuesj-22	138	27	and	and	CCONJ
cuesj-22	138	28	observation	observation	NOUN
cuesj-22	138	29	yt	yt	NOUN
cuesj-22	138	30	.	.	PUNCT
cuesj-22	139	1	[	[	X
cuesj-22	139	2	11	11	NUM
cuesj-22	139	3	]	]	PUNCT
cuesj-22	139	4	we	we	PRON
cuesj-22	139	5	have	have	VERB
cuesj-22	139	6	two	two	NUM
cuesj-22	139	7	equations	equation	NOUN
cuesj-22	139	8	:	:	PUNCT
cuesj-22	139	9	figure	figure	NOUN
cuesj-22	139	10	1	1	NUM
cuesj-22	139	11	:	:	PUNCT
cuesj-22	139	12	the	the	DET
cuesj-22	139	13	relationship	relationship	NOUN
cuesj-22	139	14	between	between	ADP
cuesj-22	139	15	the	the	DET
cuesj-22	139	16	γ(h	γ(h	NOUN
cuesj-22	139	17	)	)	PUNCT
cuesj-22	139	18	and	and	CCONJ
cuesj-22	139	19	c(h	c(h	VERB
cuesj-22	139	20	)	)	PUNCT
cuesj-22	139	21	for	for	ADP
cuesj-22	139	22	the	the	DET
cuesj-22	139	23	second	second	ADJ
cuesj-22	139	24	-	-	PUNCT
cuesj-22	139	25	order	order	NOUN
cuesj-22	139	26	stationary	stationary	NOUN
cuesj-22	139	27	of	of	ADP
cuesj-22	139	28	a	a	DET
cuesj-22	139	29	spatial	spatial	ADJ
cuesj-22	139	30	random	random	ADJ
cuesj-22	139	31	variable	variable	ADJ
cuesj-22	139	32	figure	figure	NOUN
cuesj-22	139	33	2	2	NUM
cuesj-22	139	34	:	:	PUNCT
cuesj-22	139	35	the	the	DET
cuesj-22	139	36	gaussian	gaussian	ADJ
cuesj-22	139	37	semivariogram	semivariogram	NOUN
cuesj-22	139	38	model	model	NOUN
cuesj-22	139	39	with	with	ADP
cuesj-22	139	40	parameters	parameter	NOUN
cuesj-22	139	41	mawlood	mawlood	NOUN
cuesj-22	139	42	and	and	CCONJ
cuesj-22	139	43	omer	omer	PROPN
cuesj-22	139	44	:	:	PUNCT
cuesj-22	139	45	prediction	prediction	NOUN
cuesj-22	139	46	the	the	DET
cuesj-22	139	47	groundwater	groundwater	NOUN
cuesj-22	139	48	depth	depth	NOUN
cuesj-22	139	49	45	45	NUM
cuesj-22	139	50	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	139	51	cuesj	cuesj	NOUN
cuesj-22	139	52	2019	2019	NUM
cuesj-22	139	53	,	,	PUNCT
cuesj-22	139	54	3	3	NUM
cuesj-22	139	55	(	(	PUNCT
cuesj-22	139	56	1	1	NUM
cuesj-22	139	57	):	):	PUNCT
cuesj-22	139	58	42	42	NUM
cuesj-22	139	59	-	-	SYM
cuesj-22	139	60	49	49	NUM
cuesj-22	139	61	1	1	NUM
cuesj-22	139	62	.	.	PUNCT
cuesj-22	140	1	observation	observation	NOUN
cuesj-22	140	2	equation	equation	NOUN
cuesj-22	140	3	:	:	PUNCT
cuesj-22	141	1	y	y	PROPN
cuesj-22	141	2	ft	ft	PROPN
cuesj-22	141	3	t	t	PROPN
cuesj-22	141	4	t	t	X
cuesj-22	141	5	t=	t=	ADJ
cuesj-22	142	1	+	+	PROPN
cuesj-22	142	2	θ	θ	PROPN
cuesj-22	142	3	υ	υ	ADJ
cuesj-22	142	4	2	2	NUM
cuesj-22	142	5	.	.	PUNCT
cuesj-22	142	6	system	system	NOUN
cuesj-22	142	7	equation	equation	NOUN
cuesj-22	142	8	:	:	PUNCT
cuesj-22	142	9	θ	θ	X
cuesj-22	142	10	θ	θ	X
cuesj-22	142	11	ωt	ωt	ADP
cuesj-22	142	12	t	t	PROPN
cuesj-22	142	13	t	t	PROPN
cuesj-22	142	14	tg=	tg=	VERB
cuesj-22	142	15	+	+	PROPN
cuesj-22	142	16	−1	−1	NOUN
cuesj-22	142	17	the	the	DET
cuesj-22	142	18	initial	initial	ADJ
cuesj-22	142	19	state	state	NOUN
cuesj-22	142	20	vector	vector	NOUN
cuesj-22	142	21	has	have	VERB
cuesj-22	142	22	a	a	DET
cuesj-22	142	23	mean	mean	ADJ
cuesj-22	142	24	θ0	θ0	NOUN
cuesj-22	142	25	and	and	CCONJ
cuesj-22	142	26	a	a	DET
cuesj-22	142	27	covariance	covariance	NOUN
cuesj-22	142	28	matrix	matrix	NOUN
cuesj-22	142	29	p0	p0	NOUN
cuesj-22	142	30	that	that	PRON
cuesj-22	142	31	is	be	AUX
cuesj-22	142	32	:	:	PUNCT
cuesj-22	142	33	0	0	NUM
cuesj-22	142	34	0	0	NUM
cuesj-22	142	35	0	0	NUM
cuesj-22	142	36	0	0	NUM
cuesj-22	143	1	ˆ	ˆ	NOUN
cuesj-22	143	2	[	[	X
cuesj-22	143	3	]	]	X
cuesj-22	143	4	=	=	PUNCT
cuesj-22	143	5	and	and	CCONJ
cuesj-22	143	6	[	[	PUNCT
cuesj-22	143	7	]	]	X
cuesj-22	143	8	=	=	SYM
cuesj-22	143	9	e	e	X
cuesj-22	143	10	var	var	NOUN
cuesj-22	143	11	p	p	PROPN
cuesj-22	143	12			PROPN
cuesj-22	144	1			PROPN
cuesj-22	144	2	yt	yt	PROPN
cuesj-22	144	3	is	be	AUX
cuesj-22	144	4	the	the	DET
cuesj-22	144	5	vector	vector	NOUN
cuesj-22	144	6	of	of	ADP
cuesj-22	144	7	noisy	noisy	ADJ
cuesj-22	144	8	observation	observation	NOUN
cuesj-22	144	9	at	at	ADP
cuesj-22	144	10	time	time	NOUN
cuesj-22	144	11	t	t	PROPN
cuesj-22	144	12	,	,	PUNCT
cuesj-22	144	13	of	of	ADP
cuesj-22	144	14	dimension	dimension	NOUN
cuesj-22	144	15	m×1	m×1	NOUN
cuesj-22	144	16	.	.	PUNCT
cuesj-22	145	1	ft	ft	PROPN
cuesj-22	145	2	is	be	AUX
cuesj-22	145	3	the	the	DET
cuesj-22	145	4	known	know	VERB
cuesj-22	145	5	matrix	matrix	NOUN
cuesj-22	145	6	at	at	ADP
cuesj-22	145	7	time	time	NOUN
cuesj-22	145	8	t	t	PROPN
cuesj-22	145	9	,	,	PUNCT
cuesj-22	145	10	of	of	ADP
cuesj-22	145	11	dimension	dimension	NOUN
cuesj-22	145	12	m×p	m×p	PROPN
cuesj-22	145	13	relates	relate	VERB
cuesj-22	145	14	the	the	DET
cuesj-22	145	15	state	state	NOUN
cuesj-22	145	16	vector	vector	NOUN
cuesj-22	145	17	to	to	ADP
cuesj-22	145	18	the	the	DET
cuesj-22	145	19	observation	observation	NOUN
cuesj-22	145	20	vector	vector	NOUN
cuesj-22	145	21	yt	yt	PROPN
cuesj-22	145	22	.	.	PUNCT
cuesj-22	145	23	υt	υt	PROPN
cuesj-22	145	24	is	be	AUX
cuesj-22	145	25	the	the	DET
cuesj-22	145	26	vector	vector	NOUN
cuesj-22	145	27	of	of	ADP
cuesj-22	145	28	observation	observation	NOUN
cuesj-22	145	29	error	error	NOUN
cuesj-22	145	30	(	(	PUNCT
cuesj-22	145	31	stochastic	stochastic	ADJ
cuesj-22	145	32	error	error	NOUN
cuesj-22	145	33	term	term	NOUN
cuesj-22	145	34	)	)	PUNCT
cuesj-22	145	35	at	at	ADP
cuesj-22	145	36	time	time	NOUN
cuesj-22	145	37	t	t	PROPN
cuesj-22	145	38	,	,	PUNCT
cuesj-22	145	39	having	have	VERB
cuesj-22	145	40	normal	normal	ADJ
cuesj-22	145	41	distribution	distribution	NOUN
cuesj-22	145	42	with	with	ADP
cuesj-22	145	43	zero	zero	NUM
cuesj-22	145	44	mean	mean	NOUN
cuesj-22	145	45	and	and	CCONJ
cuesj-22	145	46	covariance	covariance	NOUN
cuesj-22	145	47	vt	vt	PROPN
cuesj-22	145	48	,	,	PUNCT
cuesj-22	145	49	of	of	ADP
cuesj-22	145	50	dimension	dimension	NOUN
cuesj-22	145	51	m×θt	m×θt	PROPN
cuesj-22	145	52	is	be	AUX
cuesj-22	145	53	the	the	DET
cuesj-22	145	54	vector	vector	NOUN
cuesj-22	145	55	of	of	ADP
cuesj-22	145	56	unknown	unknown	ADJ
cuesj-22	145	57	state	state	NOUN
cuesj-22	145	58	parameters	parameter	NOUN
cuesj-22	145	59	(	(	PUNCT
cuesj-22	145	60	signal	signal	NOUN
cuesj-22	145	61	)	)	PUNCT
cuesj-22	145	62	at	at	ADP
cuesj-22	145	63	time	time	NOUN
cuesj-22	145	64	t	t	PROPN
cuesj-22	145	65	,	,	PUNCT
cuesj-22	145	66	of	of	ADP
cuesj-22	145	67	dimension	dimension	NOUN
cuesj-22	145	68	p×1	p×1	ADJ
cuesj-22	145	69	,	,	PUNCT
cuesj-22	145	70	system	system	NOUN
cuesj-22	145	71	states	state	NOUN
cuesj-22	145	72	.	.	PUNCT
cuesj-22	146	1	gt	gt	PROPN
cuesj-22	146	2	is	be	AUX
cuesj-22	146	3	the	the	DET
cuesj-22	146	4	matrix	matrix	NOUN
cuesj-22	146	5	of	of	ADP
cuesj-22	146	6	time	time	NOUN
cuesj-22	146	7	-	-	PUNCT
cuesj-22	146	8	varying	vary	VERB
cuesj-22	146	9	state	state	NOUN
cuesj-22	146	10	transition	transition	NOUN
cuesj-22	146	11	matrix	matrix	NOUN
cuesj-22	146	12	dimension	dimension	NOUN
cuesj-22	146	13	pxp	pxp	PROPN
cuesj-22	146	14	.	.	PUNCT
cuesj-22	147	1	ωt	ωt	PROPN
cuesj-22	147	2	is	be	AUX
cuesj-22	147	3	the	the	DET
cuesj-22	147	4	system	system	NOUN
cuesj-22	147	5	error	error	NOUN
cuesj-22	147	6	(	(	PUNCT
cuesj-22	147	7	stochastic	stochastic	ADJ
cuesj-22	147	8	error	error	NOUN
cuesj-22	147	9	term	term	NOUN
cuesj-22	147	10	)	)	PUNCT
cuesj-22	147	11	at	at	ADP
cuesj-22	147	12	time	time	NOUN
cuesj-22	147	13	t	t	PROPN
cuesj-22	147	14	,	,	PUNCT
cuesj-22	147	15	having	have	VERB
cuesj-22	147	16	normal	normal	ADJ
cuesj-22	147	17	distribution	distribution	NOUN
cuesj-22	147	18	with	with	ADP
cuesj-22	147	19	zero	zero	NUM
cuesj-22	147	20	mean	mean	NOUN
cuesj-22	147	21	and	and	CCONJ
cuesj-22	147	22	covariance	covariance	NOUN
cuesj-22	147	23	wt	wt	ADP
cuesj-22	147	24	,	,	PUNCT
cuesj-22	147	25	of	of	ADP
cuesj-22	147	26	dimension	dimension	NOUN
cuesj-22	147	27	p×1	p×1	ADJ
cuesj-22	147	28	.	.	PUNCT
cuesj-22	148	1	where	where	SCONJ
cuesj-22	148	2	,	,	PUNCT
cuesj-22	148	3	vt	vt	PROPN
cuesj-22	148	4	and	and	CCONJ
cuesj-22	148	5	wt	wt	PROPN
cuesj-22	148	6	are	be	AUX
cuesj-22	148	7	variance	variance	NOUN
cuesj-22	148	8	-	-	PUNCT
cuesj-22	148	9	covariance	covariance	NOUN
cuesj-22	148	10	measurement	measurement	NOUN
cuesj-22	148	11	and	and	CCONJ
cuesj-22	148	12	process	process	NOUN
cuesj-22	148	13	noise	noise	NOUN
cuesj-22	148	14	matrices	matrix	NOUN
cuesj-22	148	15	,	,	PUNCT
cuesj-22	148	16	respectively	respectively	ADV
cuesj-22	148	17	.	.	PUNCT
cuesj-22	149	1	the	the	DET
cuesj-22	149	2	noise	noise	NOUN
cuesj-22	149	3	vectors	vector	NOUN
cuesj-22	149	4	ωt	ωt	VERB
cuesj-22	149	5	and	and	CCONJ
cuesj-22	149	6	υt	υt	NOUN
cuesj-22	149	7	process	process	NOUN
cuesj-22	149	8	and	and	CCONJ
cuesj-22	149	9	measurement	measurement	NOUN
cuesj-22	149	10	noise	noise	NOUN
cuesj-22	149	11	,	,	PUNCT
cuesj-22	149	12	respectively	respectively	ADV
cuesj-22	149	13	,	,	PUNCT
cuesj-22	149	14	are	be	AUX
cuesj-22	149	15	uncorrected	uncorrecte	VERB
cuesj-22	149	16	white	white	ADJ
cuesj-22	149	17	noises	noise	NOUN
cuesj-22	149	18	(	(	PUNCT
cuesj-22	149	19	have	have	AUX
cuesj-22	149	20	zero	zero	NUM
cuesj-22	149	21	means	mean	NOUN
cuesj-22	149	22	)	)	PUNCT
cuesj-22	149	23	they	they	PRON
cuesj-22	149	24	are	be	AUX
cuesj-22	149	25	independent	independent	ADJ
cuesj-22	149	26	of	of	ADP
cuesj-22	149	27	each	each	DET
cuesj-22	149	28	other	other	ADJ
cuesj-22	149	29	and	and	CCONJ
cuesj-22	149	30	have	have	VERB
cuesj-22	149	31	normal	normal	ADJ
cuesj-22	149	32	probability	probability	NOUN
cuesj-22	149	33	distributions	distribution	NOUN
cuesj-22	149	34	.	.	PUNCT
cuesj-22	150	1	the	the	DET
cuesj-22	150	2	disturbances	disturbance	NOUN
cuesj-22	150	3	υt	υt	VERB
cuesj-22	150	4	and	and	CCONJ
cuesj-22	150	5	ωt	ωt	AUX
cuesj-22	150	6	uncorrected	uncorrecte	VERB
cuesj-22	150	7	with	with	ADP
cuesj-22	150	8	the	the	DET
cuesj-22	150	9	initial	initial	ADJ
cuesj-22	150	10	state	state	NOUN
cuesj-22	150	11	that	that	SCONJ
cuesj-22	150	12	as	as	ADP
cuesj-22	150	13	:	:	PUNCT
cuesj-22	150	14	e	e	NOUN
cuesj-22	150	15	(	(	PUNCT
cuesj-22	150	16	)	)	PUNCT
cuesj-22	150	17	=	=	SYM
cuesj-22	150	18	0υ	0υ	VERB
cuesj-22	150	19	θt	θt	PROPN
cuesj-22	150	20	t	t	PROPN
cuesj-22	150	21	0	0	PUNCT
cuesj-22	150	22	and	and	CCONJ
cuesj-22	150	23	e	e	NOUN
cuesj-22	150	24	(	(	PUNCT
cuesj-22	150	25	)	)	PUNCT
cuesj-22	150	26	=	=	SYM
cuesj-22	150	27	0ω	0ω	NOUN
cuesj-22	150	28	θt	θt	PROPN
cuesj-22	150	29	t	t	PROPN
cuesj-22	150	30	0	0	NUM
cuesj-22	150	31	for	for	ADP
cuesj-22	150	32	t=1	t=1	PROPN
cuesj-22	150	33	,	,	PUNCT
cuesj-22	150	34	2	2	NUM
cuesj-22	150	35	,	,	PUNCT
cuesj-22	150	36	3,	3,	NUM
cuesj-22	150	37	…	…	PUNCT
cuesj-22	150	38	,t	,t	PUNCT
cuesj-22	150	39	bayesian	bayesian	NOUN
cuesj-22	150	40	estimation	estimation	NOUN
cuesj-22	150	41	of	of	ADP
cuesj-22	150	42	dynamic	dynamic	ADJ
cuesj-22	150	43	linear	linear	NOUN
cuesj-22	150	44	models	model	NOUN
cuesj-22	150	45	kalman	kalman	NOUN
cuesj-22	150	46	filter	filter	PROPN
cuesj-22	150	47	is	be	AUX
cuesj-22	150	48	used	use	VERB
cuesj-22	150	49	for	for	ADP
cuesj-22	150	50	estimating	estimate	VERB
cuesj-22	150	51	a	a	DET
cuesj-22	150	52	state	state	NOUN
cuesj-22	150	53	of	of	ADP
cuesj-22	150	54	a	a	DET
cuesj-22	150	55	dynamic	dynamic	ADJ
cuesj-22	150	56	system	system	NOUN
cuesj-22	150	57	which	which	PRON
cuesj-22	150	58	gives	give	VERB
cuesj-22	150	59	the	the	DET
cuesj-22	150	60	best	well	ADV
cuesj-22	150	61	-	-	PUNCT
cuesj-22	150	62	unbiased	unbiased	ADJ
cuesj-22	150	63	estimator	estimator	NOUN
cuesj-22	150	64	using	use	VERB
cuesj-22	150	65	previous	previous	ADJ
cuesj-22	150	66	measurement	measurement	NOUN
cuesj-22	150	67	knowledge	knowledge	NOUN
cuesj-22	150	68	.	.	PUNCT
cuesj-22	151	1	previous	previous	ADJ
cuesj-22	151	2	algorithms	algorithm	NOUN
cuesj-22	151	3	stand	stand	VERB
cuesj-22	151	4	from	from	ADP
cuesj-22	151	5	the	the	DET
cuesj-22	151	6	use	use	NOUN
cuesj-22	151	7	of	of	ADP
cuesj-22	151	8	all	all	DET
cuesj-22	151	9	the	the	DET
cuesj-22	151	10	previous	previous	ADJ
cuesj-22	151	11	information	information	NOUN
cuesj-22	151	12	to	to	PART
cuesj-22	151	13	estimate	estimate	VERB
cuesj-22	151	14	the	the	DET
cuesj-22	151	15	state	state	NOUN
cuesj-22	151	16	of	of	ADP
cuesj-22	151	17	the	the	DET
cuesj-22	151	18	system	system	NOUN
cuesj-22	151	19	at	at	ADP
cuesj-22	151	20	the	the	DET
cuesj-22	151	21	time	time	NOUN
cuesj-22	151	22	of	of	ADP
cuesj-22	151	23	the	the	DET
cuesj-22	151	24	next	next	ADJ
cuesj-22	151	25	step	step	NOUN
cuesj-22	151	26	.	.	PUNCT
cuesj-22	152	1	this	this	DET
cuesj-22	152	2	dynamic	dynamic	ADJ
cuesj-22	152	3	linear	linear	NOUN
cuesj-22	152	4	model	model	NOUN
cuesj-22	152	5	is	be	AUX
cuesj-22	152	6	the	the	DET
cuesj-22	152	7	simple	simple	ADJ
cuesj-22	152	8	model	model	NOUN
cuesj-22	152	9	represented	represent	VERB
cuesj-22	152	10	as	as	ADP
cuesj-22	152	11	:	:	PUNCT
cuesj-22	152	12	observation	observation	NOUN
cuesj-22	152	13	equation	equation	NOUN
cuesj-22	152	14	yt	yt	NOUN
cuesj-22	152	15	=	=	ADJ
cuesj-22	152	16	μt+εt	μt+εt	NOUN
cuesj-22	152	17	system	system	NOUN
cuesj-22	152	18	equation	equation	NOUN
cuesj-22	152	19	μt	μt	ADP
cuesj-22	152	20	=	=	NOUN
cuesj-22	152	21	μt−1+σμ2	μt−1+σμ2	NOUN
cuesj-22	152	22	where	where	SCONJ
cuesj-22	152	23	the	the	DET
cuesj-22	152	24	errors	error	NOUN
cuesj-22	152	25	εt	εt	PROPN
cuesj-22	152	26	and	and	CCONJ
cuesj-22	152	27	ωt	ωt	PROPN
cuesj-22	152	28	are	be	AUX
cuesj-22	152	29	mutually	mutually	ADV
cuesj-22	152	30	independent	independent	ADJ
cuesj-22	152	31	and	and	CCONJ
cuesj-22	152	32	independent	independent	ADJ
cuesj-22	152	33	of	of	ADP
cuesj-22	152	34	initial	initial	ADJ
cuesj-22	152	35	information	information	NOUN
cuesj-22	152	36	probability	probability	NOUN
cuesj-22	152	37	(	(	PUNCT
cuesj-22	152	38	μ0|d0	μ0|d0	NOUN
cuesj-22	152	39	)	)	PUNCT
cuesj-22	152	40	initial	initial	ADJ
cuesj-22	152	41	information	information	NOUN
cuesj-22	152	42	probability	probability	NOUN
cuesj-22	152	43	(	(	PUNCT
cuesj-22	152	44	μ0|d0	μ0|d0	NUM
cuesj-22	152	45	)	)	PUNCT
cuesj-22	152	46	represents	represent	VERB
cuesj-22	152	47	forecaster	forecaster	NOUN
cuesj-22	152	48	’s	’s	PART
cuesj-22	152	49	probabilistic	probabilistic	ADJ
cuesj-22	152	50	information	information	NOUN
cuesj-22	152	51	of	of	ADP
cuesj-22	152	52	about	about	ADP
cuesj-22	152	53	the	the	DET
cuesj-22	152	54	μ0	μ0	PROPN
cuesj-22	152	55	at	at	ADP
cuesj-22	152	56	time	time	NOUN
cuesj-22	152	57	t=0	t=0	X
cuesj-22	152	58	.	.	PUNCT
cuesj-22	153	1	the	the	DET
cuesj-22	153	2	mean	mean	ADJ
cuesj-22	153	3	m0	m0	NOUN
cuesj-22	153	4	and	and	CCONJ
cuesj-22	153	5	the	the	DET
cuesj-22	153	6	variance	variance	NOUN
cuesj-22	153	7	c0	c0	NOUN
cuesj-22	153	8	are	be	AUX
cuesj-22	153	9	a	a	DET
cuesj-22	153	10	point	point	NOUN
cuesj-22	153	11	estimate	estimate	NOUN
cuesj-22	153	12	and	and	CCONJ
cuesj-22	153	13	the	the	DET
cuesj-22	153	14	associated	associated	ADJ
cuesj-22	153	15	uncertainty	uncertainty	NOUN
cuesj-22	153	16	of	of	ADP
cuesj-22	153	17	μ0	μ0	PROPN
cuesj-22	153	18	.	.	PUNCT
cuesj-22	154	1	dt	dt	PUNCT
cuesj-22	154	2	consists	consist	VERB
cuesj-22	154	3	all	all	DET
cuesj-22	154	4	the	the	DET
cuesj-22	154	5	information	information	NOUN
cuesj-22	154	6	available	available	ADJ
cuesj-22	154	7	up	up	ADP
cuesj-22	154	8	until	until	ADP
cuesj-22	154	9	time	time	NOUN
cuesj-22	154	10	t	t	PROPN
cuesj-22	154	11	,	,	PUNCT
cuesj-22	154	12	including	include	VERB
cuesj-22	154	13	d0	d0	NOUN
cuesj-22	154	14	,	,	PUNCT
cuesj-22	154	15	the	the	DET
cuesj-22	154	16	values	value	NOUN
cuesj-22	154	17	variances	variance	VERB
cuesj-22	154	18	values	value	NOUN
cuesj-22	154	19	{	{	PUNCT
cuesj-22	154	20	,	,	PUNCT
cuesj-22	154	21	:	:	PUNCT
cuesj-22	154	22	}	}	PUNCT
cuesj-22	154	23	σ	σ	PROPN
cuesj-22	154	24	σε	σε	PROPN
cuesj-22	154	25	µ	µ	PROPN
cuesj-22	154	26	2	2	NUM
cuesj-22	154	27	2	2	NUM
cuesj-22	154	28	0	0	NUM
cuesj-22	154	29	t	t	NOUN
cuesj-22	154	30	>	>	X
cuesj-22	154	31	,	,	PUNCT
cuesj-22	154	32	and	and	CCONJ
cuesj-22	154	33	the	the	DET
cuesj-22	154	34	the	the	DET
cuesj-22	154	35	observations	observation	NOUN
cuesj-22	154	36	yt	yt	VERB
cuesj-22	154	37	,	,	PUNCT
cuesj-22	154	38	yt−1,	yt−1,	NOUN
cuesj-22	154	39	…	…	PUNCT
cuesj-22	154	40	,y1	,y1	PUNCT
cuesj-22	154	41	or	or	CCONJ
cuesj-22	154	42	,	,	PUNCT
cuesj-22	154	43	the	the	DET
cuesj-22	154	44	only	only	ADJ
cuesj-22	154	45	new	new	ADJ
cuesj-22	154	46	information	information	NOUN
cuesj-22	154	47	becomes	become	VERB
cuesj-22	154	48	available	available	ADJ
cuesj-22	154	49	at	at	ADP
cuesj-22	154	50	any	any	DET
cuesj-22	154	51	time	time	NOUN
cuesj-22	154	52	t	t	NOUN
cuesj-22	154	53	is	be	AUX
cuesj-22	154	54	the	the	DET
cuesj-22	154	55	observed	observe	VERB
cuesj-22	154	56	value	value	NOUN
cuesj-22	154	57	yt	yt	NOUN
cuesj-22	154	58	,	,	PUNCT
cuesj-22	154	59	where	where	SCONJ
cuesj-22	154	60	dt={yt	dt={yt	PROPN
cuesj-22	154	61	,	,	PUNCT
cuesj-22	154	62	dt−1	dt−1	NOUN
cuesj-22	154	63	}	}	PUNCT
cuesj-22	154	64	.	.	PUNCT
cuesj-22	155	1	here	here	ADV
cuesj-22	155	2	,	,	PUNCT
cuesj-22	155	3	we	we	PRON
cuesj-22	155	4	start	start	VERB
cuesj-22	155	5	expressing	express	VERB
cuesj-22	155	6	initial	initial	ADJ
cuesj-22	155	7	information	information	NOUN
cuesj-22	155	8	concerning	concern	VERB
cuesj-22	155	9	the	the	DET
cuesj-22	155	10	parameter	parameter	NOUN
cuesj-22	155	11	μ0	μ0	PROPN
cuesj-22	155	12	was	be	AUX
cuesj-22	155	13	described	describe	VERB
cuesj-22	155	14	in	in	ADP
cuesj-22	155	15	the	the	DET
cuesj-22	155	16	form	form	NOUN
cuesj-22	155	17	of	of	ADP
cuesj-22	155	18	a	a	DET
cuesj-22	155	19	normal	normal	ADJ
cuesj-22	155	20	probability	probability	NOUN
cuesj-22	155	21	distribution	distribution	NOUN
cuesj-22	155	22	with	with	ADP
cuesj-22	155	23	mean	mean	ADJ
cuesj-22	155	24	m0	m0	NOUN
cuesj-22	155	25	and	and	CCONJ
cuesj-22	155	26	variance	variance	NOUN
cuesj-22	155	27	c0	c0	NOUN
cuesj-22	155	28	is	be	AUX
cuesj-22	155	29	:	:	PUNCT
cuesj-22	155	30	0	0	NUM
cuesj-22	155	31	0	0	NUM
cuesj-22	155	32	0~	0~	NOUN
cuesj-22	155	33	(	(	PUNCT
cuesj-22	155	34	,	,	PUNCT
cuesj-22	155	35	)	)	PUNCT
cuesj-22	155	36	n	n	CCONJ
cuesj-22	155	37	m	m	VERB
cuesj-22	155	38	c	c	NOUN
cuesj-22	155	39	using	use	VERB
cuesj-22	155	40	a	a	DET
cuesj-22	155	41	mathematical	mathematical	ADJ
cuesj-22	155	42	process	process	NOUN
cuesj-22	155	43	and	and	CCONJ
cuesj-22	155	44	bayes	bayes	PROPN
cuesj-22	155	45	’	'	PUNCT
cuesj-22	155	46	theorem	theorem	VERB
cuesj-22	155	47	at	at	ADP
cuesj-22	155	48	the	the	DET
cuesj-22	155	49	time	time	NOUN
cuesj-22	155	50	(	(	PUNCT
cuesj-22	155	51	t−1	t−1	NOUN
cuesj-22	155	52	)	)	PUNCT
cuesj-22	155	53	of	of	ADP
cuesj-22	155	54	the	the	DET
cuesj-22	155	55	parameter	parameter	NOUN
cuesj-22	155	56	μt−1	μt−1	PROPN
cuesj-22	155	57	is	be	AUX
cuesj-22	155	58	:	:	PUNCT
cuesj-22	155	59	1	1	NUM
cuesj-22	155	60	1	1	NUM
cuesj-22	155	61	1	1	NUM
cuesj-22	155	62	1	1	NUM
cuesj-22	155	63	(	(	PUNCT
cuesj-22	155	64	|	|	ADV
cuesj-22	155	65	)	)	PUNCT
cuesj-22	155	66	~	~	PUNCT
cuesj-22	155	67	(	(	PUNCT
cuesj-22	155	68	,	,	PUNCT
cuesj-22	155	69	)	)	PUNCT
cuesj-22	155	70	t	t	PROPN
cuesj-22	155	71	t	t	PROPN
cuesj-22	155	72	t	t	PROPN
cuesj-22	155	73	t	t	PROPN
cuesj-22	155	74	td	td	PROPN
cuesj-22	155	75	n	n	CCONJ
cuesj-22	155	76	m	m	NOUN
cuesj-22	155	77	c	c	NUM
cuesj-22	156	1	−	−	NOUN
cuesj-22	157	1	−	−	NOUN
cuesj-22	158	1	−	−	PROPN
cuesj-22	158	2	−	−	PROPN
cuesj-22	158	3	where	where	SCONJ
cuesj-22	158	4	,	,	PUNCT
cuesj-22	158	5	dt−1	dt−1	NOUN
cuesj-22	158	6	is	be	AUX
cuesj-22	158	7	d	d	PROPN
cuesj-22	158	8	yt	yt	ADP
cuesj-22	158	9	t	t	NOUN
cuesj-22	158	10	−	−	PROPN
cuesj-22	158	11	−=1	−=1	VERB
cuesj-22	158	12	1	1	NUM
cuesj-22	158	13	2	2	NUM
cuesj-22	158	14	2	2	NUM
cuesj-22	158	15	{	{	PUNCT
cuesj-22	158	16	,	,	PUNCT
cuesj-22	158	17	,	,	PUNCT
cuesj-22	158	18	}	}	PUNCT
cuesj-22	158	19	σ	σ	PROPN
cuesj-22	158	20	σε	σε	PROPN
cuesj-22	158	21	µ	µ	PROPN
cuesj-22	158	22	.	.	PUNCT
cuesj-22	159	1	we	we	PRON
cuesj-22	159	2	aim	aim	VERB
cuesj-22	159	3	now	now	ADV
cuesj-22	159	4	to	to	PART
cuesj-22	159	5	find	find	VERB
cuesj-22	159	6	a	a	DET
cuesj-22	159	7	final	final	ADJ
cuesj-22	159	8	distribution	distribution	NOUN
cuesj-22	159	9	for	for	ADP
cuesj-22	159	10	the	the	DET
cuesj-22	159	11	parameter	parameter	NOUN
cuesj-22	159	12	μt	μt	NOUN
cuesj-22	159	13	at	at	ADP
cuesj-22	159	14	last	last	ADJ
cuesj-22	159	15	time	time	NOUN
cuesj-22	159	16	is	be	AUX
cuesj-22	159	17	the	the	DET
cuesj-22	159	18	time	time	NOUN
cuesj-22	159	19	appointed	appoint	VERB
cuesj-22	159	20	by	by	ADP
cuesj-22	159	21	our	our	PRON
cuesj-22	159	22	knowledge	knowledge	NOUN
cuesj-22	159	23	of	of	ADP
cuesj-22	159	24	all	all	DET
cuesj-22	159	25	available	available	ADJ
cuesj-22	159	26	data	datum	NOUN
cuesj-22	159	27	,	,	PUNCT
cuesj-22	159	28	or	or	CCONJ
cuesj-22	159	29	the	the	DET
cuesj-22	159	30	time	time	NOUN
cuesj-22	159	31	that	that	PRON
cuesj-22	159	32	we	we	PRON
cuesj-22	159	33	want	want	VERB
cuesj-22	159	34	to	to	PART
cuesj-22	159	35	filter	filter	VERB
cuesj-22	159	36	.	.	PUNCT
cuesj-22	160	1	we	we	PRON
cuesj-22	160	2	recompense	recompense	VERB
cuesj-22	160	3	an	an	DET
cuesj-22	160	4	extremely	extremely	ADJ
cuesj-22	160	5	presence	presence	NOUN
cuesj-22	160	6	of	of	ADP
cuesj-22	160	7	the	the	DET
cuesj-22	160	8	information	information	NOUN
cuesj-22	160	9	is	be	AUX
cuesj-22	160	10	:	:	PUNCT
cuesj-22	161	1	d	d	X
cuesj-22	161	2	y	y	PROPN
cuesj-22	161	3	dt	dt	X
cuesj-22	161	4	t	t	PROPN
cuesj-22	161	5	t=	t=	PROPN
cuesj-22	161	6	−	−	PROPN
cuesj-22	161	7	{	{	PUNCT
cuesj-22	161	8	,	,	PUNCT
cuesj-22	161	9	}	}	PUNCT
cuesj-22	161	10	1	1	NUM
cuesj-22	161	11	.	.	PUNCT
cuesj-22	162	1	then	then	ADV
cuesj-22	162	2	bayes	bayes	PROPN
cuesj-22	162	3	’	'	PUNCT
cuesj-22	162	4	theorem	theorem	NOUN
cuesj-22	162	5	is	be	AUX
cuesj-22	162	6	posterior	posterior	ADJ
cuesj-22	162	7	∝	∝	NOUN
cuesj-22	162	8	observed	observe	VERB
cuesj-22	162	9	likelihood×prior	likelihood×prior	ADJ
cuesj-22	162	10	p	p	PROPN
cuesj-22	163	1	d	d	X
cuesj-22	163	2	p	p	X
cuesj-22	163	3	y	y	PROPN
cuesj-22	163	4	p	p	X
cuesj-22	163	5	dt	dt	X
cuesj-22	163	6	t	t	PROPN
cuesj-22	163	7	t	t	PROPN
cuesj-22	163	8	t	t	PROPN
cuesj-22	163	9	t	t	PROPN
cuesj-22	163	10	t	t	PROPN
cuesj-22	163	11	(	(	PUNCT
cuesj-22	163	12	|	|	ADV
cuesj-22	163	13	)	)	PUNCT
cuesj-22	163	14	(	(	PUNCT
cuesj-22	163	15	|	|	ADV
cuesj-22	163	16	)	)	PUNCT
cuesj-22	163	17	*	*	PUNCT
cuesj-22	163	18	(	(	PUNCT
cuesj-22	163	19	|	|	ADV
cuesj-22	163	20	)	)	PUNCT
cuesj-22	163	21	µ	µ	NOUN
cuesj-22	163	22	α	α	X
cuesj-22	163	23	µ	µ	X
cuesj-22	163	24	µ	µ	X
cuesj-22	163	25	−1	−1	NOUN
cuesj-22	163	26	(	(	PUNCT
cuesj-22	163	27	16	16	NUM
cuesj-22	163	28	)	)	PUNCT
cuesj-22	163	29	where	where	SCONJ
cuesj-22	163	30	p(yt|μt	p(yt|μt	NOUN
cuesj-22	163	31	)	)	PUNCT
cuesj-22	163	32	represents	represent	VERB
cuesj-22	163	33	the	the	DET
cuesj-22	163	34	likelihood	likelihood	NOUN
cuesj-22	163	35	distribution	distribution	NOUN
cuesj-22	163	36	function	function	NOUN
cuesj-22	163	37	at	at	ADP
cuesj-22	163	38	time	time	NOUN
cuesj-22	163	39	t	t	PROPN
cuesj-22	163	40	p(μt|dt−1	p(μt|dt−1	PROPN
cuesj-22	163	41	)	)	PUNCT
cuesj-22	163	42	represents	represent	VERB
cuesj-22	163	43	the	the	DET
cuesj-22	163	44	prior	prior	ADJ
cuesj-22	163	45	distribution	distribution	NOUN
cuesj-22	163	46	at	at	ADP
cuesj-22	163	47	time	time	NOUN
cuesj-22	163	48	t	t	PROPN
cuesj-22	163	49	both	both	PRON
cuesj-22	163	50	of	of	ADP
cuesj-22	163	51	them	they	PRON
cuesj-22	163	52	are	be	AUX
cuesj-22	163	53	distributed	distribute	VERB
cuesj-22	163	54	normally	normally	ADV
cuesj-22	163	55	now	now	ADV
cuesj-22	163	56	to	to	PART
cuesj-22	163	57	find	find	VERB
cuesj-22	163	58	all	all	PRON
cuesj-22	163	59	of	of	ADP
cuesj-22	163	60	the	the	DET
cuesj-22	163	61	weighting	weighting	NOUN
cuesj-22	163	62	function	function	NOUN
cuesj-22	163	63	p(μt|dt−1	p(μt|dt−1	PROPN
cuesj-22	163	64	)	)	PUNCT
cuesj-22	163	65	and	and	CCONJ
cuesj-22	163	66	the	the	DET
cuesj-22	163	67	prior	prior	ADJ
cuesj-22	163	68	probability	probability	NOUN
cuesj-22	163	69	before	before	ADP
cuesj-22	163	70	observation	observation	NOUN
cuesj-22	163	71	yt	yt	NOUN
cuesj-22	163	72	,	,	PUNCT
cuesj-22	163	73	using	use	VERB
cuesj-22	163	74	dynamic	dynamic	ADJ
cuesj-22	163	75	linear	linear	NOUN
cuesj-22	163	76	model	model	NOUN
cuesj-22	163	77	and	and	CCONJ
cuesj-22	163	78	probability	probability	NOUN
cuesj-22	163	79	distribution	distribution	NOUN
cuesj-22	163	80	as	as	ADP
cuesj-22	163	81	:	:	PUNCT
cuesj-22	163	82	e	e	NOUN
cuesj-22	163	83	y	y	NOUN
cuesj-22	163	84	e	e	X
cuesj-22	163	85	e	e	PROPN
cuesj-22	163	86	e	e	PROPN
cuesj-22	163	87	t	t	PROPN
cuesj-22	163	88	t	t	PROPN
cuesj-22	163	89	t	t	PROPN
cuesj-22	163	90	t	t	PROPN
cuesj-22	163	91	t	t	PROPN
cuesj-22	163	92	t	t	PROPN
cuesj-22	163	93	t	t	PROPN
cuesj-22	163	94	t	t	PROPN
cuesj-22	163	95	t	t	PROPN
cuesj-22	163	96	t	t	PROPN
cuesj-22	163	97	(	(	PUNCT
cuesj-22	163	98	|	|	ADV
cuesj-22	163	99	)	)	PUNCT
cuesj-22	163	100	(	(	PUNCT
cuesj-22	163	101	|	|	ADV
cuesj-22	163	102	)	)	PUNCT
cuesj-22	163	103	(	(	PUNCT
cuesj-22	163	104	|	|	ADV
cuesj-22	163	105	)	)	PUNCT
cuesj-22	163	106	(	(	PUNCT
cuesj-22	163	107	|	|	ADV
cuesj-22	163	108	)	)	PUNCT
cuesj-22	163	109	µ	µ	PROPN
cuesj-22	163	110	µ	µ	X
cuesj-22	163	111	ε	ε	PROPN
cuesj-22	163	112	µ	µ	X
cuesj-22	163	113	µ	µ	X
cuesj-22	163	114	µ	µ	X
cuesj-22	163	115	ε	ε	PROPN
cuesj-22	163	116	µ	µ	X
cuesj-22	163	117	µ	µ	X
cuesj-22	163	118	=	=	PUNCT
cuesj-22	164	1	+	+	CCONJ
cuesj-22	164	2	=	=	SYM
cuesj-22	165	1	+	+	CCONJ
cuesj-22	165	2	=	=	SYM
cuesj-22	165	3	and	and	CCONJ
cuesj-22	165	4	v	v	ADP
cuesj-22	165	5	y	y	PROPN
cuesj-22	165	6	v	v	NUM
cuesj-22	165	7	v	v	NUM
cuesj-22	165	8	v	v	ADP
cuesj-22	165	9	t	t	PROPN
cuesj-22	165	10	t	t	PROPN
cuesj-22	165	11	t	t	PROPN
cuesj-22	165	12	t	t	PROPN
cuesj-22	165	13	t	t	PROPN
cuesj-22	165	14	t	t	PROPN
cuesj-22	165	15	t	t	PROPN
cuesj-22	165	16	t	t	PROPN
cuesj-22	165	17	t	t	PROPN
cuesj-22	165	18	(	(	PUNCT
cuesj-22	165	19	|	|	ADV
cuesj-22	165	20	)	)	PUNCT
cuesj-22	165	21	(	(	PUNCT
cuesj-22	165	22	|	|	ADV
cuesj-22	165	23	)	)	PUNCT
cuesj-22	165	24	(	(	PUNCT
cuesj-22	165	25	|	|	ADV
cuesj-22	165	26	)	)	PUNCT
cuesj-22	165	27	(	(	PUNCT
cuesj-22	165	28	|	|	ADV
cuesj-22	165	29	)	)	PUNCT
cuesj-22	165	30	µ	µ	PROPN
cuesj-22	165	31	µ	µ	X
cuesj-22	165	32	ε	ε	PROPN
cuesj-22	165	33	µ	µ	X
cuesj-22	165	34	µ	µ	X
cuesj-22	165	35	µ	µ	X
cuesj-22	165	36	ε	ε	PROPN
cuesj-22	165	37	µ	µ	X
cuesj-22	165	38	σε	σε	X
cuesj-22	165	39	=	=	PUNCT
cuesj-22	165	40	+	+	PUNCT
cuesj-22	165	41	=	=	SYM
cuesj-22	165	42	+	+	CCONJ
cuesj-22	165	43	=	=	SYM
cuesj-22	165	44	2	2	NUM
cuesj-22	165	45	or	or	CCONJ
cuesj-22	165	46	(	(	PUNCT
cuesj-22	165	47	|	|	ADV
cuesj-22	165	48	)	)	PUNCT
cuesj-22	165	49	~	~	PUNCT
cuesj-22	165	50	(	(	PUNCT
cuesj-22	165	51	,	,	PUNCT
cuesj-22	165	52	)	)	PUNCT
cuesj-22	165	53	y	y	PROPN
cuesj-22	165	54	nt	not	PART
cuesj-22	165	55	t	t	PROPN
cuesj-22	165	56	tµ	tµ	PROPN
cuesj-22	165	57	µ	µ	X
cuesj-22	165	58	σε	σε	X
cuesj-22	165	59	2	2	NUM
cuesj-22	165	60	and	and	CCONJ
cuesj-22	165	61	from	from	ADP
cuesj-22	165	62	the	the	DET
cuesj-22	165	63	system	system	NOUN
cuesj-22	165	64	equation	equation	NOUN
cuesj-22	165	65	:	:	PUNCT
cuesj-22	165	66	e	e	X
cuesj-22	165	67	d	d	X
cuesj-22	165	68	e	e	X
cuesj-22	165	69	e	e	X
cuesj-22	165	70	d	d	X
cuesj-22	165	71	e	e	PROPN
cuesj-22	165	72	d	d	NOUN
cuesj-22	165	73	m	m	VERB
cuesj-22	165	74	t	t	NOUN
cuesj-22	165	75	t	t	PROPN
cuesj-22	165	76	t	t	PROPN
cuesj-22	165	77	t	t	PROPN
cuesj-22	165	78	t	t	PROPN
cuesj-22	165	79	t	t	PROPN
cuesj-22	165	80	t	t	PROPN
cuesj-22	165	81	t	t	PROPN
cuesj-22	165	82	t	t	PROPN
cuesj-22	165	83	(	(	PUNCT
cuesj-22	165	84	|	|	ADV
cuesj-22	165	85	)	)	PUNCT
cuesj-22	165	86	(	(	PUNCT
cuesj-22	165	87	)	)	PUNCT
cuesj-22	165	88	(	(	PUNCT
cuesj-22	165	89	|	|	ADV
cuesj-22	165	90	)	)	PUNCT
cuesj-22	165	91	(	(	PUNCT
cuesj-22	165	92	|	|	ADV
cuesj-22	165	93	)	)	PUNCT
cuesj-22	165	94	µ	µ	PROPN
cuesj-22	165	95	µ	µ	X
cuesj-22	165	96	σ	σ	X
cuesj-22	165	97	µ	µ	PROPN
cuesj-22	165	98	σ	σ	X
cuesj-22	165	99	µ	µ	X
cuesj-22	165	100	µ	µ	X
cuesj-22	165	101	−	−	ADP
cuesj-22	165	102	−	−	PROPN
cuesj-22	165	103	−	−	PROPN
cuesj-22	166	1	−	−	NOUN
cuesj-22	167	1	−	−	PROPN
cuesj-22	168	1	=	=	PUNCT
cuesj-22	169	1	+	+	PUNCT
cuesj-22	170	1	=	=	SYM
cuesj-22	171	1	+	+	CCONJ
cuesj-22	171	2	=	=	SYM
cuesj-22	171	3	1	1	NUM
cuesj-22	171	4	1	1	NUM
cuesj-22	171	5	1	1	NUM
cuesj-22	171	6	1	1	NUM
cuesj-22	171	7	1	1	NUM
cuesj-22	171	8	and	and	CCONJ
cuesj-22	171	9	v	v	NOUN
cuesj-22	172	1	d	d	PROPN
cuesj-22	172	2	v	v	NOUN
cuesj-22	172	3	v	v	NOUN
cuesj-22	172	4	d	d	PROPN
cuesj-22	172	5	v	v	NOUN
cuesj-22	173	1	d	d	X
cuesj-22	173	2	c	c	NOUN
cuesj-22	173	3	r	r	NOUN
cuesj-22	173	4	t	t	PROPN
cuesj-22	173	5	t	t	PROPN
cuesj-22	173	6	t	t	PROPN
cuesj-22	173	7	t	t	PROPN
cuesj-22	173	8	t	t	PROPN
cuesj-22	173	9	t	t	PROPN
cuesj-22	173	10	t	t	PROPN
cuesj-22	173	11	t	t	PROPN
cuesj-22	173	12	t	t	PROPN
cuesj-22	173	13	t	t	PROPN
cuesj-22	173	14	t	t	PROPN
cuesj-22	173	15	(	(	PUNCT
cuesj-22	173	16	|	|	ADV
cuesj-22	173	17	)	)	PUNCT
cuesj-22	173	18	(	(	PUNCT
cuesj-22	173	19	)	)	PUNCT
cuesj-22	173	20	(	(	PUNCT
cuesj-22	173	21	|	|	ADV
cuesj-22	173	22	)	)	PUNCT
cuesj-22	173	23	(	(	PUNCT
cuesj-22	173	24	|	|	ADV
cuesj-22	173	25	)	)	PUNCT
cuesj-22	173	26	µ	µ	PROPN
cuesj-22	173	27	µ	µ	X
cuesj-22	173	28	σ	σ	X
cuesj-22	173	29	µ	µ	PROPN
cuesj-22	173	30	σ	σ	PROPN
cuesj-22	173	31	σ	σ	PROPN
cuesj-22	173	32	µ	µ	PROPN
cuesj-22	173	33	µ	µ	X
cuesj-22	173	34	µ	µ	X
cuesj-22	173	35	−	−	ADP
cuesj-22	173	36	−	−	PROPN
cuesj-22	173	37	−	−	PROPN
cuesj-22	174	1	−	−	NOUN
cuesj-22	175	1	−	−	PROPN
cuesj-22	176	1	=	=	PUNCT
cuesj-22	177	1	+	+	PUNCT
cuesj-22	178	1	=	=	SYM
cuesj-22	179	1	+	+	PUNCT
cuesj-22	180	1	=	=	SYM
cuesj-22	181	1	+	+	CCONJ
cuesj-22	181	2	=	=	SYM
cuesj-22	181	3	1	1	NUM
cuesj-22	181	4	1	1	NUM
cuesj-22	181	5	1	1	NUM
cuesj-22	181	6	1	1	NUM
cuesj-22	181	7	1	1	NUM
cuesj-22	181	8	2	2	NUM
cuesj-22	181	9	or	or	CCONJ
cuesj-22	181	10	1	1	NUM
cuesj-22	181	11	1	1	NUM
cuesj-22	181	12	(	(	PUNCT
cuesj-22	181	13	|	|	ADV
cuesj-22	181	14	)	)	PUNCT
cuesj-22	181	15	~	~	PUNCT
cuesj-22	181	16	(	(	PUNCT
cuesj-22	181	17	,	,	PUNCT
cuesj-22	181	18	)	)	PUNCT
cuesj-22	181	19	t	t	PROPN
cuesj-22	181	20	t	t	PROPN
cuesj-22	181	21	t	t	PROPN
cuesj-22	181	22	tp	tp	PROPN
cuesj-22	181	23	d	d	PROPN
cuesj-22	181	24	n	n	PROPN
cuesj-22	181	25	m	m	PROPN
cuesj-22	181	26	r	r	NUM
cuesj-22	181	27	−	−	NOUN
cuesj-22	181	28	−	−	PROPN
cuesj-22	181	29	then	then	ADV
cuesj-22	181	30	by	by	ADP
cuesj-22	181	31	substituting	substitute	VERB
cuesj-22	181	32	each	each	PRON
cuesj-22	181	33	of	of	ADP
cuesj-22	181	34	(	(	PUNCT
cuesj-22	181	35	yt|μt	yt|μt	NOUN
cuesj-22	181	36	)	)	PUNCT
cuesj-22	181	37	,	,	PUNCT
cuesj-22	181	38	p(μt|dt−1	p(μt|dt−1	PROPN
cuesj-22	181	39	)	)	PUNCT
cuesj-22	181	40	in	in	ADP
cuesj-22	181	41	equation	equation	NOUN
cuesj-22	181	42	(	(	PUNCT
cuesj-22	181	43	2.10.1	2.10.1	NUM
cuesj-22	181	44	)	)	PUNCT
cuesj-22	181	45	we	we	PRON
cuesj-22	181	46	get	get	VERB
cuesj-22	181	47	mawlood	mawlood	NOUN
cuesj-22	181	48	and	and	CCONJ
cuesj-22	181	49	omer	omer	PROPN
cuesj-22	181	50	:	:	PUNCT
cuesj-22	181	51	prediction	prediction	NOUN
cuesj-22	181	52	the	the	DET
cuesj-22	181	53	groundwater	groundwater	NOUN
cuesj-22	181	54	depth	depth	NOUN
cuesj-22	181	55	46	46	NUM
cuesj-22	181	56	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	181	57	cuesj	cuesj	NOUN
cuesj-22	181	58	2019	2019	NUM
cuesj-22	181	59	,	,	PUNCT
cuesj-22	181	60	3	3	NUM
cuesj-22	181	61	(	(	PUNCT
cuesj-22	181	62	1	1	NUM
cuesj-22	181	63	):	):	PUNCT
cuesj-22	181	64	42	42	NUM
cuesj-22	181	65	-	-	SYM
cuesj-22	181	66	49	49	NUM
cuesj-22	181	67	p	p	NOUN
cuesj-22	181	68	t	t	NOUN
cuesj-22	181	69	dt	dt	X
cuesj-22	181	70	e	e	PROPN
cuesj-22	181	71	yt	yt	PROPN
cuesj-22	181	72	t	t	PROPN
cuesj-22	181	73	rt	rt	PROPN
cuesj-22	181	74	t	t	PROPN
cuesj-22	181	75	mt	mt	PROPN
cuesj-22	181	76	(	(	PUNCT
cuesj-22	181	77	|	|	ADV
cuesj-22	181	78	)	)	PUNCT
cuesj-22	181	79	[	[	PUNCT
cuesj-22	181	80	(	(	PUNCT
cuesj-22	181	81	)	)	PUNCT
cuesj-22	181	82	(	(	PUNCT
cuesj-22	181	83	)	)	PUNCT
cuesj-22	181	84	]	]	X
cuesj-22	181	85	µ	µ	X
cuesj-22	181	86	α	α	PRON
cuesj-22	181	87	σε	σε	PROPN
cuesj-22	181	88	µ	µ	PRON
cuesj-22	181	89	µ−	µ−	NOUN
cuesj-22	181	90	−	−	PROPN
cuesj-22	182	1	+	+	CCONJ
cuesj-22	183	1	−	−	PROPN
cuesj-22	183	2	−	−	NUM
cuesj-22	183	3	1	1	NUM
cuesj-22	183	4	2	2	NUM
cuesj-22	183	5	1	1	NUM
cuesj-22	183	6	2	2	NUM
cuesj-22	183	7	1	1	NUM
cuesj-22	183	8	1	1	NUM
cuesj-22	183	9	(	(	PUNCT
cuesj-22	183	10	17	17	NUM
cuesj-22	183	11	)	)	PUNCT
cuesj-22	183	12	we	we	PRON
cuesj-22	183	13	can	can	AUX
cuesj-22	183	14	find	find	VERB
cuesj-22	183	15	that	that	SCONJ
cuesj-22	183	16	:	:	PUNCT
cuesj-22	183	17	p	p	X
cuesj-22	183	18	t	t	NOUN
cuesj-22	183	19	dt	dt	X
cuesj-22	183	20	e	e	PROPN
cuesj-22	183	21	ct	ct	PROPN
cuesj-22	183	22	t	t	PROPN
cuesj-22	183	23	mt	mt	PROPN
cuesj-22	183	24	(	(	PUNCT
cuesj-22	183	25	|	|	ADV
cuesj-22	183	26	)	)	PUNCT
cuesj-22	183	27	[	[	PUNCT
cuesj-22	183	28	(	(	PUNCT
cuesj-22	183	29	)	)	PUNCT
cuesj-22	183	30	]	]	X
cuesj-22	183	31	µ	µ	PRON
cuesj-22	183	32	α	α	DET
cuesj-22	183	33	µ−	µ−	NOUN
cuesj-22	183	34	−	−	NUM
cuesj-22	183	35	1	1	NUM
cuesj-22	183	36	2	2	NUM
cuesj-22	183	37	1	1	NUM
cuesj-22	183	38	(	(	PUNCT
cuesj-22	183	39	18	18	NUM
cuesj-22	183	40	)	)	PUNCT
cuesj-22	183	41	now	now	ADV
cuesj-22	183	42	we	we	PRON
cuesj-22	183	43	get	get	VERB
cuesj-22	183	44	mean	mean	PROPN
cuesj-22	183	45	mt	mt	PROPN
cuesj-22	183	46	and	and	CCONJ
cuesj-22	183	47	variance	variance	NOUN
cuesj-22	183	48	ct	ct	NUM
cuesj-22	183	49	of	of	ADP
cuesj-22	183	50	final	final	ADJ
cuesj-22	183	51	distribution	distribution	NOUN
cuesj-22	183	52	,	,	PUNCT
cuesj-22	183	53	respectively	respectively	ADV
cuesj-22	183	54	,	,	PUNCT
cuesj-22	183	55	are	be	AUX
cuesj-22	183	56	:	:	PUNCT
cuesj-22	183	57	m	m	VERB
cuesj-22	184	1	y	y	VERB
cuesj-22	184	2	r	r	NOUN
cuesj-22	184	3	m	m	VERB
cuesj-22	184	4	r	r	NOUN
cuesj-22	184	5	c	c	NOUN
cuesj-22	184	6	r	r	NOUN
cuesj-22	184	7	rt	rt	PROPN
cuesj-22	184	8	t	t	PROPN
cuesj-22	184	9	t	t	PROPN
cuesj-22	184	10	t	t	PROPN
cuesj-22	184	11	t	t	PROPN
cuesj-22	184	12	t	t	PROPN
cuesj-22	184	13	t	t	PROPN
cuesj-22	184	14	=	=	PUNCT
cuesj-22	185	1	+	+	PUNCT
cuesj-22	186	1	+	+	PUNCT
cuesj-22	186	2	=	=	SYM
cuesj-22	187	1	+	+	CCONJ
cuesj-22	187	2	+	+	CCONJ
cuesj-22	187	3	−	−	NUM
cuesj-22	187	4	1	1	NUM
cuesj-22	187	5	1	1	NUM
cuesj-22	187	6	1	1	NUM
cuesj-22	187	7	1	1	NUM
cuesj-22	187	8	2	2	NUM
cuesj-22	187	9	1	1	NUM
cuesj-22	187	10	2	2	NUM
cuesj-22	187	11	2	2	NUM
cuesj-22	187	12	2	2	NUM
cuesj-22	187	13	σ	σ	PROPN
cuesj-22	187	14	σ	σ	PROPN
cuesj-22	187	15	σ	σ	PROPN
cuesj-22	187	16	σ	σ	PROPN
cuesj-22	187	17	ε	ε	PROPN
cuesj-22	187	18	ε	ε	PROPN
cuesj-22	187	19	ε	ε	PROPN
cuesj-22	187	20	ε	ε	PROPN
cuesj-22	187	21	,	,	PUNCT
cuesj-22	187	22	and	and	CCONJ
cuesj-22	187	23	then	then	ADV
cuesj-22	187	24	,	,	PUNCT
cuesj-22	187	25	the	the	DET
cuesj-22	187	26	final	final	ADJ
cuesj-22	187	27	distribution	distribution	NOUN
cuesj-22	187	28	for	for	ADP
cuesj-22	187	29	parameter	parameter	NOUN
cuesj-22	187	30	μt	μt	NOUN
cuesj-22	187	31	at	at	ADP
cuesj-22	187	32	time	time	NOUN
cuesj-22	187	33	t	t	PROPN
cuesj-22	187	34	is	be	AUX
cuesj-22	187	35	(	(	PUNCT
cuesj-22	187	36	|	|	ADV
cuesj-22	187	37	)	)	PUNCT
cuesj-22	187	38	~	~	PUNCT
cuesj-22	187	39	(	(	PUNCT
cuesj-22	187	40	,	,	PUNCT
cuesj-22	187	41	)	)	PUNCT
cuesj-22	187	42	t	t	PROPN
cuesj-22	187	43	t	t	PROPN
cuesj-22	187	44	t	t	PROPN
cuesj-22	187	45	tp	tp	PROPN
cuesj-22	187	46	d	d	PROPN
cuesj-22	187	47	n	n	VERB
cuesj-22	187	48	m	m	VERB
cuesj-22	187	49	c	c	ADJ
cuesj-22	187	50	we	we	PRON
cuesj-22	187	51	can	can	AUX
cuesj-22	187	52	write	write	VERB
cuesj-22	187	53	the	the	DET
cuesj-22	187	54	mean	mean	NOUN
cuesj-22	187	55	and	and	CCONJ
cuesj-22	187	56	variance	variance	NOUN
cuesj-22	187	57	of	of	ADP
cuesj-22	187	58	posterior	posterior	ADJ
cuesj-22	187	59	probability	probability	NOUN
cuesj-22	187	60	distribution	distribution	NOUN
cuesj-22	187	61	after	after	ADP
cuesj-22	187	62	simplifying	simplify	VERB
cuesj-22	187	63	them	they	PRON
cuesj-22	187	64	as	as	ADP
cuesj-22	187	65	:	:	PUNCT
cuesj-22	187	66	m	m	VERB
cuesj-22	187	67	m	m	VERB
cuesj-22	187	68	k	k	PROPN
cuesj-22	187	69	y	y	PROPN
cuesj-22	187	70	mt	mt	PROPN
cuesj-22	187	71	t	t	PROPN
cuesj-22	187	72	t	t	PROPN
cuesj-22	187	73	t	t	PROPN
cuesj-22	187	74	t=	t=	PROPN
cuesj-22	188	1	+	+	CCONJ
cuesj-22	188	2	−−	−−	NOUN
cuesj-22	188	3	−1	−1	NOUN
cuesj-22	188	4	1	1	NUM
cuesj-22	188	5	(	(	PUNCT
cuesj-22	188	6	)	)	PUNCT
cuesj-22	188	7	(	(	PUNCT
cuesj-22	188	8	19	19	NUM
cuesj-22	188	9	)	)	PUNCT
cuesj-22	188	10	c	c	NOUN
cuesj-22	189	1	kt	kt	X
cuesj-22	189	2	t=	t=	PROPN
cuesj-22	189	3	*	*	PUNCT
cuesj-22	189	4	σε	σε	X
cuesj-22	189	5	2	2	NUM
cuesj-22	189	6	(	(	PUNCT
cuesj-22	189	7	20	20	NUM
cuesj-22	189	8	)	)	PUNCT
cuesj-22	190	1	where	where	SCONJ
cuesj-22	190	2	k	k	PROPN
cuesj-22	191	1	r	r	NOUN
cuesj-22	191	2	rt	rt	PROPN
cuesj-22	191	3	t	t	PROPN
cuesj-22	191	4	t=	t=	PRON
cuesj-22	192	1	+	+	CCONJ
cuesj-22	192	2	−	−	PROPN
cuesj-22	192	3	(	(	PUNCT
cuesj-22	192	4	)	)	PUNCT
cuesj-22	192	5	σε	σε	PROPN
cuesj-22	192	6	2	2	NUM
cuesj-22	192	7	1	1	NUM
cuesj-22	192	8	(	(	PUNCT
cuesj-22	192	9	21	21	NUM
cuesj-22	192	10	)	)	PUNCT
cuesj-22	192	11	where	where	SCONJ
cuesj-22	192	12	the	the	DET
cuesj-22	192	13	equation	equation	NOUN
cuesj-22	192	14	(	(	PUNCT
cuesj-22	192	15	19	19	NUM
cuesj-22	192	16	)	)	PUNCT
cuesj-22	192	17	called	call	VERB
cuesj-22	192	18	kalman	kalman	PROPN
cuesj-22	192	19	filter	filter	PROPN
cuesj-22	192	20	,	,	PUNCT
cuesj-22	192	21	sometimes	sometimes	ADV
cuesj-22	192	22	called	call	VERB
cuesj-22	192	23	for	for	ADP
cuesj-22	192	24	two	two	NUM
cuesj-22	192	25	equations	equation	NOUN
cuesj-22	192	26	(	(	PUNCT
cuesj-22	192	27	19	19	NUM
cuesj-22	192	28	)	)	PUNCT
cuesj-22	192	29	,	,	PUNCT
cuesj-22	192	30	(	(	PUNCT
cuesj-22	192	31	20	20	NUM
cuesj-22	192	32	)	)	PUNCT
cuesj-22	192	33	together	together	ADV
cuesj-22	192	34	kalman	kalman	PROPN
cuesj-22	192	35	filter	filter	PROPN
cuesj-22	192	36	,	,	PUNCT
cuesj-22	192	37	and	and	CCONJ
cuesj-22	192	38	equation	equation	NOUN
cuesj-22	192	39	(	(	PUNCT
cuesj-22	192	40	21	21	NUM
cuesj-22	192	41	)	)	PUNCT
cuesj-22	192	42	is	be	AUX
cuesj-22	192	43	kalman	kalman	NOUN
cuesj-22	192	44	gain	gain	NOUN
cuesj-22	192	45	.	.	PUNCT
cuesj-22	193	1	forecasting	forecast	VERB
cuesj-22	193	2	dynamic	dynamic	ADJ
cuesj-22	193	3	linear	linear	NOUN
cuesj-22	193	4	models	model	NOUN
cuesj-22	193	5	we	we	PRON
cuesj-22	193	6	mean	mean	VERB
cuesj-22	193	7	by	by	ADP
cuesj-22	193	8	forecasting	forecasting	NOUN
cuesj-22	193	9	finding	find	VERB
cuesj-22	193	10	the	the	DET
cuesj-22	193	11	h	h	NOUN
cuesj-22	193	12	-	-	PUNCT
cuesj-22	193	13	step	step	NOUN
cuesj-22	193	14	ahead	ahead	ADV
cuesj-22	193	15	for	for	ADP
cuesj-22	193	16	each	each	DET
cuesj-22	193	17	distribution	distribution	NOUN
cuesj-22	193	18	given	give	VERB
cuesj-22	193	19	the	the	DET
cuesj-22	193	20	data	datum	NOUN
cuesj-22	193	21	dynamic	dynamic	ADJ
cuesj-22	193	22	linear	linear	NOUN
cuesj-22	193	23	model	model	NOUN
cuesj-22	193	24	y1,y2.,yt	y1,y2.,yt	PROPN
cuesj-22	193	25	.	.	PUNCT
cuesj-22	194	1	for	for	ADP
cuesj-22	194	2	a	a	DET
cuesj-22	194	3	dynamic	dynamic	ADJ
cuesj-22	194	4	linear	linear	NOUN
cuesj-22	194	5	model	model	NOUN
cuesj-22	194	6	,	,	PUNCT
cuesj-22	194	7	the	the	DET
cuesj-22	194	8	h	h	NOUN
cuesj-22	194	9	-	-	PUNCT
cuesj-22	194	10	step	step	NOUN
cuesj-22	194	11	-	-	PUNCT
cuesj-22	194	12	ahead	ahead	NOUN
cuesj-22	194	13	forecasting	forecasting	NOUN
cuesj-22	194	14	distributions	distribution	NOUN
cuesj-22	194	15	,	,	PUNCT
cuesj-22	194	16	for	for	ADP
cuesj-22	194	17	states	state	NOUN
cuesj-22	194	18	and	and	CCONJ
cuesj-22	194	19	observations	observation	NOUN
cuesj-22	194	20	,	,	PUNCT
cuesj-22	194	21	are	be	AUX
cuesj-22	194	22	obtained	obtain	VERB
cuesj-22	194	23	as	as	ADP
cuesj-22	194	24	a	a	DET
cuesj-22	194	25	product	product	NOUN
cuesj-22	194	26	of	of	ADP
cuesj-22	194	27	the	the	DET
cuesj-22	194	28	kalman	kalman	PROPN
cuesj-22	194	29	filter.[12,13	filter.[12,13	PROPN
cuesj-22	194	30	]	]	PUNCT
cuesj-22	194	31	from	from	ADP
cuesj-22	194	32	the	the	DET
cuesj-22	194	33	observations	observation	NOUN
cuesj-22	194	34	y1	y1	PROPN
cuesj-22	194	35	:	:	PUNCT
cuesj-22	194	36	t	t	NOUN
cuesj-22	194	37	to	to	ADP
cuesj-22	194	38	yt+h	yt+h	NUM
cuesj-22	194	39	we	we	PRON
cuesj-22	194	40	noticed	notice	VERB
cuesj-22	194	41	that	that	SCONJ
cuesj-22	194	42	the	the	DET
cuesj-22	194	43	data	data	NOUN
cuesj-22	194	44	y1	y1	PROPN
cuesj-22	194	45	:	:	PUNCT
cuesj-22	194	46	t	t	NOUN
cuesj-22	194	47	provide	provide	VERB
cuesj-22	194	48	information	information	NOUN
cuesj-22	194	49	about	about	ADP
cuesj-22	194	50	θt	θt	PROPN
cuesj-22	194	51	,	,	PUNCT
cuesj-22	194	52	which	which	PRON
cuesj-22	194	53	,	,	PUNCT
cuesj-22	194	54	in	in	ADP
cuesj-22	194	55	turn	turn	NOUN
cuesj-22	194	56	,	,	PUNCT
cuesj-22	194	57	gives	give	VERB
cuesj-22	194	58	information	information	NOUN
cuesj-22	194	59	about	about	ADP
cuesj-22	194	60	the	the	DET
cuesj-22	194	61	future	future	ADJ
cuesj-22	194	62	state	state	NOUN
cuesj-22	194	63	evolution	evolution	NOUN
cuesj-22	194	64	up	up	ADP
cuesj-22	194	65	to	to	ADP
cuesj-22	194	66	θt+k	θt+k	PROPN
cuesj-22	194	67	and	and	CCONJ
cuesj-22	194	68	consequently	consequently	ADV
cuesj-22	194	69	on	on	ADP
cuesj-22	194	70	yt+h	yt+h	NUM
cuesj-22	194	71	.	.	PUNCT
cuesj-22	195	1	[	[	X
cuesj-22	195	2	13,14	13,14	X
cuesj-22	195	3	]	]	PUNCT
cuesj-22	195	4	then	then	ADV
cuesj-22	195	5	,	,	PUNCT
cuesj-22	195	6	the	the	DET
cuesj-22	195	7	general	general	ADJ
cuesj-22	195	8	dynamic	dynamic	ADJ
cuesj-22	195	9	linear	linear	NOUN
cuesj-22	195	10	model	model	NOUN
cuesj-22	195	11	for	for	ADP
cuesj-22	195	12	h	h	NOUN
cuesj-22	195	13	step	step	NOUN
cuesj-22	195	14	is	be	AUX
cuesj-22	195	15	:	:	PUNCT
cuesj-22	195	16	observation	observation	NOUN
cuesj-22	195	17	equation	equation	NOUN
cuesj-22	195	18	:	:	PUNCT
cuesj-22	195	19	y	y	PROPN
cuesj-22	195	20	f	f	PROPN
cuesj-22	195	21	n	n	PROPN
cuesj-22	195	22	vt	vt	PROPN
cuesj-22	195	23	h	h	PROPN
cuesj-22	195	24	t	t	PROPN
cuesj-22	195	25	h	h	PROPN
cuesj-22	196	1	t	t	PROPN
cuesj-22	196	2	h	h	NOUN
cuesj-22	196	3	t	t	PROPN
cuesj-22	196	4	h	h	PROPN
cuesj-22	196	5	t	t	PROPN
cuesj-22	196	6	t+	t+	NOUN
cuesj-22	196	7	+	+	PUNCT
cuesj-22	197	1	+	+	PUNCT
cuesj-22	197	2	+	+	ADJ
cuesj-22	197	3	=	=	ADJ
cuesj-22	197	4	+	+	ADJ
cuesj-22	197	5	θ	θ	PROPN
cuesj-22	197	6	υ	υ	NOUN
cuesj-22	197	7	υ	υ	X
cuesj-22	197	8	~	~	PUNCT
cuesj-22	197	9	[	[	PUNCT
cuesj-22	197	10	,	,	PUNCT
cuesj-22	197	11	]	]	X
cuesj-22	197	12	0	0	NUM
cuesj-22	197	13	(	(	PUNCT
cuesj-22	197	14	22	22	NUM
cuesj-22	197	15	)	)	PUNCT
cuesj-22	197	16	system	system	NOUN
cuesj-22	197	17	equation	equation	NOUN
cuesj-22	197	18	:	:	PUNCT
cuesj-22	198	1	θ	θ	PROPN
cuesj-22	198	2	θ	θ	PROPN
cuesj-22	198	3	ω	ω	NUM
cuesj-22	198	4	ωt	ωt	INTJ
cuesj-22	198	5	h	h	NOUN
cuesj-22	199	1	t	t	PROPN
cuesj-22	199	2	h	h	NOUN
cuesj-22	199	3	t	t	PROPN
cuesj-22	199	4	h	h	NOUN
cuesj-22	200	1	t	t	PROPN
cuesj-22	200	2	h	h	NOUN
cuesj-22	200	3	t	t	PROPN
cuesj-22	200	4	tg	tg	PROPN
cuesj-22	200	5	n	n	PROPN
cuesj-22	200	6	w+	w+	AUX
cuesj-22	200	7	+	+	PUNCT
cuesj-22	201	1	+	+	CCONJ
cuesj-22	202	1	−	−	X
cuesj-22	202	2	+	+	NOUN
cuesj-22	202	3	=	=	NOUN
cuesj-22	202	4	+1	+1	ADJ
cuesj-22	202	5	0~	0~	NOUN
cuesj-22	202	6	[	[	PUNCT
cuesj-22	202	7	,	,	PUNCT
cuesj-22	202	8	]	]	X
cuesj-22	202	9	(	(	PUNCT
cuesj-22	202	10	23	23	NUM
cuesj-22	202	11	)	)	PUNCT
cuesj-22	202	12	let	let	VERB
cuesj-22	202	13	a0	a0	PROPN
cuesj-22	202	14	=	=	PROPN
cuesj-22	202	15	mt	mt	PROPN
cuesj-22	202	16	,	,	PUNCT
cuesj-22	202	17	r0	r0	NOUN
cuesj-22	202	18	=	=	PROPN
cuesj-22	202	19	ct	ct	PROPN
cuesj-22	202	20	,	,	PUNCT
cuesj-22	202	21	then	then	ADV
cuesj-22	202	22	for	for	ADP
cuesj-22	202	23	h≥1	h≥1	NOUN
cuesj-22	202	24	,	,	PUNCT
cuesj-22	202	25	for	for	ADP
cuesj-22	202	26	forecasting	forecast	VERB
cuesj-22	202	27	at	at	ADP
cuesj-22	202	28	time	time	NOUN
cuesj-22	202	29	t	t	PROPN
cuesj-22	202	30	,	,	PUNCT
cuesj-22	202	31	the	the	DET
cuesj-22	202	32	forecaster	forecaster	NOUN
cuesj-22	202	33	requires	require	VERB
cuesj-22	202	34	the	the	DET
cuesj-22	202	35	h	h	NOUN
cuesj-22	202	36	-	-	PUNCT
cuesj-22	202	37	step	step	NOUN
cuesj-22	202	38	ahead	ahead	ADV
cuesj-22	202	39	marginal	marginal	ADJ
cuesj-22	202	40	distributions	distribution	NOUN
cuesj-22	202	41	,	,	PUNCT
cuesj-22	202	42	(	(	PUNCT
cuesj-22	202	43	yt+h|dt	yt+h|dt	NOUN
cuesj-22	202	44	)	)	PUNCT
cuesj-22	202	45	and	and	CCONJ
cuesj-22	202	46	(	(	PUNCT
cuesj-22	202	47	θt+h|dt	θt+h|dt	NOUN
cuesj-22	202	48	)	)	PUNCT
cuesj-22	202	49	,	,	PUNCT
cuesj-22	202	50	predictions	prediction	NOUN
cuesj-22	202	51	start	start	VERB
cuesj-22	202	52	from	from	ADP
cuesj-22	202	53	the	the	DET
cuesj-22	202	54	posterior	posterior	ADJ
cuesj-22	202	55	estimate	estimate	NOUN
cuesj-22	202	56	of	of	ADP
cuesj-22	202	57	the	the	DET
cuesj-22	202	58	state	state	NOUN
cuesj-22	202	59	,	,	PUNCT
cuesj-22	202	60	obtained	obtain	VERB
cuesj-22	202	61	from	from	ADP
cuesj-22	202	62	the	the	DET
cuesj-22	202	63	kalman	kalman	NOUN
cuesj-22	202	64	filter	filter	NOUN
cuesj-22	202	65	,	,	PUNCT
cuesj-22	202	66	on	on	ADP
cuesj-22	202	67	the	the	DET
cuesj-22	202	68	time	time	NOUN
cuesj-22	202	69	(	(	PUNCT
cuesj-22	202	70	t	t	NOUN
cuesj-22	202	71	)	)	PUNCT
cuesj-22	202	72	on	on	ADP
cuesj-22	202	73	which	which	PRON
cuesj-22	202	74	the	the	DET
cuesj-22	202	75	forecast	forecast	NOUN
cuesj-22	202	76	is	be	AUX
cuesj-22	202	77	made	make	VERB
cuesj-22	202	78	.	.	PUNCT
cuesj-22	203	1	the	the	DET
cuesj-22	203	2	mean	mean	NOUN
cuesj-22	203	3	and	and	CCONJ
cuesj-22	203	4	variance	variance	NOUN
cuesj-22	203	5	for	for	ADP
cuesj-22	203	6	the	the	DET
cuesj-22	203	7	observation	observation	NOUN
cuesj-22	203	8	equation	equation	NOUN
cuesj-22	203	9	are	be	AUX
cuesj-22	203	10	:	:	PUNCT
cuesj-22	203	11	1	1	NUM
cuesj-22	203	12	1	1	NUM
cuesj-22	203	13	1	1	NUM
cuesj-22	203	14	ˆ	ˆ	PROPN
cuesj-22	203	15	(	(	PUNCT
cuesj-22	203	16	|	|	ADV
cuesj-22	203	17	)	)	PUNCT
cuesj-22	203	18	(	(	PUNCT
cuesj-22	203	19	|	|	ADV
cuesj-22	203	20	)	)	PUNCT
cuesj-22	203	21	t	t	PROPN
cuesj-22	203	22	h	h	NOUN
cuesj-22	203	23	t	t	PROPN
cuesj-22	203	24	h	h	PROPN
cuesj-22	204	1	t	t	PROPN
cuesj-22	204	2	t	t	PROPN
cuesj-22	204	3	t	t	PROPN
cuesj-22	204	4	t	t	PROPN
cuesj-22	204	5	t	t	PROPN
cuesj-22	205	1	k	k	PROPN
cuesj-22	205	2	t	t	PROPN
cuesj-22	205	3	y	y	PROPN
cuesj-22	206	1	e	e	PROPN
cuesj-22	206	2	y	y	PROPN
cuesj-22	206	3	d	d	X
cuesj-22	206	4	e	e	X
cuesj-22	207	1	f	f	PROPN
cuesj-22	207	2	d	d	X
cuesj-22	207	3	fg	fg	PROPN
cuesj-22	207	4	m	m	VERB
cuesj-22	207	5			X
cuesj-22	207	6			PROPN
cuesj-22	208	1	+	+	PUNCT
cuesj-22	209	1	+	+	PUNCT
cuesj-22	210	1	+	+	PUNCT
cuesj-22	210	2	+	+	PUNCT
cuesj-22	210	3	+	+	NOUN
cuesj-22	210	4	=	=	SYM
cuesj-22	210	5	=	=	SYM
cuesj-22	210	6	+	+	NUM
cuesj-22	210	7	=	=	SYM
cuesj-22	210	8	(	(	PUNCT
cuesj-22	210	9	24	24	NUM
cuesj-22	210	10	)	)	PUNCT
cuesj-22	210	11	q	q	PROPN
cuesj-22	211	1	t	t	PROPN
cuesj-22	211	2	var	var	NOUN
cuesj-22	211	3	y	y	PROPN
cuesj-22	211	4	d	d	PROPN
cuesj-22	211	5	fg	fg	PROPN
cuesj-22	212	1	c	c	PROPN
cuesj-22	212	2	g	g	PROPN
cuesj-22	212	3	f	f	PROPN
cuesj-22	212	4	fg	fg	PROPN
cuesj-22	212	5	w	w	PROPN
cuesj-22	212	6	g	g	PROPN
cuesj-22	212	7	f	f	PROPN
cuesj-22	212	8	v	v	ADP
cuesj-22	212	9	t	t	PROPN
cuesj-22	212	10	t	t	PROPN
cuesj-22	213	1	h	h	NOUN
cuesj-22	214	1	t	t	PROPN
cuesj-22	215	1	h	h	NOUN
cuesj-22	216	1	t	t	PROPN
cuesj-22	217	1	k	k	PROPN
cuesj-22	217	2	t	t	PROPN
cuesj-22	217	3	t	t	PROPN
cuesj-22	218	1	i	i	PRON
cuesj-22	219	1	i	i	PRON
cuesj-22	219	2	t	t	NOUN
cuesj-22	220	1	t	t	X
cuesj-22	220	2	i	i	PRON
cuesj-22	220	3	h	h	NOUN
cuesj-22	220	4	(	(	PUNCT
cuesj-22	220	5	)	)	PUNCT
cuesj-22	220	6	(	(	PUNCT
cuesj-22	220	7	|	|	ADV
cuesj-22	220	8	)	)	PUNCT
cuesj-22	220	9	(	(	PUNCT
cuesj-22	220	10	)	)	PUNCT
cuesj-22	220	11	(	(	PUNCT
cuesj-22	220	12	)	)	PUNCT
cuesj-22	220	13	=	=	PUNCT
cuesj-22	221	1	=	=	PUNCT
cuesj-22	222	1	+	+	PUNCT
cuesj-22	223	1	+	+	PUNCT
cuesj-22	223	2	+	+	CCONJ
cuesj-22	223	3	=	=	SYM
cuesj-22	223	4	−	−	ADP
cuesj-22	223	5	∑	∑	NOUN
cuesj-22	223	6	0	0	NUM
cuesj-22	223	7	1	1	NUM
cuesj-22	223	8	(	(	PUNCT
cuesj-22	223	9	25	25	NUM
cuesj-22	223	10	)	)	PUNCT
cuesj-22	223	11	application	application	NOUN
cuesj-22	223	12	,	,	PUNCT
cuesj-22	223	13	results	result	NOUN
cuesj-22	223	14	and	and	CCONJ
cuesj-22	223	15	discussion	discussion	VERB
cuesj-22	223	16	the	the	DET
cuesj-22	223	17	study	study	NOUN
cuesj-22	223	18	area	area	NOUN
cuesj-22	223	19	and	and	CCONJ
cuesj-22	223	20	data	datum	NOUN
cuesj-22	223	21	collection	collection	NOUN
cuesj-22	223	22	spatial	spatial	ADJ
cuesj-22	223	23	variability	variability	NOUN
cuesj-22	223	24	of	of	ADP
cuesj-22	223	25	any	any	DET
cuesj-22	223	26	regional	regional	ADJ
cuesj-22	223	27	variable	variable	NOUN
cuesj-22	223	28	is	be	AUX
cuesj-22	223	29	a	a	DET
cuesj-22	223	30	result	result	NOUN
cuesj-22	223	31	of	of	ADP
cuesj-22	223	32	complex	complex	ADJ
cuesj-22	223	33	processes	process	NOUN
cuesj-22	223	34	which	which	PRON
cuesj-22	223	35	is	be	AUX
cuesj-22	223	36	working	work	VERB
cuesj-22	223	37	at	at	ADP
cuesj-22	223	38	the	the	DET
cuesj-22	223	39	same	same	ADJ
cuesj-22	223	40	time	time	NOUN
cuesj-22	223	41	and	and	CCONJ
cuesj-22	223	42	over	over	ADP
cuesj-22	223	43	long	long	ADJ
cuesj-22	223	44	periods	period	NOUN
cuesj-22	223	45	of	of	ADP
cuesj-22	223	46	time	time	NOUN
cuesj-22	223	47	.	.	PUNCT
cuesj-22	224	1	variation	variation	NOUN
cuesj-22	224	2	of	of	ADP
cuesj-22	224	3	a	a	DET
cuesj-22	224	4	regional	regional	ADJ
cuesj-22	224	5	variable	variable	NOUN
cuesj-22	224	6	has	have	AUX
cuesj-22	224	7	never	never	ADV
cuesj-22	224	8	been	be	AUX
cuesj-22	224	9	an	an	DET
cuesj-22	224	10	easy	easy	ADJ
cuesj-22	224	11	task	task	NOUN
cuesj-22	224	12	or	or	CCONJ
cuesj-22	224	13	work	work	NOUN
cuesj-22	224	14	.	.	PUNCT
cuesj-22	225	1	many	many	ADJ
cuesj-22	225	2	regional	regional	ADJ
cuesj-22	225	3	random	random	ADJ
cuesj-22	225	4	variables	variable	NOUN
cuesj-22	225	5	vary	vary	VERB
cuesj-22	225	6	not	not	PART
cuesj-22	225	7	only	only	ADV
cuesj-22	225	8	horizontally	horizontally	ADV
cuesj-22	225	9	but	but	CCONJ
cuesj-22	225	10	also	also	ADV
cuesj-22	225	11	with	with	ADP
cuesj-22	225	12	depth	depth	NOUN
cuesj-22	225	13	such	such	ADJ
cuesj-22	225	14	as	as	ADP
cuesj-22	225	15	wells	well	NOUN
cuesj-22	225	16	of	of	ADP
cuesj-22	225	17	water	water	NOUN
cuesj-22	225	18	,	,	PUNCT
cuesj-22	225	19	oil	oil	NOUN
cuesj-22	225	20	,	,	PUNCT
cuesj-22	225	21	and	and	CCONJ
cuesj-22	225	22	gas	gas	NOUN
cuesj-22	225	23	.	.	PUNCT
cuesj-22	226	1	the	the	DET
cuesj-22	226	2	spatial	spatial	ADJ
cuesj-22	226	3	attribute	attribute	NOUN
cuesj-22	226	4	includes	include	VERB
cuesj-22	226	5	approximately	approximately	ADV
cuesj-22	226	6	all	all	DET
cuesj-22	226	7	wells	well	NOUN
cuesj-22	226	8	at	at	ADP
cuesj-22	226	9	fields	field	NOUN
cuesj-22	226	10	that	that	PRON
cuesj-22	226	11	exist	exist	VERB
cuesj-22	226	12	which	which	PRON
cuesj-22	226	13	spatially	spatially	ADV
cuesj-22	226	14	distributed	distribute	VERB
cuesj-22	226	15	across	across	ADP
cuesj-22	226	16	the	the	DET
cuesj-22	226	17	study	study	NOUN
cuesj-22	226	18	area	area	NOUN
cuesj-22	226	19	to	to	PART
cuesj-22	226	20	represent	represent	VERB
cuesj-22	226	21	the	the	DET
cuesj-22	226	22	fluctuations	fluctuation	NOUN
cuesj-22	226	23	of	of	ADP
cuesj-22	226	24	levels	level	NOUN
cuesj-22	226	25	from	from	ADP
cuesj-22	226	26	place	place	NOUN
cuesj-22	226	27	to	to	ADP
cuesj-22	226	28	another	another	DET
cuesj-22	226	29	place	place	NOUN
cuesj-22	226	30	in	in	ADP
cuesj-22	226	31	whole	whole	ADJ
cuesj-22	226	32	area	area	NOUN
cuesj-22	226	33	.	.	PUNCT
cuesj-22	227	1	the	the	DET
cuesj-22	227	2	source	source	NOUN
cuesj-22	227	3	of	of	ADP
cuesj-22	227	4	dataset	dataset	NOUN
cuesj-22	227	5	is	be	AUX
cuesj-22	227	6	the	the	DET
cuesj-22	227	7	observed	observe	VERB
cuesj-22	227	8	values	value	NOUN
cuesj-22	227	9	of	of	ADP
cuesj-22	227	10	the	the	DET
cuesj-22	227	11	295	295	NUM
cuesj-22	227	12	wells	well	NOUN
cuesj-22	227	13	that	that	PRON
cuesj-22	227	14	had	have	AUX
cuesj-22	227	15	been	be	AUX
cuesj-22	227	16	taken	take	VERB
cuesj-22	227	17	from	from	ADP
cuesj-22	227	18	the	the	DET
cuesj-22	227	19	known	know	VERB
cuesj-22	227	20	specific	specific	ADJ
cuesj-22	227	21	place	place	NOUN
cuesj-22	227	22	which	which	PRON
cuesj-22	227	23	called	call	VERB
cuesj-22	227	24	shaqlawa	shaqlawa	PROPN
cuesj-22	227	25	in	in	ADP
cuesj-22	227	26	erbil	erbil	PROPN
cuesj-22	227	27	governorate	governorate	PROPN
cuesj-22	227	28	.	.	PUNCT
cuesj-22	228	1	these	these	DET
cuesj-22	228	2	observations	observation	NOUN
cuesj-22	228	3	are	be	AUX
cuesj-22	228	4	collected	collect	VERB
cuesj-22	228	5	by	by	ADP
cuesj-22	228	6	gps	gps	PROPN
cuesj-22	228	7	by	by	ADP
cuesj-22	228	8	the	the	DET
cuesj-22	228	9	ministry	ministry	PROPN
cuesj-22	228	10	of	of	ADP
cuesj-22	228	11	agriculture	agriculture	PROPN
cuesj-22	228	12	,	,	PUNCT
cuesj-22	228	13	figure	figure	NOUN
cuesj-22	228	14	3	3	NUM
cuesj-22	228	15	expresses	express	VERB
cuesj-22	228	16	the	the	DET
cuesj-22	228	17	location	location	NOUN
cuesj-22	228	18	of	of	ADP
cuesj-22	228	19	each	each	PRON
cuesj-22	228	20	well	well	ADV
cuesj-22	228	21	,	,	PUNCT
cuesj-22	228	22	and	and	CCONJ
cuesj-22	228	23	each	each	DET
cuesj-22	228	24	point	point	NOUN
cuesj-22	228	25	of	of	ADP
cuesj-22	228	26	the	the	DET
cuesj-22	228	27	dataset	dataset	NOUN
cuesj-22	228	28	has	have	VERB
cuesj-22	228	29	its	its	PRON
cuesj-22	228	30	name	name	NOUN
cuesj-22	228	31	and	and	CCONJ
cuesj-22	228	32	properties	property	NOUN
cuesj-22	228	33	.	.	PUNCT
cuesj-22	229	1	furthermore	furthermore	ADV
cuesj-22	229	2	,	,	PUNCT
cuesj-22	229	3	each	each	DET
cuesj-22	229	4	point	point	NOUN
cuesj-22	229	5	has	have	VERB
cuesj-22	229	6	its	its	PRON
cuesj-22	229	7	goal	goal	NOUN
cuesj-22	229	8	for	for	ADP
cuesj-22	229	9	drilling	drill	VERB
cuesj-22	229	10	the	the	DET
cuesj-22	229	11	well	well	NOUN
cuesj-22	229	12	,	,	PUNCT
cuesj-22	229	13	some	some	PRON
cuesj-22	229	14	of	of	ADP
cuesj-22	229	15	them	they	PRON
cuesj-22	229	16	for	for	ADP
cuesj-22	229	17	drinking	drinking	NOUN
cuesj-22	229	18	or	or	CCONJ
cuesj-22	229	19	irrigation	irrigation	NOUN
cuesj-22	229	20	and	and	CCONJ
cuesj-22	229	21	agriculture	agriculture	NOUN
cuesj-22	229	22	.	.	PUNCT
cuesj-22	230	1	figure	figure	NOUN
cuesj-22	230	2	3	3	NUM
cuesj-22	230	3	shows	show	VERB
cuesj-22	230	4	the	the	DET
cuesj-22	230	5	geographical	geographical	ADJ
cuesj-22	230	6	location	location	NOUN
cuesj-22	230	7	of	of	ADP
cuesj-22	230	8	the	the	DET
cuesj-22	230	9	data	data	NOUN
cuesj-22	230	10	points	point	NOUN
cuesj-22	230	11	of	of	ADP
cuesj-22	230	12	shaqlawa	shaqlawa	NOUN
cuesj-22	230	13	or	or	CCONJ
cuesj-22	230	14	all	all	DET
cuesj-22	230	15	wells	well	NOUN
cuesj-22	230	16	.	.	PUNCT
cuesj-22	231	1	they	they	PRON
cuesj-22	231	2	are	be	AUX
cuesj-22	231	3	shown	show	VERB
cuesj-22	231	4	as	as	ADP
cuesj-22	231	5	a	a	DET
cuesj-22	231	6	surface	surface	NOUN
cuesj-22	231	7	which	which	PRON
cuesj-22	231	8	can	can	AUX
cuesj-22	231	9	be	be	AUX
cuesj-22	231	10	represented	represent	VERB
cuesj-22	231	11	by	by	ADP
cuesj-22	231	12	the	the	DET
cuesj-22	231	13	most	most	ADV
cuesj-22	231	14	probably	probably	ADV
cuesj-22	231	15	prediction	prediction	NOUN
cuesj-22	231	16	map	map	NOUN
cuesj-22	231	17	and	and	CCONJ
cuesj-22	231	18	also	also	ADV
cuesj-22	231	19	by	by	ADP
cuesj-22	231	20	estimated	estimate	VERB
cuesj-22	231	21	prediction	prediction	NOUN
cuesj-22	231	22	when	when	SCONJ
cuesj-22	231	23	we	we	PRON
cuesj-22	231	24	are	be	AUX
cuesj-22	231	25	applying	apply	VERB
cuesj-22	231	26	a	a	DET
cuesj-22	231	27	model	model	NOUN
cuesj-22	231	28	of	of	ADP
cuesj-22	231	29	the	the	DET
cuesj-22	231	30	simple	simple	ADJ
cuesj-22	231	31	kriging	krige	VERB
cuesj-22	231	32	interpolation	interpolation	NOUN
cuesj-22	231	33	.	.	PUNCT
cuesj-22	232	1	the	the	DET
cuesj-22	232	2	figure	figure	NOUN
cuesj-22	232	3	shows	show	VERB
cuesj-22	232	4	cycles	cycle	NOUN
cuesj-22	232	5	that	that	PRON
cuesj-22	232	6	are	be	AUX
cuesj-22	232	7	many	many	ADJ
cuesj-22	232	8	points	point	NOUN
cuesj-22	232	9	closer	close	ADV
cuesj-22	232	10	together	together	ADV
cuesj-22	232	11	tend	tend	VERB
cuesj-22	232	12	to	to	PART
cuesj-22	232	13	be	be	AUX
cuesj-22	232	14	more	more	ADV
cuesj-22	232	15	alike	alike	ADJ
cuesj-22	232	16	than	than	ADP
cuesj-22	232	17	things	thing	NOUN
cuesj-22	232	18	that	that	PRON
cuesj-22	232	19	are	be	AUX
cuesj-22	232	20	farther	far	ADV
cuesj-22	232	21	apart	apart	ADV
cuesj-22	232	22	(	(	PUNCT
cuesj-22	232	23	quantified	quantify	VERB
cuesj-22	232	24	here	here	ADV
cuesj-22	232	25	as	as	ADP
cuesj-22	232	26	spatial	spatial	ADJ
cuesj-22	232	27	autocorrelation	autocorrelation	NOUN
cuesj-22	232	28	)	)	PUNCT
cuesj-22	232	29	.	.	PUNCT
cuesj-22	233	1	results	result	NOUN
cuesj-22	233	2	of	of	ADP
cuesj-22	233	3	groundwater	groundwater	NOUN
cuesj-22	233	4	-	-	PUNCT
cuesj-22	233	5	surface	surface	NOUN
cuesj-22	233	6	interpolation	interpolation	NOUN
cuesj-22	233	7	figure	figure	NOUN
cuesj-22	233	8	4	4	NUM
cuesj-22	233	9	can	can	AUX
cuesj-22	233	10	be	be	AUX
cuesj-22	233	11	described	describe	VERB
cuesj-22	233	12	as	as	ADP
cuesj-22	233	13	continuous	continuous	ADJ
cuesj-22	233	14	data	datum	NOUN
cuesj-22	233	15	and	and	CCONJ
cuesj-22	233	16	represented	represent	VERB
cuesj-22	233	17	the	the	DET
cuesj-22	233	18	random	random	ADJ
cuesj-22	233	19	field	field	NOUN
cuesj-22	233	20	.	.	PUNCT
cuesj-22	234	1	it	it	PRON
cuesj-22	234	2	shows	show	VERB
cuesj-22	234	3	a	a	DET
cuesj-22	234	4	histogram	histogram	NOUN
cuesj-22	234	5	of	of	ADP
cuesj-22	234	6	the	the	DET
cuesj-22	234	7	observed	observed	ADJ
cuesj-22	234	8	values	value	NOUN
cuesj-22	234	9	and	and	CCONJ
cuesj-22	234	10	how	how	SCONJ
cuesj-22	234	11	they	they	PRON
cuesj-22	234	12	distributed	distribute	VERB
cuesj-22	234	13	in	in	ADP
cuesj-22	234	14	the	the	DET
cuesj-22	234	15	region	region	NOUN
cuesj-22	234	16	.	.	PUNCT
cuesj-22	235	1	it	it	PRON
cuesj-22	235	2	represents	represent	VERB
cuesj-22	235	3	the	the	DET
cuesj-22	235	4	curve	curve	NOUN
cuesj-22	235	5	of	of	ADP
cuesj-22	235	6	the	the	DET
cuesj-22	235	7	observations	observation	NOUN
cuesj-22	235	8	values	value	NOUN
cuesj-22	235	9	,	,	PUNCT
cuesj-22	235	10	and	and	CCONJ
cuesj-22	235	11	the	the	DET
cuesj-22	235	12	results	result	NOUN
cuesj-22	235	13	after	after	SCONJ
cuesj-22	235	14	taken	take	VERB
cuesj-22	235	15	the	the	DET
cuesj-22	235	16	suitable	suitable	ADJ
cuesj-22	235	17	transformation	transformation	NOUN
cuesj-22	235	18	that	that	PRON
cuesj-22	235	19	has	have	VERB
cuesj-22	235	20	a	a	DET
cuesj-22	235	21	small	small	ADJ
cuesj-22	235	22	standard	standard	ADJ
cuesj-22	235	23	deviation	deviation	NOUN
cuesj-22	235	24	equal	equal	ADJ
cuesj-22	235	25	to	to	ADP
cuesj-22	235	26	0.37924	0.37924	NUM
cuesj-22	235	27	,	,	PUNCT
cuesj-22	235	28	the	the	DET
cuesj-22	235	29	skewness	skewness	NOUN
cuesj-22	235	30	of	of	ADP
cuesj-22	235	31	dataset	dataset	NOUN
cuesj-22	235	32	is	be	AUX
cuesj-22	235	33	equal	equal	ADJ
cuesj-22	235	34	to	to	ADP
cuesj-22	235	35	−0.61614	−0.61614	NUM
cuesj-22	235	36	,	,	PUNCT
cuesj-22	235	37	near	near	ADP
cuesj-22	235	38	to	to	ADP
cuesj-22	235	39	zero	zero	NUM
cuesj-22	235	40	and	and	CCONJ
cuesj-22	235	41	the	the	DET
cuesj-22	235	42	kurtosis	kurtosis	NOUN
cuesj-22	235	43	of	of	ADP
cuesj-22	235	44	the	the	DET
cuesj-22	235	45	dataset	dataset	NOUN
cuesj-22	235	46	is	be	AUX
cuesj-22	235	47	equal	equal	ADJ
cuesj-22	235	48	to	to	ADP
cuesj-22	235	49	4.5715	4.5715	NUM
cuesj-22	235	50	not	not	PART
cuesj-22	235	51	near	near	ADV
cuesj-22	235	52	to	to	ADP
cuesj-22	235	53	three	three	NUM
cuesj-22	235	54	.	.	PUNCT
cuesj-22	236	1	these	these	PRON
cuesj-22	236	2	indicated	indicate	VERB
cuesj-22	236	3	that	that	SCONJ
cuesj-22	236	4	the	the	DET
cuesj-22	236	5	distributions	distribution	NOUN
cuesj-22	236	6	of	of	ADP
cuesj-22	236	7	depth	depth	NOUN
cuesj-22	236	8	in	in	ADP
cuesj-22	236	9	sample	sample	NOUN
cuesj-22	236	10	data	datum	NOUN
cuesj-22	236	11	points	point	NOUN
cuesj-22	236	12	were	be	AUX
cuesj-22	236	13	approximate	approximate	ADJ
cuesj-22	236	14	to	to	ADP
cuesj-22	236	15	normal	normal	ADJ
cuesj-22	236	16	distribution	distribution	NOUN
cuesj-22	236	17	.	.	PUNCT
cuesj-22	237	1	results	result	NOUN
cuesj-22	237	2	of	of	ADP
cuesj-22	237	3	gaussian	gaussian	ADJ
cuesj-22	237	4	semivariogram	semivariogram	NOUN
cuesj-22	237	5	simple	simple	ADJ
cuesj-22	237	6	kriging	krige	VERB
cuesj-22	237	7	surface	surface	NOUN
cuesj-22	237	8	interpolation	interpolation	NOUN
cuesj-22	237	9	spatial	spatial	ADJ
cuesj-22	237	10	dependency	dependency	NOUN
cuesj-22	237	11	can	can	AUX
cuesj-22	237	12	be	be	AUX
cuesj-22	237	13	detected	detect	VERB
cuesj-22	237	14	in	in	ADP
cuesj-22	237	15	this	this	DET
cuesj-22	237	16	dataset	dataset	NOUN
cuesj-22	237	17	using	use	VERB
cuesj-22	237	18	several	several	ADJ
cuesj-22	237	19	tools	tool	NOUN
cuesj-22	237	20	available	available	ADJ
cuesj-22	237	21	in	in	ADP
cuesj-22	237	22	geostatistical	geostatistical	ADJ
cuesj-22	237	23	analysis	analysis	NOUN
cuesj-22	237	24	exploratory	exploratory	ADJ
cuesj-22	237	25	spatial	spatial	ADJ
cuesj-22	237	26	data	data	NOUN
cuesj-22	237	27	analysis	analysis	NOUN
cuesj-22	237	28	and	and	CCONJ
cuesj-22	237	29	geostatistical	geostatistical	ADJ
cuesj-22	237	30	wizard	wizard	NOUN
cuesj-22	237	31	in	in	ADP
cuesj-22	237	32	gis	gis	PROPN
cuesj-22	237	33	software	software	NOUN
cuesj-22	237	34	.	.	PUNCT
cuesj-22	238	1	figure	figure	VERB
cuesj-22	238	2	3	3	NUM
cuesj-22	238	3	:	:	PUNCT
cuesj-22	238	4	geographical	geographical	ADJ
cuesj-22	238	5	location	location	NOUN
cuesj-22	238	6	of	of	ADP
cuesj-22	238	7	the	the	DET
cuesj-22	238	8	dataset	dataset	NOUN
cuesj-22	238	9	mawlood	mawlood	NOUN
cuesj-22	238	10	and	and	CCONJ
cuesj-22	238	11	omer	omer	PROPN
cuesj-22	238	12	:	:	PUNCT
cuesj-22	238	13	prediction	prediction	NOUN
cuesj-22	238	14	the	the	DET
cuesj-22	238	15	groundwater	groundwater	NOUN
cuesj-22	238	16	depth	depth	NOUN
cuesj-22	238	17	47	47	NUM
cuesj-22	238	18	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	238	19	cuesj	cuesj	NOUN
cuesj-22	238	20	2019	2019	NUM
cuesj-22	238	21	,	,	PUNCT
cuesj-22	238	22	3	3	NUM
cuesj-22	238	23	(	(	PUNCT
cuesj-22	238	24	1	1	NUM
cuesj-22	238	25	):	):	PUNCT
cuesj-22	238	26	42	42	NUM
cuesj-22	238	27	-	-	SYM
cuesj-22	238	28	49	49	NUM
cuesj-22	238	29	in	in	ADP
cuesj-22	238	30	geostatistics	geostatistic	NOUN
cuesj-22	238	31	semivariogram	semivariogram	NOUN
cuesj-22	238	32	is	be	AUX
cuesj-22	238	33	called	call	VERB
cuesj-22	238	34	spatial	spatial	ADJ
cuesj-22	238	35	modeling	modeling	NOUN
cuesj-22	238	36	(	(	PUNCT
cuesj-22	238	37	called	call	VERB
cuesj-22	238	38	structural	structural	ADJ
cuesj-22	238	39	analysis	analysis	NOUN
cuesj-22	238	40	or	or	CCONJ
cuesj-22	238	41	variography	variography	NOUN
cuesj-22	238	42	)	)	PUNCT
cuesj-22	238	43	.	.	PUNCT
cuesj-22	239	1	table	table	NOUN
cuesj-22	239	2	2	2	NUM
cuesj-22	239	3	shows	show	VERB
cuesj-22	239	4	the	the	DET
cuesj-22	239	5	model	model	NOUN
cuesj-22	239	6	called	call	VERB
cuesj-22	239	7	the	the	DET
cuesj-22	239	8	gaussian	gaussian	ADJ
cuesj-22	239	9	semivariogram	semivariogram	NOUN
cuesj-22	239	10	.	.	PUNCT
cuesj-22	240	1	this	this	PRON
cuesj-22	240	2	gives	give	VERB
cuesj-22	240	3	a	a	DET
cuesj-22	240	4	quantitative	quantitative	ADJ
cuesj-22	240	5	description	description	NOUN
cuesj-22	240	6	of	of	ADP
cuesj-22	240	7	variability	variability	NOUN
cuesj-22	240	8	about	about	ADP
cuesj-22	240	9	the	the	DET
cuesj-22	240	10	same	same	ADJ
cuesj-22	240	11	regional	regional	ADJ
cuesj-22	240	12	variation	variation	NOUN
cuesj-22	240	13	.	.	PUNCT
cuesj-22	241	1	the	the	DET
cuesj-22	241	2	important	important	ADJ
cuesj-22	241	3	part	part	NOUN
cuesj-22	241	4	of	of	ADP
cuesj-22	241	5	the	the	DET
cuesj-22	241	6	variogram	variogram	NOUN
cuesj-22	241	7	is	be	AUX
cuesj-22	241	8	the	the	DET
cuesj-22	241	9	rang	rang	NOUN
cuesj-22	241	10	which	which	PRON
cuesj-22	241	11	describes	describe	VERB
cuesj-22	241	12	the	the	DET
cuesj-22	241	13	distance	distance	NOUN
cuesj-22	241	14	.	.	PUNCT
cuesj-22	242	1	it	it	PRON
cuesj-22	242	2	shows	show	VERB
cuesj-22	242	3	the	the	DET
cuesj-22	242	4	isotropy	isotropy	NOUN
cuesj-22	242	5	of	of	ADP
cuesj-22	242	6	the	the	DET
cuesj-22	242	7	gaussian	gaussian	ADJ
cuesj-22	242	8	semivariogram	semivariogram	NOUN
cuesj-22	242	9	model	model	NOUN
cuesj-22	242	10	for	for	ADP
cuesj-22	242	11	(	(	PUNCT
cuesj-22	242	12	295	295	NUM
cuesj-22	242	13	)	)	PUNCT
cuesj-22	242	14	dataset	dataset	NOUN
cuesj-22	242	15	of	of	ADP
cuesj-22	242	16	groundwater	groundwater	NOUN
cuesj-22	242	17	of	of	ADP
cuesj-22	242	18	wells	well	NOUN
cuesj-22	242	19	in	in	ADP
cuesj-22	242	20	shaqlawa	shaqlawa	PROPN
cuesj-22	242	21	,	,	PUNCT
cuesj-22	242	22	which	which	PRON
cuesj-22	242	23	all	all	DET
cuesj-22	242	24	points	point	NOUN
cuesj-22	242	25	have	have	VERB
cuesj-22	242	26	equal	equal	ADJ
cuesj-22	242	27	directional	directional	NOUN
cuesj-22	242	28	of	of	ADP
cuesj-22	242	29	variability	variability	NOUN
cuesj-22	242	30	as	as	ADP
cuesj-22	242	31	being	be	AUX
cuesj-22	242	32	north	north	NOUN
cuesj-22	242	33	-	-	PUNCT
cuesj-22	242	34	south	south	NOUN
cuesj-22	242	35	and	and	CCONJ
cuesj-22	242	36	east	east	NOUN
cuesj-22	242	37	-	-	PUNCT
cuesj-22	242	38	west	west	PROPN
cuesj-22	242	39	.	.	PUNCT
cuesj-22	243	1	furthermore	furthermore	ADV
cuesj-22	243	2	,	,	PUNCT
cuesj-22	243	3	the	the	DET
cuesj-22	243	4	anisotropy	anisotropy	NOUN
cuesj-22	243	5	of	of	ADP
cuesj-22	243	6	gaussian	gaussian	ADJ
cuesj-22	243	7	semivariogram	semivariogram	NOUN
cuesj-22	243	8	model	model	NOUN
cuesj-22	243	9	expresses	express	VERB
cuesj-22	243	10	the	the	DET
cuesj-22	243	11	same	same	ADJ
cuesj-22	243	12	data	datum	NOUN
cuesj-22	243	13	(	(	PUNCT
cuesj-22	243	14	554	554	NUM
cuesj-22	243	15	)	)	PUNCT
cuesj-22	243	16	that	that	PRON
cuesj-22	243	17	change	change	VERB
cuesj-22	243	18	with	with	ADP
cuesj-22	243	19	the	the	DET
cuesj-22	243	20	direction	direction	NOUN
cuesj-22	243	21	which	which	PRON
cuesj-22	243	22	described	describe	VERB
cuesj-22	243	23	an	an	DET
cuesj-22	243	24	ellipsoid	ellipsoid	NOUN
cuesj-22	243	25	,	,	PUNCT
cuesj-22	243	26	and	and	CCONJ
cuesj-22	243	27	this	this	DET
cuesj-22	243	28	ellipsoid	ellipsoid	NOUN
cuesj-22	243	29	specified	specify	VERB
cuesj-22	243	30	by	by	ADP
cuesj-22	243	31	the	the	DET
cuesj-22	243	32	length	length	NOUN
cuesj-22	243	33	of	of	ADP
cuesj-22	243	34	two	two	NUM
cuesj-22	243	35	orthogonal	orthogonal	ADJ
cuesj-22	243	36	axis	axis	NOUN
cuesj-22	243	37	(	(	PUNCT
cuesj-22	243	38	major	major	ADJ
cuesj-22	243	39	and	and	CCONJ
cuesj-22	243	40	minor	minor	ADJ
cuesj-22	243	41	)	)	PUNCT
cuesj-22	243	42	with	with	ADP
cuesj-22	243	43	its	its	PRON
cuesj-22	243	44	orientation	orientation	NOUN
cuesj-22	243	45	angle	angle	NOUN
cuesj-22	243	46	θ	θ	PROPN
cuesj-22	243	47	.	.	PUNCT
cuesj-22	243	48	figure	figure	NOUN
cuesj-22	243	49	5	5	NUM
cuesj-22	243	50	shows	show	VERB
cuesj-22	243	51	gaussian	gaussian	ADJ
cuesj-22	243	52	semivariogram	semivariogram	NOUN
cuesj-22	243	53	model	model	NOUN
cuesj-22	243	54	for	for	ADP
cuesj-22	243	55	isotropy	isotropy	VERB
cuesj-22	243	56	and	and	CCONJ
cuesj-22	243	57	anisotropy	anisotropy	NOUN
cuesj-22	243	58	surfaces	surface	NOUN
cuesj-22	243	59	,	,	PUNCT
cuesj-22	243	60	these	these	PRON
cuesj-22	243	61	used	use	VERB
cuesj-22	243	62	to	to	PART
cuesj-22	243	63	predict	predict	VERB
cuesj-22	243	64	of	of	ADP
cuesj-22	243	65	random	random	ADJ
cuesj-22	243	66	field	field	NOUN
cuesj-22	243	67	(	(	PUNCT
cuesj-22	243	68	spatial	spatial	ADJ
cuesj-22	243	69	field	field	NOUN
cuesj-22	243	70	)	)	PUNCT
cuesj-22	243	71	of	of	ADP
cuesj-22	243	72	groundwater	groundwater	NOUN
cuesj-22	243	73	well	well	NOUN
cuesj-22	243	74	z(s0	z(s0	NUM
cuesj-22	243	75	)	)	PUNCT
cuesj-22	243	76	unknown	unknown	ADJ
cuesj-22	243	77	value	value	NOUN
cuesj-22	243	78	in	in	ADP
cuesj-22	243	79	the	the	DET
cuesj-22	243	80	same	same	ADJ
cuesj-22	243	81	area	area	NOUN
cuesj-22	243	82	,	,	PUNCT
cuesj-22	243	83	which	which	PRON
cuesj-22	243	84	depends	depend	VERB
cuesj-22	243	85	on	on	ADP
cuesj-22	243	86	only	only	ADV
cuesj-22	243	87	maximum	maximum	ADJ
cuesj-22	243	88	(	(	PUNCT
cuesj-22	243	89	5	5	NUM
cuesj-22	243	90	)	)	PUNCT
cuesj-22	243	91	neighbors	neighbor	NOUN
cuesj-22	243	92	value	value	NOUN
cuesj-22	243	93	of	of	ADP
cuesj-22	243	94	the	the	DET
cuesj-22	243	95	measured	measure	VERB
cuesj-22	243	96	values	value	NOUN
cuesj-22	243	97	.	.	PUNCT
cuesj-22	244	1	table	table	NOUN
cuesj-22	244	2	2	2	NUM
cuesj-22	244	3	contains	contain	VERB
cuesj-22	244	4	all	all	PRON
cuesj-22	244	5	cross	cross	NOUN
cuesj-22	244	6	-	-	NOUN
cuesj-22	244	7	validation	validation	NOUN
cuesj-22	244	8	of	of	ADP
cuesj-22	244	9	isotropy	isotropy	VERB
cuesj-22	244	10	and	and	CCONJ
cuesj-22	244	11	anisotropy	anisotropy	NOUN
cuesj-22	244	12	of	of	ADP
cuesj-22	244	13	simple	simple	ADJ
cuesj-22	244	14	kriging	kriging	ADJ
cuesj-22	244	15	gaussian	gaussian	ADJ
cuesj-22	244	16	semivariogram	semivariogram	NOUN
cuesj-22	244	17	model	model	NOUN
cuesj-22	244	18	and	and	CCONJ
cuesj-22	244	19	the	the	DET
cuesj-22	244	20	prediction	prediction	NOUN
cuesj-22	244	21	value	value	NOUN
cuesj-22	244	22	of	of	ADP
cuesj-22	244	23	the	the	DET
cuesj-22	244	24	depth	depth	NOUN
cuesj-22	244	25	of	of	ADP
cuesj-22	244	26	unknown	unknown	ADJ
cuesj-22	244	27	new	new	ADJ
cuesj-22	244	28	value	value	NOUN
cuesj-22	244	29	of	of	ADP
cuesj-22	244	30	well	well	ADV
cuesj-22	244	31	.	.	PUNCT
cuesj-22	245	1	table	table	NOUN
cuesj-22	245	2	3	3	NUM
cuesj-22	245	3	shows	show	VERB
cuesj-22	245	4	all	all	DET
cuesj-22	245	5	information	information	NOUN
cuesj-22	245	6	about	about	ADP
cuesj-22	245	7	simple	simple	ADJ
cuesj-22	245	8	kriging	kriging	NOUN
cuesj-22	245	9	by	by	ADP
cuesj-22	245	10	guassian	guassian	PROPN
cuesj-22	245	11	semivariogram	semivariogram	NOUN
cuesj-22	245	12	model	model	NOUN
cuesj-22	245	13	to	to	PART
cuesj-22	245	14	predict	predict	VERB
cuesj-22	245	15	a	a	DET
cuesj-22	245	16	new	new	ADJ
cuesj-22	245	17	location	location	NOUN
cuesj-22	245	18	of	of	ADP
cuesj-22	245	19	groundwater	groundwater	NOUN
cuesj-22	245	20	in	in	ADP
cuesj-22	245	21	the	the	DET
cuesj-22	245	22	same	same	ADJ
cuesj-22	245	23	area	area	NOUN
cuesj-22	245	24	for	for	ADP
cuesj-22	245	25	both	both	PRON
cuesj-22	245	26	of	of	ADP
cuesj-22	245	27	isotropy	isotropy	VERB
cuesj-22	245	28	and	and	CCONJ
cuesj-22	245	29	anisotropy	anisotropy	VERB
cuesj-22	245	30	.	.	PUNCT
cuesj-22	246	1	the	the	DET
cuesj-22	246	2	prediction	prediction	NOUN
cuesj-22	246	3	value	value	NOUN
cuesj-22	246	4	of	of	ADP
cuesj-22	246	5	unknown	unknown	ADJ
cuesj-22	246	6	is	be	AUX
cuesj-22	246	7	measured	measure	VERB
cuesj-22	246	8	also	also	ADV
cuesj-22	246	9	depending	depend	VERB
cuesj-22	246	10	on	on	ADP
cuesj-22	246	11	(	(	PUNCT
cuesj-22	246	12	5	5	NUM
cuesj-22	246	13	)	)	PUNCT
cuesj-22	246	14	maximum	maximum	NOUN
cuesj-22	246	15	and	and	CCONJ
cuesj-22	246	16	(	(	PUNCT
cuesj-22	246	17	2	2	NUM
cuesj-22	246	18	)	)	PUNCT
cuesj-22	246	19	minimum	minimum	ADJ
cuesj-22	246	20	value	value	NOUN
cuesj-22	246	21	of	of	ADP
cuesj-22	246	22	neighbors	neighbor	NOUN
cuesj-22	246	23	from	from	ADP
cuesj-22	246	24	measured	measure	VERB
cuesj-22	246	25	value	value	NOUN
cuesj-22	246	26	for	for	ADP
cuesj-22	246	27	the	the	DET
cuesj-22	246	28	depth	depth	NOUN
cuesj-22	246	29	of	of	ADP
cuesj-22	246	30	new	new	ADJ
cuesj-22	246	31	well	well	NOUN
cuesj-22	246	32	with	with	ADP
cuesj-22	246	33	longitude	longitude	NOUN
cuesj-22	246	34	and	and	CCONJ
cuesj-22	246	35	latitude	latitude	NOUN
cuesj-22	246	36	.	.	PUNCT
cuesj-22	247	1	in	in	ADP
cuesj-22	247	2	anisotropy	anisotropy	NOUN
cuesj-22	247	3	gaussian	gaussian	ADJ
cuesj-22	247	4	semivariogram	semivariogram	NOUN
cuesj-22	247	5	model	model	NOUN
cuesj-22	247	6	is	be	AUX
cuesj-22	247	7	equal	equal	ADJ
cuesj-22	247	8	to	to	ADP
cuesj-22	247	9	173.2582	173.2582	NUM
cuesj-22	247	10	and	and	CCONJ
cuesj-22	247	11	its	its	PRON
cuesj-22	247	12	greater	great	ADJ
cuesj-22	247	13	than	than	ADP
cuesj-22	247	14	the	the	DET
cuesj-22	247	15	depth	depth	NOUN
cuesj-22	247	16	of	of	ADP
cuesj-22	247	17	the	the	DET
cuesj-22	247	18	isotropy	isotropy	ADJ
cuesj-22	247	19	exponential	exponential	ADJ
cuesj-22	247	20	semivariogram	semivariogram	NOUN
cuesj-22	247	21	model	model	NOUN
cuesj-22	247	22	which	which	PRON
cuesj-22	247	23	equal	equal	ADJ
cuesj-22	247	24	to	to	ADP
cuesj-22	247	25	132.3405	132.3405	NUM
cuesj-22	247	26	.	.	PUNCT
cuesj-22	248	1	estimation	estimation	NOUN
cuesj-22	248	2	the	the	DET
cuesj-22	248	3	depth	depth	NOUN
cuesj-22	248	4	of	of	ADP
cuesj-22	248	5	groundwater	groundwater	NOUN
cuesj-22	248	6	using	use	VERB
cuesj-22	248	7	kalman	kalman	NOUN
cuesj-22	248	8	filter	filter	VERB
cuesj-22	248	9	the	the	DET
cuesj-22	248	10	estimation	estimation	NOUN
cuesj-22	248	11	process	process	NOUN
cuesj-22	248	12	of	of	ADP
cuesj-22	248	13	parameters	parameter	NOUN
cuesj-22	248	14	is	be	AUX
cuesj-22	248	15	made	make	VERB
cuesj-22	248	16	using	use	VERB
cuesj-22	248	17	the	the	DET
cuesj-22	248	18	initial	initial	ADJ
cuesj-22	248	19	values	value	NOUN
cuesj-22	248	20	of	of	ADP
cuesj-22	248	21	the	the	DET
cuesj-22	248	22	depth	depth	NOUN
cuesj-22	248	23	of	of	ADP
cuesj-22	248	24	groundwater	groundwater	NOUN
cuesj-22	248	25	mean	mean	NOUN
cuesj-22	248	26	and	and	CCONJ
cuesj-22	248	27	variances	variance	NOUN
cuesj-22	248	28	are	be	AUX
cuesj-22	248	29	being	be	AUX
cuesj-22	248	30	of	of	ADP
cuesj-22	248	31	an	an	DET
cuesj-22	248	32	estimate	estimate	NOUN
cuesj-22	248	33	of	of	ADP
cuesj-22	248	34	it	it	PRON
cuesj-22	248	35	is	be	AUX
cuesj-22	248	36	values	value	NOUN
cuesj-22	248	37	.	.	PUNCT
cuesj-22	249	1	equations	equation	NOUN
cuesj-22	249	2	(	(	PUNCT
cuesj-22	249	3	19	19	NUM
cuesj-22	249	4	)	)	PUNCT
cuesj-22	249	5	,	,	PUNCT
cuesj-22	249	6	(	(	PUNCT
cuesj-22	249	7	20	20	NUM
cuesj-22	249	8	)	)	PUNCT
cuesj-22	249	9	,	,	PUNCT
cuesj-22	249	10	and	and	CCONJ
cuesj-22	249	11	(	(	PUNCT
cuesj-22	249	12	21	21	NUM
cuesj-22	249	13	)	)	PUNCT
cuesj-22	249	14	are	be	AUX
cuesj-22	249	15	used	use	VERB
cuesj-22	249	16	to	to	PART
cuesj-22	249	17	estimate	estimate	VERB
cuesj-22	249	18	parameters	parameter	NOUN
cuesj-22	249	19	,	,	PUNCT
cuesj-22	249	20	error	error	NOUN
cuesj-22	249	21	covariance	covariance	NOUN
cuesj-22	249	22	,	,	PUNCT
cuesj-22	249	23	and	and	CCONJ
cuesj-22	249	24	kalman	kalman	NOUN
cuesj-22	249	25	gain	gain	NOUN
cuesj-22	249	26	for	for	ADP
cuesj-22	249	27	univariate	univariate	ADJ
cuesj-22	249	28	simple	simple	ADJ
cuesj-22	249	29	dynamic	dynamic	ADJ
cuesj-22	249	30	linear	linear	NOUN
cuesj-22	249	31	model	model	NOUN
cuesj-22	249	32	for	for	ADP
cuesj-22	249	33	each	each	DET
cuesj-22	249	34	observation	observation	NOUN
cuesj-22	249	35	of	of	ADP
cuesj-22	249	36	the	the	DET
cuesj-22	249	37	groundwater	groundwater	NOUN
cuesj-22	249	38	depth	depth	NOUN
cuesj-22	249	39	.	.	PUNCT
cuesj-22	250	1	table	table	NOUN
cuesj-22	250	2	4	4	NUM
cuesj-22	250	3	gives	give	VERB
cuesj-22	250	4	the	the	DET
cuesj-22	250	5	actual	actual	ADJ
cuesj-22	250	6	value	value	NOUN
cuesj-22	250	7	,	,	PUNCT
cuesj-22	250	8	estimated	estimate	VERB
cuesj-22	250	9	groundwater	groundwater	NOUN
cuesj-22	250	10	depth	depth	NOUN
cuesj-22	250	11	parameter	parameter	NOUN
cuesj-22	250	12	,	,	PUNCT
cuesj-22	250	13	error	error	NOUN
cuesj-22	250	14	covariance	covariance	NOUN
cuesj-22	250	15	,	,	PUNCT
cuesj-22	250	16	and	and	CCONJ
cuesj-22	250	17	kalman	kalman	PROPN
cuesj-22	250	18	gain	gain	NOUN
cuesj-22	250	19	.	.	PUNCT
cuesj-22	251	1	examining	examine	VERB
cuesj-22	251	2	the	the	DET
cuesj-22	251	3	table	table	NOUN
cuesj-22	251	4	reveals	reveal	VERB
cuesj-22	251	5	the	the	DET
cuesj-22	251	6	following	following	NOUN
cuesj-22	251	7	:	:	PUNCT
cuesj-22	251	8	the	the	DET
cuesj-22	251	9	error	error	NOUN
cuesj-22	251	10	covariance	covariance	NOUN
cuesj-22	251	11	is	be	AUX
cuesj-22	251	12	convergence	convergence	NOUN
cuesj-22	251	13	at	at	ADP
cuesj-22	251	14	a	a	DET
cuesj-22	251	15	time	time	NOUN
cuesj-22	251	16	point	point	NOUN
cuesj-22	251	17	(	(	PUNCT
cuesj-22	251	18	t=18	t=18	NOUN
cuesj-22	251	19	)	)	PUNCT
cuesj-22	251	20	with	with	ADP
cuesj-22	251	21	the	the	DET
cuesj-22	251	22	value	value	NOUN
cuesj-22	251	23	(	(	PUNCT
cuesj-22	251	24	802.1407	802.1407	NUM
cuesj-22	251	25	)	)	PUNCT
cuesj-22	251	26	.	.	PUNCT
cuesj-22	252	1	kalman	kalman	PROPN
cuesj-22	252	2	gain	gain	NOUN
cuesj-22	252	3	is	be	AUX
cuesj-22	252	4	convergence	convergence	NOUN
cuesj-22	252	5	at	at	ADP
cuesj-22	252	6	a	a	DET
cuesj-22	252	7	time	time	NOUN
cuesj-22	252	8	point	point	NOUN
cuesj-22	252	9	(	(	PUNCT
cuesj-22	252	10	t=16	t=16	NOUN
cuesj-22	252	11	)	)	PUNCT
cuesj-22	252	12	with	with	ADP
cuesj-22	252	13	the	the	DET
cuesj-22	252	14	value	value	NOUN
cuesj-22	252	15	(	(	PUNCT
cuesj-22	252	16	0.383983	0.383983	NUM
cuesj-22	252	17	)	)	PUNCT
cuesj-22	252	18	.	.	PUNCT
cuesj-22	253	1	there	there	PRON
cuesj-22	253	2	is	be	VERB
cuesj-22	253	3	two	two	NUM
cuesj-22	253	4	convergences	convergence	NOUN
cuesj-22	253	5	time	time	NOUN
cuesj-22	253	6	and	and	CCONJ
cuesj-22	253	7	the	the	DET
cuesj-22	253	8	last	last	ADJ
cuesj-22	253	9	time	time	NOUN
cuesj-22	253	10	convergence	convergence	NOUN
cuesj-22	253	11	is	be	AUX
cuesj-22	253	12	the	the	DET
cuesj-22	253	13	best	good	ADJ
cuesj-22	253	14	estimate	estimate	NOUN
cuesj-22	253	15	for	for	ADP
cuesj-22	253	16	the	the	DET
cuesj-22	253	17	same	same	ADJ
cuesj-22	253	18	time	time	NOUN
cuesj-22	253	19	.	.	PUNCT
cuesj-22	254	1	a	a	DET
cuesj-22	254	2	compression	compression	NOUN
cuesj-22	254	3	between	between	ADP
cuesj-22	254	4	groundwater	groundwater	NOUN
cuesj-22	254	5	depth	depth	NOUN
cuesj-22	254	6	resulting	result	VERB
cuesj-22	254	7	from	from	ADP
cuesj-22	254	8	the	the	DET
cuesj-22	254	9	estimate	estimate	NOUN
cuesj-22	254	10	and	and	CCONJ
cuesj-22	254	11	forecasting	forecasting	NOUN
cuesj-22	254	12	groundwater	groundwater	NOUN
cuesj-22	254	13	depth	depth	NOUN
cuesj-22	254	14	values	value	NOUN
cuesj-22	254	15	for	for	ADP
cuesj-22	254	16	all	all	DET
cuesj-22	254	17	observations	observation	NOUN
cuesj-22	254	18	is	be	AUX
cuesj-22	254	19	shown	show	VERB
cuesj-22	254	20	in	in	ADP
cuesj-22	254	21	figure	figure	NOUN
cuesj-22	254	22	6	6	NUM
cuesj-22	254	23	.	.	PUNCT
cuesj-22	255	1	the	the	DET
cuesj-22	255	2	results	result	NOUN
cuesj-22	255	3	show	show	VERB
cuesj-22	255	4	how	how	SCONJ
cuesj-22	255	5	figure	figure	NOUN
cuesj-22	255	6	6	6	NUM
cuesj-22	255	7	:	:	PUNCT
cuesj-22	255	8	difference	difference	NOUN
cuesj-22	255	9	between	between	ADP
cuesj-22	255	10	estimate	estimate	NOUN
cuesj-22	255	11	values	value	NOUN
cuesj-22	255	12	and	and	CCONJ
cuesj-22	255	13	forecast	forecast	NOUN
cuesj-22	255	14	values	value	NOUN
cuesj-22	255	15	for	for	ADP
cuesj-22	255	16	groundwater	groundwater	NOUN
cuesj-22	255	17	depth	depth	NOUN
cuesj-22	255	18	figure	figure	NOUN
cuesj-22	255	19	4	4	NUM
cuesj-22	255	20	:	:	PUNCT
cuesj-22	255	21	the	the	DET
cuesj-22	255	22	histogram	histogram	NOUN
cuesj-22	255	23	of	of	ADP
cuesj-22	255	24	the	the	DET
cuesj-22	255	25	dataset	dataset	ADJ
cuesj-22	255	26	figure	figure	NOUN
cuesj-22	255	27	5	5	NUM
cuesj-22	255	28	:	:	PUNCT
cuesj-22	255	29	(	(	PUNCT
cuesj-22	255	30	a	a	PRON
cuesj-22	255	31	and	and	CCONJ
cuesj-22	255	32	b	b	NOUN
cuesj-22	255	33	)	)	PUNCT
cuesj-22	255	34	surfaces	surface	NOUN
cuesj-22	255	35	of	of	ADP
cuesj-22	255	36	the	the	DET
cuesj-22	255	37	gaussian	gaussian	ADJ
cuesj-22	255	38	semivariogram	semivariogram	NOUN
cuesj-22	255	39	model	model	VERB
cuesj-22	255	40	a	a	DET
cuesj-22	255	41	b	b	NOUN
cuesj-22	255	42	mawlood	mawlood	NOUN
cuesj-22	255	43	and	and	CCONJ
cuesj-22	255	44	omer	omer	PROPN
cuesj-22	255	45	:	:	PUNCT
cuesj-22	255	46	prediction	prediction	NOUN
cuesj-22	255	47	the	the	DET
cuesj-22	255	48	groundwater	groundwater	NOUN
cuesj-22	255	49	depth	depth	NOUN
cuesj-22	255	50	48	48	NUM
cuesj-22	255	51	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	255	52	cuesj	cuesj	NOUN
cuesj-22	255	53	2019	2019	NUM
cuesj-22	255	54	,	,	PUNCT
cuesj-22	255	55	3	3	NUM
cuesj-22	255	56	(	(	PUNCT
cuesj-22	255	57	1	1	NUM
cuesj-22	255	58	):	):	PUNCT
cuesj-22	255	59	42	42	NUM
cuesj-22	255	60	-	-	SYM
cuesj-22	255	61	49	49	NUM
cuesj-22	255	62	closely	closely	ADV
cuesj-22	255	63	the	the	DET
cuesj-22	255	64	estimated	estimate	VERB
cuesj-22	255	65	model	model	NOUN
cuesj-22	255	66	matches	match	VERB
cuesj-22	255	67	the	the	DET
cuesj-22	255	68	forecasting	forecasting	NOUN
cuesj-22	255	69	electricity	electricity	NOUN
cuesj-22	255	70	groundwater	groundwater	NOUN
cuesj-22	255	71	depth	depth	NOUN
cuesj-22	255	72	.	.	PUNCT
cuesj-22	256	1	the	the	DET
cuesj-22	256	2	blue	blue	ADJ
cuesj-22	256	3	line	line	NOUN
cuesj-22	256	4	is	be	AUX
cuesj-22	256	5	estimate	estimate	NOUN
cuesj-22	256	6	values	value	NOUN
cuesj-22	256	7	and	and	CCONJ
cuesj-22	256	8	the	the	DET
cuesj-22	256	9	red	red	ADJ
cuesj-22	256	10	line	line	NOUN
cuesj-22	256	11	is	be	AUX
cuesj-22	256	12	forecasting	forecast	VERB
cuesj-22	256	13	values	value	NOUN
cuesj-22	256	14	.	.	PUNCT
cuesj-22	257	1	evaluating	evaluate	VERB
cuesj-22	257	2	accuracy	accuracy	NOUN
cuesj-22	257	3	(	(	PUNCT
cuesj-22	257	4	or	or	CCONJ
cuesj-22	257	5	error	error	NOUN
cuesj-22	257	6	)	)	PUNCT
cuesj-22	257	7	measures	measure	NOUN
cuesj-22	257	8	for	for	ADP
cuesj-22	257	9	estimation	estimation	NOUN
cuesj-22	257	10	for	for	ADP
cuesj-22	257	11	selections	selection	NOUN
cuesj-22	257	12	,	,	PUNCT
cuesj-22	257	13	the	the	DET
cuesj-22	257	14	best	good	ADJ
cuesj-22	257	15	approach	approach	NOUN
cuesj-22	257	16	(	(	PUNCT
cuesj-22	257	17	method	method	NOUN
cuesj-22	257	18	)	)	PUNCT
cuesj-22	257	19	for	for	ADP
cuesj-22	257	20	prediction	prediction	NOUN
cuesj-22	257	21	the	the	DET
cuesj-22	257	22	groundwater	groundwater	NOUN
cuesj-22	257	23	depth	depth	NOUN
cuesj-22	257	24	two	two	NUM
cuesj-22	257	25	accuracy	accuracy	NOUN
cuesj-22	257	26	criteria	criterion	NOUN
cuesj-22	257	27	were	be	AUX
cuesj-22	257	28	used	use	VERB
cuesj-22	257	29	,	,	PUNCT
cuesj-22	257	30	which	which	PRON
cuesj-22	257	31	are	be	AUX
cuesj-22	257	32	(	(	PUNCT
cuesj-22	257	33	mean	mean	ADJ
cuesj-22	257	34	absolute	absolute	ADJ
cuesj-22	257	35	error	error	NOUN
cuesj-22	257	36	[	[	X
cuesj-22	257	37	mae	mae	X
cuesj-22	257	38	]	]	X
cuesj-22	257	39	and	and	CCONJ
cuesj-22	257	40	root	root	NOUN
cuesj-22	257	41	mean	mean	NOUN
cuesj-22	257	42	square	square	ADJ
cuesj-22	257	43	error	error	NOUN
cuesj-22	257	44	[	[	X
cuesj-22	257	45	rmse	rmse	NOUN
cuesj-22	257	46	]	]	X
cuesj-22	257	47	)	)	PUNCT
cuesj-22	257	48	.	.	PUNCT
cuesj-22	258	1	they	they	PRON
cuesj-22	258	2	are	be	AUX
cuesj-22	258	3	based	base	VERB
cuesj-22	258	4	on	on	ADP
cuesj-22	258	5	the	the	DET
cuesj-22	258	6	error	error	NOUN
cuesj-22	258	7	estimate	estimate	NOUN
cuesj-22	258	8	,	,	PUNCT
cuesj-22	258	9	which	which	PRON
cuesj-22	258	10	is	be	AUX
cuesj-22	258	11	the	the	DET
cuesj-22	258	12	difference	difference	NOUN
cuesj-22	258	13	between	between	ADP
cuesj-22	258	14	the	the	DET
cuesj-22	258	15	estimated	estimate	VERB
cuesj-22	258	16	and	and	CCONJ
cuesj-22	258	17	forecast	forecast	NOUN
cuesj-22	258	18	groundwater	groundwater	NOUN
cuesj-22	258	19	depth	depth	NOUN
cuesj-22	258	20	values	value	NOUN
cuesj-22	258	21	.	.	PUNCT
cuesj-22	259	1	the	the	DET
cuesj-22	259	2	results	result	NOUN
cuesj-22	259	3	are	be	AUX
cuesj-22	259	4	shown	show	VERB
cuesj-22	259	5	in	in	ADP
cuesj-22	259	6	table	table	NOUN
cuesj-22	259	7	5	5	NUM
cuesj-22	259	8	.	.	PUNCT
cuesj-22	259	9	table	table	NOUN
cuesj-22	259	10	3	3	NUM
cuesj-22	259	11	represents	represent	VERB
cuesj-22	259	12	that	that	SCONJ
cuesj-22	259	13	kalman	kalman	PROPN
cuesj-22	259	14	filter	filter	NOUN
cuesj-22	259	15	is	be	AUX
cuesj-22	259	16	the	the	DET
cuesj-22	259	17	better	well	ADJ
cuesj-22	259	18	approach	approach	NOUN
cuesj-22	259	19	based	base	VERB
cuesj-22	259	20	on	on	ADP
cuesj-22	259	21	the	the	DET
cuesj-22	259	22	two	two	NUM
cuesj-22	259	23	criteria	criterion	NOUN
cuesj-22	259	24	,	,	PUNCT
cuesj-22	259	25	where	where	SCONJ
cuesj-22	259	26	each	each	PRON
cuesj-22	259	27	of	of	ADP
cuesj-22	259	28	them	they	PRON
cuesj-22	259	29	has	have	VERB
cuesj-22	259	30	a	a	DET
cuesj-22	259	31	small	small	ADJ
cuesj-22	259	32	value	value	NOUN
cuesj-22	259	33	than	than	ADP
cuesj-22	259	34	simple	simple	ADJ
cuesj-22	259	35	kriging	kriging	NOUN
cuesj-22	259	36	using	use	VERB
cuesj-22	259	37	gaussian	gaussian	ADJ
cuesj-22	259	38	semivariogram	semivariogram	NOUN
cuesj-22	259	39	model	model	NOUN
cuesj-22	259	40	for	for	ADP
cuesj-22	259	41	both	both	PRON
cuesj-22	259	42	of	of	ADP
cuesj-22	259	43	isotropy	isotropy	VERB
cuesj-22	259	44	and	and	CCONJ
cuesj-22	259	45	anisotropy	anisotropy	VERB
cuesj-22	259	46	.	.	PUNCT
cuesj-22	260	1	conclusions	conclusion	NOUN
cuesj-22	260	2	the	the	DET
cuesj-22	260	3	major	major	ADJ
cuesj-22	260	4	results	result	NOUN
cuesj-22	260	5	of	of	ADP
cuesj-22	260	6	performing	perform	VERB
cuesj-22	260	7	the	the	DET
cuesj-22	260	8	analysis	analysis	NOUN
cuesj-22	260	9	of	of	ADP
cuesj-22	260	10	two	two	NUM
cuesj-22	260	11	approaches	approach	NOUN
cuesj-22	260	12	(	(	PUNCT
cuesj-22	260	13	simple	simple	ADJ
cuesj-22	260	14	kriging	kriging	NOUN
cuesj-22	260	15	and	and	CCONJ
cuesj-22	260	16	kalman	kalman	PROPN
cuesj-22	260	17	filter	filter	PROPN
cuesj-22	260	18	)	)	PUNCT
cuesj-22	260	19	,	,	PUNCT
cuesj-22	260	20	the	the	DET
cuesj-22	260	21	following	follow	VERB
cuesj-22	260	22	main	main	ADJ
cuesj-22	260	23	conclusions	conclusion	NOUN
cuesj-22	260	24	have	have	AUX
cuesj-22	260	25	been	be	AUX
cuesj-22	260	26	achieved	achieve	VERB
cuesj-22	260	27	:	:	PUNCT
cuesj-22	261	1	1	1	X
cuesj-22	261	2	.	.	PUNCT
cuesj-22	261	3	the	the	DET
cuesj-22	261	4	data	datum	NOUN
cuesj-22	261	5	follow	follow	VERB
cuesj-22	261	6	approximately	approximately	ADV
cuesj-22	261	7	to	to	ADP
cuesj-22	261	8	normal	normal	ADJ
cuesj-22	261	9	distribution	distribution	NOUN
cuesj-22	261	10	after	after	SCONJ
cuesj-22	261	11	taken	take	VERB
cuesj-22	261	12	log	log	NOUN
cuesj-22	261	13	transformation	transformation	NOUN
cuesj-22	261	14	for	for	ADP
cuesj-22	261	15	variability	variability	NOUN
cuesj-22	261	16	and	and	CCONJ
cuesj-22	261	17	secondorder	secondorder	NOUN
cuesj-22	261	18	stationary	stationary	NOUN
cuesj-22	261	19	to	to	PART
cuesj-22	261	20	remove	remove	VERB
cuesj-22	261	21	the	the	DET
cuesj-22	261	22	trend	trend	NOUN
cuesj-22	261	23	.	.	PUNCT
cuesj-22	262	1	2	2	X
cuesj-22	262	2	.	.	X
cuesj-22	262	3	the	the	DET
cuesj-22	262	4	variability	variability	NOUN
cuesj-22	262	5	of	of	ADP
cuesj-22	262	6	the	the	DET
cuesj-22	262	7	depth	depth	NOUN
cuesj-22	262	8	of	of	ADP
cuesj-22	262	9	groundwater	groundwater	NOUN
cuesj-22	262	10	wells	well	NOUN
cuesj-22	262	11	elevation	elevation	NOUN
cuesj-22	262	12	in	in	ADP
cuesj-22	262	13	the	the	DET
cuesj-22	262	14	region	region	NOUN
cuesj-22	262	15	changes	change	NOUN
cuesj-22	262	16	from	from	ADP
cuesj-22	262	17	north	north	NOUN
cuesj-22	262	18	to	to	ADP
cuesj-22	262	19	south	south	NOUN
cuesj-22	262	20	or	or	CCONJ
cuesj-22	262	21	from	from	ADP
cuesj-22	262	22	westnorth	westnorth	NOUN
cuesj-22	262	23	to	to	ADP
cuesj-22	262	24	south	south	NOUN
cuesj-22	262	25	-	-	PUNCT
cuesj-22	262	26	east	east	NOUN
cuesj-22	262	27	,	,	PUNCT
cuesj-22	262	28	this	this	PRON
cuesj-22	262	29	is	be	AUX
cuesj-22	262	30	due	due	ADJ
cuesj-22	262	31	to	to	ADP
cuesj-22	262	32	the	the	DET
cuesj-22	262	33	spatial	spatial	ADJ
cuesj-22	262	34	dependency	dependency	NOUN
cuesj-22	262	35	in	in	ADP
cuesj-22	262	36	the	the	DET
cuesj-22	262	37	area	area	NOUN
cuesj-22	262	38	which	which	PRON
cuesj-22	262	39	is	be	AUX
cuesj-22	262	40	one	one	NUM
cuesj-22	262	41	of	of	ADP
cuesj-22	262	42	the	the	DET
cuesj-22	262	43	reasons	reason	NOUN
cuesj-22	262	44	of	of	ADP
cuesj-22	262	45	the	the	DET
cuesj-22	262	46	depth	depth	NOUN
cuesj-22	262	47	of	of	ADP
cuesj-22	262	48	well	well	ADV
cuesj-22	262	49	.	.	PUNCT
cuesj-22	263	1	3	3	X
cuesj-22	263	2	.	.	X
cuesj-22	263	3	for	for	ADP
cuesj-22	263	4	unmeasured	unmeasured	ADJ
cuesj-22	263	5	value	value	NOUN
cuesj-22	263	6	used	use	VERB
cuesj-22	263	7	simple	simple	ADJ
cuesj-22	263	8	kriging	krige	VERB
cuesj-22	263	9	with	with	ADP
cuesj-22	263	10	gaussian	gaussian	ADJ
cuesj-22	263	11	semivariogram	semivariogram	NOUN
cuesj-22	263	12	model	model	NOUN
cuesj-22	263	13	,	,	PUNCT
cuesj-22	263	14	in	in	ADP
cuesj-22	263	15	this	this	DET
cuesj-22	263	16	model	model	NOUN
cuesj-22	263	17	the	the	DET
cuesj-22	263	18	predicted	predict	VERB
cuesj-22	263	19	value	value	NOUN
cuesj-22	263	20	by	by	ADP
cuesj-22	263	21	isotropy	isotropy	ADJ
cuesj-22	263	22	semivariogram	semivariogram	NOUN
cuesj-22	263	23	model	model	NOUN
cuesj-22	263	24	is	be	AUX
cuesj-22	263	25	better	well	ADJ
cuesj-22	263	26	than	than	ADP
cuesj-22	263	27	the	the	DET
cuesj-22	263	28	anisotropy	anisotropy	NOUN
cuesj-22	263	29	semivariogram	semivariogram	NOUN
cuesj-22	263	30	model	model	NOUN
cuesj-22	263	31	depending	depend	VERB
cuesj-22	263	32	on	on	ADP
cuesj-22	263	33	the	the	DET
cuesj-22	263	34	value	value	NOUN
cuesj-22	263	35	of	of	ADP
cuesj-22	263	36	the	the	DET
cuesj-22	263	37	depth	depth	NOUN
cuesj-22	263	38	of	of	ADP
cuesj-22	263	39	groundwater	groundwater	NOUN
cuesj-22	263	40	[	[	X
cuesj-22	263	41	table	table	NOUN
cuesj-22	263	42	2	2	NUM
cuesj-22	263	43	]	]	PUNCT
cuesj-22	263	44	.	.	PUNCT
cuesj-22	264	1	4	4	X
cuesj-22	264	2	.	.	X
cuesj-22	264	3	when	when	SCONJ
cuesj-22	264	4	applied	apply	VERB
cuesj-22	264	5	the	the	DET
cuesj-22	264	6	kalman	kalman	NOUN
cuesj-22	264	7	filter	filter	NOUN
cuesj-22	264	8	for	for	ADP
cuesj-22	264	9	estimating	estimate	VERB
cuesj-22	264	10	and	and	CCONJ
cuesj-22	264	11	forecasting	forecast	VERB
cuesj-22	264	12	the	the	DET
cuesj-22	264	13	depth	depth	NOUN
cuesj-22	264	14	of	of	ADP
cuesj-22	264	15	groundwater	groundwater	NOUN
cuesj-22	264	16	,	,	PUNCT
cuesj-22	264	17	we	we	PRON
cuesj-22	264	18	found	find	VERB
cuesj-22	264	19	that	that	SCONJ
cuesj-22	264	20	the	the	DET
cuesj-22	264	21	kf	kf	PROPN
cuesj-22	264	22	is	be	AUX
cuesj-22	264	23	suitable	suitable	ADJ
cuesj-22	264	24	for	for	ADP
cuesj-22	264	25	estimation	estimation	NOUN
cuesj-22	264	26	and	and	CCONJ
cuesj-22	264	27	yield	yield	VERB
cuesj-22	264	28	good	good	ADJ
cuesj-22	264	29	results	result	NOUN
cuesj-22	264	30	,	,	PUNCT
cuesj-22	264	31	because	because	SCONJ
cuesj-22	264	32	in	in	ADP
cuesj-22	264	33	the	the	DET
cuesj-22	264	34	presence	presence	NOUN
cuesj-22	264	35	of	of	ADP
cuesj-22	264	36	white	white	ADJ
cuesj-22	264	37	gaussian	gaussian	PROPN
cuesj-22	264	38	noise	noise	NOUN
cuesj-22	264	39	because	because	SCONJ
cuesj-22	264	40	when	when	SCONJ
cuesj-22	264	41	the	the	DET
cuesj-22	264	42	normality	normality	NOUN
cuesj-22	264	43	condition	condition	NOUN
cuesj-22	264	44	holds	hold	VERB
cuesj-22	264	45	,	,	PUNCT
cuesj-22	264	46	the	the	DET
cuesj-22	264	47	kf	kf	PROPN
cuesj-22	264	48	will	will	AUX
cuesj-22	264	49	give	give	VERB
cuesj-22	264	50	optimum	optimum	ADJ
cuesj-22	264	51	and	and	CCONJ
cuesj-22	264	52	sufficient	sufficient	ADJ
cuesj-22	264	53	results	result	NOUN
cuesj-22	264	54	.	.	PUNCT
cuesj-22	265	1	5	5	X
cuesj-22	265	2	.	.	PUNCT
cuesj-22	265	3	the	the	DET
cuesj-22	265	4	result	result	NOUN
cuesj-22	265	5	shows	show	VERB
cuesj-22	265	6	that	that	SCONJ
cuesj-22	265	7	the	the	DET
cuesj-22	265	8	bayesian	bayesian	NOUN
cuesj-22	265	9	kalman	kalman	NOUN
cuesj-22	265	10	filter	filter	NOUN
cuesj-22	265	11	approach	approach	NOUN
cuesj-22	265	12	has	have	VERB
cuesj-22	265	13	the	the	DET
cuesj-22	265	14	best	good	ADJ
cuesj-22	265	15	estimation	estimation	NOUN
cuesj-22	265	16	and	and	CCONJ
cuesj-22	265	17	forecasting	forecasting	NOUN
cuesj-22	265	18	results	result	NOUN
cuesj-22	265	19	compared	compare	VERB
cuesj-22	265	20	to	to	ADP
cuesj-22	265	21	simple	simple	ADJ
cuesj-22	265	22	kriging	kriging	NOUN
cuesj-22	265	23	using	use	VERB
cuesj-22	265	24	the	the	DET
cuesj-22	265	25	accuracy	accuracy	NOUN
cuesj-22	265	26	criteria	criterion	NOUN
cuesj-22	265	27	(	(	PUNCT
cuesj-22	265	28	mae	mae	PROPN
cuesj-22	265	29	and	and	CCONJ
cuesj-22	265	30	rmse	rmse	PROPN
cuesj-22	265	31	)	)	PUNCT
cuesj-22	265	32	.	.	PUNCT
cuesj-22	266	1	table	table	NOUN
cuesj-22	266	2	2	2	NUM
cuesj-22	266	3	:	:	PUNCT
cuesj-22	266	4	results	result	NOUN
cuesj-22	266	5	of	of	ADP
cuesj-22	266	6	gaussian	gaussian	ADJ
cuesj-22	266	7	semivariogram	semivariogram	NOUN
cuesj-22	266	8	model	model	NOUN
cuesj-22	266	9	simple	simple	ADJ
cuesj-22	266	10	kriging	krige	VERB
cuesj-22	266	11	exponential	exponential	ADJ
cuesj-22	266	12	semivariogram	semivariogram	NOUN
cuesj-22	266	13	isotropy	isotropy	NOUN
cuesj-22	266	14	anisotropy	anisotropy	NOUN
cuesj-22	266	15	nugget	nugget	NOUN
cuesj-22	266	16	(	(	PUNCT
cuesj-22	266	17	c0	c0	NOUN
cuesj-22	266	18	)	)	PUNCT
cuesj-22	266	19	0.0714	0.0714	NUM
cuesj-22	266	20	0	0	NUM
cuesj-22	266	21	rang	ring	VERB
cuesj-22	266	22	(	(	PUNCT
cuesj-22	266	23	α	α	NOUN
cuesj-22	266	24	)	)	PUNCT
cuesj-22	266	25	2	2	NUM
cuesj-22	266	26	2	2	NUM
cuesj-22	266	27	major	major	NOUN
cuesj-22	266	28	rang	ring	VERB
cuesj-22	266	29	2554.326	2554.326	NUM
cuesj-22	266	30	4998.32	4998.32	NUM
cuesj-22	266	31	minor	minor	ADJ
cuesj-22	266	32	rang	ring	VERB
cuesj-22	266	33	2554.326	2554.326	NUM
cuesj-22	266	34	1949.16	1949.16	NUM
cuesj-22	266	35	partial	partial	ADJ
cuesj-22	266	36	sill	sill	NOUN
cuesj-22	266	37	(	(	PUNCT
cuesj-22	266	38	c	c	NOUN
cuesj-22	266	39	)	)	PUNCT
cuesj-22	266	40	0.081173	0.081173	NUM
cuesj-22	266	41	0.093287	0.093287	NUM
cuesj-22	266	42	lag	lag	NOUN
cuesj-22	266	43	size	size	NOUN
cuesj-22	266	44	(	(	PUNCT
cuesj-22	266	45	h	h	NOUN
cuesj-22	266	46	)	)	PUNCT
cuesj-22	266	47	416.5267	416.5267	NOUN
cuesj-22	266	48	416.5267	416.5267	NUM
cuesj-22	266	49	number	number	NOUN
cuesj-22	266	50	of	of	ADP
cuesj-22	266	51	lag	lag	NOUN
cuesj-22	266	52	12	12	NUM
cuesj-22	266	53	12	12	NUM
cuesj-22	266	54	table	table	NOUN
cuesj-22	266	55	3	3	NUM
cuesj-22	266	56	:	:	PUNCT
cuesj-22	266	57	accuracy	accuracy	NOUN
cuesj-22	266	58	criteria	criterion	NOUN
cuesj-22	266	59	for	for	ADP
cuesj-22	266	60	each	each	DET
cuesj-22	266	61	approach	approach	NOUN
cuesj-22	266	62	methods	method	NOUN
cuesj-22	266	63	mae	mae	PROPN
cuesj-22	266	64	rmse	rmse	PROPN
cuesj-22	266	65	kalman	kalman	PROPN
cuesj-22	266	66	filter	filter	PROPN
cuesj-22	266	67	13.2975	13.2975	NUM
cuesj-22	266	68	18.078	18.078	NUM
cuesj-22	266	69	simple	simple	ADJ
cuesj-22	266	70	kriging	kriging	ADJ
cuesj-22	266	71	/	/	SYM
cuesj-22	266	72	gaussian	gaussian	ADJ
cuesj-22	266	73	model	model	NOUN
cuesj-22	266	74	29.7364	29.7364	NUM
cuesj-22	266	75	43.719	43.719	NUM
cuesj-22	266	76	mae	mae	PROPN
cuesj-22	266	77	:	:	PUNCT
cuesj-22	266	78	mean	mean	VERB
cuesj-22	266	79	absolute	absolute	ADJ
cuesj-22	266	80	error	error	NOUN
cuesj-22	266	81	,	,	PUNCT
cuesj-22	266	82	rmse	rmse	NOUN
cuesj-22	266	83	:	:	PUNCT
cuesj-22	266	84	root	root	NOUN
cuesj-22	266	85	mean	mean	VERB
cuesj-22	266	86	square	square	ADJ
cuesj-22	266	87	error	error	NOUN
cuesj-22	266	88	table	table	NOUN
cuesj-22	266	89	4	4	NUM
cuesj-22	266	90	:	:	PUNCT
cuesj-22	266	91	results	result	NOUN
cuesj-22	266	92	of	of	ADP
cuesj-22	266	93	simple	simple	ADJ
cuesj-22	266	94	kriging	kriging	ADJ
cuesj-22	266	95	gaussian	gaussian	ADJ
cuesj-22	266	96	semivariogram	semivariogram	NOUN
cuesj-22	266	97	model	model	NOUN
cuesj-22	266	98	simple	simple	ADJ
cuesj-22	266	99	kriging	krige	VERB
cuesj-22	266	100	exponential	exponential	ADJ
cuesj-22	266	101	semivariogram	semivariogram	NOUN
cuesj-22	266	102	isotropy	isotropy	NOUN
cuesj-22	266	103	anisotropy	anisotropy	VERB
cuesj-22	266	104	maximum	maximum	ADJ
cuesj-22	266	105	neighbors	neighbor	NOUN
cuesj-22	266	106	5	5	NUM
cuesj-22	266	107	5	5	NUM
cuesj-22	266	108	minimum	minimum	ADJ
cuesj-22	266	109	neighbors	neighbor	NOUN
cuesj-22	266	110	2	2	NUM
cuesj-22	266	111	2	2	NUM
cuesj-22	266	112	predicted	predict	VERB
cuesj-22	266	113	value	value	NOUN
cuesj-22	266	114	x	x	NOUN
cuesj-22	266	115	400,739	400,739	NUM
cuesj-22	266	116	400,739	400,739	NUM
cuesj-22	266	117	y	y	NOUN
cuesj-22	266	118	421,531	421,531	NUM
cuesj-22	266	119	421,531	421,531	NUM
cuesj-22	266	120	depth	depth	NOUN
cuesj-22	266	121	(	(	PUNCT
cuesj-22	266	122	m	m	NOUN
cuesj-22	266	123	)	)	PUNCT
cuesj-22	266	124	132.3405	132.3405	NUM
cuesj-22	266	125	173.2582	173.2582	NUM
cuesj-22	266	126	table	table	NOUN
cuesj-22	266	127	5	5	NUM
cuesj-22	266	128	:	:	PUNCT
cuesj-22	266	129	estimated	estimate	VERB
cuesj-22	266	130	parameters	parameter	NOUN
cuesj-22	266	131	when	when	SCONJ
cuesj-22	266	132	(	(	PUNCT
cuesj-22	266	133	̂	̂	X
cuesj-22	266	134	0	0	NUM
cuesj-22	267	1	=	=	SYM
cuesj-22	267	2	126.02	126.02	NUM
cuesj-22	267	3	,	,	PUNCT
cuesj-22	267	4	p̂0	p̂0	X
cuesj-22	268	1	=	=	SYM
cuesj-22	268	2	2089.088	2089.088	NUM
cuesj-22	268	3	,	,	PUNCT
cuesj-22	268	4	and	and	CCONJ
cuesj-22	268	5	w=500	w=500	NUM
cuesj-22	268	6	)	)	PUNCT
cuesj-22	268	7	convergence	convergence	NOUN
cuesj-22	268	8	time	time	NOUN
cuesj-22	268	9	actual	actual	ADJ
cuesj-22	268	10	values	value	NOUN
cuesj-22	268	11	estimate	estimate	VERB
cuesj-22	268	12	error	error	NOUN
cuesj-22	268	13	covariance	covariance	NOUN
cuesj-22	268	14	kalman	kalman	NOUN
cuesj-22	268	15	gain	gain	VERB
cuesj-22	268	16	1	1	NUM
cuesj-22	268	17	189	189	NUM
cuesj-22	268	18	126.02	126.02	NUM
cuesj-22	268	19	2089.088	2089.088	NUM
cuesj-22	268	20	0.73123	0.73123	NUM
cuesj-22	268	21	2	2	NUM
cuesj-22	268	22	201	201	NUM
cuesj-22	268	23	167.5177	167.5177	NUM
cuesj-22	268	24	1156.157	1156.157	NUM
cuesj-22	268	25	0.55345	0.55345	NUM
cuesj-22	268	26	3	3	NUM
cuesj-22	268	27	91	91	NUM
cuesj-22	268	28	133.6806	133.6806	NUM
cuesj-22	268	29	923.7829	923.7829	NUM
cuesj-22	268	30	0.442213	0.442213	NUM
cuesj-22	268	31	4	4	NUM
cuesj-22	268	32	142	142	NUM
cuesj-22	268	33	137.0526	137.0526	NUM
cuesj-22	268	34	846.7026	846.7026	NUM
cuesj-22	268	35	0.405315	0.405315	NUM
cuesj-22	268	36	5	5	NUM
cuesj-22	268	37	100	100	NUM
cuesj-22	268	38	122.529	122.529	NUM
cuesj-22	268	39	818.8316	818.8316	NUM
cuesj-22	268	40	0.391973	0.391973	NUM
cuesj-22	268	41	6	6	NUM
cuesj-22	268	42	130	130	NUM
cuesj-22	268	43	125.4203	125.4203	NUM
cuesj-22	268	44	808.4435	808.4435	NUM
cuesj-22	268	45	0.387	0.387	NUM
cuesj-22	268	46	7	7	NUM
cuesj-22	268	47	160	160	NUM
cuesj-22	268	48	138.7378	138.7378	NUM
cuesj-22	268	49	804.528	804.528	NUM
cuesj-22	268	50	0.385126	0.385126	NUM
cuesj-22	268	51	8	8	NUM
cuesj-22	268	52	140	140	NUM
cuesj-22	268	53	139.223	139.223	NUM
cuesj-22	268	54	803.046	803.046	NUM
cuesj-22	268	55	0.384416	0.384416	NUM
cuesj-22	268	56	9	9	NUM
cuesj-22	268	57	50	50	NUM
cuesj-22	268	58	104.9482	104.9482	NUM
cuesj-22	268	59	802.4841	802.4841	NUM
cuesj-22	268	60	0.384147	0.384147	NUM
cuesj-22	268	61	10	10	NUM
cuesj-22	268	62	151	151	NUM
cuesj-22	268	63	122.6342	122.6342	NUM
cuesj-22	268	64	802.271	802.271	NUM
cuesj-22	268	65	0.384045	0.384045	NUM
cuesj-22	268	66	11	11	NUM
cuesj-22	268	67	183	183	NUM
cuesj-22	268	68	145.8151	145.8151	NUM
cuesj-22	268	69	802.1901	802.1901	NUM
cuesj-22	268	70	0.384007	0.384007	NUM
cuesj-22	268	71	12	12	NUM
cuesj-22	268	72	100	100	NUM
cuesj-22	268	73	128.2224	128.2224	NUM
cuesj-22	268	74	802.1594	802.1594	NUM
cuesj-22	268	75	0.383992	0.383992	NUM
cuesj-22	268	76	13	13	NUM
cuesj-22	268	77	100	100	NUM
cuesj-22	268	78	117.3854	117.3854	NUM
cuesj-22	268	79	802.1478	802.1478	NUM
cuesj-22	268	80	0.383986	0.383986	NUM
cuesj-22	268	81	14	14	NUM
cuesj-22	268	82	220	220	NUM
cuesj-22	268	83	156.7878	156.7878	NUM
cuesj-22	268	84	802.1434	802.1434	NUM
cuesj-22	268	85	0.383984	0.383984	NUM
cuesj-22	268	86	15	15	NUM
cuesj-22	268	87	193	193	NUM
cuesj-22	268	88	170.6927	170.6927	NUM
cuesj-22	268	89	802.1417	802.1417	NUM
cuesj-22	268	90	0.383984	0.383984	NUM
cuesj-22	268	91	16	16	NUM
cuesj-22	268	92	180	180	NUM
cuesj-22	268	93	174.2665	174.2665	NUM
cuesj-22	268	94	802.1411	802.1411	NUM
cuesj-22	268	95	0.383983	0.383983	NUM
cuesj-22	268	96	17	17	NUM
cuesj-22	268	97	200	200	NUM
cuesj-22	268	98	184.1478	184.1478	NUM
cuesj-22	268	99	802.1408	802.1408	NUM
cuesj-22	268	100	0.383983	0.383983	NUM
cuesj-22	268	101	18	18	NUM
cuesj-22	268	102	147	147	NUM
cuesj-22	268	103	169.8836	169.8836	NUM
cuesj-22	268	104	802.1407	802.1407	NUM
cuesj-22	268	105	0.383983	0.383983	NUM
cuesj-22	268	106	19	19	NUM
cuesj-22	268	107	202	202	NUM
cuesj-22	268	108	182.2158	182.2158	NUM
cuesj-22	268	109	802.1407	802.1407	NUM
cuesj-22	268	110	0.383983	0.383983	NUM
cuesj-22	268	111	20	20	NUM
cuesj-22	268	112	151	151	NUM
cuesj-22	268	113	170.2295	170.2295	NUM
cuesj-22	268	114	802.1407	802.1407	NUM
cuesj-22	268	115	0.383983	0.383983	NUM
cuesj-22	268	116	.	.	PUNCT
cuesj-22	268	117	.	.	PUNCT
cuesj-22	268	118	.	.	PUNCT
cuesj-22	268	119	.	.	PUNCT
cuesj-22	268	120	.	.	PUNCT
cuesj-22	268	121	.	.	PUNCT
cuesj-22	268	122	.	.	PUNCT
cuesj-22	268	123	.	.	PUNCT
cuesj-22	268	124	.	.	PUNCT
cuesj-22	269	1	.	.	PUNCT
cuesj-22	270	1	291	291	NUM
cuesj-22	270	2	239	239	NUM
cuesj-22	270	3	164.2836	164.2836	NUM
cuesj-22	270	4	802.1407	802.1407	NUM
cuesj-22	270	5	0.383983	0.383983	NUM
cuesj-22	270	6	292	292	NUM
cuesj-22	270	7	211	211	NUM
cuesj-22	270	8	182.2219	182.2219	NUM
cuesj-22	270	9	802.1407	802.1407	NUM
cuesj-22	270	10	0.383983	0.383983	NUM
cuesj-22	270	11	293	293	NUM
cuesj-22	270	12	73	73	NUM
cuesj-22	270	13	140.2825	140.2825	NUM
cuesj-22	270	14	802.1407	802.1407	NUM
cuesj-22	270	15	0.383983	0.383983	NUM
cuesj-22	270	16	294	294	NUM
cuesj-22	270	17	91	91	NUM
cuesj-22	270	18	121.3589	121.3589	NUM
cuesj-22	270	19	802.1407	802.1407	NUM
cuesj-22	270	20	0.383983	0.383983	NUM
cuesj-22	270	21	295	295	NUM
cuesj-22	270	22	82	82	NUM
cuesj-22	270	23	106.2469	106.2469	NUM
cuesj-22	270	24	802.1407	802.1407	NUM
cuesj-22	270	25	0.383983	0.383983	NUM
cuesj-22	270	26	mawlood	mawlood	NOUN
cuesj-22	270	27	and	and	CCONJ
cuesj-22	270	28	omer	omer	PROPN
cuesj-22	270	29	:	:	PUNCT
cuesj-22	270	30	prediction	prediction	NOUN
cuesj-22	270	31	the	the	DET
cuesj-22	270	32	groundwater	groundwater	NOUN
cuesj-22	270	33	depth	depth	NOUN
cuesj-22	270	34	49	49	NUM
cuesj-22	270	35	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-22	270	36	cuesj	cuesj	NOUN
cuesj-22	270	37	2019	2019	NUM
cuesj-22	270	38	,	,	PUNCT
cuesj-22	270	39	3	3	NUM
cuesj-22	270	40	(	(	PUNCT
cuesj-22	270	41	1	1	NUM
cuesj-22	270	42	):	):	PUNCT
cuesj-22	270	43	42	42	NUM
cuesj-22	270	44	-	-	SYM
cuesj-22	270	45	49	49	NUM
cuesj-22	270	46	references	reference	NOUN
cuesj-22	270	47	1	1	NUM
cuesj-22	270	48	.	.	PUNCT
cuesj-22	270	49	s.	s.	PROPN
cuesj-22	270	50	i.	i.	PROPN
cuesj-22	270	51	gou	gou	PROPN
cuesj-22	270	52	.	.	PUNCT
cuesj-22	271	1	“	"	PUNCT
cuesj-22	271	2	identifying	identify	VERB
cuesj-22	271	3	groundwater	groundwater	NOUN
cuesj-22	271	4	dependent	dependent	ADJ
cuesj-22	271	5	ecosystems	ecosystem	NOUN
cuesj-22	271	6	in	in	ADP
cuesj-22	271	7	the	the	DET
cuesj-22	271	8	edwards	edwards	PROPN
cuesj-22	271	9	aquifer	aquifer	PROPN
cuesj-22	271	10	area	area	NOUN
cuesj-22	271	11	,	,	PUNCT
cuesj-22	271	12	zachry	zachry	PROPN
cuesj-22	271	13	department	department	PROPN
cuesj-22	271	14	of	of	ADP
cuesj-22	271	15	civil	civil	ADJ
cuesj-22	271	16	engineering	engineering	NOUN
cuesj-22	271	17	”	"	PUNCT
cuesj-22	271	18	.	.	PUNCT
cuesj-22	272	1	texas	texas	PROPN
cuesj-22	272	2	:	:	PUNCT
cuesj-22	273	1	texas	texas	PROPN
cuesj-22	273	2	a	a	PROPN
cuesj-22	273	3	and	and	CCONJ
cuesj-22	273	4	m	m	PROPN
cuesj-22	273	5	university	university	NOUN
cuesj-22	273	6	,	,	PUNCT
cuesj-22	273	7	2010	2010	NUM
cuesj-22	273	8	.	.	PUNCT
cuesj-22	274	1	2	2	NUM
cuesj-22	274	2	.	.	X
cuesj-22	274	3	r.	r.	PROPN
cuesj-22	274	4	webster	webster	PROPN
cuesj-22	274	5	and	and	CCONJ
cuesj-22	274	6	m.	m.	NOUN
cuesj-22	274	7	olivier	olivier	NOUN
cuesj-22	274	8	.	.	PUNCT
cuesj-22	275	1	“	"	PUNCT
cuesj-22	275	2	geostatistics	geostatistics	PROPN
cuesj-22	275	3	environmental	environmental	ADJ
cuesj-22	275	4	scientists	scientist	NOUN
cuesj-22	275	5	”	"	PUNCT
cuesj-22	275	6	.	.	PUNCT
cuesj-22	276	1	2nd	2nd	ADJ
cuesj-22	276	2	ed	ed	NOUN
cuesj-22	276	3	.	.	PUNCT
cuesj-22	277	1	new	new	PROPN
cuesj-22	277	2	york	york	PROPN
cuesj-22	277	3	,	,	PUNCT
cuesj-22	277	4	usa	usa	PROPN
cuesj-22	277	5	:	:	PUNCT
cuesj-22	277	6	john	john	PROPN
cuesj-22	277	7	wiley	wiley	PROPN
cuesj-22	277	8	and	and	CCONJ
cuesj-22	277	9	sons	son	NOUN
cuesj-22	277	10	,	,	PUNCT
cuesj-22	277	11	2007	2007	NUM
cuesj-22	277	12	.	.	PUNCT
cuesj-22	278	1	3	3	X
cuesj-22	278	2	.	.	X
cuesj-22	278	3	l.	l.	PROPN
cuesj-22	278	4	marcin	marcin	PROPN
cuesj-22	278	5	and	and	CCONJ
cuesj-22	278	6	k.	k.	PROPN
cuesj-22	278	7	marek	marek	PROPN
cuesj-22	278	8	.	.	PUNCT
cuesj-22	279	1	“	"	PUNCT
cuesj-22	279	2	simple	simple	ADJ
cuesj-22	279	3	spatial	spatial	ADJ
cuesj-22	279	4	prediction	prediction	NOUN
cuesj-22	279	5	least	least	ADJ
cuesj-22	279	6	squares	square	NOUN
cuesj-22	279	7	prediction	prediction	NOUN
cuesj-22	279	8	,	,	PUNCT
cuesj-22	279	9	simple	simple	ADJ
cuesj-22	279	10	kriging	kriging	NOUN
cuesj-22	279	11	and	and	CCONJ
cuesj-22	279	12	conditional	conditional	ADJ
cuesj-22	279	13	expectation	expectation	NOUN
cuesj-22	279	14	of	of	ADP
cuesj-22	279	15	normal	normal	ADJ
cuesj-22	279	16	vector	vector	NOUN
cuesj-22	279	17	”	"	PUNCT
cuesj-22	279	18	.	.	PUNCT
cuesj-22	280	1	poland	poland	PROPN
cuesj-22	280	2	:	:	PUNCT
cuesj-22	280	3	department	department	NOUN
cuesj-22	280	4	of	of	ADP
cuesj-22	280	5	geomatics	geomatics	PROPN
cuesj-22	280	6	,	,	PUNCT
cuesj-22	280	7	agh	agh	PROPN
cuesj-22	280	8	university	university	PROPN
cuesj-22	280	9	of	of	ADP
cuesj-22	280	10	science	science	NOUN
cuesj-22	280	11	and	and	CCONJ
cuesj-22	280	12	technology	technology	NOUN
cuesj-22	280	13	in	in	ADP
cuesj-22	280	14	krakow	krakow	PROPN
cuesj-22	280	15	,	,	PUNCT
cuesj-22	280	16	2010	2010	NUM
cuesj-22	280	17	.	.	PUNCT
cuesj-22	281	1	4	4	NUM
cuesj-22	281	2	.	.	X
cuesj-22	282	1	d	d	X
cuesj-22	282	2	,	,	PUNCT
cuesj-22	282	3	sarma	sarma	PROPN
cuesj-22	282	4	.	.	PUNCT
cuesj-22	283	1	“	"	PUNCT
cuesj-22	283	2	geostatistics	geostatistic	NOUN
cuesj-22	283	3	with	with	ADP
cuesj-22	283	4	application	application	NOUN
cuesj-22	283	5	in	in	ADP
cuesj-22	283	6	earth	earth	NOUN
cuesj-22	283	7	science	science	NOUN
cuesj-22	283	8	”	"	PUNCT
cuesj-22	283	9	.	.	PUNCT
cuesj-22	284	1	2nd	2nd	ADJ
cuesj-22	284	2	ed	ed	NOUN
cuesj-22	284	3	.	.	PUNCT
cuesj-22	285	1	dordrecht	dordrecht	PROPN
cuesj-22	285	2	:	:	PUNCT
cuesj-22	285	3	springer	springer	NOUN
cuesj-22	285	4	,	,	PUNCT
cuesj-22	285	5	captial	captial	PROPN
cuesj-22	285	6	publishing	publishing	NOUN
cuesj-22	285	7	company	company	NOUN
cuesj-22	285	8	,	,	PUNCT
cuesj-22	285	9	2009	2009	NUM
cuesj-22	285	10	.	.	PUNCT
cuesj-22	286	1	5	5	NUM
cuesj-22	286	2	.	.	X
cuesj-22	286	3	r.	r.	PROPN
cuesj-22	286	4	sluiter	sluiter	PROPN
cuesj-22	286	5	.	.	PUNCT
cuesj-22	287	1	“	"	PUNCT
cuesj-22	287	2	interpolation	interpolation	NOUN
cuesj-22	287	3	methods	method	NOUN
cuesj-22	287	4	for	for	ADP
cuesj-22	287	5	climate	climate	NOUN
cuesj-22	287	6	data	datum	NOUN
cuesj-22	287	7	;	;	PUNCT
cuesj-22	287	8	literature	literature	NOUN
cuesj-22	287	9	review	review	PROPN
cuesj-22	287	10	”	"	PUNCT
cuesj-22	287	11	.	.	PUNCT
cuesj-22	288	1	knmi	knmi	NOUN
cuesj-22	288	2	,	,	PUNCT
cuesj-22	288	3	r	r	NOUN
cuesj-22	288	4	and	and	CCONJ
cuesj-22	288	5	d	d	NOUN
cuesj-22	288	6	information	information	NOUN
cuesj-22	288	7	and	and	CCONJ
cuesj-22	288	8	observation	observation	NOUN
cuesj-22	288	9	technology	technology	NOUN
cuesj-22	288	10	,	,	PUNCT
cuesj-22	288	11	2009	2009	NUM
cuesj-22	288	12	.	.	PUNCT
cuesj-22	289	1	6	6	NUM
cuesj-22	289	2	.	.	PUNCT
cuesj-22	289	3	i.	i.	PROPN
cuesj-22	289	4	clark	clark	PROPN
cuesj-22	289	5	and	and	CCONJ
cuesj-22	289	6	w.	w.	PROPN
cuesj-22	289	7	v.	v.	PROPN
cuesj-22	289	8	harper	harper	PROPN
cuesj-22	289	9	.	.	PUNCT
cuesj-22	290	1	“	"	PUNCT
cuesj-22	290	2	practical	practical	ADJ
cuesj-22	290	3	geostatistics	geostatistic	NOUN
cuesj-22	290	4	”	"	PUNCT
cuesj-22	290	5	.	.	PUNCT
cuesj-22	291	1	columbus	columbus	PROPN
cuesj-22	291	2	,	,	PUNCT
cuesj-22	291	3	ohio	ohio	PROPN
cuesj-22	291	4	:	:	PUNCT
cuesj-22	291	5	ecosse	ecosse	PROPN
cuesj-22	291	6	north	north	PROPN
cuesj-22	291	7	america	america	PROPN
cuesj-22	291	8	,	,	PUNCT
cuesj-22	291	9	2000	2000	NUM
cuesj-22	291	10	.	.	PUNCT
cuesj-22	292	1	7	7	X
cuesj-22	292	2	.	.	X
cuesj-22	292	3	m.	m.	NOUN
cuesj-22	292	4	disse	disse	PROPN
cuesj-22	292	5	and	and	CCONJ
cuesj-22	292	6	k.	k.	PROPN
cuesj-22	292	7	amin	amin	PROPN
cuesj-22	292	8	.	.	PUNCT
cuesj-22	293	1	“	"	PUNCT
cuesj-22	293	2	review	review	NOUN
cuesj-22	293	3	of	of	ADP
cuesj-22	293	4	various	various	ADJ
cuesj-22	293	5	methods	method	NOUN
cuesj-22	293	6	for	for	ADP
cuesj-22	293	7	interpolation	interpolation	NOUN
cuesj-22	293	8	of	of	ADP
cuesj-22	293	9	rainfall	rainfall	NOUN
cuesj-22	293	10	and	and	CCONJ
cuesj-22	293	11	their	their	PRON
cuesj-22	293	12	applications	application	NOUN
cuesj-22	293	13	in	in	ADP
cuesj-22	293	14	hydrology	hydrology	NOUN
cuesj-22	293	15	,	,	PUNCT
cuesj-22	293	16	thesis	thesis	NOUN
cuesj-22	293	17	of	of	ADP
cuesj-22	293	18	chair	chair	NOUN
cuesj-22	293	19	of	of	ADP
cuesj-22	293	20	hydrology	hydrology	NOUN
cuesj-22	293	21	and	and	CCONJ
cuesj-22	293	22	river	river	NOUN
cuesj-22	293	23	basin	basin	NOUN
cuesj-22	293	24	management	management	NOUN
cuesj-22	293	25	”	"	PUNCT
cuesj-22	293	26	.	.	PUNCT
cuesj-22	294	1	munich	munich	PROPN
cuesj-22	294	2	:	:	PUNCT
cuesj-22	294	3	technical	technical	PROPN
cuesj-22	294	4	university	university	PROPN
cuesj-22	294	5	munich	munich	PROPN
cuesj-22	294	6	,	,	PUNCT
cuesj-22	294	7	2017	2017	NUM
cuesj-22	294	8	.	.	PUNCT
cuesj-22	295	1	8	8	NUM
cuesj-22	295	2	.	.	X
cuesj-22	295	3	r.	r.	PROPN
cuesj-22	295	4	e.	e.	PROPN
cuesj-22	295	5	kalman	kalman	PROPN
cuesj-22	295	6	.	.	PUNCT
cuesj-22	296	1	“	"	PUNCT
cuesj-22	296	2	a	a	DET
cuesj-22	296	3	new	new	ADJ
cuesj-22	296	4	approach	approach	NOUN
cuesj-22	296	5	to	to	ADP
cuesj-22	296	6	linear	linear	ADJ
cuesj-22	296	7	filtering	filtering	NOUN
cuesj-22	296	8	and	and	CCONJ
cuesj-22	296	9	prediction	prediction	NOUN
cuesj-22	296	10	problems	problem	NOUN
cuesj-22	296	11	”	"	PUNCT
cuesj-22	296	12	.	.	PUNCT
cuesj-22	297	1	journal	journal	PROPN
cuesj-22	297	2	of	of	ADP
cuesj-22	297	3	basic	basic	ADJ
cuesj-22	297	4	engineering	engineering	NOUN
cuesj-22	297	5	,	,	PUNCT
cuesj-22	297	6	vol	vol	NOUN
cuesj-22	297	7	.	.	PROPN
cuesj-22	297	8	82	82	NUM
cuesj-22	297	9	,	,	PUNCT
cuesj-22	297	10	pp	pp	ADJ
cuesj-22	297	11	.	.	PUNCT
cuesj-22	298	1	35	35	NUM
cuesj-22	298	2	-	-	SYM
cuesj-22	298	3	45	45	NUM
cuesj-22	298	4	,	,	PUNCT
cuesj-22	298	5	1960	1960	NUM
cuesj-22	298	6	.	.	PUNCT
cuesj-22	299	1	9	9	NUM
cuesj-22	299	2	.	.	X
cuesj-22	299	3	r.	r.	PROPN
cuesj-22	299	4	e.	e.	PROPN
cuesj-22	299	5	kalman	kalman	PROPN
cuesj-22	299	6	and	and	CCONJ
cuesj-22	299	7	r.	r.	PROPN
cuesj-22	299	8	s.	s.	PROPN
cuesj-22	299	9	bucy	bucy	PROPN
cuesj-22	299	10	.	.	PUNCT
cuesj-22	300	1	“	"	PUNCT
cuesj-22	300	2	new	new	ADJ
cuesj-22	300	3	linear	linear	ADJ
cuesj-22	300	4	filtering	filtering	NOUN
cuesj-22	300	5	and	and	CCONJ
cuesj-22	300	6	prediction	prediction	NOUN
cuesj-22	300	7	problems	problem	NOUN
cuesj-22	300	8	”	"	PUNCT
cuesj-22	300	9	.	.	PUNCT
cuesj-22	301	1	journal	journal	PROPN
cuesj-22	301	2	of	of	ADP
cuesj-22	301	3	basic	basic	ADJ
cuesj-22	301	4	engineering	engineering	NOUN
cuesj-22	301	5	,	,	PUNCT
cuesj-22	301	6	vol	vol	NOUN
cuesj-22	301	7	.	.	PROPN
cuesj-22	301	8	83	83	NUM
cuesj-22	301	9	,	,	PUNCT
cuesj-22	301	10	pp	pp	ADJ
cuesj-22	301	11	.	.	PUNCT
cuesj-22	302	1	95	95	NUM
cuesj-22	302	2	-	-	SYM
cuesj-22	302	3	108	108	NUM
cuesj-22	302	4	,	,	PUNCT
cuesj-22	302	5	1961	1961	NUM
cuesj-22	302	6	.	.	PUNCT
cuesj-22	303	1	10	10	NUM
cuesj-22	303	2	.	.	PUNCT
cuesj-22	303	3	r.	r.	PROPN
cuesj-22	303	4	eubank	eubank	PROPN
cuesj-22	303	5	.	.	PUNCT
cuesj-22	304	1	“	"	PUNCT
cuesj-22	304	2	a	a	DET
cuesj-22	304	3	kalman	kalman	PROPN
cuesj-22	304	4	filter	filter	NOUN
cuesj-22	304	5	primer	primer	NOUN
cuesj-22	304	6	”	"	PUNCT
cuesj-22	304	7	.	.	PUNCT
cuesj-22	305	1	united	united	PROPN
cuesj-22	305	2	states	states	PROPN
cuesj-22	305	3	of	of	ADP
cuesj-22	305	4	america	america	PROPN
cuesj-22	305	5	:	:	PUNCT
cuesj-22	305	6	crc	crc	NOUN
cuesj-22	305	7	press	press	NOUN
cuesj-22	305	8	publishing	publishing	NOUN
cuesj-22	305	9	,	,	PUNCT
cuesj-22	305	10	2006	2006	NUM
cuesj-22	305	11	.	.	PUNCT
cuesj-22	306	1	11	11	NUM
cuesj-22	306	2	.	.	PUNCT
cuesj-22	306	3	b.	b.	PROPN
cuesj-22	306	4	d.	d.	PROPN
cuesj-22	306	5	anderson	anderson	PROPN
cuesj-22	306	6	and	and	CCONJ
cuesj-22	306	7	b.	b.	PROPN
cuesj-22	306	8	g.	g.	PROPN
cuesj-22	306	9	moore	moore	PROPN
cuesj-22	306	10	.	.	PUNCT
cuesj-22	307	1	“	"	PUNCT
cuesj-22	307	2	optimal	optimal	ADJ
cuesj-22	307	3	filtering	filtering	NOUN
cuesj-22	307	4	”	"	PUNCT
cuesj-22	307	5	.	.	PUNCT
cuesj-22	308	1	new	new	ADJ
cuesj-22	308	2	south	south	PROPN
cuesj-22	308	3	wales	wales	PROPN
cuesj-22	308	4	,	,	PUNCT
cuesj-22	308	5	australia	australia	PROPN
cuesj-22	308	6	:	:	PUNCT
cuesj-22	308	7	prentice	prentice	NOUN
cuesj-22	308	8	-	-	PUNCT
cuesj-22	308	9	hall	hall	NOUN
cuesj-22	308	10	publishing	publishing	NOUN
cuesj-22	308	11	,	,	PUNCT
cuesj-22	308	12	university	university	PROPN
cuesj-22	308	13	of	of	ADP
cuesj-22	308	14	newcastle	newcastle	PROPN
cuesj-22	308	15	,	,	PUNCT
cuesj-22	308	16	1979	1979	NUM
cuesj-22	308	17	.	.	PUNCT
cuesj-22	309	1	12	12	NUM
cuesj-22	309	2	.	.	PUNCT
cuesj-22	310	1	m.	m.	PROPN
cuesj-22	310	2	meng	meng	PROPN
cuesj-22	310	3	,	,	PUNCT
cuesj-22	310	4	d	d	PROPN
cuesj-22	310	5	,	,	PUNCT
cuesj-22	310	6	niu	niu	PROPN
cuesj-22	310	7	d	d	PROPN
cuesj-22	310	8	and	and	CCONJ
cuesj-22	310	9	w.	w.	PROPN
cuesj-22	310	10	sun	sun	PROPN
cuesj-22	310	11	.	.	PUNCT
cuesj-22	311	1	“	"	PUNCT
cuesj-22	311	2	forecasting	forecast	VERB
cuesj-22	311	3	monthly	monthly	ADJ
cuesj-22	311	4	electric	electric	ADJ
cuesj-22	311	5	energy	energy	NOUN
cuesj-22	311	6	consumption	consumption	NOUN
cuesj-22	311	7	using	use	VERB
cuesj-22	311	8	feature	feature	NOUN
cuesj-22	311	9	extraction	extraction	NOUN
cuesj-22	311	10	”	"	PUNCT
cuesj-22	311	11	.	.	PUNCT
cuesj-22	312	1	journal	journal	PROPN
cuesj-22	312	2	energies	energy	NOUN
cuesj-22	312	3	,	,	PUNCT
cuesj-22	312	4	vol	vol	NOUN
cuesj-22	312	5	.	.	PROPN
cuesj-22	312	6	4	4	NUM
cuesj-22	312	7	,	,	PUNCT
cuesj-22	312	8	pp	pp	ADJ
cuesj-22	312	9	.	.	PUNCT
cuesj-22	313	1	1495	1495	NUM
cuesj-22	313	2	-	-	SYM
cuesj-22	313	3	1507	1507	NUM
cuesj-22	313	4	,	,	PUNCT
cuesj-22	313	5	2011	2011	NUM
cuesj-22	313	6	.	.	PUNCT
cuesj-22	314	1	13	13	NUM
cuesj-22	314	2	.	.	PUNCT
cuesj-22	314	3	m.	m.	PROPN
cuesj-22	314	4	west	west	PROPN
cuesj-22	314	5	and	and	CCONJ
cuesj-22	314	6	j.	j.	PROPN
cuesj-22	314	7	harrison	harrison	PROPN
cuesj-22	314	8	.	.	PUNCT
cuesj-22	315	1	“	"	PUNCT
cuesj-22	315	2	bayesian	bayesian	NOUN
cuesj-22	315	3	forecasting	forecasting	NOUN
cuesj-22	315	4	and	and	CCONJ
cuesj-22	315	5	dynamic	dynamic	ADJ
cuesj-22	315	6	models	model	NOUN
cuesj-22	315	7	”	"	PUNCT
cuesj-22	315	8	.	.	PUNCT
cuesj-22	316	1	2nd	2nd	ADJ
cuesj-22	316	2	ed	ed	NOUN
cuesj-22	316	3	.	.	PUNCT
cuesj-22	317	1	new	new	PROPN
cuesj-22	317	2	york	york	PROPN
cuesj-22	317	3	:	:	PUNCT
cuesj-22	317	4	springer	springer	NOUN
cuesj-22	317	5	-	-	PUNCT
cuesj-22	317	6	verlag	verlag	PROPN
cuesj-22	317	7	,	,	PUNCT
cuesj-22	317	8	1997	1997	NUM
cuesj-22	317	9	.	.	PUNCT
cuesj-22	318	1	14	14	NUM
cuesj-22	318	2	.	.	PUNCT
cuesj-22	318	3	r.	r.	PROPN
cuesj-22	318	4	gentleman	gentleman	PROPN
cuesj-22	318	5	,	,	PUNCT
cuesj-22	318	6	k.	k.	PROPN
cuesj-22	318	7	hornik	hornik	PROPN
cuesj-22	318	8	and	and	CCONJ
cuesj-22	318	9	g.	g.	PROPN
cuesj-22	318	10	parmigiani	parmigiani	PROPN
cuesj-22	318	11	.	.	PUNCT
cuesj-22	319	1	“	"	PUNCT
cuesj-22	319	2	dynamic	dynamic	ADJ
cuesj-22	319	3	linear	linear	NOUN
cuesj-22	319	4	models	model	NOUN
cuesj-22	319	5	with	with	ADP
cuesj-22	319	6	r	r	NOUN
cuesj-22	319	7	”	"	PUNCT
cuesj-22	319	8	.	.	PUNCT
cuesj-22	320	1	new	new	PROPN
cuesj-22	320	2	york	york	PROPN
cuesj-22	320	3	:	:	PUNCT
cuesj-22	320	4	springer	springer	NOUN
cuesj-22	320	5	science	science	NOUN
cuesj-22	320	6	and	and	CCONJ
cuesj-22	320	7	business	business	NOUN
cuesj-22	320	8	media	medium	NOUN
cuesj-22	320	9	publishing	publishing	NOUN
cuesj-22	320	10	,	,	PUNCT
cuesj-22	320	11	2009	2009	NUM
cuesj-22	320	12	.	.	PUNCT