id	sid	tid	token	lemma	pos
geusb-8357	1	1	research	research	NOUN
geusb-8357	1	2	article	article	NOUN
geusb-8357	1	3	|	|	ADV
geusb-8357	1	4	short	short	ADV
geusb-8357	1	5	dahl	dahl	INTJ
geusb-8357	1	6	et	et	PROPN
geusb-8357	1	7	al	al	PROPN
geusb-8357	1	8	.	.	PROPN
geusb-8357	1	9	2023	2023	NUM
geusb-8357	1	10	:	:	PUNCT
geusb-8357	1	11	geus	geus	NOUN
geusb-8357	1	12	bulletin	bulletin	NOUN
geusb-8357	1	13	53	53	NUM
geusb-8357	1	14	.	.	PUNCT
geusb-8357	1	15	8357	8357	NUM
geusb-8357	1	16	.	.	PUNCT
geusb-8357	2	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	2	2	1	1	NUM
geusb-8357	2	3	of	of	ADP
geusb-8357	2	4	7	7	NUM
geusb-8357	2	5	neural	neural	ADJ
geusb-8357	2	6	network	network	NOUN
geusb-8357	2	7	predictions	prediction	NOUN
geusb-8357	2	8	of	of	ADP
geusb-8357	2	9	drawdown	drawdown	NOUN
geusb-8357	2	10	from	from	ADP
geusb-8357	2	11	groundwater	groundwater	NOUN
geusb-8357	2	12	abstraction	abstraction	NOUN
geusb-8357	2	13	in	in	ADP
geusb-8357	2	14	the	the	DET
geusb-8357	2	15	egebjerg	egebjerg	PROPN
geusb-8357	2	16	catchment	catchment	NOUN
geusb-8357	2	17	,	,	PUNCT
geusb-8357	2	18	denmark	denmark	PROPN
geusb-8357	2	19	mathias	mathias	PROPN
geusb-8357	2	20	busk	busk	PROPN
geusb-8357	2	21	dahl*1,2	dahl*1,2	ADV
geusb-8357	2	22	,	,	PUNCT
geusb-8357	2	23	troels	troel	NOUN
geusb-8357	2	24	norvin	norvin	PROPN
geusb-8357	2	25	vilhelmsen2	vilhelmsen2	PROPN
geusb-8357	2	26	,	,	PUNCT
geusb-8357	2	27	trine	trine	INTJ
geusb-8357	2	28	enemark3	enemark3	PROPN
geusb-8357	2	29	,	,	PUNCT
geusb-8357	2	30	thomas	thomas	PROPN
geusb-8357	2	31	mejer	mejer	NOUN
geusb-8357	2	32	hansen1	hansen1	PROPN
geusb-8357	2	33	1department	1department	NUM
geusb-8357	2	34	of	of	ADP
geusb-8357	2	35	geoscience	geoscience	NOUN
geusb-8357	2	36	,	,	PUNCT
geusb-8357	2	37	aarhus	aarhus	PROPN
geusb-8357	2	38	university	university	PROPN
geusb-8357	2	39	,	,	PUNCT
geusb-8357	2	40	aarhus	aarhus	PROPN
geusb-8357	2	41	,	,	PUNCT
geusb-8357	2	42	denmark	denmark	NOUN
geusb-8357	2	43	;	;	PUNCT
geusb-8357	2	44	2niras	2niras	NUM
geusb-8357	2	45	,	,	PUNCT
geusb-8357	2	46	aarhus	aarhu	NOUN
geusb-8357	2	47	,	,	PUNCT
geusb-8357	2	48	denmark	denmark	PROPN
geusb-8357	2	49	;	;	PUNCT
geusb-8357	2	50	3department	3department	NUM
geusb-8357	2	51	of	of	ADP
geusb-8357	2	52	 	 	SPACE
geusb-8357	2	53	geosciences	geoscience	NOUN
geusb-8357	2	54	and	and	CCONJ
geusb-8357	2	55	 	 	SPACE
geusb-8357	2	56	natural	natural	ADJ
geusb-8357	2	57	resource	resource	NOUN
geusb-8357	2	58	management	management	NOUN
geusb-8357	2	59	,	,	PUNCT
geusb-8357	2	60	university	university	NOUN
geusb-8357	2	61	of	of	ADP
geusb-8357	2	62	 	 	SPACE
geusb-8357	2	63	copenhagen	copenhagen	PROPN
geusb-8357	2	64	,	,	PUNCT
geusb-8357	2	65	copenhagen	copenhagen	PROPN
geusb-8357	2	66	,	,	PUNCT
geusb-8357	2	67	denmark	denmark	PROPN
geusb-8357	2	68	abstract	abstract	ADJ
geusb-8357	2	69	results	result	NOUN
geusb-8357	2	70	from	from	ADP
geusb-8357	2	71	numerical	numerical	ADJ
geusb-8357	2	72	simulations	simulation	NOUN
geusb-8357	2	73	play	play	VERB
geusb-8357	2	74	a	a	DET
geusb-8357	2	75	vital	vital	ADJ
geusb-8357	2	76	role	role	NOUN
geusb-8357	2	77	in	in	ADP
geusb-8357	2	78	the	the	DET
geusb-8357	2	79	decision	decision	NOUN
geusb-8357	2	80	process	process	NOUN
geusb-8357	2	81	of	of	ADP
geusb-8357	2	82	everyday	everyday	ADJ
geusb-8357	2	83	groundwater	groundwater	NOUN
geusb-8357	2	84	management	management	NOUN
geusb-8357	2	85	.	.	PUNCT
geusb-8357	3	1	however	however	ADV
geusb-8357	3	2	,	,	PUNCT
geusb-8357	3	3	these	these	DET
geusb-8357	3	4	simulations	simulation	NOUN
geusb-8357	3	5	can	can	AUX
geusb-8357	3	6	be	be	AUX
geusb-8357	3	7	time	time	NOUN
geusb-8357	3	8	-	-	PUNCT
geusb-8357	3	9	consuming	consume	VERB
geusb-8357	3	10	for	for	ADP
geusb-8357	3	11	large	large	ADJ
geusb-8357	3	12	-	-	PUNCT
geusb-8357	3	13	scale	scale	NOUN
geusb-8357	3	14	investigations	investigation	NOUN
geusb-8357	3	15	,	,	PUNCT
geusb-8357	3	16	and	and	CCONJ
geusb-8357	3	17	it	it	PRON
geusb-8357	3	18	can	can	AUX
geusb-8357	3	19	be	be	AUX
geusb-8357	3	20	necessary	necessary	ADJ
geusb-8357	3	21	to	to	PART
geusb-8357	3	22	apply	apply	VERB
geusb-8357	3	23	approximate	approximate	ADJ
geusb-8357	3	24	methods	method	NOUN
geusb-8357	3	25	instead	instead	ADV
geusb-8357	3	26	.	.	PUNCT
geusb-8357	4	1	this	this	DET
geusb-8357	4	2	study	study	NOUN
geusb-8357	4	3	investigates	investigate	VERB
geusb-8357	4	4	the	the	DET
geusb-8357	4	5	abilities	ability	NOUN
geusb-8357	4	6	of	of	ADP
geusb-8357	4	7	a	a	DET
geusb-8357	4	8	neural	neural	ADJ
geusb-8357	4	9	network	network	NOUN
geusb-8357	4	10	to	to	PART
geusb-8357	4	11	replicate	replicate	VERB
geusb-8357	4	12	simulated	simulated	ADJ
geusb-8357	4	13	drawdown	drawdown	NOUN
geusb-8357	4	14	from	from	ADP
geusb-8357	4	15	groundwater	groundwater	NOUN
geusb-8357	4	16	abstraction	abstraction	NOUN
geusb-8357	4	17	in	in	ADP
geusb-8357	4	18	a	a	DET
geusb-8357	4	19	numerical	numerical	ADJ
geusb-8357	4	20	groundwater	groundwater	NOUN
geusb-8357	4	21	model	model	NOUN
geusb-8357	4	22	of	of	ADP
geusb-8357	4	23	the	the	DET
geusb-8357	4	24	egebjerg	egebjerg	PROPN
geusb-8357	4	25	catchment	catchment	NOUN
geusb-8357	4	26	,	,	PUNCT
geusb-8357	4	27	denmark	denmark	NOUN
geusb-8357	4	28	.	.	PUNCT
geusb-8357	5	1	we	we	PRON
geusb-8357	5	2	follow	follow	VERB
geusb-8357	5	3	a	a	DET
geusb-8357	5	4	generalised	generalise	VERB
geusb-8357	5	5	methodology	methodology	NOUN
geusb-8357	5	6	that	that	PRON
geusb-8357	5	7	uses	use	VERB
geusb-8357	5	8	the	the	DET
geusb-8357	5	9	information	information	NOUN
geusb-8357	5	10	within	within	ADP
geusb-8357	5	11	the	the	DET
geusb-8357	5	12	deterministic	deterministic	ADJ
geusb-8357	5	13	numerical	numerical	ADJ
geusb-8357	5	14	model	model	NOUN
geusb-8357	5	15	to	to	PART
geusb-8357	5	16	create	create	VERB
geusb-8357	5	17	a	a	DET
geusb-8357	5	18	training	training	NOUN
geusb-8357	5	19	set	set	NOUN
geusb-8357	5	20	for	for	ADP
geusb-8357	5	21	the	the	DET
geusb-8357	5	22	neural	neural	ADJ
geusb-8357	5	23	network	network	NOUN
geusb-8357	5	24	to	to	PART
geusb-8357	5	25	learn	learn	VERB
geusb-8357	5	26	from	from	ADP
geusb-8357	5	27	and	and	CCONJ
geusb-8357	5	28	extend	extend	VERB
geusb-8357	5	29	the	the	DET
geusb-8357	5	30	method	method	NOUN
geusb-8357	5	31	to	to	PART
geusb-8357	5	32	work	work	VERB
geusb-8357	5	33	in	in	ADP
geusb-8357	5	34	a	a	DET
geusb-8357	5	35	3d	3d	NUM
geusb-8357	5	36	danish	danish	ADJ
geusb-8357	5	37	groundwater	groundwater	NOUN
geusb-8357	5	38	model	model	NOUN
geusb-8357	5	39	case	case	NOUN
geusb-8357	5	40	.	.	PUNCT
geusb-8357	6	1	we	we	PRON
geusb-8357	6	2	compare	compare	VERB
geusb-8357	6	3	the	the	DET
geusb-8357	6	4	abilities	ability	NOUN
geusb-8357	6	5	of	of	ADP
geusb-8357	6	6	the	the	DET
geusb-8357	6	7	trained	train	VERB
geusb-8357	6	8	neural	neural	ADJ
geusb-8357	6	9	network	network	NOUN
geusb-8357	6	10	with	with	ADP
geusb-8357	6	11	the	the	DET
geusb-8357	6	12	results	result	NOUN
geusb-8357	6	13	of	of	ADP
geusb-8357	6	14	conventional	conventional	ADJ
geusb-8357	6	15	computations	computation	NOUN
geusb-8357	6	16	in	in	ADP
geusb-8357	6	17	terms	term	NOUN
geusb-8357	6	18	of	of	ADP
geusb-8357	6	19	speed	speed	NOUN
geusb-8357	6	20	and	and	CCONJ
geusb-8357	6	21	accuracy	accuracy	NOUN
geusb-8357	6	22	and	and	CCONJ
geusb-8357	6	23	argue	argue	VERB
geusb-8357	6	24	that	that	SCONJ
geusb-8357	6	25	this	this	DET
geusb-8357	6	26	approach	approach	NOUN
geusb-8357	6	27	has	have	VERB
geusb-8357	6	28	the	the	DET
geusb-8357	6	29	potential	potential	NOUN
geusb-8357	6	30	to	to	PART
geusb-8357	6	31	improve	improve	VERB
geusb-8357	6	32	decision	decision	NOUN
geusb-8357	6	33	support	support	NOUN
geusb-8357	6	34	for	for	ADP
geusb-8357	6	35	decision	decision	NOUN
geusb-8357	6	36	-	-	PUNCT
geusb-8357	6	37	makers	maker	NOUN
geusb-8357	6	38	within	within	ADP
geusb-8357	6	39	groundwater	groundwater	NOUN
geusb-8357	6	40	management	management	NOUN
geusb-8357	6	41	.	.	PUNCT
geusb-8357	7	1	*	*	PUNCT
geusb-8357	7	2	correspondence	correspondence	NOUN
geusb-8357	7	3	:	:	PUNCT
geusb-8357	7	4	dahl@geo.au.dk	dahl@geo.au.dk	NOUN
geusb-8357	7	5	received	receive	VERB
geusb-8357	7	6	:	:	PUNCT
geusb-8357	7	7	20	20	NUM
geusb-8357	7	8	jun	jun	PROPN
geusb-8357	7	9	2023	2023	NUM
geusb-8357	7	10	revised	revise	VERB
geusb-8357	7	11	:	:	PUNCT
geusb-8357	7	12	18	18	NUM
geusb-8357	7	13	aug	aug	PROPN
geusb-8357	7	14	2023	2023	NUM
geusb-8357	7	15	accepted	accept	VERB
geusb-8357	7	16	:	:	PUNCT
geusb-8357	7	17	22	22	NUM
geusb-8357	7	18	sep	sep	NOUN
geusb-8357	7	19	2023	2023	NUM
geusb-8357	7	20	published	publish	VERB
geusb-8357	7	21	:	:	PUNCT
geusb-8357	7	22	10	10	NUM
geusb-8357	7	23	nov	nov	NOUN
geusb-8357	7	24	2023	2023	NUM
geusb-8357	7	25	keywords	keyword	NOUN
geusb-8357	7	26	:	:	PUNCT
geusb-8357	7	27	decision	decision	NOUN
geusb-8357	7	28	support	support	NOUN
geusb-8357	7	29	,	,	PUNCT
geusb-8357	7	30	groundwater	groundwater	NOUN
geusb-8357	7	31	modelling	modelling	NOUN
geusb-8357	7	32	,	,	PUNCT
geusb-8357	7	33	machine	machine	NOUN
geusb-8357	7	34	learning	learning	NOUN
geusb-8357	7	35	,	,	PUNCT
geusb-8357	7	36	probabilistic	probabilistic	ADJ
geusb-8357	7	37	neural	neural	ADJ
geusb-8357	7	38	network	network	NOUN
geusb-8357	7	39	,	,	PUNCT
geusb-8357	7	40	resource	resource	NOUN
geusb-8357	7	41	management	management	NOUN
geusb-8357	7	42	abbreviations	abbreviation	NOUN
geusb-8357	7	43	drn	drn	VERB
geusb-8357	7	44	:	:	PUNCT
geusb-8357	7	45	drain	drain	NOUN
geusb-8357	7	46	package	package	NOUN
geusb-8357	7	47	ghb	ghb	PROPN
geusb-8357	7	48	:	:	PUNCT
geusb-8357	7	49	general	general	ADJ
geusb-8357	7	50	head	head	NOUN
geusb-8357	7	51	boundary	boundary	ADJ
geusb-8357	7	52	package	package	NOUN
geusb-8357	7	53	gelu	gelu	ADV
geusb-8357	7	54	:	:	PUNCT
geusb-8357	7	55	gaussian	gaussian	ADJ
geusb-8357	7	56	error	error	NOUN
geusb-8357	7	57	linear	linear	PROPN
geusb-8357	7	58	unit	unit	NOUN
geusb-8357	7	59	rch	rch	NOUN
geusb-8357	7	60	:	:	PUNCT
geusb-8357	7	61	recharge	recharge	VERB
geusb-8357	7	62	package	package	PROPN
geusb-8357	7	63	riv	riv	PROPN
geusb-8357	7	64	:	:	PUNCT
geusb-8357	7	65	river	river	NOUN
geusb-8357	7	66	package	package	NOUN
geusb-8357	7	67	wel	wel	PROPN
geusb-8357	7	68	:	:	PUNCT
geusb-8357	7	69	well	well	INTJ
geusb-8357	7	70	package	package	NOUN
geusb-8357	7	71	geus	geus	NOUN
geusb-8357	7	72	bulletin	bulletin	NOUN
geusb-8357	7	73	(	(	PUNCT
geusb-8357	7	74	eissn	eissn	NOUN
geusb-8357	7	75	:	:	PUNCT
geusb-8357	7	76	2597	2597	NUM
geusb-8357	7	77	-	-	SYM
geusb-8357	7	78	2154	2154	NUM
geusb-8357	7	79	)	)	PUNCT
geusb-8357	7	80	is	be	AUX
geusb-8357	7	81	an	an	DET
geusb-8357	7	82	open	open	ADJ
geusb-8357	7	83	access	access	NOUN
geusb-8357	7	84	,	,	PUNCT
geusb-8357	7	85	peer	peer	NOUN
geusb-8357	7	86	-	-	PUNCT
geusb-8357	7	87	reviewed	review	VERB
geusb-8357	7	88	journal	journal	NOUN
geusb-8357	7	89	published	publish	VERB
geusb-8357	7	90	by	by	ADP
geusb-8357	7	91	the	the	DET
geusb-8357	7	92	geological	geological	ADJ
geusb-8357	7	93	survey	survey	NOUN
geusb-8357	7	94	of	of	ADP
geusb-8357	7	95	denmark	denmark	NOUN
geusb-8357	7	96	and	and	CCONJ
geusb-8357	7	97	greenland	greenland	PROPN
geusb-8357	7	98	(	(	PUNCT
geusb-8357	7	99	geus	geus	NOUN
geusb-8357	7	100	)	)	PUNCT
geusb-8357	7	101	.	.	PUNCT
geusb-8357	8	1	this	this	DET
geusb-8357	8	2	article	article	NOUN
geusb-8357	8	3	is	be	AUX
geusb-8357	8	4	distributed	distribute	VERB
geusb-8357	8	5	under	under	ADP
geusb-8357	8	6	a	a	DET
geusb-8357	8	7	cc	cc	NOUN
geusb-8357	8	8	-	-	PUNCT
geusb-8357	8	9	by	by	ADP
geusb-8357	8	10	4.0	4.0	NUM
geusb-8357	8	11	licence	licence	NOUN
geusb-8357	8	12	,	,	PUNCT
geusb-8357	8	13	permitting	permit	VERB
geusb-8357	8	14	free	free	ADJ
geusb-8357	8	15	redistribution	redistribution	NOUN
geusb-8357	8	16	,	,	PUNCT
geusb-8357	8	17	and	and	CCONJ
geusb-8357	8	18	reproduction	reproduction	NOUN
geusb-8357	8	19	for	for	ADP
geusb-8357	8	20	any	any	DET
geusb-8357	8	21	purpose	purpose	NOUN
geusb-8357	8	22	,	,	PUNCT
geusb-8357	8	23	even	even	ADV
geusb-8357	8	24	commercial	commercial	ADJ
geusb-8357	8	25	,	,	PUNCT
geusb-8357	8	26	provided	provide	VERB
geusb-8357	8	27	proper	proper	ADJ
geusb-8357	8	28	citation	citation	NOUN
geusb-8357	8	29	of	of	ADP
geusb-8357	8	30	the	the	DET
geusb-8357	8	31	original	original	ADJ
geusb-8357	8	32	work	work	NOUN
geusb-8357	8	33	.	.	PUNCT
geusb-8357	9	1	author(s	author(s	NOUN
geusb-8357	9	2	)	)	PUNCT
geusb-8357	9	3	retain	retain	VERB
geusb-8357	9	4	copyright	copyright	NOUN
geusb-8357	9	5	.	.	PUNCT
geusb-8357	10	1	edited	edit	VERB
geusb-8357	10	2	by	by	ADP
geusb-8357	10	3	:	:	PUNCT
geusb-8357	10	4	hyojin	hyojin	PROPN
geusb-8357	10	5	kim	kim	PROPN
geusb-8357	10	6	(	(	PUNCT
geusb-8357	10	7	geus	geus	NOUN
geusb-8357	10	8	,	,	PUNCT
geusb-8357	10	9	denmark	denmark	PROPN
geusb-8357	10	10	)	)	PUNCT
geusb-8357	10	11	reviewed	review	VERB
geusb-8357	10	12	by	by	ADP
geusb-8357	10	13	:	:	PUNCT
geusb-8357	10	14	robin	robin	PROPN
geusb-8357	10	15	thibaut	thibaut	PROPN
geusb-8357	10	16	(	(	PUNCT
geusb-8357	10	17	ghent	ghent	PROPN
geusb-8357	10	18	university	university	PROPN
geusb-8357	10	19	,	,	PUNCT
geusb-8357	10	20	belgium	belgium	PROPN
geusb-8357	10	21	)	)	PUNCT
geusb-8357	10	22	and	and	CCONJ
geusb-8357	10	23	two	two	NUM
geusb-8357	10	24	anonymous	anonymous	ADJ
geusb-8357	10	25	reviewers	reviewer	NOUN
geusb-8357	10	26	funding	funding	NOUN
geusb-8357	10	27	:	:	PUNCT
geusb-8357	10	28	see	see	VERB
geusb-8357	10	29	page	page	NOUN
geusb-8357	10	30	6	6	NUM
geusb-8357	10	31	competing	compete	VERB
geusb-8357	10	32	interests	interest	NOUN
geusb-8357	10	33	:	:	PUNCT
geusb-8357	10	34	see	see	VERB
geusb-8357	10	35	page	page	NOUN
geusb-8357	10	36	6	6	NUM
geusb-8357	10	37	additional	additional	ADJ
geusb-8357	10	38	files	file	NOUN
geusb-8357	10	39	:	:	PUNCT
geusb-8357	10	40	see	see	VERB
geusb-8357	10	41	page	page	NOUN
geusb-8357	10	42	6	6	NUM
geusb-8357	10	43	introduction	introduction	NOUN
geusb-8357	10	44	groundwater	groundwater	NOUN
geusb-8357	10	45	models	model	NOUN
geusb-8357	10	46	informed	inform	VERB
geusb-8357	10	47	by	by	ADP
geusb-8357	10	48	data	datum	NOUN
geusb-8357	10	49	on	on	ADP
geusb-8357	10	50	geology	geology	NOUN
geusb-8357	10	51	and	and	CCONJ
geusb-8357	10	52	hydrological	hydrological	ADJ
geusb-8357	10	53	properties	property	NOUN
geusb-8357	10	54	of	of	ADP
geusb-8357	10	55	the	the	DET
geusb-8357	10	56	subsurface	subsurface	NOUN
geusb-8357	10	57	are	be	AUX
geusb-8357	10	58	important	important	ADJ
geusb-8357	10	59	tools	tool	NOUN
geusb-8357	10	60	in	in	ADP
geusb-8357	10	61	groundwater	groundwater	NOUN
geusb-8357	10	62	management	management	NOUN
geusb-8357	10	63	(	(	PUNCT
geusb-8357	10	64	gorelick	gorelick	NOUN
geusb-8357	10	65	1983	1983	NUM
geusb-8357	10	66	;	;	PUNCT
geusb-8357	10	67	pisinaras	pisinaras	PROPN
geusb-8357	10	68	et	et	PROPN
geusb-8357	10	69	al	al	PROPN
geusb-8357	10	70	.	.	PROPN
geusb-8357	10	71	2007	2007	NUM
geusb-8357	10	72	;	;	PUNCT
geusb-8357	10	73	hadded	hadde	VERB
geusb-8357	10	74	et	et	PROPN
geusb-8357	10	75	al	al	PROPN
geusb-8357	10	76	.	.	PROPN
geusb-8357	10	77	2013	2013	NUM
geusb-8357	10	78	)	)	PUNCT
geusb-8357	10	79	.	.	PUNCT
geusb-8357	11	1	these	these	DET
geusb-8357	11	2	models	model	NOUN
geusb-8357	11	3	simulate	simulate	VERB
geusb-8357	11	4	groundwater	groundwater	NOUN
geusb-8357	11	5	flow	flow	NOUN
geusb-8357	11	6	in	in	ADP
geusb-8357	11	7	the	the	DET
geusb-8357	11	8	subsurface	subsurface	NOUN
geusb-8357	11	9	and	and	CCONJ
geusb-8357	11	10	are	be	AUX
geusb-8357	11	11	used	use	VERB
geusb-8357	11	12	to	to	PART
geusb-8357	11	13	investigate	investigate	VERB
geusb-8357	11	14	the	the	DET
geusb-8357	11	15	environmental	environmental	ADJ
geusb-8357	11	16	effects	effect	NOUN
geusb-8357	11	17	of	of	ADP
geusb-8357	11	18	external	external	ADJ
geusb-8357	11	19	interventions	intervention	NOUN
geusb-8357	11	20	on	on	ADP
geusb-8357	11	21	the	the	DET
geusb-8357	11	22	groundwater	groundwater	NOUN
geusb-8357	11	23	system	system	NOUN
geusb-8357	11	24	,	,	PUNCT
geusb-8357	11	25	such	such	ADJ
geusb-8357	11	26	as	as	ADP
geusb-8357	11	27	the	the	DET
geusb-8357	11	28	establishment	establishment	NOUN
geusb-8357	11	29	of	of	ADP
geusb-8357	11	30	new	new	ADJ
geusb-8357	11	31	abstraction	abstraction	NOUN
geusb-8357	11	32	wells	well	NOUN
geusb-8357	11	33	.	.	PUNCT
geusb-8357	12	1	dahl	dahl	PROPN
geusb-8357	12	2	et	et	PROPN
geusb-8357	12	3	al	al	PROPN
geusb-8357	12	4	.	.	PROPN
geusb-8357	13	1	(	(	PUNCT
geusb-8357	13	2	2023	2023	NUM
geusb-8357	13	3	)	)	PUNCT
geusb-8357	13	4	provide	provide	VERB
geusb-8357	13	5	an	an	DET
geusb-8357	13	6	approach	approach	NOUN
geusb-8357	13	7	for	for	ADP
geusb-8357	13	8	training	train	VERB
geusb-8357	13	9	a	a	DET
geusb-8357	13	10	neural	neural	ADJ
geusb-8357	13	11	network	network	NOUN
geusb-8357	13	12	to	to	PART
geusb-8357	13	13	replicate	replicate	VERB
geusb-8357	13	14	the	the	DET
geusb-8357	13	15	results	result	NOUN
geusb-8357	13	16	of	of	ADP
geusb-8357	13	17	drawdown	drawdown	NOUN
geusb-8357	13	18	from	from	ADP
geusb-8357	13	19	an	an	DET
geusb-8357	13	20	abstraction	abstraction	NOUN
geusb-8357	13	21	well	well	ADV
geusb-8357	13	22	as	as	SCONJ
geusb-8357	13	23	simulated	simulate	VERB
geusb-8357	13	24	in	in	ADP
geusb-8357	13	25	a	a	DET
geusb-8357	13	26	modflow	modflow	ADJ
geusb-8357	13	27	model	model	NOUN
geusb-8357	13	28	(	(	PUNCT
geusb-8357	13	29	harbaugh	harbaugh	PROPN
geusb-8357	13	30	2005	2005	NUM
geusb-8357	13	31	)	)	PUNCT
geusb-8357	13	32	with	with	ADP
geusb-8357	13	33	a	a	DET
geusb-8357	13	34	probabilistic	probabilistic	ADJ
geusb-8357	13	35	output	output	NOUN
geusb-8357	13	36	.	.	PUNCT
geusb-8357	14	1	the	the	DET
geusb-8357	14	2	network	network	NOUN
geusb-8357	14	3	is	be	AUX
geusb-8357	14	4	trained	train	VERB
geusb-8357	14	5	to	to	PART
geusb-8357	14	6	model	model	VERB
geusb-8357	14	7	the	the	DET
geusb-8357	14	8	mapping	mapping	NOUN
geusb-8357	14	9	between	between	ADP
geusb-8357	14	10	a	a	DET
geusb-8357	14	11	few	few	ADJ
geusb-8357	14	12	influential	influential	ADJ
geusb-8357	14	13	model	model	NOUN
geusb-8357	14	14	attributes	attribute	NOUN
geusb-8357	14	15	and	and	CCONJ
geusb-8357	14	16	simulated	simulate	VERB
geusb-8357	14	17	drawdown	drawdown	NOUN
geusb-8357	14	18	.	.	PUNCT
geusb-8357	15	1	once	once	ADV
geusb-8357	15	2	trained	train	VERB
geusb-8357	15	3	,	,	PUNCT
geusb-8357	15	4	the	the	DET
geusb-8357	15	5	network	network	NOUN
geusb-8357	15	6	carries	carry	VERB
geusb-8357	15	7	out	out	ADP
geusb-8357	15	8	this	this	DET
geusb-8357	15	9	mapping	mapping	NOUN
geusb-8357	15	10	at	at	ADP
geusb-8357	15	11	a	a	DET
geusb-8357	15	12	speed	speed	NOUN
geusb-8357	15	13	exceeding	exceed	VERB
geusb-8357	15	14	100	100	NUM
geusb-8357	15	15	times	time	NOUN
geusb-8357	15	16	that	that	PRON
geusb-8357	15	17	of	of	ADP
geusb-8357	15	18	conventional	conventional	ADJ
geusb-8357	15	19	modflow	modflow	ADJ
geusb-8357	15	20	simulations	simulation	NOUN
geusb-8357	15	21	.	.	PUNCT
geusb-8357	16	1	dahl	dahl	PROPN
geusb-8357	16	2	et	et	PROPN
geusb-8357	16	3	al	al	PROPN
geusb-8357	16	4	.	.	PROPN
geusb-8357	17	1	(	(	PUNCT
geusb-8357	17	2	2023	2023	NUM
geusb-8357	17	3	)	)	PUNCT
geusb-8357	17	4	make	make	VERB
geusb-8357	17	5	use	use	NOUN
geusb-8357	17	6	of	of	ADP
geusb-8357	17	7	a	a	DET
geusb-8357	17	8	synthetic	synthetic	ADJ
geusb-8357	17	9	groundwater	groundwater	NOUN
geusb-8357	17	10	model	model	NOUN
geusb-8357	17	11	and	and	CCONJ
geusb-8357	17	12	limit	limit	VERB
geusb-8357	17	13	the	the	DET
geusb-8357	17	14	drawdown	drawdown	NOUN
geusb-8357	17	15	predictions	prediction	NOUN
geusb-8357	17	16	to	to	ADP
geusb-8357	17	17	the	the	DET
geusb-8357	17	18	layer	layer	NOUN
geusb-8357	17	19	of	of	ADP
geusb-8357	17	20	pumping	pumping	NOUN
geusb-8357	17	21	.	.	PUNCT
geusb-8357	18	1	here	here	ADV
geusb-8357	18	2	,	,	PUNCT
geusb-8357	18	3	we	we	PRON
geusb-8357	18	4	extend	extend	VERB
geusb-8357	18	5	the	the	DET
geusb-8357	18	6	method	method	NOUN
geusb-8357	18	7	presented	present	VERB
geusb-8357	18	8	in	in	ADP
geusb-8357	18	9	dahl	dahl	PROPN
geusb-8357	18	10	et	et	PROPN
geusb-8357	18	11	al	al	PROPN
geusb-8357	18	12	.	.	PROPN
geusb-8357	19	1	(	(	PUNCT
geusb-8357	19	2	2023	2023	NUM
geusb-8357	19	3	)	)	PUNCT
geusb-8357	20	1	to	to	PART
geusb-8357	20	2	allow	allow	VERB
geusb-8357	20	3	abstraction	abstraction	NOUN
geusb-8357	20	4	from	from	ADP
geusb-8357	20	5	multiple	multiple	ADJ
geusb-8357	20	6	suitable	suitable	ADJ
geusb-8357	20	7	model	model	NOUN
geusb-8357	20	8	layers	layer	NOUN
geusb-8357	20	9	and	and	CCONJ
geusb-8357	20	10	predict	predict	VERB
geusb-8357	20	11	drawdown	drawdown	NOUN
geusb-8357	20	12	in	in	ADP
geusb-8357	20	13	a	a	DET
geusb-8357	20	14	full	full	ADJ
geusb-8357	20	15	3d	3d	NOUN
geusb-8357	20	16	real	real	ADJ
geusb-8357	20	17	field	field	NOUN
geusb-8357	20	18	-	-	PUNCT
geusb-8357	20	19	based	base	VERB
geusb-8357	20	20	groundwater	groundwater	NOUN
geusb-8357	20	21	model	model	NOUN
geusb-8357	20	22	of	of	ADP
geusb-8357	20	23	the	the	DET
geusb-8357	20	24	egebjerg	egebjerg	PROPN
geusb-8357	20	25	catchment	catchment	NOUN
geusb-8357	20	26	,	,	PUNCT
geusb-8357	20	27	east	east	PROPN
geusb-8357	20	28	jylland	jylland	NOUN
geusb-8357	20	29	,	,	PUNCT
geusb-8357	20	30	denmark	denmark	NOUN
geusb-8357	20	31	(	(	PUNCT
geusb-8357	20	32	approx	approx	PROPN
geusb-8357	20	33	.	.	PUNCT
geusb-8357	20	34	55.9748	55.9748	NUM
geusb-8357	20	35	°	°	PROPN
geusb-8357	20	36	n	n	CCONJ
geusb-8357	20	37	,	,	PUNCT
geusb-8357	20	38	9.8129	9.8129	NUM
geusb-8357	20	39	°	°	PRON
geusb-8357	20	40	e	e	NOUN
geusb-8357	20	41	to	to	ADP
geusb-8357	20	42	55.8581	55.8581	NUM
geusb-8357	20	43	°	°	NOUN
geusb-8357	20	44	n	n	CCONJ
geusb-8357	20	45	,	,	PUNCT
geusb-8357	20	46	9.9645	9.9645	NUM
geusb-8357	20	47	°	°	NUM
geusb-8357	20	48	e	e	NOUN
geusb-8357	20	49	)	)	PUNCT
geusb-8357	21	1	.	.	PUNCT
geusb-8357	22	1	predictions	prediction	NOUN
geusb-8357	22	2	from	from	ADP
geusb-8357	22	3	the	the	DET
geusb-8357	22	4	neural	neural	ADJ
geusb-8357	22	5	network	network	NOUN
geusb-8357	22	6	are	be	AUX
geusb-8357	22	7	compared	compare	VERB
geusb-8357	22	8	with	with	ADP
geusb-8357	22	9	modflow	modflow	ADJ
geusb-8357	22	10	simulation	simulation	NOUN
geusb-8357	22	11	results	result	NOUN
geusb-8357	22	12	,	,	PUNCT
geusb-8357	22	13	and	and	CCONJ
geusb-8357	22	14	we	we	PRON
geusb-8357	22	15	discuss	discuss	VERB
geusb-8357	22	16	the	the	DET
geusb-8357	22	17	generalisation	generalisation	NOUN
geusb-8357	22	18	potential	potential	NOUN
geusb-8357	22	19	of	of	ADP
geusb-8357	22	20	the	the	DET
geusb-8357	22	21	tested	test	VERB
geusb-8357	22	22	method	method	NOUN
geusb-8357	22	23	.	.	PUNCT
geusb-8357	23	1	materials	material	NOUN
geusb-8357	23	2	and	and	CCONJ
geusb-8357	23	3	methods	method	NOUN
geusb-8357	23	4	the	the	DET
geusb-8357	23	5	methodology	methodology	NOUN
geusb-8357	23	6	of	of	ADP
geusb-8357	23	7	this	this	DET
geusb-8357	23	8	paper	paper	NOUN
geusb-8357	23	9	is	be	AUX
geusb-8357	23	10	based	base	VERB
geusb-8357	23	11	on	on	ADP
geusb-8357	23	12	that	that	PRON
geusb-8357	23	13	presented	present	VERB
geusb-8357	23	14	by	by	ADP
geusb-8357	23	15	dahl	dahl	PROPN
geusb-8357	23	16	et	et	PROPN
geusb-8357	23	17	al	al	PROPN
geusb-8357	23	18	.	.	PROPN
geusb-8357	23	19	(	(	PUNCT
geusb-8357	23	20	2023	2023	NUM
geusb-8357	23	21	)	)	PUNCT
geusb-8357	23	22	.	.	PUNCT
geusb-8357	24	1	our	our	PRON
geusb-8357	24	2	aim	aim	NOUN
geusb-8357	24	3	is	be	AUX
geusb-8357	24	4	to	to	PART
geusb-8357	24	5	generalise	generalise	VERB
geusb-8357	24	6	and	and	CCONJ
geusb-8357	24	7	test	test	VERB
geusb-8357	24	8	the	the	DET
geusb-8357	24	9	method	method	NOUN
geusb-8357	24	10	on	on	ADP
geusb-8357	24	11	a	a	DET
geusb-8357	24	12	danish	danish	ADJ
geusb-8357	24	13	groundwater	groundwater	NOUN
geusb-8357	24	14	model	model	NOUN
geusb-8357	24	15	case	case	NOUN
geusb-8357	24	16	and	and	CCONJ
geusb-8357	24	17	explore	explore	VERB
geusb-8357	24	18	its	its	PRON
geusb-8357	24	19	implications	implication	NOUN
geusb-8357	24	20	for	for	ADP
geusb-8357	24	21	groundwater	groundwater	NOUN
geusb-8357	24	22	management	management	NOUN
geusb-8357	24	23	.	.	PUNCT
geusb-8357	25	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	25	2	https://orcid.org/0000-0003-4816-0241	https://orcid.org/0000-0003-4816-0241	PROPN
geusb-8357	25	3	https://orcid.org/0000-0002-6399-8563	https://orcid.org/0000-0002-6399-8563	PROPN
geusb-8357	25	4	https://orcid.org/0000-0002-3881-2739	https://orcid.org/0000-0002-3881-2739	PROPN
geusb-8357	25	5	https://orcid.org/0000-0003-4529-0112	https://orcid.org/0000-0003-4529-0112	PROPN
geusb-8357	25	6	mailto:dahl@geo.au.dk	mailto:dahl@geo.au.dk	PROPN
geusb-8357	25	7	https://creativecommons.org/licenses/by/4.0/deed.ast	https://creativecommons.org/licenses/by/4.0/deed.ast	VERB
geusb-8357	25	8	dahl	dahl	PROPN
geusb-8357	25	9	et	et	PROPN
geusb-8357	25	10	al	al	PROPN
geusb-8357	25	11	.	.	PROPN
geusb-8357	25	12	2023	2023	NUM
geusb-8357	25	13	:	:	PUNCT
geusb-8357	25	14	geus	geus	NOUN
geusb-8357	25	15	bulletin	bulletin	NOUN
geusb-8357	25	16	53	53	NUM
geusb-8357	25	17	.	.	PUNCT
geusb-8357	25	18	8357	8357	NUM
geusb-8357	25	19	.	.	PUNCT
geusb-8357	26	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	26	2	2	2	NUM
geusb-8357	26	3	of	of	ADP
geusb-8357	26	4	7	7	NUM
geusb-8357	26	5	www.geusbul	www.geusbul	NOUN
geusb-8357	26	6	let	let	VERB
geusb-8357	26	7	in.org	in.org	ADJ
geusb-8357	26	8	the	the	DET
geusb-8357	26	9	core	core	ADJ
geusb-8357	26	10	idea	idea	NOUN
geusb-8357	26	11	is	be	AUX
geusb-8357	26	12	to	to	PART
geusb-8357	26	13	train	train	VERB
geusb-8357	26	14	a	a	DET
geusb-8357	26	15	neural	neural	ADJ
geusb-8357	26	16	network	network	NOUN
geusb-8357	26	17	to	to	PART
geusb-8357	26	18	predict	predict	VERB
geusb-8357	26	19	drawdown	drawdown	NOUN
geusb-8357	26	20	from	from	ADP
geusb-8357	26	21	groundwater	groundwater	NOUN
geusb-8357	26	22	abstraction	abstraction	NOUN
geusb-8357	26	23	using	use	VERB
geusb-8357	26	24	results	result	NOUN
geusb-8357	26	25	and	and	CCONJ
geusb-8357	26	26	features	feature	NOUN
geusb-8357	26	27	from	from	ADP
geusb-8357	26	28	a	a	DET
geusb-8357	26	29	numerical	numerical	ADJ
geusb-8357	26	30	groundwater	groundwater	NOUN
geusb-8357	26	31	model	model	NOUN
geusb-8357	26	32	to	to	PART
geusb-8357	26	33	significantly	significantly	ADV
geusb-8357	26	34	reduce	reduce	VERB
geusb-8357	26	35	computation	computation	NOUN
geusb-8357	26	36	times	time	NOUN
geusb-8357	26	37	.	.	PUNCT
geusb-8357	27	1	whilst	whilst	SCONJ
geusb-8357	27	2	the	the	DET
geusb-8357	27	3	method	method	NOUN
geusb-8357	27	4	can	can	AUX
geusb-8357	27	5	generally	generally	ADV
geusb-8357	27	6	be	be	AUX
geusb-8357	27	7	applied	apply	VERB
geusb-8357	27	8	to	to	ADP
geusb-8357	27	9	most	most	ADJ
geusb-8357	27	10	groundwater	groundwater	NOUN
geusb-8357	27	11	models	model	NOUN
geusb-8357	27	12	,	,	PUNCT
geusb-8357	27	13	a	a	DET
geusb-8357	27	14	few	few	ADJ
geusb-8357	27	15	site	site	NOUN
geusb-8357	27	16	-	-	PUNCT
geusb-8357	27	17	specific	specific	ADJ
geusb-8357	27	18	decisions	decision	NOUN
geusb-8357	27	19	need	need	VERB
geusb-8357	27	20	to	to	PART
geusb-8357	27	21	be	be	AUX
geusb-8357	27	22	made	make	VERB
geusb-8357	27	23	.	.	PUNCT
geusb-8357	28	1	our	our	PRON
geusb-8357	28	2	objective	objective	NOUN
geusb-8357	28	3	is	be	AUX
geusb-8357	28	4	to	to	PART
geusb-8357	28	5	make	make	VERB
geusb-8357	28	6	minimal	minimal	ADJ
geusb-8357	28	7	changes	change	NOUN
geusb-8357	28	8	to	to	ADP
geusb-8357	28	9	the	the	DET
geusb-8357	28	10	method	method	NOUN
geusb-8357	28	11	to	to	PART
geusb-8357	28	12	accommodate	accommodate	VERB
geusb-8357	28	13	the	the	DET
geusb-8357	28	14	new	new	ADJ
geusb-8357	28	15	groundwater	groundwater	NOUN
geusb-8357	28	16	model	model	NOUN
geusb-8357	28	17	.	.	PUNCT
geusb-8357	29	1	we	we	PRON
geusb-8357	29	2	also	also	ADV
geusb-8357	29	3	aim	aim	VERB
geusb-8357	29	4	to	to	PART
geusb-8357	29	5	expand	expand	VERB
geusb-8357	29	6	the	the	DET
geusb-8357	29	7	method	method	NOUN
geusb-8357	29	8	’s	’s	PART
geusb-8357	29	9	capabilities	capability	NOUN
geusb-8357	29	10	from	from	ADP
geusb-8357	29	11	2d	2d	NUM
geusb-8357	29	12	to	to	ADP
geusb-8357	29	13	a	a	DET
geusb-8357	29	14	full	full	ADJ
geusb-8357	29	15	3d	3d	NOUN
geusb-8357	29	16	model	model	NOUN
geusb-8357	29	17	by	by	ADP
geusb-8357	29	18	training	train	VERB
geusb-8357	29	19	the	the	DET
geusb-8357	29	20	neural	neural	ADJ
geusb-8357	29	21	network	network	NOUN
geusb-8357	29	22	with	with	ADP
geusb-8357	29	23	simulated	simulate	VERB
geusb-8357	29	24	drawdown	drawdown	NOUN
geusb-8357	29	25	data	datum	NOUN
geusb-8357	29	26	from	from	ADP
geusb-8357	29	27	all	all	DET
geusb-8357	29	28	model	model	NOUN
geusb-8357	29	29	layers	layer	NOUN
geusb-8357	29	30	,	,	PUNCT
geusb-8357	29	31	which	which	PRON
geusb-8357	29	32	come	come	VERB
geusb-8357	29	33	from	from	ADP
geusb-8357	29	34	wells	well	NOUN
geusb-8357	29	35	situated	situate	VERB
geusb-8357	29	36	in	in	ADP
geusb-8357	29	37	various	various	ADJ
geusb-8357	29	38	aquifers	aquifer	NOUN
geusb-8357	29	39	.	.	PUNCT
geusb-8357	30	1	all	all	DET
geusb-8357	30	2	the	the	DET
geusb-8357	30	3	work	work	NOUN
geusb-8357	30	4	is	be	AUX
geusb-8357	30	5	performed	perform	VERB
geusb-8357	30	6	on	on	ADP
geusb-8357	30	7	a	a	DET
geusb-8357	30	8	computer	computer	NOUN
geusb-8357	30	9	with	with	ADP
geusb-8357	30	10	an	an	DET
geusb-8357	30	11	13th	13th	ADJ
geusb-8357	30	12	gen	gen	PROPN
geusb-8357	30	13	intel	intel	PROPN
geusb-8357	30	14	core	core	NOUN
geusb-8357	30	15	i9	i9	NOUN
geusb-8357	30	16	3.00	3.00	NUM
geusb-8357	30	17	ghz	ghz	NOUN
geusb-8357	30	18	processor	processor	NOUN
geusb-8357	30	19	and	and	CCONJ
geusb-8357	30	20	128	128	NUM
geusb-8357	30	21	gb	gb	NOUN
geusb-8357	30	22	ram	ram	NOUN
geusb-8357	30	23	.	.	PUNCT
geusb-8357	31	1	egebjerg	egebjerg	PROPN
geusb-8357	31	2	groundwater	groundwater	NOUN
geusb-8357	31	3	model	model	NOUN
geusb-8357	31	4	for	for	ADP
geusb-8357	31	5	the	the	DET
geusb-8357	31	6	egebjerg	egebjerg	PROPN
geusb-8357	31	7	catchment	catchment	NOUN
geusb-8357	31	8	,	,	PUNCT
geusb-8357	31	9	a	a	DET
geusb-8357	31	10	groundwater	groundwater	NOUN
geusb-8357	31	11	flow	flow	NOUN
geusb-8357	31	12	model	model	NOUN
geusb-8357	31	13	has	have	AUX
geusb-8357	31	14	been	be	AUX
geusb-8357	31	15	developed	develop	VERB
geusb-8357	31	16	using	use	VERB
geusb-8357	31	17	modflow	modflow	NOUN
geusb-8357	31	18	6	6	NUM
geusb-8357	31	19	(	(	PUNCT
geusb-8357	31	20	langevin	langevin	PROPN
geusb-8357	31	21	et	et	PROPN
geusb-8357	31	22	 	 	SPACE
geusb-8357	31	23	al	al	PROPN
geusb-8357	31	24	.	.	PROPN
geusb-8357	31	25	2017	2017	NUM
geusb-8357	31	26	,	,	PUNCT
geusb-8357	31	27	2019	2019	NUM
geusb-8357	31	28	)	)	PUNCT
geusb-8357	31	29	.	.	PUNCT
geusb-8357	32	1	this	this	DET
geusb-8357	32	2	model	model	NOUN
geusb-8357	32	3	employs	employ	VERB
geusb-8357	32	4	a	a	DET
geusb-8357	32	5	grid	grid	NOUN
geusb-8357	32	6	size	size	NOUN
geusb-8357	32	7	of	of	ADP
geusb-8357	32	8	144	144	NUM
geusb-8357	32	9	×	×	NOUN
geusb-8357	32	10	121	121	NUM
geusb-8357	32	11	with	with	ADP
geusb-8357	32	12	a	a	DET
geusb-8357	32	13	100	100	NUM
geusb-8357	32	14	×	×	NOUN
geusb-8357	32	15	100	100	NUM
geusb-8357	32	16	m	m	NOUN
geusb-8357	32	17	discretisation	discretisation	NOUN
geusb-8357	32	18	across	across	ADP
geusb-8357	32	19	its	its	PRON
geusb-8357	32	20	14	14	NUM
geusb-8357	32	21	layers	layer	NOUN
geusb-8357	32	22	of	of	ADP
geusb-8357	32	23	varying	vary	VERB
geusb-8357	32	24	thicknesses	thickness	NOUN
geusb-8357	32	25	(	(	PUNCT
geusb-8357	32	26	enemark	enemark	PROPN
geusb-8357	32	27	et	et	PROPN
geusb-8357	32	28	al	al	PROPN
geusb-8357	32	29	.	.	PROPN
geusb-8357	32	30	2022	2022	NUM
geusb-8357	32	31	)	)	PUNCT
geusb-8357	32	32	.	.	PUNCT
geusb-8357	33	1	boundary	boundary	ADJ
geusb-8357	33	2	conditions	condition	NOUN
geusb-8357	33	3	are	be	AUX
geusb-8357	33	4	modelled	model	VERB
geusb-8357	33	5	using	use	VERB
geusb-8357	33	6	the	the	DET
geusb-8357	33	7	general	general	ADJ
geusb-8357	33	8	head	head	NOUN
geusb-8357	33	9	boundary	boundary	ADJ
geusb-8357	33	10	package	package	NOUN
geusb-8357	33	11	(	(	PUNCT
geusb-8357	33	12	ghb	ghb	NOUN
geusb-8357	33	13	)	)	PUNCT
geusb-8357	33	14	for	for	ADP
geusb-8357	33	15	lakes	lake	NOUN
geusb-8357	33	16	and	and	CCONJ
geusb-8357	33	17	coastal	coastal	ADJ
geusb-8357	33	18	areas	area	NOUN
geusb-8357	33	19	,	,	PUNCT
geusb-8357	33	20	the	the	DET
geusb-8357	33	21	recharge	recharge	NOUN
geusb-8357	33	22	package	package	NOUN
geusb-8357	33	23	(	(	PUNCT
geusb-8357	33	24	rch	rch	NOUN
geusb-8357	33	25	)	)	PUNCT
geusb-8357	33	26	for	for	ADP
geusb-8357	33	27	the	the	DET
geusb-8357	33	28	simulation	simulation	NOUN
geusb-8357	33	29	of	of	ADP
geusb-8357	33	30	groundwater	groundwater	NOUN
geusb-8357	33	31	recharge	recharge	NOUN
geusb-8357	33	32	in	in	ADP
geusb-8357	33	33	the	the	DET
geusb-8357	33	34	top	top	ADJ
geusb-8357	33	35	active	active	ADJ
geusb-8357	33	36	cells	cell	NOUN
geusb-8357	33	37	,	,	PUNCT
geusb-8357	33	38	the	the	DET
geusb-8357	33	39	drain	drain	NOUN
geusb-8357	33	40	package	package	NOUN
geusb-8357	33	41	(	(	PUNCT
geusb-8357	33	42	drn	drn	NOUN
geusb-8357	33	43	)	)	PUNCT
geusb-8357	33	44	for	for	ADP
geusb-8357	33	45	simulating	simulate	VERB
geusb-8357	33	46	stream	stream	NOUN
geusb-8357	33	47	flow	flow	NOUN
geusb-8357	33	48	(	(	PUNCT
geusb-8357	33	49	drn	drn	NOUN
geusb-8357	33	50	-	-	PUNCT
geusb-8357	33	51	riv	riv	NOUN
geusb-8357	33	52	in	in	ADP
geusb-8357	33	53	fig	fig	NOUN
geusb-8357	33	54	.	.	PUNCT
geusb-8357	34	1	1a	1a	NOUN
geusb-8357	34	2	)	)	PUNCT
geusb-8357	35	1	and	and	CCONJ
geusb-8357	35	2	drainage	drainage	NOUN
geusb-8357	35	3	in	in	ADP
geusb-8357	35	4	all	all	DET
geusb-8357	35	5	top	top	ADJ
geusb-8357	35	6	active	active	ADJ
geusb-8357	35	7	cells	cell	NOUN
geusb-8357	35	8	and	and	CCONJ
geusb-8357	35	9	the	the	DET
geusb-8357	35	10	well	well	ADJ
geusb-8357	35	11	package	package	NOUN
geusb-8357	35	12	(	(	PUNCT
geusb-8357	35	13	wel	wel	PROPN
geusb-8357	35	14	)	)	PUNCT
geusb-8357	35	15	for	for	ADP
geusb-8357	35	16	simulating	simulate	VERB
geusb-8357	35	17	groundwater	groundwater	NOUN
geusb-8357	35	18	abstraction	abstraction	NOUN
geusb-8357	35	19	in	in	ADP
geusb-8357	35	20	active	active	ADJ
geusb-8357	35	21	wells	well	NOUN
geusb-8357	35	22	.	.	PUNCT
geusb-8357	36	1	topography	topography	NOUN
geusb-8357	36	2	and	and	CCONJ
geusb-8357	36	3	the	the	DET
geusb-8357	36	4	location	location	NOUN
geusb-8357	36	5	of	of	ADP
geusb-8357	36	6	the	the	DET
geusb-8357	36	7	boundary	boundary	ADJ
geusb-8357	36	8	conditions	condition	NOUN
geusb-8357	36	9	in	in	ADP
geusb-8357	36	10	the	the	DET
geusb-8357	36	11	model	model	NOUN
geusb-8357	36	12	are	be	AUX
geusb-8357	36	13	visualised	visualise	VERB
geusb-8357	36	14	in	in	ADP
geusb-8357	36	15	fig	fig	NOUN
geusb-8357	36	16	.	.	PUNCT
geusb-8357	37	1	1a	1a	X
geusb-8357	37	2	.	.	PUNCT
geusb-8357	38	1	the	the	DET
geusb-8357	38	2	geological	geological	ADJ
geusb-8357	38	3	layers	layer	NOUN
geusb-8357	38	4	alternate	alternate	VERB
geusb-8357	38	5	between	between	ADP
geusb-8357	38	6	sand	sand	NOUN
geusb-8357	38	7	and	and	CCONJ
geusb-8357	38	8	clay	clay	NOUN
geusb-8357	38	9	deposits	deposit	NOUN
geusb-8357	38	10	with	with	ADP
geusb-8357	38	11	a	a	DET
geusb-8357	38	12	thick	thick	ADJ
geusb-8357	38	13	chalk	chalk	NOUN
geusb-8357	38	14	aquifer	aquifer	NOUN
geusb-8357	38	15	beneath	beneath	ADP
geusb-8357	38	16	.	.	PUNCT
geusb-8357	39	1	the	the	DET
geusb-8357	39	2	downwards	downwards	ADJ
geusb-8357	39	3	extent	extent	NOUN
geusb-8357	39	4	of	of	ADP
geusb-8357	39	5	the	the	DET
geusb-8357	39	6	chalk	chalk	NOUN
geusb-8357	39	7	layer	layer	NOUN
geusb-8357	39	8	is	be	AUX
geusb-8357	39	9	unknown	unknown	ADJ
geusb-8357	39	10	but	but	CCONJ
geusb-8357	39	11	is	be	AUX
geusb-8357	39	12	assumed	assume	VERB
geusb-8357	39	13	to	to	PART
geusb-8357	39	14	be	be	AUX
geusb-8357	39	15	–	–	PUNCT
geusb-8357	39	16	550	550	NUM
geusb-8357	39	17	m	m	NOUN
geusb-8357	39	18	below	below	ADP
geusb-8357	39	19	sea	sea	NOUN
geusb-8357	39	20	level	level	NOUN
geusb-8357	39	21	.	.	PUNCT
geusb-8357	40	1	to	to	PART
geusb-8357	40	2	simulate	simulate	VERB
geusb-8357	40	3	layers	layer	NOUN
geusb-8357	40	4	that	that	PRON
geusb-8357	40	5	pinch	pinch	VERB
geusb-8357	40	6	out	out	ADP
geusb-8357	40	7	,	,	PUNCT
geusb-8357	40	8	the	the	DET
geusb-8357	40	9	vertical	vertical	ADJ
geusb-8357	40	10	pass	pass	VERB
geusb-8357	40	11	-	-	PUNCT
geusb-8357	40	12	through	through	ADP
geusb-8357	40	13	option	option	NOUN
geusb-8357	40	14	in	in	ADP
geusb-8357	40	15	modflow	modflow	ADJ
geusb-8357	40	16	6	6	NUM
geusb-8357	40	17	is	be	AUX
geusb-8357	40	18	used	use	VERB
geusb-8357	40	19	.	.	PUNCT
geusb-8357	41	1	this	this	DET
geusb-8357	41	2	option	option	NOUN
geusb-8357	41	3	is	be	AUX
geusb-8357	41	4	applied	apply	VERB
geusb-8357	41	5	for	for	ADP
geusb-8357	41	6	cells	cell	NOUN
geusb-8357	41	7	with	with	ADP
geusb-8357	41	8	a	a	DET
geusb-8357	41	9	thickness	thickness	NOUN
geusb-8357	41	10	of	of	ADP
geusb-8357	41	11	<	<	PROPN
geusb-8357	41	12	0.5	0.5	NUM
geusb-8357	41	13	m	m	NOUN
geusb-8357	41	14	where	where	SCONJ
geusb-8357	41	15	flow	flow	NOUN
geusb-8357	41	16	is	be	AUX
geusb-8357	41	17	distributed	distribute	VERB
geusb-8357	41	18	downwards	downward	NOUN
geusb-8357	41	19	to	to	ADP
geusb-8357	41	20	the	the	DET
geusb-8357	41	21	subsequent	subsequent	ADJ
geusb-8357	41	22	active	active	ADJ
geusb-8357	41	23	cell	cell	NOUN
geusb-8357	41	24	.	.	PUNCT
geusb-8357	42	1	data	data	NOUN
geusb-8357	42	2	-	-	PUNCT
geusb-8357	42	3	set	set	VERB
geusb-8357	42	4	construction	construction	NOUN
geusb-8357	42	5	the	the	DET
geusb-8357	42	6	data	datum	NOUN
geusb-8357	42	7	set	set	VERB
geusb-8357	42	8	for	for	ADP
geusb-8357	42	9	training	train	VERB
geusb-8357	42	10	the	the	DET
geusb-8357	42	11	neural	neural	ADJ
geusb-8357	42	12	network	network	NOUN
geusb-8357	42	13	consists	consist	VERB
geusb-8357	42	14	of	of	ADP
geusb-8357	42	15	sets	set	NOUN
geusb-8357	42	16	of	of	ADP
geusb-8357	42	17	target	target	NOUN
geusb-8357	42	18	data	datum	NOUN
geusb-8357	42	19	y	y	PROPN
geusb-8357	42	20	and	and	CCONJ
geusb-8357	42	21	input	input	NOUN
geusb-8357	42	22	features	feature	NOUN
geusb-8357	42	23	stored	store	VERB
geusb-8357	42	24	in	in	ADP
geusb-8357	42	25	a	a	DET
geusb-8357	42	26	vector	vector	NOUN
geusb-8357	42	27	x.	x.	NOUN
geusb-8357	42	28	features	feature	NOUN
geusb-8357	42	29	are	be	AUX
geusb-8357	42	30	related	relate	VERB
geusb-8357	42	31	to	to	ADP
geusb-8357	42	32	targets	target	NOUN
geusb-8357	42	33	with	with	ADP
geusb-8357	42	34	an	an	DET
geusb-8357	42	35	unknown	unknown	ADJ
geusb-8357	42	36	function	function	NOUN
geusb-8357	42	37	f	f	NOUN
geusb-8357	42	38	,	,	PUNCT
geusb-8357	42	39	such	such	ADJ
geusb-8357	42	40	that	that	SCONJ
geusb-8357	42	41	:	:	PUNCT
geusb-8357	42	42	y	y	X
geusb-8357	42	43	 	 	SPACE
geusb-8357	42	44	=	=	NOUN
geusb-8357	42	45	 	 	SPACE
geusb-8357	42	46	f	f	PROPN
geusb-8357	42	47	(	(	PUNCT
geusb-8357	42	48	x	x	NOUN
geusb-8357	42	49	)	)	PUNCT
geusb-8357	42	50	,	,	PUNCT
geusb-8357	42	51	(	(	PUNCT
geusb-8357	42	52	1	1	X
geusb-8357	42	53	)	)	PUNCT
geusb-8357	42	54	where	where	SCONJ
geusb-8357	42	55	f	f	PROPN
geusb-8357	42	56	is	be	AUX
geusb-8357	42	57	determined	determine	VERB
geusb-8357	42	58	through	through	ADP
geusb-8357	42	59	training	training	NOUN
geusb-8357	42	60	of	of	ADP
geusb-8357	42	61	the	the	DET
geusb-8357	42	62	neural	neural	ADJ
geusb-8357	42	63	network	network	NOUN
geusb-8357	42	64	(	(	PUNCT
geusb-8357	42	65	gardner	gardner	NOUN
geusb-8357	42	66	&	&	CCONJ
geusb-8357	42	67	dorling	dorling	PROPN
geusb-8357	42	68	1998	1998	NUM
geusb-8357	42	69	)	)	PUNCT
geusb-8357	42	70	.	.	PUNCT
geusb-8357	43	1	here	here	ADV
geusb-8357	43	2	,	,	PUNCT
geusb-8357	43	3	the	the	DET
geusb-8357	43	4	target	target	NOUN
geusb-8357	43	5	data	data	VERB
geusb-8357	43	6	y	y	PROPN
geusb-8357	43	7	in	in	ADP
geusb-8357	43	8	eq	eq	ADP
geusb-8357	43	9	.	.	PUNCT
geusb-8357	44	1	(	(	PUNCT
geusb-8357	44	2	1	1	X
geusb-8357	44	3	)	)	PUNCT
geusb-8357	44	4	is	be	AUX
geusb-8357	44	5	the	the	DET
geusb-8357	44	6	simulated	simulate	VERB
geusb-8357	44	7	drawdown	drawdown	NOUN
geusb-8357	44	8	caused	cause	VERB
geusb-8357	44	9	by	by	ADP
geusb-8357	44	10	groundwater	groundwater	NOUN
geusb-8357	44	11	abstraction	abstraction	NOUN
geusb-8357	44	12	in	in	ADP
geusb-8357	44	13	the	the	DET
geusb-8357	44	14	model	model	NOUN
geusb-8357	44	15	.	.	PUNCT
geusb-8357	45	1	the	the	DET
geusb-8357	45	2	training	training	NOUN
geusb-8357	45	3	data	datum	NOUN
geusb-8357	45	4	set	set	VERB
geusb-8357	45	5	is	be	AUX
geusb-8357	45	6	created	create	VERB
geusb-8357	45	7	by	by	ADP
geusb-8357	45	8	conducting	conduct	VERB
geusb-8357	45	9	1000	1000	NUM
geusb-8357	45	10	simulations	simulation	NOUN
geusb-8357	45	11	.	.	PUNCT
geusb-8357	46	1	in	in	ADP
geusb-8357	46	2	each	each	DET
geusb-8357	46	3	simulation	simulation	NOUN
geusb-8357	46	4	,	,	PUNCT
geusb-8357	46	5	a	a	DET
geusb-8357	46	6	new	new	ADJ
geusb-8357	46	7	well	well	NOUN
geusb-8357	46	8	is	be	AUX
geusb-8357	46	9	introduced	introduce	VERB
geusb-8357	46	10	into	into	ADP
geusb-8357	46	11	the	the	DET
geusb-8357	46	12	groundwater	groundwater	NOUN
geusb-8357	46	13	model	model	NOUN
geusb-8357	46	14	.	.	PUNCT
geusb-8357	47	1	the	the	DET
geusb-8357	47	2	well	well	PROPN
geusb-8357	47	3	’s	’s	PART
geusb-8357	47	4	location	location	NOUN
geusb-8357	47	5	is	be	AUX
geusb-8357	47	6	randomly	randomly	ADV
geusb-8357	47	7	chosen	choose	VERB
geusb-8357	47	8	from	from	ADP
geusb-8357	47	9	one	one	NUM
geusb-8357	47	10	of	of	ADP
geusb-8357	47	11	the	the	DET
geusb-8357	47	12	layers	layer	NOUN
geusb-8357	47	13	containing	contain	VERB
geusb-8357	47	14	water	water	NOUN
geusb-8357	47	15	and	and	CCONJ
geusb-8357	47	16	having	have	VERB
geusb-8357	47	17	a	a	DET
geusb-8357	47	18	thickness	thickness	NOUN
geusb-8357	47	19	>	>	SYM
geusb-8357	47	20	10	10	NUM
geusb-8357	47	21	m.	m.	NOUN
geusb-8357	47	22	additionally	additionally	ADV
geusb-8357	47	23	,	,	PUNCT
geusb-8357	47	24	the	the	DET
geusb-8357	47	25	pumping	pumping	NOUN
geusb-8357	47	26	rate	rate	NOUN
geusb-8357	47	27	for	for	ADP
geusb-8357	47	28	the	the	DET
geusb-8357	47	29	well	well	NOUN
geusb-8357	47	30	is	be	AUX
geusb-8357	47	31	drawn	draw	VERB
geusb-8357	47	32	randomly	randomly	ADV
geusb-8357	47	33	from	from	ADP
geusb-8357	47	34	a	a	DET
geusb-8357	47	35	uniform	uniform	ADJ
geusb-8357	47	36	range	range	NOUN
geusb-8357	47	37	spanning	span	VERB
geusb-8357	47	38	100–5000	100–5000	NUM
geusb-8357	47	39	m3·day–1	m3·day–1	NOUN
geusb-8357	47	40	.	.	PUNCT
geusb-8357	48	1	a	a	DET
geusb-8357	48	2	single	single	ADJ
geusb-8357	48	3	modflow	modflow	ADJ
geusb-8357	48	4	simulation	simulation	NOUN
geusb-8357	48	5	takes	take	VERB
geusb-8357	48	6	3.2	3.2	NUM
geusb-8357	48	7	±	±	NUM
geusb-8357	48	8	0.1	0.1	NUM
geusb-8357	48	9	s	s	NOUN
geusb-8357	48	10	to	to	PART
geusb-8357	48	11	run	run	VERB
geusb-8357	48	12	.	.	PUNCT
geusb-8357	49	1	the	the	DET
geusb-8357	49	2	change	change	NOUN
geusb-8357	49	3	in	in	ADP
geusb-8357	49	4	drawdown	drawdown	NOUN
geusb-8357	49	5	in	in	ADP
geusb-8357	49	6	each	each	DET
geusb-8357	49	7	cell	cell	NOUN
geusb-8357	49	8	caused	cause	VERB
geusb-8357	49	9	by	by	ADP
geusb-8357	49	10	the	the	DET
geusb-8357	49	11	abstraction	abstraction	NOUN
geusb-8357	49	12	is	be	AUX
geusb-8357	49	13	saved	save	VERB
geusb-8357	49	14	for	for	ADP
geusb-8357	49	15	the	the	DET
geusb-8357	49	16	target	target	NOUN
geusb-8357	49	17	data	datum	NOUN
geusb-8357	49	18	.	.	PUNCT
geusb-8357	50	1	dahl	dahl	PROPN
geusb-8357	50	2	et	et	PROPN
geusb-8357	50	3	al	al	PROPN
geusb-8357	50	4	.	.	PROPN
geusb-8357	51	1	(	(	PUNCT
geusb-8357	51	2	2023	2023	NUM
geusb-8357	51	3	)	)	PUNCT
geusb-8357	51	4	proposed	propose	VERB
geusb-8357	51	5	to	to	PART
geusb-8357	51	6	use	use	VERB
geusb-8357	51	7	a	a	DET
geusb-8357	51	8	subset	subset	NOUN
geusb-8357	51	9	of	of	ADP
geusb-8357	51	10	the	the	DET
geusb-8357	51	11	full	full	ADJ
geusb-8357	51	12	information	information	NOUN
geusb-8357	51	13	in	in	ADP
geusb-8357	51	14	the	the	DET
geusb-8357	51	15	model	model	NOUN
geusb-8357	51	16	for	for	ADP
geusb-8357	51	17	the	the	DET
geusb-8357	51	18	input	input	NOUN
geusb-8357	51	19	feature	feature	NOUN
geusb-8357	51	20	vector	vector	NOUN
geusb-8357	51	21	x.	x.	NOUN
geusb-8357	52	1	the	the	DET
geusb-8357	52	2	subset	subset	NOUN
geusb-8357	52	3	contains	contain	VERB
geusb-8357	52	4	12	12	NUM
geusb-8357	52	5	influencing	influence	VERB
geusb-8357	52	6	features	feature	NOUN
geusb-8357	52	7	that	that	PRON
geusb-8357	52	8	are	be	AUX
geusb-8357	52	9	generally	generally	ADV
geusb-8357	52	10	available	available	ADJ
geusb-8357	52	11	in	in	ADP
geusb-8357	52	12	most	most	ADJ
geusb-8357	52	13	groundwater	groundwater	NOUN
geusb-8357	52	14	models	model	NOUN
geusb-8357	52	15	.	.	PUNCT
geusb-8357	53	1	we	we	PRON
geusb-8357	53	2	extend	extend	VERB
geusb-8357	53	3	the	the	DET
geusb-8357	53	4	method	method	NOUN
geusb-8357	53	5	from	from	ADP
geusb-8357	53	6	only	only	ADV
geusb-8357	53	7	observing	observe	VERB
geusb-8357	53	8	drawdown	drawdown	NOUN
geusb-8357	53	9	from	from	ADP
geusb-8357	53	10	abstraction	abstraction	NOUN
geusb-8357	53	11	in	in	ADP
geusb-8357	53	12	a	a	DET
geusb-8357	53	13	single	single	ADJ
geusb-8357	53	14	layer	layer	NOUN
geusb-8357	53	15	to	to	ADP
geusb-8357	53	16	the	the	DET
geusb-8357	53	17	full	full	ADJ
geusb-8357	53	18	3d	3d	PROPN
geusb-8357	53	19	model	model	NOUN
geusb-8357	53	20	,	,	PUNCT
geusb-8357	53	21	where	where	SCONJ
geusb-8357	53	22	a	a	DET
geusb-8357	53	23	well	well	NOUN
geusb-8357	53	24	can	can	AUX
geusb-8357	53	25	be	be	AUX
geusb-8357	53	26	placed	place	VERB
geusb-8357	53	27	at	at	ADP
geusb-8357	53	28	different	different	ADJ
geusb-8357	53	29	depths	depth	NOUN
geusb-8357	53	30	,	,	PUNCT
geusb-8357	53	31	and	and	CCONJ
geusb-8357	53	32	changes	change	NOUN
geusb-8357	53	33	in	in	ADP
geusb-8357	53	34	drawdown	drawdown	NOUN
geusb-8357	53	35	are	be	AUX
geusb-8357	53	36	predicted	predict	VERB
geusb-8357	53	37	for	for	ADP
geusb-8357	53	38	all	all	DET
geusb-8357	53	39	layers	layer	NOUN
geusb-8357	53	40	.	.	PUNCT
geusb-8357	54	1	the	the	DET
geusb-8357	54	2	features	feature	NOUN
geusb-8357	54	3	from	from	ADP
geusb-8357	54	4	the	the	DET
geusb-8357	54	5	egebjerg	egebjerg	PROPN
geusb-8357	54	6	groundwater	groundwater	NOUN
geusb-8357	54	7	model	model	NOUN
geusb-8357	54	8	used	use	VERB
geusb-8357	54	9	for	for	ADP
geusb-8357	54	10	training	training	NOUN
geusb-8357	54	11	include	include	VERB
geusb-8357	54	12	the	the	DET
geusb-8357	54	13	hydraulic	hydraulic	ADJ
geusb-8357	54	14	head	head	NOUN
geusb-8357	54	15	before	before	ADP
geusb-8357	54	16	pumping	pumping	NOUN
geusb-8357	54	17	,	,	PUNCT
geusb-8357	54	18	distance	distance	NOUN
geusb-8357	54	19	to	to	ADP
geusb-8357	54	20	well	well	INTJ
geusb-8357	54	21	(	(	PUNCT
geusb-8357	54	22	fig	fig	NOUN
geusb-8357	54	23	.	.	PUNCT
geusb-8357	54	24	1b	1b	NUM
geusb-8357	54	25	)	)	PUNCT
geusb-8357	54	26	,	,	PUNCT
geusb-8357	54	27	travel	travel	NOUN
geusb-8357	54	28	time	time	NOUN
geusb-8357	54	29	to	to	PART
geusb-8357	54	30	well	well	INTJ
geusb-8357	54	31	(	(	PUNCT
geusb-8357	54	32	fig	fig	NOUN
geusb-8357	54	33	.	.	PUNCT
geusb-8357	55	1	1c	1c	NOUN
geusb-8357	55	2	)	)	PUNCT
geusb-8357	55	3	,	,	PUNCT
geusb-8357	55	4	distance	distance	NOUN
geusb-8357	55	5	to	to	PART
geusb-8357	55	6	head	head	VERB
geusb-8357	55	7	boundary	boundary	ADJ
geusb-8357	55	8	(	(	PUNCT
geusb-8357	55	9	ghb	ghb	NOUN
geusb-8357	55	10	package	package	NOUN
geusb-8357	55	11	;	;	PUNCT
geusb-8357	55	12	fig	fig	NOUN
geusb-8357	55	13	.	.	PUNCT
geusb-8357	56	1	1d	1d	NUM
geusb-8357	56	2	)	)	PUNCT
geusb-8357	57	1	,	,	PUNCT
geusb-8357	57	2	distance	distance	NOUN
geusb-8357	57	3	to	to	PART
geusb-8357	57	4	stream	stream	VERB
geusb-8357	57	5	(	(	PUNCT
geusb-8357	57	6	drn	drn	NOUN
geusb-8357	57	7	package	package	NOUN
geusb-8357	57	8	;	;	PUNCT
geusb-8357	57	9	fig	fig	NOUN
geusb-8357	57	10	.	.	PUNCT
geusb-8357	58	1	1e	1e	NUM
geusb-8357	58	2	)	)	PUNCT
geusb-8357	59	1	,	,	PUNCT
geusb-8357	59	2	pumping	pump	VERB
geusb-8357	59	3	rate	rate	NOUN
geusb-8357	59	4	,	,	PUNCT
geusb-8357	59	5	hydraulic	hydraulic	ADJ
geusb-8357	59	6	conductivity	conductivity	NOUN
geusb-8357	59	7	and	and	CCONJ
geusb-8357	59	8	the	the	DET
geusb-8357	59	9	logarithmic	logarithmic	ADJ
geusb-8357	59	10	hydraulic	hydraulic	ADJ
geusb-8357	59	11	conductivity	conductivity	NOUN
geusb-8357	59	12	in	in	ADP
geusb-8357	59	13	the	the	DET
geusb-8357	59	14	current	current	ADJ
geusb-8357	59	15	cell	cell	NOUN
geusb-8357	59	16	,	,	PUNCT
geusb-8357	59	17	well	well	INTJ
geusb-8357	59	18	location	location	NOUN
geusb-8357	59	19	(	(	PUNCT
geusb-8357	59	20	row	row	NOUN
geusb-8357	59	21	,	,	PUNCT
geusb-8357	59	22	column	column	NOUN
geusb-8357	59	23	and	and	CCONJ
geusb-8357	59	24	layer	layer	NOUN
geusb-8357	59	25	)	)	PUNCT
geusb-8357	59	26	and	and	CCONJ
geusb-8357	59	27	the	the	DET
geusb-8357	59	28	layer	layer	NOUN
geusb-8357	59	29	of	of	ADP
geusb-8357	59	30	the	the	DET
geusb-8357	59	31	current	current	ADJ
geusb-8357	59	32	cell	cell	NOUN
geusb-8357	59	33	.	.	PUNCT
geusb-8357	60	1	the	the	DET
geusb-8357	60	2	travel	travel	NOUN
geusb-8357	60	3	time	time	NOUN
geusb-8357	60	4	feature	feature	NOUN
geusb-8357	60	5	does	do	AUX
geusb-8357	60	6	not	not	PART
geusb-8357	60	7	represent	represent	VERB
geusb-8357	60	8	a	a	DET
geusb-8357	60	9	specific	specific	ADJ
geusb-8357	60	10	physical	physical	ADJ
geusb-8357	60	11	measure	measure	NOUN
geusb-8357	60	12	but	but	CCONJ
geusb-8357	60	13	is	be	AUX
geusb-8357	60	14	a	a	DET
geusb-8357	60	15	proxy	proxy	NOUN
geusb-8357	60	16	of	of	ADP
geusb-8357	60	17	water	water	NOUN
geusb-8357	60	18	’s	’s	PART
geusb-8357	60	19	ability	ability	NOUN
geusb-8357	60	20	to	to	PART
geusb-8357	60	21	travel	travel	VERB
geusb-8357	60	22	to	to	ADP
geusb-8357	60	23	a	a	DET
geusb-8357	60	24	certain	certain	ADJ
geusb-8357	60	25	location	location	NOUN
geusb-8357	60	26	depending	depend	VERB
geusb-8357	60	27	on	on	ADP
geusb-8357	60	28	the	the	DET
geusb-8357	60	29	distance	distance	NOUN
geusb-8357	60	30	and	and	CCONJ
geusb-8357	60	31	flow	flow	VERB
geusb-8357	60	32	resistance	resistance	NOUN
geusb-8357	60	33	along	along	ADP
geusb-8357	60	34	the	the	DET
geusb-8357	60	35	travel	travel	NOUN
geusb-8357	60	36	route	route	NOUN
geusb-8357	60	37	(	(	PUNCT
geusb-8357	60	38	hydraulic	hydraulic	ADJ
geusb-8357	60	39	conductivity	conductivity	NOUN
geusb-8357	60	40	)	)	PUNCT
geusb-8357	60	41	.	.	PUNCT
geusb-8357	61	1	the	the	DET
geusb-8357	61	2	fast	fast	ADV
geusb-8357	61	3	-	-	PUNCT
geusb-8357	61	4	marching	marching	NOUN
geusb-8357	61	5	algorithm	algorithm	NOUN
geusb-8357	61	6	is	be	AUX
geusb-8357	61	7	used	use	VERB
geusb-8357	61	8	to	to	PART
geusb-8357	61	9	compute	compute	VERB
geusb-8357	61	10	these	these	DET
geusb-8357	61	11	travel	travel	NOUN
geusb-8357	61	12	times	time	NOUN
geusb-8357	61	13	in	in	ADP
geusb-8357	61	14	a	a	DET
geusb-8357	61	15	2d	2d	NUM
geusb-8357	61	16	plane	plane	NOUN
geusb-8357	61	17	of	of	ADP
geusb-8357	61	18	the	the	DET
geusb-8357	61	19	active	active	ADJ
geusb-8357	61	20	cells	cell	NOUN
geusb-8357	61	21	just	just	ADV
geusb-8357	61	22	below	below	ADP
geusb-8357	61	23	topography	topography	NOUN
geusb-8357	61	24	,	,	PUNCT
geusb-8357	61	25	as	as	SCONJ
geusb-8357	61	26	implemented	implement	VERB
geusb-8357	61	27	in	in	ADP
geusb-8357	61	28	the	the	DET
geusb-8357	61	29	scikit	scikit	NOUN
geusb-8357	61	30	-	-	PUNCT
geusb-8357	61	31	fmm	fmm	PROPN
geusb-8357	61	32	python	python	NOUN
geusb-8357	61	33	extension	extension	NOUN
geusb-8357	61	34	module	module	NOUN
geusb-8357	61	35	(	(	PUNCT
geusb-8357	61	36	furtney	furtney	NOUN
geusb-8357	61	37	2021	2021	NUM
geusb-8357	61	38	)	)	PUNCT
geusb-8357	61	39	and	and	CCONJ
geusb-8357	61	40	applied	apply	VERB
geusb-8357	61	41	in	in	ADP
geusb-8357	61	42	other	other	ADJ
geusb-8357	61	43	studies	study	NOUN
geusb-8357	61	44	for	for	ADP
geusb-8357	61	45	training	training	NOUN
geusb-8357	61	46	machine	machine	NOUN
geusb-8357	61	47	learning	learning	NOUN
geusb-8357	61	48	models	model	NOUN
geusb-8357	61	49	with	with	ADP
geusb-8357	61	50	simulated	simulated	ADJ
geusb-8357	61	51	data	datum	NOUN
geusb-8357	61	52	(	(	PUNCT
geusb-8357	61	53	thibaut	thibaut	PROPN
geusb-8357	61	54	et	et	PROPN
geusb-8357	61	55	al	al	PROPN
geusb-8357	61	56	.	.	PROPN
geusb-8357	61	57	2021	2021	NUM
geusb-8357	61	58	)	)	PUNCT
geusb-8357	61	59	.	.	PUNCT
geusb-8357	62	1	neural	neural	ADJ
geusb-8357	62	2	network	network	NOUN
geusb-8357	62	3	setup	setup	VERB
geusb-8357	62	4	the	the	DET
geusb-8357	62	5	structure	structure	NOUN
geusb-8357	62	6	of	of	ADP
geusb-8357	62	7	the	the	DET
geusb-8357	62	8	neural	neural	ADJ
geusb-8357	62	9	network	network	NOUN
geusb-8357	62	10	consists	consist	VERB
geusb-8357	62	11	of	of	ADP
geusb-8357	62	12	an	an	DET
geusb-8357	62	13	input	input	NOUN
geusb-8357	62	14	layer	layer	NOUN
geusb-8357	62	15	of	of	ADP
geusb-8357	62	16	size	size	NOUN
geusb-8357	62	17	12	12	NUM
geusb-8357	62	18	(	(	PUNCT
geusb-8357	62	19	the	the	DET
geusb-8357	62	20	number	number	NOUN
geusb-8357	62	21	of	of	ADP
geusb-8357	62	22	input	input	NOUN
geusb-8357	62	23	features	feature	NOUN
geusb-8357	62	24	)	)	PUNCT
geusb-8357	62	25	,	,	PUNCT
geusb-8357	62	26	fully	fully	ADV
geusb-8357	62	27	connected	connect	VERB
geusb-8357	62	28	to	to	ADP
geusb-8357	62	29	three	three	NUM
geusb-8357	62	30	hidden	hidden	ADJ
geusb-8357	62	31	layers	layer	NOUN
geusb-8357	62	32	,	,	PUNCT
geusb-8357	62	33	each	each	PRON
geusb-8357	62	34	with	with	ADP
geusb-8357	62	35	75	75	NUM
geusb-8357	62	36	neurons	neuron	NOUN
geusb-8357	62	37	as	as	ADP
geusb-8357	62	38	in	in	ADP
geusb-8357	62	39	dahl	dahl	PROPN
geusb-8357	62	40	et	et	PROPN
geusb-8357	62	41	al	al	PROPN
geusb-8357	62	42	.	.	PROPN
geusb-8357	63	1	(	(	PUNCT
geusb-8357	63	2	2023	2023	NUM
geusb-8357	63	3	)	)	PUNCT
geusb-8357	63	4	.	.	PUNCT
geusb-8357	64	1	the	the	DET
geusb-8357	64	2	output	output	NOUN
geusb-8357	64	3	layer	layer	NOUN
geusb-8357	64	4	has	have	VERB
geusb-8357	64	5	two	two	NUM
geusb-8357	64	6	neurons	neuron	NOUN
geusb-8357	64	7	for	for	ADP
geusb-8357	64	8	estimating	estimate	VERB
geusb-8357	64	9	the	the	DET
geusb-8357	64	10	mean	mean	ADJ
geusb-8357	64	11	and	and	CCONJ
geusb-8357	64	12	standard	standard	ADJ
geusb-8357	64	13	deviation	deviation	NOUN
geusb-8357	64	14	of	of	ADP
geusb-8357	64	15	a	a	DET
geusb-8357	64	16	normal	normal	ADJ
geusb-8357	64	17	distribution	distribution	NOUN
geusb-8357	64	18	representing	represent	VERB
geusb-8357	64	19	the	the	DET
geusb-8357	64	20	drawdown	drawdown	NOUN
geusb-8357	64	21	.	.	PUNCT
geusb-8357	65	1	we	we	PRON
geusb-8357	65	2	apply	apply	VERB
geusb-8357	65	3	the	the	DET
geusb-8357	65	4	gaussian	gaussian	ADJ
geusb-8357	65	5	error	error	NOUN
geusb-8357	65	6	linear	linear	NOUN
geusb-8357	65	7	unit	unit	NOUN
geusb-8357	65	8	(	(	PUNCT
geusb-8357	65	9	gelu	gelu	NOUN
geusb-8357	65	10	)	)	PUNCT
geusb-8357	65	11	activation	activation	NOUN
geusb-8357	65	12	function	function	NOUN
geusb-8357	65	13	(	(	PUNCT
geusb-8357	65	14	hendrycks	hendryck	NOUN
geusb-8357	65	15	&	&	CCONJ
geusb-8357	65	16	gimpel	gimpel	PROPN
geusb-8357	65	17	2016	2016	NUM
geusb-8357	65	18	)	)	PUNCT
geusb-8357	65	19	between	between	ADP
geusb-8357	65	20	the	the	DET
geusb-8357	65	21	input	input	NOUN
geusb-8357	65	22	and	and	CCONJ
geusb-8357	65	23	hidden	hidden	ADJ
geusb-8357	65	24	layers	layer	NOUN
geusb-8357	65	25	and	and	CCONJ
geusb-8357	65	26	a	a	DET
geusb-8357	65	27	linear	linear	ADJ
geusb-8357	65	28	activation	activation	NOUN
geusb-8357	65	29	function	function	NOUN
geusb-8357	65	30	between	between	ADP
geusb-8357	65	31	the	the	DET
geusb-8357	65	32	last	last	ADJ
geusb-8357	65	33	hidden	hide	VERB
geusb-8357	65	34	layer	layer	NOUN
geusb-8357	65	35	and	and	CCONJ
geusb-8357	65	36	the	the	DET
geusb-8357	65	37	output	output	NOUN
geusb-8357	65	38	layer	layer	NOUN
geusb-8357	65	39	.	.	PUNCT
geusb-8357	66	1	the	the	DET
geusb-8357	66	2	adam	adam	PROPN
geusb-8357	66	3	algorithm	algorithm	PROPN
geusb-8357	66	4	(	(	PUNCT
geusb-8357	66	5	kingma	kingma	PROPN
geusb-8357	66	6	&	&	CCONJ
geusb-8357	66	7	ba	ba	PROPN
geusb-8357	66	8	2014	2014	NUM
geusb-8357	66	9	)	)	PUNCT
geusb-8357	66	10	is	be	AUX
geusb-8357	66	11	used	use	VERB
geusb-8357	66	12	to	to	PART
geusb-8357	66	13	minimise	minimise	VERB
geusb-8357	66	14	the	the	DET
geusb-8357	66	15	loss	loss	NOUN
geusb-8357	66	16	function	function	NOUN
geusb-8357	66	17	,	,	PUNCT
geusb-8357	66	18	which	which	PRON
geusb-8357	66	19	is	be	AUX
geusb-8357	66	20	defined	define	VERB
geusb-8357	66	21	as	as	ADP
geusb-8357	66	22	the	the	DET
geusb-8357	66	23	negative	negative	ADJ
geusb-8357	66	24	log	log	NOUN
geusb-8357	66	25	-	-	PUNCT
geusb-8357	66	26	likelihood	likelihood	NOUN
geusb-8357	66	27	of	of	ADP
geusb-8357	66	28	1d	1d	NUM
geusb-8357	66	29	gaussian	gaussian	ADJ
geusb-8357	66	30	distribution	distribution	NOUN
geusb-8357	66	31	.	.	PUNCT
geusb-8357	67	1	this	this	PRON
geusb-8357	67	2	allows	allow	VERB
geusb-8357	67	3	us	we	PRON
geusb-8357	67	4	to	to	PART
geusb-8357	67	5	interpret	interpret	VERB
geusb-8357	67	6	the	the	DET
geusb-8357	67	7	output	output	NOUN
geusb-8357	67	8	of	of	ADP
geusb-8357	67	9	the	the	DET
geusb-8357	67	10	neural	neural	ADJ
geusb-8357	67	11	network	network	NOUN
geusb-8357	67	12	as	as	ADP
geusb-8357	67	13	a	a	DET
geusb-8357	67	14	gaussian	gaussian	ADJ
geusb-8357	67	15	distribution	distribution	NOUN
geusb-8357	67	16	as	as	ADP
geusb-8357	67	17	describing	describe	VERB
geusb-8357	67	18	the	the	DET
geusb-8357	67	19	drawdown	drawdown	NOUN
geusb-8357	67	20	(	(	PUNCT
geusb-8357	67	21	dahl	dahl	PROPN
geusb-8357	67	22	et	et	PROPN
geusb-8357	67	23	al	al	PROPN
geusb-8357	67	24	.	.	PROPN
geusb-8357	67	25	2023	2023	NUM
geusb-8357	67	26	)	)	PUNCT
geusb-8357	67	27	.	.	PUNCT
geusb-8357	68	1	we	we	PRON
geusb-8357	68	2	use	use	VERB
geusb-8357	68	3	the	the	DET
geusb-8357	68	4	python	python	NOUN
geusb-8357	68	5	libraries	library	NOUN
geusb-8357	68	6	tensorflow	tensorflow	VERB
geusb-8357	68	7	and	and	CCONJ
geusb-8357	68	8	tensorflow	tensorflow	NOUN
geusb-8357	68	9	probability	probability	NOUN
geusb-8357	68	10	(	(	PUNCT
geusb-8357	68	11	abadi	abadi	PROPN
geusb-8357	68	12	et	et	PROPN
geusb-8357	68	13	al	al	PROPN
geusb-8357	68	14	.	.	PROPN
geusb-8357	68	15	2016	2016	NUM
geusb-8357	68	16	;	;	PUNCT
geusb-8357	68	17	dillon	dillon	PROPN
geusb-8357	68	18	et	et	PROPN
geusb-8357	68	19	al	al	PROPN
geusb-8357	68	20	.	.	PROPN
geusb-8357	68	21	2017	2017	NUM
geusb-8357	68	22	)	)	PUNCT
geusb-8357	68	23	for	for	ADP
geusb-8357	68	24	the	the	DET
geusb-8357	68	25	construction	construction	NOUN
geusb-8357	68	26	and	and	CCONJ
geusb-8357	68	27	training	training	NOUN
geusb-8357	68	28	of	of	ADP
geusb-8357	68	29	the	the	DET
geusb-8357	68	30	neural	neural	ADJ
geusb-8357	68	31	network	network	NOUN
geusb-8357	68	32	.	.	PUNCT
geusb-8357	69	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	69	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	69	3	dahl	dahl	PROPN
geusb-8357	69	4	et	et	PROPN
geusb-8357	69	5	al	al	PROPN
geusb-8357	69	6	.	.	PROPN
geusb-8357	69	7	2023	2023	NUM
geusb-8357	69	8	:	:	PUNCT
geusb-8357	69	9	geus	geus	NOUN
geusb-8357	69	10	bulletin	bulletin	NOUN
geusb-8357	69	11	53	53	NUM
geusb-8357	69	12	.	.	PUNCT
geusb-8357	69	13	8357	8357	NUM
geusb-8357	69	14	.	.	PUNCT
geusb-8357	70	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	70	2	3	3	NUM
geusb-8357	70	3	of	of	ADP
geusb-8357	70	4	7	7	NUM
geusb-8357	70	5	www.geusbul	www.geusbul	NOUN
geusb-8357	70	6	let	let	VERB
geusb-8357	70	7	in.org	in.org	ADJ
geusb-8357	70	8	the	the	DET
geusb-8357	70	9	holding	hold	VERB
geusb-8357	70	10	input	input	NOUN
geusb-8357	70	11	features	feature	NOUN
geusb-8357	70	12	of	of	ADP
geusb-8357	70	13	the	the	DET
geusb-8357	70	14	constructed	construct	VERB
geusb-8357	70	15	data	datum	NOUN
geusb-8357	70	16	set	set	VERB
geusb-8357	70	17	and	and	CCONJ
geusb-8357	70	18	simulated	simulate	VERB
geusb-8357	70	19	modflow	modflow	ADJ
geusb-8357	70	20	responses	response	NOUN
geusb-8357	70	21	are	be	AUX
geusb-8357	70	22	split	split	VERB
geusb-8357	70	23	80/20	80/20	NUM
geusb-8357	70	24	into	into	ADP
geusb-8357	70	25	a	a	DET
geusb-8357	70	26	training	training	NOUN
geusb-8357	70	27	data	datum	NOUN
geusb-8357	70	28	set	set	VERB
geusb-8357	70	29	and	and	CCONJ
geusb-8357	70	30	a	a	DET
geusb-8357	70	31	validation	validation	NOUN
geusb-8357	70	32	data	datum	NOUN
geusb-8357	70	33	set	set	VERB
geusb-8357	70	34	and	and	CCONJ
geusb-8357	70	35	standardised	standardise	VERB
geusb-8357	70	36	using	use	VERB
geusb-8357	70	37	a	a	DET
geusb-8357	70	38	standard	standard	ADJ
geusb-8357	70	39	scaler	scaler	NOUN
geusb-8357	70	40	.	.	PUNCT
geusb-8357	71	1	the	the	DET
geusb-8357	71	2	validation	validation	NOUN
geusb-8357	71	3	set	set	NOUN
geusb-8357	71	4	is	be	AUX
geusb-8357	71	5	withheld	withhold	VERB
geusb-8357	71	6	from	from	ADP
geusb-8357	71	7	training	training	NOUN
geusb-8357	71	8	and	and	CCONJ
geusb-8357	71	9	used	use	VERB
geusb-8357	71	10	to	to	PART
geusb-8357	71	11	validate	validate	VERB
geusb-8357	71	12	the	the	DET
geusb-8357	71	13	ongoing	ongoing	ADJ
geusb-8357	71	14	training	training	NOUN
geusb-8357	71	15	phase	phase	NOUN
geusb-8357	71	16	to	to	PART
geusb-8357	71	17	prevent	prevent	VERB
geusb-8357	71	18	overfitting	overfitting	NOUN
geusb-8357	71	19	.	.	PUNCT
geusb-8357	72	1	during	during	ADP
geusb-8357	72	2	training	training	NOUN
geusb-8357	72	3	,	,	PUNCT
geusb-8357	72	4	if	if	SCONJ
geusb-8357	72	5	loss	loss	NOUN
geusb-8357	72	6	improves	improve	VERB
geusb-8357	72	7	for	for	ADP
geusb-8357	72	8	the	the	DET
geusb-8357	72	9	training	training	NOUN
geusb-8357	72	10	data	datum	NOUN
geusb-8357	72	11	but	but	CCONJ
geusb-8357	72	12	not	not	PART
geusb-8357	72	13	for	for	ADP
geusb-8357	72	14	the	the	DET
geusb-8357	72	15	validation	validation	NOUN
geusb-8357	72	16	data	datum	NOUN
geusb-8357	72	17	,	,	PUNCT
geusb-8357	72	18	the	the	DET
geusb-8357	72	19	neural	neural	ADJ
geusb-8357	72	20	network	network	NOUN
geusb-8357	72	21	is	be	AUX
geusb-8357	72	22	likely	likely	ADV
geusb-8357	72	23	overfitting	overfitte	VERB
geusb-8357	72	24	to	to	ADP
geusb-8357	72	25	the	the	DET
geusb-8357	72	26	training	training	NOUN
geusb-8357	72	27	data	datum	NOUN
geusb-8357	72	28	.	.	PUNCT
geusb-8357	73	1	we	we	PRON
geusb-8357	73	2	train	train	VERB
geusb-8357	73	3	the	the	DET
geusb-8357	73	4	network	network	NOUN
geusb-8357	73	5	for	for	ADP
geusb-8357	73	6	1000	1000	NUM
geusb-8357	73	7	epochs	epoch	NOUN
geusb-8357	73	8	and	and	CCONJ
geusb-8357	73	9	observe	observe	VERB
geusb-8357	73	10	a	a	DET
geusb-8357	73	11	stagnation	stagnation	NOUN
geusb-8357	73	12	in	in	ADP
geusb-8357	73	13	loss	loss	NOUN
geusb-8357	73	14	improvement	improvement	NOUN
geusb-8357	73	15	without	without	ADP
geusb-8357	73	16	encountering	encounter	VERB
geusb-8357	73	17	overfitting	overfitting	NOUN
geusb-8357	73	18	.	.	PUNCT
geusb-8357	74	1	additionally	additionally	ADV
geusb-8357	74	2	,	,	PUNCT
geusb-8357	74	3	an	an	DET
geusb-8357	74	4	independent	independent	ADJ
geusb-8357	74	5	third	third	ADJ
geusb-8357	74	6	test	test	NOUN
geusb-8357	74	7	data	datum	NOUN
geusb-8357	74	8	set	set	VERB
geusb-8357	74	9	is	be	AUX
geusb-8357	74	10	constructed	construct	VERB
geusb-8357	74	11	from	from	ADP
geusb-8357	74	12	80	80	NUM
geusb-8357	74	13	new	new	ADJ
geusb-8357	74	14	modflow	modflow	ADJ
geusb-8357	74	15	simulations	simulation	NOUN
geusb-8357	74	16	,	,	PUNCT
geusb-8357	74	17	separate	separate	ADJ
geusb-8357	74	18	from	from	ADP
geusb-8357	74	19	the	the	DET
geusb-8357	74	20	validation	validation	NOUN
geusb-8357	74	21	data	datum	NOUN
geusb-8357	74	22	set	set	VERB
geusb-8357	74	23	for	for	ADP
geusb-8357	74	24	performance	performance	NOUN
geusb-8357	74	25	evaluation	evaluation	NOUN
geusb-8357	74	26	.	.	PUNCT
geusb-8357	75	1	results	result	VERB
geusb-8357	75	2	the	the	DET
geusb-8357	75	3	performances	performance	NOUN
geusb-8357	75	4	of	of	ADP
geusb-8357	75	5	the	the	DET
geusb-8357	75	6	trained	train	VERB
geusb-8357	75	7	neural	neural	ADJ
geusb-8357	75	8	network	network	NOUN
geusb-8357	75	9	on	on	ADP
geusb-8357	75	10	the	the	DET
geusb-8357	75	11	validation	validation	NOUN
geusb-8357	75	12	and	and	CCONJ
geusb-8357	75	13	test	test	NOUN
geusb-8357	75	14	data	datum	NOUN
geusb-8357	75	15	are	be	AUX
geusb-8357	75	16	shown	show	VERB
geusb-8357	75	17	in	in	ADP
geusb-8357	75	18	fig	fig	NOUN
geusb-8357	75	19	.	.	PUNCT
geusb-8357	76	1	2a	2a	NUM
geusb-8357	76	2	–	–	PUNCT
geusb-8357	76	3	b	b	NOUN
geusb-8357	76	4	as	as	SCONJ
geusb-8357	76	5	normalised	normalise	VERB
geusb-8357	76	6	density	density	NOUN
geusb-8357	76	7	plots	plot	NOUN
geusb-8357	76	8	of	of	ADP
geusb-8357	76	9	modflow	modflow	ADJ
geusb-8357	76	10	simulations	simulation	NOUN
geusb-8357	76	11	(	(	PUNCT
geusb-8357	76	12	observed	observe	VERB
geusb-8357	76	13	drawdown	drawdown	NOUN
geusb-8357	76	14	)	)	PUNCT
geusb-8357	76	15	against	against	ADP
geusb-8357	76	16	mean	mean	ADJ
geusb-8357	76	17	predictions	prediction	NOUN
geusb-8357	76	18	(	(	PUNCT
geusb-8357	76	19	predicted	predict	VERB
geusb-8357	76	20	drawdown	drawdown	NOUN
geusb-8357	76	21	)	)	PUNCT
geusb-8357	76	22	with	with	ADP
geusb-8357	76	23	a	a	DET
geusb-8357	76	24	logarithmic	logarithmic	ADJ
geusb-8357	76	25	colour	colour	NOUN
geusb-8357	76	26	scale	scale	NOUN
geusb-8357	76	27	.	.	PUNCT
geusb-8357	77	1	the	the	DET
geusb-8357	77	2	sum	sum	NOUN
geusb-8357	77	3	of	of	ADP
geusb-8357	77	4	all	all	DET
geusb-8357	77	5	values	value	NOUN
geusb-8357	77	6	in	in	ADP
geusb-8357	77	7	the	the	DET
geusb-8357	77	8	figure	figure	NOUN
geusb-8357	77	9	adds	add	VERB
geusb-8357	77	10	up	up	ADP
geusb-8357	77	11	to	to	PART
geusb-8357	77	12	1	1	NUM
geusb-8357	77	13	.	.	PUNCT
geusb-8357	78	1	the	the	DET
geusb-8357	78	2	dashed	dash	VERB
geusb-8357	78	3	identity	identity	NOUN
geusb-8357	78	4	line	line	NOUN
geusb-8357	78	5	represents	represent	VERB
geusb-8357	78	6	the	the	DET
geusb-8357	78	7	ideal	ideal	ADJ
geusb-8357	78	8	agreement	agreement	NOUN
geusb-8357	78	9	between	between	ADP
geusb-8357	78	10	modflow	modflow	ADJ
geusb-8357	78	11	simulations	simulation	NOUN
geusb-8357	78	12	and	and	CCONJ
geusb-8357	78	13	predictions	prediction	NOUN
geusb-8357	78	14	of	of	ADP
geusb-8357	78	15	the	the	DET
geusb-8357	78	16	neural	neural	ADJ
geusb-8357	78	17	network	network	NOUN
geusb-8357	78	18	.	.	PUNCT
geusb-8357	79	1	in	in	ADP
geusb-8357	79	2	both	both	DET
geusb-8357	79	3	data	datum	NOUN
geusb-8357	79	4	sets	set	NOUN
geusb-8357	79	5	,	,	PUNCT
geusb-8357	79	6	we	we	PRON
geusb-8357	79	7	observe	observe	VERB
geusb-8357	79	8	the	the	DET
geusb-8357	79	9	highest	high	ADJ
geusb-8357	79	10	concentrations	concentration	NOUN
geusb-8357	79	11	exactly	exactly	ADV
geusb-8357	79	12	on	on	ADP
geusb-8357	79	13	the	the	DET
geusb-8357	79	14	identity	identity	NOUN
geusb-8357	79	15	line	line	NOUN
geusb-8357	79	16	for	for	ADP
geusb-8357	79	17	a	a	DET
geusb-8357	79	18	drawdown	drawdown	NOUN
geusb-8357	79	19	from	from	ADP
geusb-8357	79	20	0	0	NUM
geusb-8357	79	21	m	m	NOUN
geusb-8357	79	22	and	and	CCONJ
geusb-8357	79	23	up	up	ADP
geusb-8357	79	24	to	to	ADP
geusb-8357	79	25	c.	c.	PROPN
geusb-8357	79	26	3	3	NUM
geusb-8357	79	27	m.	m.	NOUN
geusb-8357	79	28	here	here	ADV
geusb-8357	79	29	lies	lie	VERB
geusb-8357	79	30	the	the	DET
geusb-8357	79	31	highest	high	ADJ
geusb-8357	79	32	density	density	NOUN
geusb-8357	79	33	region	region	NOUN
geusb-8357	79	34	containing	contain	VERB
geusb-8357	79	35	95	95	NUM
geusb-8357	79	36	%	%	NOUN
geusb-8357	79	37	of	of	ADP
geusb-8357	79	38	the	the	DET
geusb-8357	79	39	data	datum	NOUN
geusb-8357	79	40	marked	mark	VERB
geusb-8357	79	41	with	with	ADP
geusb-8357	79	42	the	the	DET
geusb-8357	79	43	white	white	ADJ
geusb-8357	79	44	contour	contour	NOUN
geusb-8357	79	45	line	line	NOUN
geusb-8357	79	46	(	(	PUNCT
geusb-8357	79	47	fig	fig	NOUN
geusb-8357	79	48	2a	2a	NUM
geusb-8357	79	49	–	–	PUNCT
geusb-8357	79	50	b	b	NOUN
geusb-8357	79	51	)	)	PUNCT
geusb-8357	79	52	.	.	PUNCT
geusb-8357	80	1	in	in	ADP
geusb-8357	80	2	this	this	DET
geusb-8357	80	3	interval	interval	NOUN
geusb-8357	80	4	,	,	PUNCT
geusb-8357	80	5	the	the	DET
geusb-8357	80	6	data	data	NOUN
geusb-8357	80	7	density	density	NOUN
geusb-8357	80	8	rapidly	rapidly	ADV
geusb-8357	80	9	decreases	decrease	VERB
geusb-8357	80	10	as	as	SCONJ
geusb-8357	80	11	we	we	PRON
geusb-8357	80	12	move	move	VERB
geusb-8357	80	13	away	away	ADV
geusb-8357	80	14	from	from	ADP
geusb-8357	80	15	the	the	DET
geusb-8357	80	16	line	line	NOUN
geusb-8357	80	17	in	in	ADP
geusb-8357	80	18	both	both	DET
geusb-8357	80	19	directions	direction	NOUN
geusb-8357	80	20	.	.	PUNCT
geusb-8357	81	1	for	for	ADP
geusb-8357	81	2	higher	high	ADJ
geusb-8357	81	3	values	value	NOUN
geusb-8357	81	4	,	,	PUNCT
geusb-8357	81	5	we	we	PRON
geusb-8357	81	6	observe	observe	VERB
geusb-8357	81	7	a	a	DET
geusb-8357	81	8	shift	shift	NOUN
geusb-8357	81	9	in	in	ADP
geusb-8357	81	10	the	the	DET
geusb-8357	81	11	validation	validation	NOUN
geusb-8357	81	12	data	datum	NOUN
geusb-8357	81	13	density	density	NOUN
geusb-8357	81	14	plot	plot	NOUN
geusb-8357	81	15	away	away	ADV
geusb-8357	81	16	from	from	ADP
geusb-8357	81	17	the	the	DET
geusb-8357	81	18	identity	identity	NOUN
geusb-8357	81	19	line	line	NOUN
geusb-8357	81	20	,	,	PUNCT
geusb-8357	81	21	and	and	CCONJ
geusb-8357	81	22	the	the	DET
geusb-8357	81	23	spread	spread	ADJ
geusb-8357	81	24	increases	increase	NOUN
geusb-8357	81	25	.	.	PUNCT
geusb-8357	82	1	figure	figure	NOUN
geusb-8357	82	2	2c	2c	NOUN
geusb-8357	82	3	shows	show	VERB
geusb-8357	82	4	histograms	histogram	NOUN
geusb-8357	82	5	depicting	depict	VERB
geusb-8357	82	6	the	the	DET
geusb-8357	82	7	differences	difference	NOUN
geusb-8357	82	8	between	between	ADP
geusb-8357	82	9	true	true	ADJ
geusb-8357	82	10	modflow	modflow	ADJ
geusb-8357	82	11	results	result	NOUN
geusb-8357	82	12	and	and	CCONJ
geusb-8357	82	13	predictions	prediction	NOUN
geusb-8357	82	14	within	within	ADP
geusb-8357	82	15	the	the	DET
geusb-8357	82	16	two	two	NUM
geusb-8357	82	17	data	datum	NOUN
geusb-8357	82	18	sets	set	NOUN
geusb-8357	82	19	,	,	PUNCT
geusb-8357	82	20	confirming	confirm	VERB
geusb-8357	82	21	a	a	DET
geusb-8357	82	22	small	small	ADJ
geusb-8357	82	23	error	error	NOUN
geusb-8357	82	24	of	of	ADP
geusb-8357	82	25	less	less	ADJ
geusb-8357	82	26	than	than	ADP
geusb-8357	82	27	0.2	0.2	NUM
geusb-8357	82	28	m	m	NOUN
geusb-8357	82	29	for	for	ADP
geusb-8357	82	30	most	most	ADJ
geusb-8357	82	31	differences	difference	NOUN
geusb-8357	82	32	.	.	PUNCT
geusb-8357	83	1	figure	figure	NOUN
geusb-8357	83	2	2d	2d	NOUN
geusb-8357	83	3	displays	display	VERB
geusb-8357	83	4	histograms	histogram	NOUN
geusb-8357	83	5	depicting	depict	VERB
geusb-8357	83	6	data	datum	NOUN
geusb-8357	83	7	set	set	VERB
geusb-8357	83	8	z	z	PROPN
geusb-8357	83	9	scores	score	NOUN
geusb-8357	83	10	,	,	PUNCT
geusb-8357	83	11	which	which	PRON
geusb-8357	83	12	quantify	quantify	VERB
geusb-8357	83	13	the	the	DET
geusb-8357	83	14	deviation	deviation	NOUN
geusb-8357	83	15	of	of	ADP
geusb-8357	83	16	modflow	modflow	ADJ
geusb-8357	83	17	results	result	NOUN
geusb-8357	83	18	xmod	xmod	PROPN
geusb-8357	83	19	from	from	ADP
geusb-8357	83	20	the	the	DET
geusb-8357	83	21	mean	mean	ADJ
geusb-8357	83	22	prediction	prediction	NOUN
geusb-8357	83	23	μnn	μnn	NOUN
geusb-8357	83	24	in	in	ADP
geusb-8357	83	25	terms	term	NOUN
geusb-8357	83	26	of	of	ADP
geusb-8357	83	27	standard	standard	ADJ
geusb-8357	83	28	deviations	deviation	NOUN
geusb-8357	83	29	,	,	PUNCT
geusb-8357	83	30	σnn	σnn	CCONJ
geusb-8357	83	31	where	where	SCONJ
geusb-8357	83	32	,	,	PUNCT
geusb-8357	83	33	(	(	PUNCT
geusb-8357	83	34	2	2	X
geusb-8357	83	35	)	)	PUNCT
geusb-8357	83	36	the	the	DET
geusb-8357	83	37	root	root	NOUN
geusb-8357	83	38	mean	mean	VERB
geusb-8357	83	39	squared	square	VERB
geusb-8357	83	40	errors	error	NOUN
geusb-8357	83	41	between	between	ADP
geusb-8357	83	42	observed	observed	ADJ
geusb-8357	83	43	and	and	CCONJ
geusb-8357	83	44	predicted	predict	VERB
geusb-8357	83	45	values	value	NOUN
geusb-8357	83	46	in	in	ADP
geusb-8357	83	47	fig	fig	NOUN
geusb-8357	83	48	.	.	PUNCT
geusb-8357	84	1	2a	2a	NUM
geusb-8357	84	2	–	–	PUNCT
geusb-8357	84	3	b	b	NOUN
geusb-8357	84	4	are	be	AUX
geusb-8357	84	5	rmsevali	rmsevali	NOUN
geusb-8357	84	6	 	 	SPACE
geusb-8357	84	7	=	=	NOUN
geusb-8357	84	8	 	 	SPACE
geusb-8357	84	9	0.19	0.19	NUM
geusb-8357	84	10	m	m	NOUN
geusb-8357	84	11	and	and	CCONJ
geusb-8357	84	12	rmsetest	rmset	ADJ
geusb-8357	84	13	=	=	SYM
geusb-8357	84	14	0.17	0.17	NUM
geusb-8357	84	15	m.	m.	NOUN
geusb-8357	84	16	the	the	DET
geusb-8357	84	17	low	low	ADJ
geusb-8357	84	18	errors	error	NOUN
geusb-8357	84	19	are	be	AUX
geusb-8357	84	20	in	in	ADP
geusb-8357	84	21	good	good	ADJ
geusb-8357	84	22	agreement	agreement	NOUN
geusb-8357	84	23	with	with	ADP
geusb-8357	84	24	the	the	DET
geusb-8357	84	25	observed	observe	VERB
geusb-8357	84	26	high	high	ADJ
geusb-8357	84	27	density	density	NOUN
geusb-8357	84	28	of	of	ADP
geusb-8357	84	29	observations	observation	NOUN
geusb-8357	84	30	near	near	ADP
geusb-8357	84	31	the	the	DET
geusb-8357	84	32	identity	identity	NOUN
geusb-8357	84	33	line	line	NOUN
geusb-8357	84	34	and	and	CCONJ
geusb-8357	84	35	the	the	DET
geusb-8357	84	36	distribution	distribution	NOUN
geusb-8357	84	37	in	in	ADP
geusb-8357	84	38	fig	fig	NOUN
geusb-8357	84	39	.	.	PUNCT
geusb-8357	85	1	2c	2c	NOUN
geusb-8357	85	2	.	.	PUNCT
geusb-8357	86	1	we	we	PRON
geusb-8357	86	2	test	test	VERB
geusb-8357	86	3	the	the	DET
geusb-8357	86	4	neural	neural	ADJ
geusb-8357	86	5	network	network	NOUN
geusb-8357	86	6	at	at	ADP
geusb-8357	86	7	two	two	NUM
geusb-8357	86	8	different	different	ADJ
geusb-8357	86	9	locations	location	NOUN
geusb-8357	86	10	outside	outside	ADP
geusb-8357	86	11	the	the	DET
geusb-8357	86	12	original	original	ADJ
geusb-8357	86	13	training	training	NOUN
geusb-8357	86	14	and	and	CCONJ
geusb-8357	86	15	validation	validation	NOUN
geusb-8357	86	16	data	datum	NOUN
geusb-8357	86	17	sets	set	NOUN
geusb-8357	86	18	and	and	CCONJ
geusb-8357	86	19	compare	compare	VERB
geusb-8357	86	20	predictions	prediction	NOUN
geusb-8357	86	21	to	to	ADP
geusb-8357	86	22	modflow	modflow	ADJ
geusb-8357	86	23	results	result	NOUN
geusb-8357	86	24	(	(	PUNCT
geusb-8357	86	25	fig	fig	NOUN
geusb-8357	86	26	.	.	PUNCT
geusb-8357	87	1	3	3	NUM
geusb-8357	87	2	)	)	PUNCT
geusb-8357	87	3	.	.	PUNCT
geusb-8357	88	1	in	in	ADP
geusb-8357	88	2	case	case	NOUN
geusb-8357	88	3	1	1	NUM
geusb-8357	88	4	(	(	PUNCT
geusb-8357	88	5	fig	fig	NOUN
geusb-8357	88	6	.	.	PUNCT
geusb-8357	88	7	3a	3a	NUM
geusb-8357	88	8	)	)	PUNCT
geusb-8357	88	9	,	,	PUNCT
geusb-8357	88	10	the	the	DET
geusb-8357	88	11	well	well	NOUN
geusb-8357	88	12	is	be	AUX
geusb-8357	88	13	placed	place	VERB
geusb-8357	88	14	in	in	ADP
geusb-8357	88	15	layer	layer	NOUN
geusb-8357	88	16	6	6	NUM
geusb-8357	88	17	at	at	ADP
geusb-8357	88	18	row	row	NOUN
geusb-8357	88	19	55	55	NUM
geusb-8357	88	20	and	and	CCONJ
geusb-8357	88	21	column	column	NOUN
geusb-8357	88	22	99	99	NUM
geusb-8357	88	23	with	with	ADP
geusb-8357	88	24	a	a	DET
geusb-8357	88	25	pumping	pumping	NOUN
geusb-8357	88	26	rate	rate	NOUN
geusb-8357	88	27	of	of	ADP
geusb-8357	88	28	798	798	NUM
geusb-8357	88	29	m3·day–1	m3·day–1	NOUN
geusb-8357	88	30	.	.	PUNCT
geusb-8357	89	1	the	the	DET
geusb-8357	89	2	drawdown	drawdown	NOUN
geusb-8357	89	3	from	from	ADP
geusb-8357	89	4	the	the	DET
geusb-8357	89	5	modflow	modflow	ADJ
geusb-8357	89	6	simulation	simulation	NOUN
geusb-8357	89	7	shown	show	VERB
geusb-8357	89	8	in	in	ADP
geusb-8357	89	9	fig	fig	NOUN
geusb-8357	89	10	.	.	PUNCT
geusb-8357	90	1	3a	3a	PROPN
geusb-8357	90	2	is	be	AUX
geusb-8357	90	3	compared	compare	VERB
geusb-8357	90	4	to	to	ADP
geusb-8357	90	5	the	the	DET
geusb-8357	90	6	predicted	predict	VERB
geusb-8357	90	7	mean	mean	NOUN
geusb-8357	90	8	and	and	CCONJ
geusb-8357	90	9	standard	standard	ADJ
geusb-8357	90	10	deviation	deviation	NOUN
geusb-8357	90	11	from	from	ADP
geusb-8357	90	12	the	the	DET
geusb-8357	90	13	network	network	NOUN
geusb-8357	90	14	(	(	PUNCT
geusb-8357	90	15	fig	fig	NOUN
geusb-8357	90	16	.	.	PUNCT
geusb-8357	91	1	3b	3b	NUM
geusb-8357	91	2	–	–	PUNCT
geusb-8357	91	3	c	c	NOUN
geusb-8357	91	4	)	)	PUNCT
geusb-8357	91	5	.	.	PUNCT
geusb-8357	92	1	the	the	DET
geusb-8357	92	2	difference	difference	NOUN
geusb-8357	92	3	between	between	ADP
geusb-8357	92	4	modflow	modflow	NOUN
geusb-8357	92	5	and	and	CCONJ
geusb-8357	92	6	the	the	DET
geusb-8357	92	7	predicted	predict	VERB
geusb-8357	92	8	mean	mean	NOUN
geusb-8357	92	9	of	of	ADP
geusb-8357	92	10	0	0	NUM
geusb-8357	92	11	20	20	NUM
geusb-8357	92	12	40	40	NUM
geusb-8357	92	13	60	60	NUM
geusb-8357	92	14	80	80	NUM
geusb-8357	92	15	100	100	NUM
geusb-8357	92	16	120	120	NUM
geusb-8357	92	17	column	column	NOUN
geusb-8357	92	18	0	0	NUM
geusb-8357	92	19	20	20	NUM
geusb-8357	92	20	40	40	NUM
geusb-8357	92	21	60	60	NUM
geusb-8357	92	22	80	80	NUM
geusb-8357	92	23	100	100	NUM
geusb-8357	92	24	120	120	NUM
geusb-8357	92	25	140	140	NUM
geusb-8357	92	26	r	r	NOUN
geusb-8357	92	27	ow	ow	NOUN
geusb-8357	92	28	a	a	DET
geusb-8357	92	29	boundary	boundary	ADJ
geusb-8357	92	30	conditions	condition	NOUN
geusb-8357	92	31	and	and	CCONJ
geusb-8357	92	32	topography	topography	NOUN
geusb-8357	92	33	0	0	NUM
geusb-8357	92	34	50	50	NUM
geusb-8357	92	35	100	100	NUM
geusb-8357	92	36	150	150	NUM
geusb-8357	92	37	topography	topography	NOUN
geusb-8357	92	38	[	[	X
geusb-8357	92	39	m	m	X
geusb-8357	92	40	]	]	X
geusb-8357	92	41	drn	drn	ADJ
geusb-8357	92	42	-	-	PUNCT
geusb-8357	92	43	riv	riv	NOUN
geusb-8357	92	44	ghb	ghb	NOUN
geusb-8357	92	45	well	well	NOUN
geusb-8357	92	46	0	0	NUM
geusb-8357	92	47	50	50	NUM
geusb-8357	92	48	100	100	NUM
geusb-8357	92	49	column	column	NOUN
geusb-8357	92	50	0	0	NUM
geusb-8357	93	1	25	25	NUM
geusb-8357	93	2	50	50	NUM
geusb-8357	93	3	75	75	NUM
geusb-8357	93	4	100	100	NUM
geusb-8357	93	5	125	125	NUM
geusb-8357	93	6	r	r	NOUN
geusb-8357	93	7	ow	ow	INTJ
geusb-8357	93	8	distance	distance	NOUN
geusb-8357	93	9	to	to	PART
geusb-8357	93	10	well	well	ADV
geusb-8357	93	11	well	well	INTJ
geusb-8357	93	12	well	well	INTJ
geusb-8357	93	13	wellwell	wellwell	VERB
geusb-8357	93	14	b	b	NOUN
geusb-8357	93	15	d	d	X
geusb-8357	93	16	e	e	PROPN
geusb-8357	93	17	c	c	NOUN
geusb-8357	93	18	0	0	NUM
geusb-8357	93	19	50	50	NUM
geusb-8357	93	20	100	100	NUM
geusb-8357	93	21	column	column	NOUN
geusb-8357	93	22	0	0	NUM
geusb-8357	93	23	25	25	NUM
geusb-8357	93	24	50	50	NUM
geusb-8357	93	25	75	75	NUM
geusb-8357	93	26	100	100	NUM
geusb-8357	93	27	125	125	NUM
geusb-8357	93	28	r	r	NOUN
geusb-8357	93	29	ow	ow	INTJ
geusb-8357	93	30	travel	travel	NOUN
geusb-8357	93	31	time	time	NOUN
geusb-8357	93	32	0	0	NUM
geusb-8357	93	33	50	50	NUM
geusb-8357	93	34	100	100	NUM
geusb-8357	93	35	column	column	NOUN
geusb-8357	93	36	0	0	NUM
geusb-8357	93	37	25	25	NUM
geusb-8357	93	38	50	50	NUM
geusb-8357	93	39	75	75	NUM
geusb-8357	93	40	100	100	NUM
geusb-8357	93	41	125	125	NUM
geusb-8357	94	1	r	r	NOUN
geusb-8357	94	2	ow	ow	INTJ
geusb-8357	94	3	distance	distance	NOUN
geusb-8357	94	4	to	to	ADP
geusb-8357	94	5	ghb	ghb	PROPN
geusb-8357	94	6	0	0	NUM
geusb-8357	94	7	50	50	NUM
geusb-8357	94	8	100	100	NUM
geusb-8357	94	9	column	column	NOUN
geusb-8357	94	10	0	0	NUM
geusb-8357	95	1	25	25	NUM
geusb-8357	95	2	50	50	NUM
geusb-8357	95	3	75	75	NUM
geusb-8357	95	4	100	100	NUM
geusb-8357	95	5	125	125	NUM
geusb-8357	95	6	r	r	NOUN
geusb-8357	95	7	ow	ow	INTJ
geusb-8357	95	8	distance	distance	NOUN
geusb-8357	95	9	to	to	PART
geusb-8357	95	10	stream	stream	VERB
geusb-8357	95	11	0	0	NUM
geusb-8357	95	12	2000	2000	NUM
geusb-8357	95	13	4000	4000	NUM
geusb-8357	95	14	6000	6000	NUM
geusb-8357	95	15	8000	8000	NUM
geusb-8357	95	16	10000	10000	NUM
geusb-8357	96	1	d	d	X
geusb-8357	96	2	istance	istance	PROPN
geusb-8357	97	1	[	[	X
geusb-8357	97	2	m	m	X
geusb-8357	97	3	]	]	X
geusb-8357	97	4	50000	50000	NUM
geusb-8357	97	5	100000	100000	NUM
geusb-8357	97	6	150000	150000	NUM
geusb-8357	97	7	200000	200000	NUM
geusb-8357	97	8	250000	250000	NUM
geusb-8357	97	9	300000	300000	NUM
geusb-8357	97	10	350000	350000	NUM
geusb-8357	97	11	400000	400000	NUM
geusb-8357	97	12	tim	tim	PROPN
geusb-8357	97	13	e	e	PROPN
geusb-8357	98	1	[	[	X
geusb-8357	98	2	d	d	X
geusb-8357	98	3	ays	ays	NOUN
geusb-8357	98	4	]	]	X
geusb-8357	98	5	0	0	NUM
geusb-8357	98	6	1000	1000	NUM
geusb-8357	98	7	2000	2000	NUM
geusb-8357	98	8	3000	3000	NUM
geusb-8357	98	9	4000	4000	NUM
geusb-8357	98	10	d	d	NOUN
geusb-8357	98	11	istance	istance	PROPN
geusb-8357	99	1	[	[	X
geusb-8357	99	2	m	m	X
geusb-8357	99	3	]	]	X
geusb-8357	99	4	0	0	NUM
geusb-8357	99	5	1000	1000	NUM
geusb-8357	99	6	2000	2000	NUM
geusb-8357	99	7	3000	3000	NUM
geusb-8357	99	8	4000	4000	NUM
geusb-8357	99	9	d	d	NOUN
geusb-8357	99	10	istance	istance	PROPN
geusb-8357	99	11	[	[	X
geusb-8357	99	12	m	m	X
geusb-8357	99	13	]	]	X
geusb-8357	99	14	input	input	NOUN
geusb-8357	99	15	features	feature	VERB
geusb-8357	99	16	fig	fig	NOUN
geusb-8357	99	17	.	.	PUNCT
geusb-8357	100	1	1	1	NUM
geusb-8357	100	2	a	a	DET
geusb-8357	100	3	visualisation	visualisation	NOUN
geusb-8357	100	4	of	of	ADP
geusb-8357	100	5	the	the	DET
geusb-8357	100	6	egebjerg	egebjerg	PROPN
geusb-8357	100	7	model	model	VERB
geusb-8357	100	8	boundary	boundary	ADJ
geusb-8357	100	9	conditions	condition	NOUN
geusb-8357	100	10	and	and	CCONJ
geusb-8357	100	11	some	some	PRON
geusb-8357	100	12	of	of	ADP
geusb-8357	100	13	the	the	DET
geusb-8357	100	14	relevant	relevant	ADJ
geusb-8357	100	15	features	feature	NOUN
geusb-8357	100	16	for	for	ADP
geusb-8357	100	17	training	training	NOUN
geusb-8357	100	18	.	.	PUNCT
geusb-8357	101	1	a	a	DET
geusb-8357	101	2	:	:	PUNCT
geusb-8357	101	3	streams	stream	NOUN
geusb-8357	101	4	(	(	PUNCT
geusb-8357	101	5	blue	blue	ADJ
geusb-8357	101	6	;	;	PUNCT
geusb-8357	101	7	drn	drn	ADJ
geusb-8357	101	8	-	-	PUNCT
geusb-8357	101	9	riv	riv	NOUN
geusb-8357	101	10	)	)	PUNCT
geusb-8357	101	11	,	,	PUNCT
geusb-8357	101	12	lakes	lake	NOUN
geusb-8357	101	13	and	and	CCONJ
geusb-8357	101	14	fjords	fjord	NOUN
geusb-8357	101	15	(	(	PUNCT
geusb-8357	101	16	green	green	NOUN
geusb-8357	101	17	et	et	PROPN
geusb-8357	101	18	al	al	PROPN
geusb-8357	101	19	.	.	PROPN
geusb-8357	101	20	2011	2011	NUM
geusb-8357	101	21	)	)	PUNCT
geusb-8357	101	22	and	and	CCONJ
geusb-8357	101	23	wells	well	NOUN
geusb-8357	101	24	(	(	PUNCT
geusb-8357	101	25	red	red	ADJ
geusb-8357	101	26	;	;	PUNCT
geusb-8357	101	27	well	well	ADV
geusb-8357	101	28	)	)	PUNCT
geusb-8357	101	29	are	be	AUX
geusb-8357	101	30	modelled	model	VERB
geusb-8357	101	31	as	as	ADP
geusb-8357	101	32	boundary	boundary	ADJ
geusb-8357	101	33	conditions	condition	NOUN
geusb-8357	101	34	shown	show	VERB
geusb-8357	101	35	on	on	ADP
geusb-8357	101	36	top	top	NOUN
geusb-8357	101	37	of	of	ADP
geusb-8357	101	38	a	a	DET
geusb-8357	101	39	topographic	topographic	NOUN
geusb-8357	101	40	map	map	NOUN
geusb-8357	101	41	.	.	PUNCT
geusb-8357	102	1	b	b	X
geusb-8357	102	2	–	–	PUNCT
geusb-8357	102	3	e	e	NOUN
geusb-8357	102	4	:	:	PUNCT
geusb-8357	102	5	examples	example	NOUN
geusb-8357	102	6	of	of	ADP
geusb-8357	102	7	input	input	NOUN
geusb-8357	102	8	features	feature	NOUN
geusb-8357	102	9	to	to	ADP
geusb-8357	102	10	the	the	DET
geusb-8357	102	11	neural	neural	ADJ
geusb-8357	102	12	network	network	NOUN
geusb-8357	102	13	.	.	PUNCT
geusb-8357	103	1	drn	drn	PROPN
geusb-8357	103	2	-	-	PUNCT
geusb-8357	103	3	riv	riv	NOUN
geusb-8357	103	4	:	:	PUNCT
geusb-8357	103	5	drain	drain	NOUN
geusb-8357	103	6	package	package	NOUN
geusb-8357	103	7	.	.	PUNCT
geusb-8357	104	1	ghb	ghb	NOUN
geusb-8357	104	2	:	:	PUNCT
geusb-8357	104	3	general	general	ADJ
geusb-8357	104	4	head	head	NOUN
geusb-8357	104	5	boundary	boundary	ADJ
geusb-8357	104	6	package	package	NOUN
geusb-8357	104	7	.	.	PUNCT
geusb-8357	105	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	105	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	105	3	dahl	dahl	PROPN
geusb-8357	105	4	et	et	PROPN
geusb-8357	105	5	al	al	PROPN
geusb-8357	105	6	.	.	PROPN
geusb-8357	105	7	2023	2023	NUM
geusb-8357	105	8	:	:	PUNCT
geusb-8357	105	9	geus	geus	NOUN
geusb-8357	105	10	bulletin	bulletin	NOUN
geusb-8357	105	11	53	53	NUM
geusb-8357	105	12	.	.	PUNCT
geusb-8357	105	13	8357	8357	NUM
geusb-8357	105	14	.	.	PUNCT
geusb-8357	106	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	106	2	4	4	NUM
geusb-8357	106	3	of	of	ADP
geusb-8357	106	4	7	7	NUM
geusb-8357	106	5	www.geusbul	www.geusbul	NOUN
geusb-8357	106	6	let	let	VERB
geusb-8357	106	7	in.org	in.org	ADJ
geusb-8357	106	8	the	the	DET
geusb-8357	106	9	network	network	NOUN
geusb-8357	106	10	is	be	AUX
geusb-8357	106	11	visualised	visualise	VERB
geusb-8357	106	12	in	in	ADP
geusb-8357	106	13	fig	fig	NOUN
geusb-8357	106	14	.	.	PUNCT
geusb-8357	107	1	3d	3d	NUM
geusb-8357	107	2	.	.	PUNCT
geusb-8357	108	1	the	the	DET
geusb-8357	108	2	network	network	NOUN
geusb-8357	108	3	replicates	replicate	VERB
geusb-8357	108	4	the	the	DET
geusb-8357	108	5	modflow	modflow	ADJ
geusb-8357	108	6	results	result	NOUN
geusb-8357	108	7	well	well	ADV
geusb-8357	108	8	with	with	ADP
geusb-8357	108	9	low	low	ADJ
geusb-8357	108	10	differences	difference	NOUN
geusb-8357	108	11	between	between	ADP
geusb-8357	108	12	the	the	DET
geusb-8357	108	13	two	two	NUM
geusb-8357	108	14	in	in	ADP
geusb-8357	108	15	most	most	ADJ
geusb-8357	108	16	parts	part	NOUN
geusb-8357	108	17	of	of	ADP
geusb-8357	108	18	the	the	DET
geusb-8357	108	19	layer	layer	NOUN
geusb-8357	108	20	.	.	PUNCT
geusb-8357	109	1	we	we	PRON
geusb-8357	109	2	observe	observe	VERB
geusb-8357	109	3	some	some	DET
geusb-8357	109	4	areas	area	NOUN
geusb-8357	109	5	with	with	ADP
geusb-8357	109	6	larger	large	ADJ
geusb-8357	109	7	differences	difference	NOUN
geusb-8357	109	8	that	that	PRON
geusb-8357	109	9	seem	seem	VERB
geusb-8357	109	10	to	to	PART
geusb-8357	109	11	correlate	correlate	VERB
geusb-8357	109	12	with	with	ADP
geusb-8357	109	13	locations	location	NOUN
geusb-8357	109	14	of	of	ADP
geusb-8357	109	15	boundary	boundary	ADJ
geusb-8357	109	16	conditions	condition	NOUN
geusb-8357	109	17	such	such	ADJ
geusb-8357	109	18	as	as	ADP
geusb-8357	109	19	simulated	simulated	ADJ
geusb-8357	109	20	rivers	river	NOUN
geusb-8357	109	21	and	and	CCONJ
geusb-8357	109	22	other	other	ADJ
geusb-8357	109	23	wells	well	NOUN
geusb-8357	109	24	.	.	PUNCT
geusb-8357	110	1	also	also	ADV
geusb-8357	110	2	,	,	PUNCT
geusb-8357	110	3	positive	positive	ADJ
geusb-8357	110	4	values	value	NOUN
geusb-8357	110	5	(	(	PUNCT
geusb-8357	110	6	in	in	ADP
geusb-8357	110	7	fig	fig	NOUN
geusb-8357	110	8	.	.	PUNCT
geusb-8357	111	1	3d	3d	NUM
geusb-8357	111	2	)	)	PUNCT
geusb-8357	111	3	are	be	AUX
geusb-8357	111	4	observed	observe	VERB
geusb-8357	111	5	near	near	ADP
geusb-8357	111	6	the	the	DET
geusb-8357	111	7	well	well	NOUN
geusb-8357	111	8	,	,	PUNCT
geusb-8357	111	9	indicating	indicate	VERB
geusb-8357	111	10	that	that	SCONJ
geusb-8357	111	11	the	the	DET
geusb-8357	111	12	neural	neural	ADJ
geusb-8357	111	13	network	network	NOUN
geusb-8357	111	14	slightly	slightly	ADV
geusb-8357	111	15	underestimates	underestimate	VERB
geusb-8357	111	16	the	the	DET
geusb-8357	111	17	drawdown	drawdown	NOUN
geusb-8357	111	18	in	in	ADP
geusb-8357	111	19	this	this	DET
geusb-8357	111	20	area	area	NOUN
geusb-8357	111	21	.	.	PUNCT
geusb-8357	112	1	these	these	DET
geusb-8357	112	2	difficult	difficult	ADJ
geusb-8357	112	3	areas	area	NOUN
geusb-8357	112	4	are	be	AUX
geusb-8357	112	5	also	also	ADV
geusb-8357	112	6	visible	visible	ADJ
geusb-8357	112	7	in	in	ADP
geusb-8357	112	8	the	the	DET
geusb-8357	112	9	standard	standard	ADJ
geusb-8357	112	10	deviation	deviation	NOUN
geusb-8357	112	11	map	map	NOUN
geusb-8357	112	12	of	of	ADP
geusb-8357	112	13	the	the	DET
geusb-8357	112	14	neural	neural	ADJ
geusb-8357	112	15	network	network	NOUN
geusb-8357	112	16	where	where	SCONJ
geusb-8357	112	17	locations	location	NOUN
geusb-8357	112	18	of	of	ADP
geusb-8357	112	19	other	other	ADJ
geusb-8357	112	20	wells	well	NOUN
geusb-8357	112	21	are	be	AUX
geusb-8357	112	22	clearly	clearly	ADV
geusb-8357	112	23	observed	observe	VERB
geusb-8357	112	24	.	.	PUNCT
geusb-8357	113	1	in	in	ADP
geusb-8357	113	2	case	case	NOUN
geusb-8357	113	3	2	2	NUM
geusb-8357	113	4	(	(	PUNCT
geusb-8357	113	5	fig	fig	NOUN
geusb-8357	113	6	.	.	PUNCT
geusb-8357	113	7	3e	3e	NOUN
geusb-8357	113	8	)	)	PUNCT
geusb-8357	113	9	,	,	PUNCT
geusb-8357	113	10	the	the	DET
geusb-8357	113	11	well	well	NOUN
geusb-8357	113	12	is	be	AUX
geusb-8357	113	13	placed	place	VERB
geusb-8357	113	14	in	in	ADP
geusb-8357	113	15	layer	layer	NOUN
geusb-8357	113	16	6	6	NUM
geusb-8357	113	17	at	at	ADP
geusb-8357	113	18	row	row	NOUN
geusb-8357	113	19	85	85	NUM
geusb-8357	113	20	and	and	CCONJ
geusb-8357	113	21	column	column	NOUN
geusb-8357	113	22	47	47	NUM
geusb-8357	113	23	with	with	ADP
geusb-8357	113	24	a	a	DET
geusb-8357	113	25	pumping	pumping	NOUN
geusb-8357	113	26	rate	rate	NOUN
geusb-8357	113	27	of	of	ADP
geusb-8357	113	28	849	849	NUM
geusb-8357	113	29	m3·day–1	m3·day–1	NOUN
geusb-8357	113	30	.	.	PUNCT
geusb-8357	113	31	again	again	ADV
geusb-8357	113	32	,	,	PUNCT
geusb-8357	113	33	modflow	modflow	ADJ
geusb-8357	113	34	results	result	NOUN
geusb-8357	113	35	are	be	AUX
geusb-8357	113	36	compared	compare	VERB
geusb-8357	113	37	to	to	ADP
geusb-8357	113	38	the	the	DET
geusb-8357	113	39	neural	neural	ADJ
geusb-8357	113	40	network	network	NOUN
geusb-8357	113	41	(	(	PUNCT
geusb-8357	113	42	fig	fig	NOUN
geusb-8357	113	43	.	.	PUNCT
geusb-8357	114	1	3f	3f	PROPN
geusb-8357	114	2	–	–	PUNCT
geusb-8357	114	3	g	g	NOUN
geusb-8357	114	4	)	)	PUNCT
geusb-8357	115	1	and	and	CCONJ
geusb-8357	115	2	subtracted	subtract	VERB
geusb-8357	115	3	to	to	PART
geusb-8357	115	4	show	show	VERB
geusb-8357	115	5	the	the	DET
geusb-8357	115	6	difference	difference	NOUN
geusb-8357	115	7	(	(	PUNCT
geusb-8357	115	8	fig	fig	NOUN
geusb-8357	115	9	.	.	PUNCT
geusb-8357	116	1	3h	3h	NUM
geusb-8357	116	2	)	)	PUNCT
geusb-8357	116	3	.	.	PUNCT
geusb-8357	117	1	the	the	DET
geusb-8357	117	2	network	network	NOUN
geusb-8357	117	3	models	model	VERB
geusb-8357	117	4	the	the	DET
geusb-8357	117	5	overall	overall	ADJ
geusb-8357	117	6	extent	extent	NOUN
geusb-8357	117	7	of	of	ADP
geusb-8357	117	8	the	the	DET
geusb-8357	117	9	change	change	NOUN
geusb-8357	117	10	in	in	ADP
geusb-8357	117	11	layer	layer	NOUN
geusb-8357	117	12	,	,	PUNCT
geusb-8357	117	13	though	though	SCONJ
geusb-8357	117	14	the	the	DET
geusb-8357	117	15	discrepancy	discrepancy	NOUN
geusb-8357	117	16	between	between	ADP
geusb-8357	117	17	modflow	modflow	ADJ
geusb-8357	117	18	and	and	CCONJ
geusb-8357	117	19	network	network	NOUN
geusb-8357	117	20	predictions	prediction	NOUN
geusb-8357	117	21	is	be	AUX
geusb-8357	117	22	larger	large	ADJ
geusb-8357	117	23	compared	compare	VERB
geusb-8357	117	24	to	to	ADP
geusb-8357	117	25	case	case	NOUN
geusb-8357	117	26	1	1	NUM
geusb-8357	117	27	.	.	PUNCT
geusb-8357	117	28	high	high	ADJ
geusb-8357	117	29	differences	difference	NOUN
geusb-8357	117	30	are	be	AUX
geusb-8357	117	31	especially	especially	ADV
geusb-8357	117	32	apparent	apparent	ADJ
geusb-8357	117	33	at	at	ADP
geusb-8357	117	34	the	the	DET
geusb-8357	117	35	nearby	nearby	ADJ
geusb-8357	117	36	boundary	boundary	ADJ
geusb-8357	117	37	conditions	condition	NOUN
geusb-8357	117	38	.	.	PUNCT
geusb-8357	118	1	again	again	ADV
geusb-8357	118	2	,	,	PUNCT
geusb-8357	118	3	this	this	DET
geusb-8357	118	4	difference	difference	NOUN
geusb-8357	118	5	between	between	ADP
geusb-8357	118	6	results	result	NOUN
geusb-8357	118	7	is	be	AUX
geusb-8357	118	8	correlated	correlate	VERB
geusb-8357	118	9	to	to	ADP
geusb-8357	118	10	the	the	DET
geusb-8357	118	11	standard	standard	ADJ
geusb-8357	118	12	deviation	deviation	NOUN
geusb-8357	118	13	map	map	NOUN
geusb-8357	118	14	in	in	ADP
geusb-8357	118	15	fig	fig	NOUN
geusb-8357	118	16	.	.	PUNCT
geusb-8357	119	1	3	3	NUM
geusb-8357	119	2	g	g	NOUN
geusb-8357	119	3	,	,	PUNCT
geusb-8357	119	4	where	where	SCONJ
geusb-8357	119	5	larger	large	ADJ
geusb-8357	119	6	standard	standard	ADJ
geusb-8357	119	7	deviations	deviation	NOUN
geusb-8357	119	8	are	be	AUX
geusb-8357	119	9	observed	observe	VERB
geusb-8357	119	10	.	.	PUNCT
geusb-8357	120	1	0	0	NUM
geusb-8357	120	2	1	1	NUM
geusb-8357	120	3	2	2	NUM
geusb-8357	120	4	3	3	NUM
geusb-8357	120	5	4	4	NUM
geusb-8357	120	6	5	5	NUM
geusb-8357	120	7	6	6	NUM
geusb-8357	120	8	7	7	NUM
geusb-8357	120	9	8	8	NUM
geusb-8357	120	10	9	9	NUM
geusb-8357	120	11	predicted	predict	VERB
geusb-8357	120	12	drawdown	drawdown	NOUN
geusb-8357	120	13	[	[	X
geusb-8357	120	14	m	m	X
geusb-8357	120	15	]	]	X
geusb-8357	120	16	0	0	NUM
geusb-8357	120	17	1	1	NUM
geusb-8357	120	18	2	2	NUM
geusb-8357	120	19	3	3	NUM
geusb-8357	120	20	4	4	NUM
geusb-8357	120	21	5	5	NUM
geusb-8357	120	22	6	6	NUM
geusb-8357	120	23	7	7	NUM
geusb-8357	120	24	8	8	NUM
geusb-8357	120	25	9	9	NUM
geusb-8357	120	26	o	o	NOUN
geusb-8357	120	27	bs	bs	NOUN
geusb-8357	120	28	er	er	INTJ
geusb-8357	120	29	ve	ve	PROPN
geusb-8357	120	30	d	d	NOUN
geusb-8357	120	31	dr	dr	PROPN
geusb-8357	121	1	aw	aw	INTJ
geusb-8357	121	2	do	do	VERB
geusb-8357	121	3	w	w	NOUN
geusb-8357	121	4	n	n	PROPN
geusb-8357	122	1	[	[	X
geusb-8357	122	2	m	m	X
geusb-8357	122	3	]	]	X
geusb-8357	122	4	a	a	DET
geusb-8357	122	5	validation	validation	NOUN
geusb-8357	122	6	data	datum	NOUN
geusb-8357	122	7	per	per	ADP
geusb-8357	122	8	formance	formance	NOUN
geusb-8357	122	9	95	95	NUM
geusb-8357	122	10	%	%	NOUN
geusb-8357	122	11	contour	contour	NOUN
geusb-8357	122	12	line	line	NOUN
geusb-8357	122	13	identity	identity	NOUN
geusb-8357	122	14	l	l	NOUN
geusb-8357	122	15	ine	ine	PROPN
geusb-8357	122	16	0	0	NUM
geusb-8357	122	17	1	1	NUM
geusb-8357	122	18	2	2	NUM
geusb-8357	122	19	3	3	NUM
geusb-8357	122	20	4	4	NUM
geusb-8357	122	21	5	5	NUM
geusb-8357	122	22	6	6	NUM
geusb-8357	122	23	7	7	NUM
geusb-8357	122	24	8	8	NUM
geusb-8357	122	25	9	9	NUM
geusb-8357	122	26	predicted	predict	VERB
geusb-8357	122	27	drawdown	drawdown	NOUN
geusb-8357	123	1	[	[	X
geusb-8357	123	2	m	m	X
geusb-8357	123	3	]	]	X
geusb-8357	123	4	0	0	NUM
geusb-8357	123	5	1	1	NUM
geusb-8357	123	6	2	2	NUM
geusb-8357	123	7	3	3	NUM
geusb-8357	123	8	4	4	NUM
geusb-8357	123	9	5	5	NUM
geusb-8357	123	10	6	6	NUM
geusb-8357	123	11	7	7	NUM
geusb-8357	123	12	8	8	NUM
geusb-8357	123	13	9	9	NUM
geusb-8357	123	14	o	o	NOUN
geusb-8357	123	15	bs	bs	NOUN
geusb-8357	123	16	er	er	INTJ
geusb-8357	123	17	ve	ve	PROPN
geusb-8357	123	18	d	d	NOUN
geusb-8357	123	19	dr	dr	PROPN
geusb-8357	123	20	aw	aw	INTJ
geusb-8357	123	21	do	do	VERB
geusb-8357	123	22	w	w	NOUN
geusb-8357	123	23	n	n	PROPN
geusb-8357	124	1	[	[	X
geusb-8357	124	2	m	m	X
geusb-8357	124	3	]	]	X
geusb-8357	124	4	b	b	X
geusb-8357	124	5	test	test	NOUN
geusb-8357	124	6	data	datum	NOUN
geusb-8357	124	7	per	per	ADP
geusb-8357	124	8	formance	formance	NOUN
geusb-8357	124	9	95	95	NUM
geusb-8357	124	10	%	%	NOUN
geusb-8357	124	11	contour	contour	NOUN
geusb-8357	124	12	line	line	NOUN
geusb-8357	124	13	identity	identity	NOUN
geusb-8357	125	1	l	l	NOUN
geusb-8357	125	2	ine	ine	PROPN
geusb-8357	125	3	true	true	PROPN
geusb-8357	125	4	predicted	predict	VERB
geusb-8357	126	1	[	[	X
geusb-8357	127	1	m	m	X
geusb-8357	127	2	]	]	X
geusb-8357	127	3	0	0	NUM
geusb-8357	127	4	5	5	NUM
geusb-8357	127	5	10	10	NUM
geusb-8357	127	6	15	15	NUM
geusb-8357	127	7	20	20	NUM
geusb-8357	127	8	25	25	NUM
geusb-8357	127	9	d	d	NOUN
geusb-8357	127	10	en	en	ADP
geusb-8357	127	11	si	si	X
geusb-8357	127	12	ty	ty	INTJ
geusb-8357	127	13	c	c	NOUN
geusb-8357	127	14	difference	difference	NOUN
geusb-8357	127	15	between	between	ADP
geusb-8357	127	16	true	true	ADJ
geusb-8357	127	17	and	and	CCONJ
geusb-8357	127	18	predicted	predict	VERB
geusb-8357	127	19	head	head	NOUN
geusb-8357	127	20	test	test	NOUN
geusb-8357	127	21	data	data	PROPN
geusb-8357	127	22	validation	validation	PROPN
geusb-8357	127	23	data	datum	NOUN
geusb-8357	127	24	z	z	NOUN
geusb-8357	127	25	-	-	PUNCT
geusb-8357	127	26	score	score	NOUN
geusb-8357	127	27	0.00	0.00	NUM
geusb-8357	127	28	0.05	0.05	NUM
geusb-8357	127	29	0.10	0.10	NUM
geusb-8357	127	30	0.15	0.15	NUM
geusb-8357	127	31	0.20	0.20	NUM
geusb-8357	127	32	0.25	0.25	NUM
geusb-8357	127	33	0.30	0.30	NUM
geusb-8357	127	34	0.35	0.35	NUM
geusb-8357	127	35	0.40	0.40	NUM
geusb-8357	127	36	d	d	NOUN
geusb-8357	127	37	en	en	ADP
geusb-8357	127	38	si	si	PROPN
geusb-8357	128	1	ty	ty	INTJ
geusb-8357	128	2	d	d	PROPN
geusb-8357	128	3	z	z	NOUN
geusb-8357	128	4	-	-	PUNCT
geusb-8357	128	5	scores	score	NOUN
geusb-8357	128	6	of	of	ADP
geusb-8357	128	7	test	test	NOUN
geusb-8357	128	8	and	and	CCONJ
geusb-8357	128	9	validation	validation	NOUN
geusb-8357	128	10	data	data	PROPN
geusb-8357	128	11	test	test	NOUN
geusb-8357	128	12	data	data	PROPN
geusb-8357	128	13	validation	validation	NOUN
geusb-8357	128	14	data	datum	NOUN
geusb-8357	128	15	log	log	VERB
geusb-8357	128	16	density	density	NOUN
geusb-8357	128	17	log	log	NOUN
geusb-8357	128	18	density	density	NOUN
geusb-8357	128	19	10	10	NUM
geusb-8357	128	20	–	–	PUNCT
geusb-8357	128	21	6	6	NUM
geusb-8357	128	22	10	10	NUM
geusb-8357	128	23	–	–	PUNCT
geusb-8357	128	24	5	5	NUM
geusb-8357	128	25	10	10	NUM
geusb-8357	128	26	–	–	PUNCT
geusb-8357	128	27	4	4	NUM
geusb-8357	128	28	10	10	NUM
geusb-8357	128	29	–	–	PUNCT
geusb-8357	128	30	3	3	NUM
geusb-8357	128	31	10	10	NUM
geusb-8357	128	32	–	–	SYM
geusb-8357	128	33	2	2	NUM
geusb-8357	128	34	10	10	NUM
geusb-8357	128	35	–	–	SYM
geusb-8357	128	36	1	1	NUM
geusb-8357	128	37	10	10	NUM
geusb-8357	128	38	–	–	PUNCT
geusb-8357	128	39	6	6	NUM
geusb-8357	128	40	10	10	NUM
geusb-8357	128	41	–	–	PUNCT
geusb-8357	128	42	5	5	NUM
geusb-8357	128	43	10	10	NUM
geusb-8357	128	44	–	–	PUNCT
geusb-8357	128	45	4	4	NUM
geusb-8357	128	46	10	10	NUM
geusb-8357	128	47	–	–	PUNCT
geusb-8357	128	48	3	3	NUM
geusb-8357	128	49	10	10	NUM
geusb-8357	128	50	–	–	SYM
geusb-8357	128	51	2	2	NUM
geusb-8357	128	52	10	10	NUM
geusb-8357	128	53	–	–	SYM
geusb-8357	128	54	1	1	NUM
geusb-8357	128	55	–	–	SYM
geusb-8357	128	56	0.6	0.6	NUM
geusb-8357	128	57	–	–	PUNCT
geusb-8357	128	58	0.4	0.4	NUM
geusb-8357	128	59	0.0–0.2	0.0–0.2	NOUN
geusb-8357	128	60	0.2	0.2	NUM
geusb-8357	128	61	0.4	0.4	NUM
geusb-8357	128	62	0.6	0.6	NUM
geusb-8357	128	63	–	–	PUNCT
geusb-8357	128	64	10.0	10.0	NUM
geusb-8357	128	65	–	–	SYM
geusb-8357	128	66	7.5	7.5	NUM
geusb-8357	128	67	–	–	SYM
geusb-8357	128	68	2.5–5.0	2.5–5.0	NUM
geusb-8357	128	69	0.0	0.0	NUM
geusb-8357	128	70	2.5	2.5	NUM
geusb-8357	128	71	5.0	5.0	NUM
geusb-8357	128	72	7.5	7.5	NUM
geusb-8357	128	73	10.0	10.0	NUM
geusb-8357	128	74	fig	fig	NOUN
geusb-8357	128	75	.	.	PUNCT
geusb-8357	129	1	2	2	NUM
geusb-8357	130	1	the	the	DET
geusb-8357	130	2	performances	performance	NOUN
geusb-8357	130	3	of	of	ADP
geusb-8357	130	4	the	the	DET
geusb-8357	130	5	trained	train	VERB
geusb-8357	130	6	neural	neural	ADJ
geusb-8357	130	7	network	network	NOUN
geusb-8357	130	8	on	on	ADP
geusb-8357	130	9	the	the	DET
geusb-8357	130	10	validation	validation	NOUN
geusb-8357	130	11	data	datum	NOUN
geusb-8357	130	12	set	set	VERB
geusb-8357	130	13	and	and	CCONJ
geusb-8357	130	14	an	an	DET
geusb-8357	130	15	independent	independent	ADJ
geusb-8357	130	16	test	test	NOUN
geusb-8357	130	17	data	datum	NOUN
geusb-8357	130	18	set	set	VERB
geusb-8357	130	19	are	be	AUX
geusb-8357	130	20	shown	show	VERB
geusb-8357	130	21	as	as	ADP
geusb-8357	130	22	density	density	NOUN
geusb-8357	130	23	plots	plot	NOUN
geusb-8357	130	24	a	a	PRON
geusb-8357	130	25	and	and	CCONJ
geusb-8357	130	26	b.	b.	VERB
geusb-8357	130	27	the	the	DET
geusb-8357	130	28	x	x	NOUN
geusb-8357	130	29	-	-	NOUN
geusb-8357	130	30	axis	axis	NOUN
geusb-8357	130	31	represents	represent	VERB
geusb-8357	130	32	the	the	DET
geusb-8357	130	33	predicted	predict	VERB
geusb-8357	130	34	mean	mean	ADJ
geusb-8357	130	35	values	value	NOUN
geusb-8357	130	36	of	of	ADP
geusb-8357	130	37	the	the	DET
geusb-8357	130	38	drawdown	drawdown	NOUN
geusb-8357	130	39	,	,	PUNCT
geusb-8357	130	40	whilst	whilst	SCONJ
geusb-8357	130	41	the	the	DET
geusb-8357	130	42	y	y	NOUN
geusb-8357	130	43	-	-	PUNCT
geusb-8357	130	44	axis	axis	NOUN
geusb-8357	130	45	represents	represent	VERB
geusb-8357	130	46	the	the	DET
geusb-8357	130	47	observed	observed	ADJ
geusb-8357	130	48	modflow	modflow	ADJ
geusb-8357	130	49	drawdown	drawdown	NOUN
geusb-8357	130	50	.	.	PUNCT
geusb-8357	131	1	each	each	DET
geusb-8357	131	2	square	square	NOUN
geusb-8357	131	3	in	in	ADP
geusb-8357	131	4	the	the	DET
geusb-8357	131	5	figures	figure	NOUN
geusb-8357	131	6	covers	cover	VERB
geusb-8357	131	7	several	several	ADJ
geusb-8357	131	8	observations	observation	NOUN
geusb-8357	131	9	visualised	visualise	VERB
geusb-8357	131	10	with	with	ADP
geusb-8357	131	11	a	a	DET
geusb-8357	131	12	normalised	normalise	VERB
geusb-8357	131	13	logarithmic	logarithmic	ADJ
geusb-8357	131	14	colour	colour	NOUN
geusb-8357	131	15	scale	scale	NOUN
geusb-8357	131	16	.	.	PUNCT
geusb-8357	132	1	the	the	DET
geusb-8357	132	2	identity	identity	NOUN
geusb-8357	132	3	line	line	NOUN
geusb-8357	132	4	(	(	PUNCT
geusb-8357	132	5	blue	blue	ADJ
geusb-8357	132	6	dashed	dash	VERB
geusb-8357	132	7	line	line	NOUN
geusb-8357	132	8	)	)	PUNCT
geusb-8357	132	9	shows	show	VERB
geusb-8357	132	10	the	the	DET
geusb-8357	132	11	one	one	NUM
geusb-8357	132	12	-	-	PUNCT
geusb-8357	132	13	toone	toone	NOUN
geusb-8357	132	14	agreement	agreement	NOUN
geusb-8357	132	15	between	between	ADP
geusb-8357	132	16	modflow	modflow	NOUN
geusb-8357	132	17	and	and	CCONJ
geusb-8357	132	18	the	the	DET
geusb-8357	132	19	network	network	NOUN
geusb-8357	132	20	.	.	PUNCT
geusb-8357	133	1	the	the	DET
geusb-8357	133	2	white	white	ADJ
geusb-8357	133	3	contour	contour	NOUN
geusb-8357	133	4	line	line	NOUN
geusb-8357	133	5	holds	hold	VERB
geusb-8357	133	6	95	95	NUM
geusb-8357	133	7	%	%	NOUN
geusb-8357	133	8	of	of	ADP
geusb-8357	133	9	the	the	DET
geusb-8357	133	10	data	datum	NOUN
geusb-8357	133	11	within	within	ADV
geusb-8357	133	12	.	.	PUNCT
geusb-8357	134	1	c	c	PROPN
geusb-8357	134	2	shows	show	VERB
geusb-8357	134	3	the	the	DET
geusb-8357	134	4	difference	difference	NOUN
geusb-8357	134	5	between	between	ADP
geusb-8357	134	6	the	the	DET
geusb-8357	134	7	true	true	ADJ
geusb-8357	134	8	modflow	modflow	ADJ
geusb-8357	134	9	values	value	NOUN
geusb-8357	134	10	and	and	CCONJ
geusb-8357	134	11	predicted	predict	VERB
geusb-8357	134	12	values	value	NOUN
geusb-8357	134	13	for	for	ADP
geusb-8357	134	14	both	both	DET
geusb-8357	134	15	data	datum	NOUN
geusb-8357	134	16	sets	set	NOUN
geusb-8357	134	17	as	as	ADP
geusb-8357	134	18	a	a	DET
geusb-8357	134	19	probability	probability	NOUN
geusb-8357	134	20	density	density	NOUN
geusb-8357	134	21	histogram	histogram	NOUN
geusb-8357	134	22	.	.	PUNCT
geusb-8357	135	1	d	d	X
geusb-8357	135	2	depicts	depict	VERB
geusb-8357	135	3	the	the	DET
geusb-8357	135	4	distribution	distribution	NOUN
geusb-8357	135	5	of	of	ADP
geusb-8357	135	6	z	z	NOUN
geusb-8357	135	7	scores	score	NOUN
geusb-8357	135	8	for	for	ADP
geusb-8357	135	9	the	the	DET
geusb-8357	135	10	validation	validation	NOUN
geusb-8357	135	11	and	and	CCONJ
geusb-8357	135	12	test	test	NOUN
geusb-8357	135	13	data	datum	NOUN
geusb-8357	135	14	sets	set	NOUN
geusb-8357	135	15	.	.	PUNCT
geusb-8357	136	1	brown	brown	ADJ
geusb-8357	136	2	shading	shading	NOUN
geusb-8357	136	3	:	:	PUNCT
geusb-8357	136	4	overlap	overlap	NOUN
geusb-8357	136	5	between	between	ADP
geusb-8357	136	6	test	test	NOUN
geusb-8357	136	7	data	datum	NOUN
geusb-8357	136	8	and	and	CCONJ
geusb-8357	136	9	validation	validation	NOUN
geusb-8357	136	10	data	datum	NOUN
geusb-8357	136	11	sets	set	NOUN
geusb-8357	136	12	.	.	PUNCT
geusb-8357	137	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	137	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	137	3	dahl	dahl	PROPN
geusb-8357	137	4	et	et	PROPN
geusb-8357	137	5	al	al	PROPN
geusb-8357	137	6	.	.	PROPN
geusb-8357	137	7	2023	2023	NUM
geusb-8357	137	8	:	:	PUNCT
geusb-8357	137	9	geus	geus	NOUN
geusb-8357	137	10	bulletin	bulletin	NOUN
geusb-8357	137	11	53	53	NUM
geusb-8357	137	12	.	.	PUNCT
geusb-8357	137	13	8357	8357	NUM
geusb-8357	137	14	.	.	PUNCT
geusb-8357	138	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	138	2	5	5	NUM
geusb-8357	138	3	of	of	ADP
geusb-8357	138	4	7	7	NUM
geusb-8357	138	5	www.geusbul	www.geusbul	NOUN
geusb-8357	138	6	let	let	VERB
geusb-8357	138	7	in.org	in.org	ADJ
geusb-8357	138	8	predicting	predict	VERB
geusb-8357	138	9	the	the	DET
geusb-8357	138	10	full	full	ADJ
geusb-8357	138	11	model	model	NOUN
geusb-8357	138	12	drawdown	drawdown	NOUN
geusb-8357	138	13	with	with	ADP
geusb-8357	138	14	the	the	DET
geusb-8357	138	15	network	network	NOUN
geusb-8357	138	16	takes	take	VERB
geusb-8357	138	17	0.60	0.60	NUM
geusb-8357	138	18	±	±	NUM
geusb-8357	138	19	0.01	0.01	NUM
geusb-8357	138	20	s	s	NOUN
geusb-8357	138	21	,	,	PUNCT
geusb-8357	138	22	related	relate	VERB
geusb-8357	138	23	to	to	ADP
geusb-8357	138	24	running	run	VERB
geusb-8357	138	25	the	the	DET
geusb-8357	138	26	neural	neural	ADJ
geusb-8357	138	27	network	network	NOUN
geusb-8357	138	28	and	and	CCONJ
geusb-8357	138	29	input	input	NOUN
geusb-8357	138	30	-	-	PUNCT
geusb-8357	138	31	feature	feature	NOUN
geusb-8357	138	32	creation	creation	NOUN
geusb-8357	138	33	.	.	PUNCT
geusb-8357	139	1	the	the	DET
geusb-8357	139	2	run	run	NOUN
geusb-8357	139	3	-	-	PUNCT
geusb-8357	139	4	time	time	NOUN
geusb-8357	139	5	is	be	AUX
geusb-8357	139	6	3.2	3.2	NUM
geusb-8357	139	7	±	±	NUM
geusb-8357	139	8	0.1	0.1	NUM
geusb-8357	139	9	s	s	NOUN
geusb-8357	139	10	for	for	ADP
geusb-8357	139	11	modflow	modflow	ADJ
geusb-8357	139	12	simulations	simulation	NOUN
geusb-8357	139	13	.	.	PUNCT
geusb-8357	140	1	with	with	ADP
geusb-8357	140	2	the	the	DET
geusb-8357	140	3	pumping	pumping	NOUN
geusb-8357	140	4	rate	rate	NOUN
geusb-8357	140	5	as	as	ADP
geusb-8357	140	6	an	an	DET
geusb-8357	140	7	input	input	NOUN
geusb-8357	140	8	feature	feature	NOUN
geusb-8357	140	9	,	,	PUNCT
geusb-8357	140	10	it	it	PRON
geusb-8357	140	11	is	be	AUX
geusb-8357	140	12	possible	possible	ADJ
geusb-8357	140	13	to	to	PART
geusb-8357	140	14	perform	perform	VERB
geusb-8357	140	15	tests	test	NOUN
geusb-8357	140	16	of	of	ADP
geusb-8357	140	17	pumping	pump	VERB
geusb-8357	140	18	series	series	NOUN
geusb-8357	140	19	where	where	SCONJ
geusb-8357	140	20	pumping	pumping	NOUN
geusb-8357	140	21	rates	rate	NOUN
geusb-8357	140	22	are	be	AUX
geusb-8357	140	23	gradually	gradually	ADV
geusb-8357	140	24	increased	increase	VERB
geusb-8357	140	25	,	,	PUNCT
geusb-8357	140	26	and	and	CCONJ
geusb-8357	140	27	drawdown	drawdown	NOUN
geusb-8357	140	28	is	be	AUX
geusb-8357	140	29	predicted	predict	VERB
geusb-8357	140	30	using	use	VERB
geusb-8357	140	31	the	the	DET
geusb-8357	140	32	neural	neural	ADJ
geusb-8357	140	33	network	network	NOUN
geusb-8357	140	34	.	.	PUNCT
geusb-8357	141	1	figure	figure	NOUN
geusb-8357	141	2	4	4	NUM
geusb-8357	141	3	shows	show	VERB
geusb-8357	141	4	a	a	DET
geusb-8357	141	5	drawdown	drawdown	NOUN
geusb-8357	141	6	in	in	ADP
geusb-8357	141	7	multiple	multiple	ADJ
geusb-8357	141	8	layers	layer	NOUN
geusb-8357	141	9	for	for	ADP
geusb-8357	141	10	this	this	DET
geusb-8357	141	11	test	test	NOUN
geusb-8357	141	12	where	where	SCONJ
geusb-8357	141	13	two	two	NUM
geusb-8357	141	14	different	different	ADJ
geusb-8357	141	15	pumping	pumping	NOUN
geusb-8357	141	16	rates	rate	NOUN
geusb-8357	141	17	are	be	AUX
geusb-8357	141	18	used	use	VERB
geusb-8357	141	19	with	with	ADP
geusb-8357	141	20	the	the	DET
geusb-8357	141	21	same	same	ADJ
geusb-8357	141	22	well	well	ADJ
geusb-8357	141	23	setup	setup	NOUN
geusb-8357	141	24	as	as	ADP
geusb-8357	141	25	in	in	ADP
geusb-8357	141	26	case	case	NOUN
geusb-8357	141	27	1	1	NUM
geusb-8357	141	28	(	(	PUNCT
geusb-8357	141	29	fig	fig	NOUN
geusb-8357	141	30	.	.	PUNCT
geusb-8357	142	1	3a	3a	NUM
geusb-8357	142	2	–	–	PUNCT
geusb-8357	142	3	d	d	NOUN
geusb-8357	142	4	)	)	PUNCT
geusb-8357	142	5	.	.	PUNCT
geusb-8357	143	1	here	here	ADV
geusb-8357	143	2	,	,	PUNCT
geusb-8357	143	3	all	all	DET
geusb-8357	143	4	input	input	NOUN
geusb-8357	143	5	features	feature	NOUN
geusb-8357	143	6	are	be	AUX
geusb-8357	143	7	determined	determine	VERB
geusb-8357	143	8	once	once	ADV
geusb-8357	143	9	,	,	PUNCT
geusb-8357	143	10	and	and	CCONJ
geusb-8357	143	11	only	only	ADV
geusb-8357	143	12	the	the	DET
geusb-8357	143	13	pumping	pump	VERB
geusb-8357	143	14	rate	rate	NOUN
geusb-8357	143	15	feature	feature	NOUN
geusb-8357	143	16	is	be	AUX
geusb-8357	143	17	changed	change	VERB
geusb-8357	143	18	for	for	ADP
geusb-8357	143	19	all	all	PRON
geusb-8357	143	20	following	follow	VERB
geusb-8357	143	21	network	network	NOUN
geusb-8357	143	22	predictions	prediction	NOUN
geusb-8357	143	23	.	.	PUNCT
geusb-8357	144	1	this	this	PRON
geusb-8357	144	2	reduces	reduce	VERB
geusb-8357	144	3	the	the	DET
geusb-8357	144	4	run	run	NOUN
geusb-8357	144	5	-	-	PUNCT
geusb-8357	144	6	time	time	NOUN
geusb-8357	144	7	to	to	ADP
geusb-8357	144	8	0.29	0.29	NUM
geusb-8357	144	9	±	±	NUM
geusb-8357	144	10	0.04	0.04	NUM
geusb-8357	144	11	s	s	NOUN
geusb-8357	144	12	per	per	ADP
geusb-8357	144	13	simulation	simulation	NOUN
geusb-8357	144	14	.	.	PUNCT
geusb-8357	145	1	discussion	discussion	NOUN
geusb-8357	145	2	we	we	PRON
geusb-8357	145	3	have	have	AUX
geusb-8357	145	4	trained	train	VERB
geusb-8357	145	5	a	a	DET
geusb-8357	145	6	neural	neural	ADJ
geusb-8357	145	7	network	network	NOUN
geusb-8357	145	8	to	to	PART
geusb-8357	145	9	replicate	replicate	VERB
geusb-8357	145	10	the	the	DET
geusb-8357	145	11	results	result	NOUN
geusb-8357	145	12	of	of	ADP
geusb-8357	145	13	simulated	simulate	VERB
geusb-8357	145	14	drawdown	drawdown	NOUN
geusb-8357	145	15	using	use	VERB
geusb-8357	145	16	modflow	modflow	NOUN
geusb-8357	145	17	due	due	ADP
geusb-8357	145	18	to	to	ADP
geusb-8357	145	19	groundwater	groundwater	NOUN
geusb-8357	145	20	abstraction	abstraction	NOUN
geusb-8357	145	21	in	in	ADP
geusb-8357	145	22	the	the	DET
geusb-8357	145	23	egebjerg	egebjerg	PROPN
geusb-8357	145	24	groundwater	groundwater	NOUN
geusb-8357	145	25	0	0	NUM
geusb-8357	146	1	20	20	NUM
geusb-8357	146	2	40	40	NUM
geusb-8357	146	3	60	60	NUM
geusb-8357	146	4	80	80	NUM
geusb-8357	146	5	100	100	NUM
geusb-8357	146	6	120	120	NUM
geusb-8357	146	7	140	140	NUM
geusb-8357	146	8	r	r	NOUN
geusb-8357	146	9	ow	ow	INTJ
geusb-8357	146	10	well	well	INTJ
geusb-8357	146	11	pos	pos	NOUN
geusb-8357	146	12	.	.	PUNCT
geusb-8357	147	1	layer	layer	NOUN
geusb-8357	147	2	:	:	PUNCT
geusb-8357	147	3	6	6	NUM
geusb-8357	147	4	row	row	NOUN
geusb-8357	147	5	:	:	PUNCT
geusb-8357	147	6	55	55	NUM
geusb-8357	147	7	col	col	NOUN
geusb-8357	147	8	.	.	PROPN
geusb-8357	147	9	:	:	PUNCT
geusb-8357	148	1	99	99	NUM
geusb-8357	148	2	a	a	DET
geusb-8357	148	3	modflow	modflow	ADJ
geusb-8357	148	4	b	b	PROPN
geusb-8357	148	5	neural	neural	ADJ
geusb-8357	148	6	network	network	NOUN
geusb-8357	148	7	mean	mean	VERB
geusb-8357	148	8	c	c	PROPN
geusb-8357	148	9	neural	neural	ADJ
geusb-8357	148	10	network	network	NOUN
geusb-8357	148	11	std	std	NOUN
geusb-8357	148	12	.	.	PUNCT
geusb-8357	149	1	d	d	NOUN
geusb-8357	149	2	difference	difference	NOUN
geusb-8357	149	3	0	0	NUM
geusb-8357	149	4	50	50	NUM
geusb-8357	149	5	100	100	NUM
geusb-8357	149	6	column	column	NOUN
geusb-8357	149	7	0	0	NUM
geusb-8357	149	8	20	20	NUM
geusb-8357	149	9	40	40	NUM
geusb-8357	149	10	60	60	NUM
geusb-8357	149	11	80	80	NUM
geusb-8357	149	12	100	100	NUM
geusb-8357	149	13	120	120	NUM
geusb-8357	149	14	140	140	NUM
geusb-8357	149	15	r	r	NOUN
geusb-8357	149	16	ow	ow	INTJ
geusb-8357	149	17	well	well	INTJ
geusb-8357	149	18	pos	pos	NOUN
geusb-8357	149	19	.	.	PUNCT
geusb-8357	150	1	layer	layer	NOUN
geusb-8357	150	2	:	:	PUNCT
geusb-8357	150	3	6	6	NUM
geusb-8357	150	4	row	row	NOUN
geusb-8357	150	5	:	:	PUNCT
geusb-8357	150	6	85	85	NUM
geusb-8357	150	7	col	col	NOUN
geusb-8357	150	8	.	.	PROPN
geusb-8357	150	9	:	:	PUNCT
geusb-8357	151	1	47	47	NUM
geusb-8357	151	2	e	e	NOUN
geusb-8357	151	3	modflow	modflow	NOUN
geusb-8357	151	4	0	0	NUM
geusb-8357	151	5	50	50	NUM
geusb-8357	151	6	100	100	NUM
geusb-8357	151	7	column	column	NOUN
geusb-8357	151	8	f	f	PROPN
geusb-8357	151	9	neural	neural	ADJ
geusb-8357	151	10	network	network	NOUN
geusb-8357	151	11	mean	mean	VERB
geusb-8357	151	12	0	0	NUM
geusb-8357	151	13	50	50	NUM
geusb-8357	151	14	100	100	NUM
geusb-8357	151	15	column	column	NOUN
geusb-8357	151	16	g	g	PROPN
geusb-8357	151	17	neural	neural	ADJ
geusb-8357	151	18	network	network	NOUN
geusb-8357	151	19	std	std	PROPN
geusb-8357	151	20	.	.	NOUN
geusb-8357	151	21	0	0	NUM
geusb-8357	151	22	50	50	NUM
geusb-8357	151	23	100	100	NUM
geusb-8357	151	24	column	column	NOUN
geusb-8357	151	25	h	h	NOUN
geusb-8357	151	26	difference	difference	NOUN
geusb-8357	151	27	0.0	0.0	NUM
geusb-8357	151	28	0.2	0.2	NUM
geusb-8357	151	29	0.4	0.4	NUM
geusb-8357	151	30	0.6	0.6	NUM
geusb-8357	151	31	0.8	0.8	NUM
geusb-8357	151	32	1.0	1.0	NUM
geusb-8357	151	33	d	d	NOUN
geusb-8357	151	34	raw	raw	ADJ
geusb-8357	151	35	dow	dow	NOUN
geusb-8357	152	1	n	n	PROPN
geusb-8357	153	1	[	[	X
geusb-8357	153	2	m	m	X
geusb-8357	153	3	]	]	X
geusb-8357	153	4	–	–	PUNCT
geusb-8357	153	5	0.2	0.2	NUM
geusb-8357	153	6	–	–	SYM
geusb-8357	153	7	0.1	0.1	NUM
geusb-8357	153	8	0.0	0.0	NUM
geusb-8357	153	9	0.1	0.1	NUM
geusb-8357	153	10	0.2	0.2	NUM
geusb-8357	153	11	d	d	NOUN
geusb-8357	153	12	ifference	ifference	NOUN
geusb-8357	154	1	[	[	X
geusb-8357	154	2	m	m	X
geusb-8357	154	3	]	]	X
geusb-8357	154	4	0.0	0.0	NUM
geusb-8357	154	5	0.2	0.2	NUM
geusb-8357	154	6	0.4	0.4	NUM
geusb-8357	154	7	0.6	0.6	NUM
geusb-8357	154	8	0.8	0.8	NUM
geusb-8357	154	9	1.0	1.0	NUM
geusb-8357	154	10	d	d	NOUN
geusb-8357	154	11	raw	raw	ADJ
geusb-8357	154	12	dow	dow	NOUN
geusb-8357	154	13	n	n	PROPN
geusb-8357	154	14	[	[	X
geusb-8357	154	15	m	m	X
geusb-8357	154	16	]	]	X
geusb-8357	154	17	–	–	PUNCT
geusb-8357	154	18	0.2	0.2	NUM
geusb-8357	154	19	–	–	SYM
geusb-8357	154	20	0.1	0.1	NUM
geusb-8357	154	21	0.0	0.0	NUM
geusb-8357	154	22	0.1	0.1	NUM
geusb-8357	154	23	0.2	0.2	NUM
geusb-8357	154	24	d	d	NOUN
geusb-8357	154	25	ifference	ifference	NOUN
geusb-8357	155	1	[	[	X
geusb-8357	155	2	m	m	X
geusb-8357	155	3	]	]	X
geusb-8357	155	4	fig	fig	NOUN
geusb-8357	155	5	.	.	PUNCT
geusb-8357	156	1	3	3	NUM
geusb-8357	156	2	results	result	NOUN
geusb-8357	156	3	and	and	CCONJ
geusb-8357	156	4	comparisons	comparison	NOUN
geusb-8357	156	5	between	between	ADP
geusb-8357	156	6	modflow	modflow	NOUN
geusb-8357	156	7	and	and	CCONJ
geusb-8357	156	8	the	the	DET
geusb-8357	156	9	neural	neural	ADJ
geusb-8357	156	10	network	network	NOUN
geusb-8357	156	11	in	in	ADP
geusb-8357	156	12	two	two	NUM
geusb-8357	156	13	test	test	NOUN
geusb-8357	156	14	cases	case	NOUN
geusb-8357	156	15	.	.	PUNCT
geusb-8357	157	1	a	a	X
geusb-8357	157	2	–	–	PUNCT
geusb-8357	157	3	d	d	NOUN
geusb-8357	157	4	:	:	PUNCT
geusb-8357	157	5	case	case	NOUN
geusb-8357	157	6	1	1	NUM
geusb-8357	157	7	.	.	PUNCT
geusb-8357	158	1	a	a	DET
geusb-8357	158	2	new	new	ADJ
geusb-8357	158	3	well	well	NOUN
geusb-8357	158	4	is	be	AUX
geusb-8357	158	5	simulated	simulate	VERB
geusb-8357	158	6	in	in	ADP
geusb-8357	158	7	layer	layer	NOUN
geusb-8357	158	8	6	6	NUM
geusb-8357	158	9	at	at	ADP
geusb-8357	158	10	row	row	NOUN
geusb-8357	158	11	55	55	NUM
geusb-8357	158	12	and	and	CCONJ
geusb-8357	158	13	column	column	NOUN
geusb-8357	158	14	99	99	NUM
geusb-8357	158	15	.	.	PUNCT
geusb-8357	159	1	the	the	DET
geusb-8357	159	2	drawdown	drawdown	NOUN
geusb-8357	159	3	of	of	ADP
geusb-8357	159	4	the	the	DET
geusb-8357	159	5	layer	layer	NOUN
geusb-8357	159	6	above	above	ADP
geusb-8357	159	7	the	the	DET
geusb-8357	159	8	abstraction	abstraction	NOUN
geusb-8357	159	9	layer	layer	NOUN
geusb-8357	159	10	is	be	AUX
geusb-8357	159	11	visualised	visualise	VERB
geusb-8357	159	12	for	for	ADP
geusb-8357	159	13	modflow	modflow	ADJ
geusb-8357	159	14	(	(	PUNCT
geusb-8357	159	15	a	a	NOUN
geusb-8357	159	16	)	)	PUNCT
geusb-8357	159	17	,	,	PUNCT
geusb-8357	159	18	the	the	DET
geusb-8357	159	19	neural	neural	ADJ
geusb-8357	159	20	network	network	NOUN
geusb-8357	159	21	mean	mean	NOUN
geusb-8357	159	22	and	and	CCONJ
geusb-8357	159	23	one	one	NUM
geusb-8357	159	24	standard	standard	ADJ
geusb-8357	159	25	deviation	deviation	NOUN
geusb-8357	159	26	(	(	PUNCT
geusb-8357	159	27	b	b	X
geusb-8357	159	28	–	–	PUNCT
geusb-8357	159	29	c	c	NOUN
geusb-8357	159	30	)	)	PUNCT
geusb-8357	159	31	and	and	CCONJ
geusb-8357	159	32	the	the	DET
geusb-8357	159	33	difference	difference	NOUN
geusb-8357	159	34	between	between	ADP
geusb-8357	159	35	modflow	modflow	NOUN
geusb-8357	159	36	and	and	CCONJ
geusb-8357	159	37	network	network	NOUN
geusb-8357	159	38	mean	mean	NOUN
geusb-8357	159	39	(	(	PUNCT
geusb-8357	159	40	d	d	NOUN
geusb-8357	159	41	)	)	PUNCT
geusb-8357	159	42	.	.	PUNCT
geusb-8357	160	1	e	e	X
geusb-8357	160	2	–	–	PUNCT
geusb-8357	160	3	h	h	NOUN
geusb-8357	160	4	:	:	PUNCT
geusb-8357	160	5	case	case	NOUN
geusb-8357	160	6	2	2	NUM
geusb-8357	160	7	.	.	PUNCT
geusb-8357	161	1	the	the	DET
geusb-8357	161	2	well	well	NOUN
geusb-8357	161	3	is	be	AUX
geusb-8357	161	4	moved	move	VERB
geusb-8357	161	5	to	to	ADP
geusb-8357	161	6	layer	layer	NOUN
geusb-8357	161	7	6	6	NUM
geusb-8357	161	8	at	at	ADP
geusb-8357	161	9	row	row	NOUN
geusb-8357	161	10	85	85	NUM
geusb-8357	161	11	and	and	CCONJ
geusb-8357	161	12	column	column	NOUN
geusb-8357	161	13	47	47	NUM
geusb-8357	161	14	,	,	PUNCT
geusb-8357	161	15	and	and	CCONJ
geusb-8357	161	16	drawdown	drawdown	NOUN
geusb-8357	161	17	is	be	AUX
geusb-8357	161	18	estimated	estimate	VERB
geusb-8357	161	19	again	again	ADV
geusb-8357	161	20	as	as	ADP
geusb-8357	161	21	in	in	ADP
geusb-8357	161	22	a	a	DET
geusb-8357	161	23	–	–	PUNCT
geusb-8357	161	24	d.	d.	NOUN
geusb-8357	161	25	row	row	NOUN
geusb-8357	161	26	0	0	NUM
geusb-8357	161	27	20	20	NUM
geusb-8357	161	28	40	40	NUM
geusb-8357	161	29	60	60	NUM
geusb-8357	161	30	80100120140	80100120140	NUM
geusb-8357	161	31	column	column	NOUN
geusb-8357	161	32	0	0	NUM
geusb-8357	161	33	20	20	NUM
geusb-8357	161	34	40	40	NUM
geusb-8357	161	35	60	60	NUM
geusb-8357	161	36	80	80	NUM
geusb-8357	161	37	100	100	NUM
geusb-8357	161	38	120	120	NUM
geusb-8357	161	39	layer	layer	NOUN
geusb-8357	161	40	2	2	NUM
geusb-8357	161	41	layer	layer	NOUN
geusb-8357	161	42	5	5	NUM
geusb-8357	161	43	layer	layer	NOUN
geusb-8357	161	44	14	14	NUM
geusb-8357	161	45	0.0	0.0	NUM
geusb-8357	161	46	0.2	0.2	NUM
geusb-8357	161	47	0.4	0.4	NUM
geusb-8357	161	48	0.6	0.6	NUM
geusb-8357	161	49	0.8	0.8	NUM
geusb-8357	161	50	1.0	1.0	NUM
geusb-8357	161	51	d	d	NOUN
geusb-8357	161	52	raw	raw	ADJ
geusb-8357	161	53	dow	dow	NOUN
geusb-8357	162	1	n	n	PROPN
geusb-8357	162	2	[	[	X
geusb-8357	162	3	m	m	X
geusb-8357	162	4	]	]	X
geusb-8357	162	5	row	row	NOUN
geusb-8357	162	6	0	0	NUM
geusb-8357	162	7	20	20	NUM
geusb-8357	162	8	40	40	NUM
geusb-8357	162	9	60	60	NUM
geusb-8357	162	10	80100120140	80100120140	NUM
geusb-8357	162	11	column	column	NOUN
geusb-8357	162	12	0	0	NUM
geusb-8357	162	13	20	20	NUM
geusb-8357	162	14	40	40	NUM
geusb-8357	162	15	60	60	NUM
geusb-8357	162	16	80	80	NUM
geusb-8357	162	17	100	100	NUM
geusb-8357	162	18	120	120	NUM
geusb-8357	162	19	layer	layer	NOUN
geusb-8357	162	20	2	2	NUM
geusb-8357	162	21	layer	layer	NOUN
geusb-8357	162	22	5	5	NUM
geusb-8357	162	23	layer	layer	NOUN
geusb-8357	162	24	14	14	NUM
geusb-8357	162	25	pumping	pumping	NOUN
geusb-8357	162	26	rate	rate	NOUN
geusb-8357	162	27	=	=	SYM
geusb-8357	162	28	800	800	NUM
geusb-8357	162	29	m3	m3	PROPN
geusb-8357	162	30	/	/	SYM
geusb-8357	162	31	day	day	NOUN
geusb-8357	162	32	0.0	0.0	NUM
geusb-8357	162	33	0.2	0.2	NUM
geusb-8357	162	34	0.4	0.4	NUM
geusb-8357	162	35	0.6	0.6	NUM
geusb-8357	162	36	0.8	0.8	NUM
geusb-8357	162	37	1.0	1.0	NUM
geusb-8357	162	38	d	d	NOUN
geusb-8357	162	39	raw	raw	ADJ
geusb-8357	162	40	dow	dow	NOUN
geusb-8357	163	1	n	n	PROPN
geusb-8357	163	2	[	[	X
geusb-8357	163	3	m	m	X
geusb-8357	163	4	]	]	X
geusb-8357	163	5	pumping	pump	VERB
geusb-8357	163	6	rate	rate	NOUN
geusb-8357	163	7	=	=	NOUN
geusb-8357	163	8	200	200	NUM
geusb-8357	163	9	m3	m3	PROPN
geusb-8357	163	10	/	/	SYM
geusb-8357	163	11	day	day	NOUN
geusb-8357	163	12	fig	fig	NOUN
geusb-8357	163	13	.	.	PUNCT
geusb-8357	164	1	4	4	NUM
geusb-8357	164	2	drawdown	drawdown	NOUN
geusb-8357	164	3	in	in	ADP
geusb-8357	164	4	multiple	multiple	ADJ
geusb-8357	164	5	layers	layer	NOUN
geusb-8357	164	6	predicted	predict	VERB
geusb-8357	164	7	with	with	ADP
geusb-8357	164	8	the	the	DET
geusb-8357	164	9	neural	neural	ADJ
geusb-8357	164	10	network	network	NOUN
geusb-8357	164	11	with	with	ADP
geusb-8357	164	12	the	the	DET
geusb-8357	164	13	well	well	ADJ
geusb-8357	164	14	setup	setup	NOUN
geusb-8357	164	15	from	from	ADP
geusb-8357	164	16	case	case	NOUN
geusb-8357	164	17	1	1	NUM
geusb-8357	164	18	.	.	PUNCT
geusb-8357	165	1	left	leave	VERB
geusb-8357	165	2	:	:	PUNCT
geusb-8357	165	3	the	the	DET
geusb-8357	165	4	pumping	pumping	NOUN
geusb-8357	165	5	rate	rate	NOUN
geusb-8357	165	6	is	be	AUX
geusb-8357	165	7	set	set	VERB
geusb-8357	165	8	to	to	ADP
geusb-8357	165	9	200	200	NUM
geusb-8357	165	10	m3·day–1	m3·day–1	NOUN
geusb-8357	165	11	.	.	PUNCT
geusb-8357	166	1	right	right	ADJ
geusb-8357	166	2	:	:	PUNCT
geusb-8357	166	3	the	the	DET
geusb-8357	166	4	pumping	pumping	NOUN
geusb-8357	166	5	rate	rate	NOUN
geusb-8357	166	6	is	be	AUX
geusb-8357	166	7	increased	increase	VERB
geusb-8357	166	8	to	to	ADP
geusb-8357	166	9	800	800	NUM
geusb-8357	166	10	m3·day–1	m3·day–1	NOUN
geusb-8357	166	11	.	.	PUNCT
geusb-8357	167	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	167	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	168	1	dahl	dahl	PROPN
geusb-8357	168	2	et	et	PROPN
geusb-8357	168	3	al	al	PROPN
geusb-8357	168	4	.	.	PROPN
geusb-8357	168	5	2023	2023	NUM
geusb-8357	168	6	:	:	PUNCT
geusb-8357	168	7	geus	geus	NOUN
geusb-8357	168	8	bulletin	bulletin	NOUN
geusb-8357	168	9	53	53	NUM
geusb-8357	168	10	.	.	PUNCT
geusb-8357	168	11	8357	8357	NUM
geusb-8357	168	12	.	.	PUNCT
geusb-8357	169	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	169	2	6	6	NUM
geusb-8357	169	3	of	of	ADP
geusb-8357	169	4	7	7	NUM
geusb-8357	169	5	www.geusbul	www.geusbul	NOUN
geusb-8357	169	6	let	let	VERB
geusb-8357	169	7	in.org	in.org	ADJ
geusb-8357	169	8	model	model	NOUN
geusb-8357	169	9	,	,	PUNCT
geusb-8357	169	10	taking	take	VERB
geusb-8357	169	11	the	the	DET
geusb-8357	169	12	same	same	ADJ
geusb-8357	169	13	approach	approach	NOUN
geusb-8357	169	14	as	as	ADP
geusb-8357	169	15	dahl	dahl	PROPN
geusb-8357	169	16	et	et	PROPN
geusb-8357	169	17	al	al	PROPN
geusb-8357	169	18	.	.	PROPN
geusb-8357	170	1	(	(	PUNCT
geusb-8357	170	2	2023	2023	NUM
geusb-8357	170	3	)	)	PUNCT
geusb-8357	170	4	with	with	ADP
geusb-8357	170	5	only	only	ADV
geusb-8357	170	6	a	a	DET
geusb-8357	170	7	few	few	ADJ
geusb-8357	170	8	changes	change	NOUN
geusb-8357	170	9	to	to	ADP
geusb-8357	170	10	input	input	NOUN
geusb-8357	170	11	features	feature	NOUN
geusb-8357	170	12	.	.	PUNCT
geusb-8357	171	1	this	this	PRON
geusb-8357	171	2	demonstrates	demonstrate	VERB
geusb-8357	171	3	that	that	SCONJ
geusb-8357	171	4	the	the	DET
geusb-8357	171	5	methodology	methodology	NOUN
geusb-8357	171	6	of	of	ADP
geusb-8357	171	7	dahl	dahl	PROPN
geusb-8357	171	8	et	et	PROPN
geusb-8357	171	9	al	al	PROPN
geusb-8357	171	10	.	.	PROPN
geusb-8357	172	1	(	(	PUNCT
geusb-8357	172	2	2023	2023	NUM
geusb-8357	172	3	)	)	PUNCT
geusb-8357	172	4	can	can	AUX
geusb-8357	172	5	be	be	AUX
geusb-8357	172	6	generalised	generalise	VERB
geusb-8357	172	7	,	,	PUNCT
geusb-8357	172	8	with	with	ADP
geusb-8357	172	9	only	only	ADJ
geusb-8357	172	10	minor	minor	ADJ
geusb-8357	172	11	modifications	modification	NOUN
geusb-8357	172	12	when	when	SCONJ
geusb-8357	172	13	applied	apply	VERB
geusb-8357	172	14	to	to	ADP
geusb-8357	172	15	new	new	ADJ
geusb-8357	172	16	areas	area	NOUN
geusb-8357	172	17	.	.	PUNCT
geusb-8357	173	1	to	to	PART
geusb-8357	173	2	do	do	VERB
geusb-8357	173	3	this	this	PRON
geusb-8357	173	4	,	,	PUNCT
geusb-8357	173	5	we	we	PRON
geusb-8357	173	6	extend	extend	VERB
geusb-8357	173	7	the	the	DET
geusb-8357	173	8	method	method	NOUN
geusb-8357	173	9	to	to	PART
geusb-8357	173	10	predict	predict	VERB
geusb-8357	173	11	drawdown	drawdown	NOUN
geusb-8357	173	12	in	in	ADP
geusb-8357	173	13	all	all	DET
geusb-8357	173	14	layers	layer	NOUN
geusb-8357	173	15	and	and	CCONJ
geusb-8357	173	16	allow	allow	VERB
geusb-8357	173	17	the	the	DET
geusb-8357	173	18	well	well	NOUN
geusb-8357	173	19	to	to	PART
geusb-8357	173	20	be	be	AUX
geusb-8357	173	21	placed	place	VERB
geusb-8357	173	22	in	in	ADP
geusb-8357	173	23	multiple	multiple	ADJ
geusb-8357	173	24	,	,	PUNCT
geusb-8357	173	25	suitable	suitable	ADJ
geusb-8357	173	26	layers	layer	NOUN
geusb-8357	173	27	.	.	PUNCT
geusb-8357	174	1	as	as	ADP
geusb-8357	174	2	a	a	DET
geusb-8357	174	3	performance	performance	NOUN
geusb-8357	174	4	test	test	NOUN
geusb-8357	174	5	of	of	ADP
geusb-8357	174	6	the	the	DET
geusb-8357	174	7	neural	neural	ADJ
geusb-8357	174	8	network	network	NOUN
geusb-8357	174	9	on	on	ADP
geusb-8357	174	10	a	a	DET
geusb-8357	174	11	validation	validation	NOUN
geusb-8357	174	12	set	set	NOUN
geusb-8357	174	13	and	and	CCONJ
geusb-8357	174	14	an	an	DET
geusb-8357	174	15	independent	independent	ADJ
geusb-8357	174	16	test	test	NOUN
geusb-8357	174	17	set	set	NOUN
geusb-8357	174	18	,	,	PUNCT
geusb-8357	174	19	results	result	NOUN
geusb-8357	174	20	of	of	ADP
geusb-8357	174	21	drawdown	drawdown	NOUN
geusb-8357	174	22	from	from	ADP
geusb-8357	174	23	multiple	multiple	ADJ
geusb-8357	174	24	well	well	ADJ
geusb-8357	174	25	locations	location	NOUN
geusb-8357	174	26	in	in	ADP
geusb-8357	174	27	different	different	ADJ
geusb-8357	174	28	layers	layer	NOUN
geusb-8357	174	29	are	be	AUX
geusb-8357	174	30	presented	present	VERB
geusb-8357	174	31	in	in	ADP
geusb-8357	174	32	fig	fig	NOUN
geusb-8357	174	33	.	.	PUNCT
geusb-8357	174	34	  	  	SPACE
geusb-8357	175	1	2	2	NUM
geusb-8357	175	2	.	.	PUNCT
geusb-8357	176	1	the	the	DET
geusb-8357	176	2	outcomes	outcome	NOUN
geusb-8357	176	3	reveal	reveal	VERB
geusb-8357	176	4	a	a	DET
geusb-8357	176	5	strong	strong	ADJ
geusb-8357	176	6	agreement	agreement	NOUN
geusb-8357	176	7	between	between	ADP
geusb-8357	176	8	the	the	DET
geusb-8357	176	9	predictions	prediction	NOUN
geusb-8357	176	10	of	of	ADP
geusb-8357	176	11	the	the	DET
geusb-8357	176	12	neural	neural	ADJ
geusb-8357	176	13	network	network	NOUN
geusb-8357	176	14	and	and	CCONJ
geusb-8357	176	15	the	the	DET
geusb-8357	176	16	results	result	NOUN
geusb-8357	176	17	obtained	obtain	VERB
geusb-8357	176	18	from	from	ADP
geusb-8357	176	19	modflow	modflow	ADJ
geusb-8357	176	20	(	(	PUNCT
geusb-8357	176	21	fig	fig	NOUN
geusb-8357	176	22	.	.	PUNCT
geusb-8357	177	1	2a	2a	NUM
geusb-8357	177	2	–	–	PUNCT
geusb-8357	177	3	c	c	NOUN
geusb-8357	177	4	)	)	PUNCT
geusb-8357	177	5	,	,	PUNCT
geusb-8357	177	6	and	and	CCONJ
geusb-8357	177	7	that	that	SCONJ
geusb-8357	177	8	a	a	DET
geusb-8357	177	9	substantial	substantial	ADJ
geusb-8357	177	10	amount	amount	NOUN
geusb-8357	177	11	of	of	ADP
geusb-8357	177	12	the	the	DET
geusb-8357	177	13	error	error	NOUN
geusb-8357	177	14	can	can	AUX
geusb-8357	177	15	be	be	AUX
geusb-8357	177	16	described	describe	VERB
geusb-8357	177	17	with	with	ADP
geusb-8357	177	18	2	2	NUM
geusb-8357	177	19	standard	standard	ADJ
geusb-8357	177	20	deviations	deviation	NOUN
geusb-8357	177	21	(	(	PUNCT
geusb-8357	177	22	fig	fig	NOUN
geusb-8357	177	23	.	.	PUNCT
geusb-8357	178	1	2d	2d	NOUN
geusb-8357	178	2	)	)	PUNCT
geusb-8357	178	3	.	.	PUNCT
geusb-8357	179	1	the	the	DET
geusb-8357	179	2	neural	neural	ADJ
geusb-8357	179	3	network	network	NOUN
geusb-8357	179	4	shows	show	VERB
geusb-8357	179	5	a	a	DET
geusb-8357	179	6	tendency	tendency	NOUN
geusb-8357	179	7	to	to	PART
geusb-8357	179	8	underestimate	underestimate	VERB
geusb-8357	179	9	higher	high	ADJ
geusb-8357	179	10	values	value	NOUN
geusb-8357	179	11	of	of	ADP
geusb-8357	179	12	drawdown	drawdown	NOUN
geusb-8357	179	13	in	in	ADP
geusb-8357	179	14	fig	fig	NOUN
geusb-8357	179	15	.	.	PUNCT
geusb-8357	180	1	2a	2a	NUM
geusb-8357	180	2	.	.	PUNCT
geusb-8357	181	1	this	this	PRON
geusb-8357	181	2	is	be	AUX
geusb-8357	181	3	also	also	ADV
geusb-8357	181	4	the	the	DET
geusb-8357	181	5	case	case	NOUN
geusb-8357	181	6	in	in	ADP
geusb-8357	181	7	dahl	dahl	PROPN
geusb-8357	181	8	et	et	PROPN
geusb-8357	181	9	al	al	PROPN
geusb-8357	181	10	.	.	PROPN
geusb-8357	182	1	(	(	PUNCT
geusb-8357	182	2	2023	2023	NUM
geusb-8357	182	3	)	)	PUNCT
geusb-8357	183	1	and	and	CCONJ
geusb-8357	183	2	is	be	AUX
geusb-8357	183	3	likely	likely	ADJ
geusb-8357	183	4	a	a	DET
geusb-8357	183	5	consequence	consequence	NOUN
geusb-8357	183	6	of	of	ADP
geusb-8357	183	7	a	a	DET
geusb-8357	183	8	few	few	ADJ
geusb-8357	183	9	high	high	ADJ
geusb-8357	183	10	-	-	PUNCT
geusb-8357	183	11	value	value	NOUN
geusb-8357	183	12	observations	observation	NOUN
geusb-8357	183	13	in	in	ADP
geusb-8357	183	14	the	the	DET
geusb-8357	183	15	training	training	NOUN
geusb-8357	183	16	data	datum	NOUN
geusb-8357	183	17	compared	compare	VERB
geusb-8357	183	18	to	to	ADP
geusb-8357	183	19	the	the	DET
geusb-8357	183	20	large	large	ADJ
geusb-8357	183	21	number	number	NOUN
geusb-8357	183	22	of	of	ADP
geusb-8357	183	23	low	low	ADJ
geusb-8357	183	24	values	value	NOUN
geusb-8357	183	25	available	available	ADJ
geusb-8357	183	26	.	.	PUNCT
geusb-8357	184	1	this	this	PRON
geusb-8357	184	2	could	could	AUX
geusb-8357	184	3	be	be	AUX
geusb-8357	184	4	solved	solve	VERB
geusb-8357	184	5	by	by	ADP
geusb-8357	184	6	sampling	sample	VERB
geusb-8357	184	7	differently	differently	ADV
geusb-8357	184	8	from	from	ADP
geusb-8357	184	9	the	the	DET
geusb-8357	184	10	training	training	NOUN
geusb-8357	184	11	data	datum	NOUN
geusb-8357	184	12	to	to	PART
geusb-8357	184	13	favour	favour	VERB
geusb-8357	184	14	underrepresented	underrepresented	ADJ
geusb-8357	184	15	data	datum	NOUN
geusb-8357	184	16	(	(	PUNCT
geusb-8357	184	17	johnson	johnson	PROPN
geusb-8357	184	18	&	&	CCONJ
geusb-8357	184	19	khoshgoftaar	khoshgoftaar	PROPN
geusb-8357	184	20	2019	2019	NUM
geusb-8357	184	21	)	)	PUNCT
geusb-8357	184	22	or	or	CCONJ
geusb-8357	184	23	by	by	ADP
geusb-8357	184	24	error	error	NOUN
geusb-8357	184	25	-	-	PUNCT
geusb-8357	184	26	correcting	correct	VERB
geusb-8357	184	27	biased	biased	ADJ
geusb-8357	184	28	predictions	prediction	NOUN
geusb-8357	184	29	(	(	PUNCT
geusb-8357	184	30	belitz	belitz	NOUN
geusb-8357	184	31	&	&	CCONJ
geusb-8357	184	32	stackelberg	stackelberg	PROPN
geusb-8357	184	33	2021	2021	NUM
geusb-8357	184	34	)	)	PUNCT
geusb-8357	184	35	.	.	PUNCT
geusb-8357	185	1	the	the	DET
geusb-8357	185	2	case	case	NOUN
geusb-8357	185	3	analyses	analyse	VERB
geusb-8357	185	4	from	from	ADP
geusb-8357	185	5	fig	fig	NOUN
geusb-8357	185	6	.	.	PUNCT
geusb-8357	186	1	3	3	NUM
geusb-8357	186	2	show	show	VERB
geusb-8357	186	3	that	that	SCONJ
geusb-8357	186	4	the	the	DET
geusb-8357	186	5	network	network	NOUN
geusb-8357	186	6	in	in	ADP
geusb-8357	186	7	general	general	ADJ
geusb-8357	186	8	replicates	replicate	VERB
geusb-8357	186	9	the	the	DET
geusb-8357	186	10	modflow	modflow	ADJ
geusb-8357	186	11	results	result	NOUN
geusb-8357	186	12	well	well	ADV
geusb-8357	186	13	.	.	PUNCT
geusb-8357	187	1	some	some	DET
geusb-8357	187	2	difficulties	difficulty	NOUN
geusb-8357	187	3	near	near	ADP
geusb-8357	187	4	simulated	simulated	ADJ
geusb-8357	187	5	boundary	boundary	ADJ
geusb-8357	187	6	conditions	condition	NOUN
geusb-8357	187	7	have	have	VERB
geusb-8357	187	8	a	a	DET
geusb-8357	187	9	large	large	ADJ
geusb-8357	187	10	effect	effect	NOUN
geusb-8357	187	11	on	on	ADP
geusb-8357	187	12	the	the	DET
geusb-8357	187	13	predicted	predict	VERB
geusb-8357	187	14	mean	mean	ADJ
geusb-8357	187	15	values	value	NOUN
geusb-8357	187	16	.	.	PUNCT
geusb-8357	188	1	however	however	ADV
geusb-8357	188	2	,	,	PUNCT
geusb-8357	188	3	these	these	DET
geusb-8357	188	4	areas	area	NOUN
geusb-8357	188	5	also	also	ADV
geusb-8357	188	6	show	show	VERB
geusb-8357	188	7	an	an	DET
geusb-8357	188	8	increase	increase	NOUN
geusb-8357	188	9	in	in	ADP
geusb-8357	188	10	the	the	DET
geusb-8357	188	11	standard	standard	ADJ
geusb-8357	188	12	deviation	deviation	NOUN
geusb-8357	188	13	maps	map	NOUN
geusb-8357	188	14	making	make	VERB
geusb-8357	188	15	them	they	PRON
geusb-8357	188	16	easier	easy	ADJ
geusb-8357	188	17	to	to	PART
geusb-8357	188	18	spot	spot	VERB
geusb-8357	188	19	and	and	CCONJ
geusb-8357	188	20	possibly	possibly	ADV
geusb-8357	188	21	remove	remove	VERB
geusb-8357	188	22	or	or	CCONJ
geusb-8357	188	23	ignore	ignore	VERB
geusb-8357	188	24	.	.	PUNCT
geusb-8357	189	1	the	the	DET
geusb-8357	189	2	standard	standard	ADJ
geusb-8357	189	3	deviation	deviation	NOUN
geusb-8357	189	4	could	could	AUX
geusb-8357	189	5	potentially	potentially	ADV
geusb-8357	189	6	act	act	VERB
geusb-8357	189	7	as	as	ADP
geusb-8357	189	8	a	a	DET
geusb-8357	189	9	verifier	verifier	NOUN
geusb-8357	189	10	of	of	ADP
geusb-8357	189	11	the	the	DET
geusb-8357	189	12	mean	mean	ADJ
geusb-8357	189	13	estimate	estimate	NOUN
geusb-8357	189	14	and	and	CCONJ
geusb-8357	189	15	identify	identify	VERB
geusb-8357	189	16	network	network	NOUN
geusb-8357	189	17	shortcomings	shortcoming	NOUN
geusb-8357	189	18	.	.	PUNCT
geusb-8357	190	1	the	the	DET
geusb-8357	190	2	accuracy	accuracy	NOUN
geusb-8357	190	3	of	of	ADP
geusb-8357	190	4	the	the	DET
geusb-8357	190	5	network	network	NOUN
geusb-8357	190	6	confirms	confirm	VERB
geusb-8357	190	7	that	that	SCONJ
geusb-8357	190	8	the	the	DET
geusb-8357	190	9	applied	apply	VERB
geusb-8357	190	10	approach	approach	NOUN
geusb-8357	190	11	generalises	generalise	VERB
geusb-8357	190	12	to	to	ADP
geusb-8357	190	13	a	a	DET
geusb-8357	190	14	danish	danish	ADJ
geusb-8357	190	15	case	case	NOUN
geusb-8357	190	16	and	and	CCONJ
geusb-8357	190	17	likely	likely	ADJ
geusb-8357	190	18	other	other	ADJ
geusb-8357	190	19	danish	danish	ADJ
geusb-8357	190	20	groundwater	groundwater	NOUN
geusb-8357	190	21	models	model	NOUN
geusb-8357	190	22	.	.	PUNCT
geusb-8357	191	1	the	the	DET
geusb-8357	191	2	neural	neural	ADJ
geusb-8357	191	3	network	network	NOUN
geusb-8357	191	4	predictions	prediction	NOUN
geusb-8357	191	5	are	be	AUX
geusb-8357	191	6	obtained	obtain	VERB
geusb-8357	191	7	five	five	NUM
geusb-8357	191	8	times	time	NOUN
geusb-8357	191	9	faster	fast	ADV
geusb-8357	191	10	than	than	ADP
geusb-8357	191	11	modflow	modflow	ADJ
geusb-8357	191	12	results	result	NOUN
geusb-8357	191	13	in	in	ADP
geusb-8357	191	14	the	the	DET
geusb-8357	191	15	full	full	ADJ
geusb-8357	191	16	model	model	NOUN
geusb-8357	191	17	.	.	PUNCT
geusb-8357	192	1	this	this	DET
geusb-8357	192	2	modest	modest	ADJ
geusb-8357	192	3	speed	speed	NOUN
geusb-8357	192	4	-	-	PUNCT
geusb-8357	192	5	up	up	NOUN
geusb-8357	192	6	can	can	AUX
geusb-8357	192	7	be	be	AUX
geusb-8357	192	8	attributed	attribute	VERB
geusb-8357	192	9	to	to	ADP
geusb-8357	192	10	the	the	DET
geusb-8357	192	11	efficient	efficient	ADJ
geusb-8357	192	12	run	run	NOUN
geusb-8357	192	13	-	-	PUNCT
geusb-8357	192	14	time	time	NOUN
geusb-8357	192	15	of	of	ADP
geusb-8357	192	16	the	the	DET
geusb-8357	192	17	egebjerg	egebjerg	PROPN
geusb-8357	192	18	groundwater	groundwater	NOUN
geusb-8357	192	19	model	model	NOUN
geusb-8357	192	20	combined	combine	VERB
geusb-8357	192	21	with	with	ADP
geusb-8357	192	22	the	the	DET
geusb-8357	192	23	computation	computation	NOUN
geusb-8357	192	24	of	of	ADP
geusb-8357	192	25	input	input	NOUN
geusb-8357	192	26	features	feature	NOUN
geusb-8357	192	27	but	but	CCONJ
geusb-8357	192	28	could	could	AUX
geusb-8357	192	29	potentially	potentially	ADV
geusb-8357	192	30	be	be	AUX
geusb-8357	192	31	much	much	ADV
geusb-8357	192	32	higher	high	ADJ
geusb-8357	192	33	in	in	ADP
geusb-8357	192	34	a	a	DET
geusb-8357	192	35	model	model	NOUN
geusb-8357	192	36	with	with	ADP
geusb-8357	192	37	higher	high	ADJ
geusb-8357	192	38	resolution	resolution	NOUN
geusb-8357	192	39	.	.	PUNCT
geusb-8357	193	1	the	the	DET
geusb-8357	193	2	neural	neural	ADJ
geusb-8357	193	3	network	network	NOUN
geusb-8357	193	4	,	,	PUNCT
geusb-8357	193	5	however	however	ADV
geusb-8357	193	6	,	,	PUNCT
geusb-8357	193	7	provides	provide	VERB
geusb-8357	193	8	inherent	inherent	ADJ
geusb-8357	193	9	flexibility	flexibility	NOUN
geusb-8357	193	10	,	,	PUNCT
geusb-8357	193	11	allowing	allow	VERB
geusb-8357	193	12	for	for	ADP
geusb-8357	193	13	predictions	prediction	NOUN
geusb-8357	193	14	within	within	ADP
geusb-8357	193	15	specific	specific	ADJ
geusb-8357	193	16	subareas	subarea	NOUN
geusb-8357	193	17	of	of	ADP
geusb-8357	193	18	the	the	DET
geusb-8357	193	19	model	model	NOUN
geusb-8357	193	20	rather	rather	ADV
geusb-8357	193	21	than	than	ADP
geusb-8357	193	22	necessitating	necessitate	VERB
geusb-8357	193	23	computations	computation	NOUN
geusb-8357	193	24	for	for	ADP
geusb-8357	193	25	the	the	DET
geusb-8357	193	26	entire	entire	ADJ
geusb-8357	193	27	model	model	NOUN
geusb-8357	193	28	each	each	DET
geusb-8357	193	29	time	time	NOUN
geusb-8357	193	30	,	,	PUNCT
geusb-8357	193	31	thereby	thereby	ADV
geusb-8357	193	32	reducing	reduce	VERB
geusb-8357	193	33	the	the	DET
geusb-8357	193	34	computational	computational	ADJ
geusb-8357	193	35	load	load	NOUN
geusb-8357	193	36	.	.	PUNCT
geusb-8357	194	1	furthermore	furthermore	ADV
geusb-8357	194	2	,	,	PUNCT
geusb-8357	194	3	for	for	ADP
geusb-8357	194	4	the	the	DET
geusb-8357	194	5	pumping	pump	VERB
geusb-8357	194	6	analysis	analysis	NOUN
geusb-8357	194	7	(	(	PUNCT
geusb-8357	194	8	fig	fig	NOUN
geusb-8357	194	9	.	.	PUNCT
geusb-8357	194	10	4	4	NUM
geusb-8357	194	11	)	)	PUNCT
geusb-8357	194	12	,	,	PUNCT
geusb-8357	194	13	all	all	PRON
geusb-8357	194	14	features	feature	NOUN
geusb-8357	194	15	,	,	PUNCT
geusb-8357	194	16	except	except	SCONJ
geusb-8357	194	17	for	for	ADP
geusb-8357	194	18	the	the	DET
geusb-8357	194	19	pumping	pumping	NOUN
geusb-8357	194	20	rate	rate	NOUN
geusb-8357	194	21	,	,	PUNCT
geusb-8357	194	22	need	need	VERB
geusb-8357	194	23	to	to	PART
geusb-8357	194	24	be	be	AUX
geusb-8357	194	25	computed	compute	VERB
geusb-8357	194	26	only	only	ADV
geusb-8357	194	27	once	once	ADV
geusb-8357	194	28	.	.	PUNCT
geusb-8357	195	1	this	this	DET
geusb-8357	195	2	results	result	NOUN
geusb-8357	195	3	in	in	ADP
geusb-8357	195	4	subsequent	subsequent	ADJ
geusb-8357	195	5	network	network	NOUN
geusb-8357	195	6	simulations	simulation	NOUN
geusb-8357	195	7	being	be	AUX
geusb-8357	195	8	11	11	NUM
geusb-8357	195	9	times	time	NOUN
geusb-8357	195	10	more	more	ADV
geusb-8357	195	11	efficient	efficient	ADJ
geusb-8357	195	12	than	than	ADP
geusb-8357	195	13	those	those	PRON
geusb-8357	195	14	using	use	VERB
geusb-8357	195	15	modflow	modflow	NOUN
geusb-8357	195	16	.	.	PUNCT
geusb-8357	196	1	such	such	DET
geusb-8357	196	2	an	an	DET
geusb-8357	196	3	analysis	analysis	NOUN
geusb-8357	196	4	for	for	ADP
geusb-8357	196	5	a	a	DET
geusb-8357	196	6	subarea	subarea	NOUN
geusb-8357	196	7	of	of	ADP
geusb-8357	196	8	the	the	DET
geusb-8357	196	9	model	model	NOUN
geusb-8357	196	10	could	could	AUX
geusb-8357	196	11	greatly	greatly	ADV
geusb-8357	196	12	reduce	reduce	VERB
geusb-8357	196	13	the	the	DET
geusb-8357	196	14	computational	computational	ADJ
geusb-8357	196	15	time	time	NOUN
geusb-8357	196	16	compared	compare	VERB
geusb-8357	196	17	to	to	ADP
geusb-8357	196	18	modflow	modflow	NOUN
geusb-8357	196	19	.	.	PUNCT
geusb-8357	197	1	the	the	DET
geusb-8357	197	2	proven	prove	VERB
geusb-8357	197	3	speed	speed	NOUN
geusb-8357	197	4	-	-	PUNCT
geusb-8357	197	5	up	up	NOUN
geusb-8357	197	6	and	and	CCONJ
geusb-8357	197	7	replication	replication	NOUN
geusb-8357	197	8	accuracy	accuracy	NOUN
geusb-8357	197	9	of	of	ADP
geusb-8357	197	10	the	the	DET
geusb-8357	197	11	network	network	NOUN
geusb-8357	197	12	make	make	VERB
geusb-8357	197	13	it	it	PRON
geusb-8357	197	14	a	a	DET
geusb-8357	197	15	great	great	ADJ
geusb-8357	197	16	addition	addition	NOUN
geusb-8357	197	17	to	to	ADP
geusb-8357	197	18	decision	decision	NOUN
geusb-8357	197	19	support	support	NOUN
geusb-8357	197	20	tools	tool	NOUN
geusb-8357	197	21	,	,	PUNCT
geusb-8357	197	22	for	for	ADP
geusb-8357	197	23	example	example	NOUN
geusb-8357	197	24	,	,	PUNCT
geusb-8357	197	25	during	during	ADP
geusb-8357	197	26	initial	initial	ADJ
geusb-8357	197	27	screening	screening	NOUN
geusb-8357	197	28	investigations	investigation	NOUN
geusb-8357	197	29	.	.	PUNCT
geusb-8357	198	1	conclusions	conclusion	NOUN
geusb-8357	198	2	in	in	ADP
geusb-8357	198	3	this	this	DET
geusb-8357	198	4	study	study	NOUN
geusb-8357	198	5	,	,	PUNCT
geusb-8357	198	6	we	we	PRON
geusb-8357	198	7	have	have	AUX
geusb-8357	198	8	trained	train	VERB
geusb-8357	198	9	a	a	DET
geusb-8357	198	10	neural	neural	ADJ
geusb-8357	198	11	network	network	NOUN
geusb-8357	198	12	to	to	PART
geusb-8357	198	13	predict	predict	VERB
geusb-8357	198	14	drawdown	drawdown	NOUN
geusb-8357	198	15	from	from	ADP
geusb-8357	198	16	groundwater	groundwater	NOUN
geusb-8357	198	17	abstraction	abstraction	NOUN
geusb-8357	198	18	to	to	PART
geusb-8357	198	19	test	test	VERB
geusb-8357	198	20	the	the	DET
geusb-8357	198	21	abilities	ability	NOUN
geusb-8357	198	22	of	of	ADP
geusb-8357	198	23	the	the	DET
geusb-8357	198	24	network	network	NOUN
geusb-8357	198	25	and	and	CCONJ
geusb-8357	198	26	the	the	DET
geusb-8357	198	27	applied	applied	ADJ
geusb-8357	198	28	approach	approach	NOUN
geusb-8357	198	29	to	to	PART
geusb-8357	198	30	generalise	generalise	VERB
geusb-8357	198	31	to	to	ADP
geusb-8357	198	32	a	a	DET
geusb-8357	198	33	danish	danish	ADJ
geusb-8357	198	34	catchment	catchment	NOUN
geusb-8357	198	35	.	.	PUNCT
geusb-8357	199	1	we	we	PRON
geusb-8357	199	2	extend	extend	VERB
geusb-8357	199	3	the	the	DET
geusb-8357	199	4	approach	approach	NOUN
geusb-8357	199	5	to	to	ADP
geusb-8357	199	6	the	the	DET
geusb-8357	199	7	full	full	ADJ
geusb-8357	199	8	3d	3d	PROPN
geusb-8357	199	9	model	model	NOUN
geusb-8357	199	10	to	to	PART
geusb-8357	199	11	increase	increase	VERB
geusb-8357	199	12	user	user	NOUN
geusb-8357	199	13	flexibility	flexibility	NOUN
geusb-8357	199	14	and	and	CCONJ
geusb-8357	199	15	general	general	ADJ
geusb-8357	199	16	applicability	applicability	NOUN
geusb-8357	199	17	with	with	ADP
geusb-8357	199	18	only	only	ADV
geusb-8357	199	19	a	a	DET
geusb-8357	199	20	few	few	ADJ
geusb-8357	199	21	modifications	modification	NOUN
geusb-8357	199	22	to	to	PART
geusb-8357	199	23	input	input	VERB
geusb-8357	199	24	features	feature	NOUN
geusb-8357	199	25	and	and	CCONJ
geusb-8357	199	26	network	network	NOUN
geusb-8357	199	27	setup	setup	NOUN
geusb-8357	199	28	.	.	PUNCT
geusb-8357	200	1	we	we	PRON
geusb-8357	200	2	find	find	VERB
geusb-8357	200	3	a	a	DET
geusb-8357	200	4	good	good	ADJ
geusb-8357	200	5	agreement	agreement	NOUN
geusb-8357	200	6	between	between	ADP
geusb-8357	200	7	modflow	modflow	ADJ
geusb-8357	200	8	results	result	NOUN
geusb-8357	200	9	and	and	CCONJ
geusb-8357	200	10	network	network	NOUN
geusb-8357	200	11	predictions	prediction	NOUN
geusb-8357	200	12	and	and	CCONJ
geusb-8357	200	13	show	show	VERB
geusb-8357	200	14	that	that	SCONJ
geusb-8357	200	15	areas	area	NOUN
geusb-8357	200	16	with	with	ADP
geusb-8357	200	17	high	high	ADJ
geusb-8357	200	18	disagreement	disagreement	NOUN
geusb-8357	200	19	correlate	correlate	VERB
geusb-8357	200	20	well	well	ADV
geusb-8357	200	21	with	with	ADP
geusb-8357	200	22	larger	large	ADJ
geusb-8357	200	23	network	network	NOUN
geusb-8357	200	24	standard	standard	ADJ
geusb-8357	200	25	deviations	deviation	NOUN
geusb-8357	200	26	.	.	PUNCT
geusb-8357	201	1	the	the	DET
geusb-8357	201	2	neural	neural	ADJ
geusb-8357	201	3	network	network	NOUN
geusb-8357	201	4	offers	offer	VERB
geusb-8357	201	5	a	a	DET
geusb-8357	201	6	5	5	NUM
geusb-8357	201	7	-	-	PUNCT
geusb-8357	201	8	time	time	NOUN
geusb-8357	201	9	speed	speed	NOUN
geusb-8357	201	10	-	-	PUNCT
geusb-8357	201	11	up	up	NOUN
geusb-8357	201	12	compared	compare	VERB
geusb-8357	201	13	to	to	ADP
geusb-8357	201	14	modflow	modflow	ADJ
geusb-8357	201	15	runtime	runtime	NOUN
geusb-8357	201	16	for	for	ADP
geusb-8357	201	17	a	a	DET
geusb-8357	201	18	single	single	ADJ
geusb-8357	201	19	simulation	simulation	NOUN
geusb-8357	201	20	and	and	CCONJ
geusb-8357	201	21	11	11	NUM
geusb-8357	201	22	-	-	PUNCT
geusb-8357	201	23	time	time	NOUN
geusb-8357	201	24	speed	speed	NOUN
geusb-8357	201	25	-	-	PUNCT
geusb-8357	201	26	up	up	NOUN
geusb-8357	201	27	in	in	ADP
geusb-8357	201	28	pumping	pump	VERB
geusb-8357	201	29	analysis	analysis	NOUN
geusb-8357	201	30	tests	test	NOUN
geusb-8357	201	31	with	with	ADP
geusb-8357	201	32	multiple	multiple	ADJ
geusb-8357	201	33	model	model	NOUN
geusb-8357	201	34	runs	run	NOUN
geusb-8357	201	35	where	where	SCONJ
geusb-8357	201	36	input	input	NOUN
geusb-8357	201	37	features	feature	NOUN
geusb-8357	201	38	are	be	AUX
geusb-8357	201	39	only	only	ADV
geusb-8357	201	40	calculated	calculate	VERB
geusb-8357	201	41	once	once	ADV
geusb-8357	201	42	.	.	PUNCT
geusb-8357	202	1	furthermore	furthermore	ADV
geusb-8357	202	2	,	,	PUNCT
geusb-8357	202	3	the	the	DET
geusb-8357	202	4	network	network	NOUN
geusb-8357	202	5	is	be	AUX
geusb-8357	202	6	flexible	flexible	ADJ
geusb-8357	202	7	and	and	CCONJ
geusb-8357	202	8	can	can	AUX
geusb-8357	202	9	be	be	AUX
geusb-8357	202	10	limited	limit	VERB
geusb-8357	202	11	to	to	PART
geusb-8357	202	12	predict	predict	VERB
geusb-8357	202	13	changes	change	NOUN
geusb-8357	202	14	in	in	ADP
geusb-8357	202	15	subareas	subarea	NOUN
geusb-8357	202	16	of	of	ADP
geusb-8357	202	17	the	the	DET
geusb-8357	202	18	model	model	NOUN
geusb-8357	202	19	reducing	reduce	VERB
geusb-8357	202	20	the	the	DET
geusb-8357	202	21	number	number	NOUN
geusb-8357	202	22	of	of	ADP
geusb-8357	202	23	computations	computation	NOUN
geusb-8357	202	24	required	require	VERB
geusb-8357	202	25	.	.	PUNCT
geusb-8357	203	1	we	we	PRON
geusb-8357	203	2	conclude	conclude	VERB
geusb-8357	203	3	that	that	SCONJ
geusb-8357	203	4	the	the	DET
geusb-8357	203	5	applied	apply	VERB
geusb-8357	203	6	approach	approach	NOUN
geusb-8357	203	7	generalises	generalise	VERB
geusb-8357	203	8	well	well	ADV
geusb-8357	203	9	to	to	ADP
geusb-8357	203	10	the	the	DET
geusb-8357	203	11	danish	danish	ADJ
geusb-8357	203	12	groundwater	groundwater	NOUN
geusb-8357	203	13	model	model	NOUN
geusb-8357	203	14	,	,	PUNCT
geusb-8357	203	15	and	and	CCONJ
geusb-8357	203	16	that	that	SCONJ
geusb-8357	203	17	the	the	DET
geusb-8357	203	18	trained	train	VERB
geusb-8357	203	19	network	network	NOUN
geusb-8357	203	20	has	have	VERB
geusb-8357	203	21	potential	potential	ADJ
geusb-8357	203	22	as	as	ADP
geusb-8357	203	23	a	a	DET
geusb-8357	203	24	decision	decision	NOUN
geusb-8357	203	25	support	support	NOUN
geusb-8357	203	26	tool	tool	NOUN
geusb-8357	203	27	.	.	PUNCT
geusb-8357	204	1	acknowledgements	acknowledgement	NOUN
geusb-8357	204	2	the	the	DET
geusb-8357	204	3	authors	author	NOUN
geusb-8357	204	4	would	would	AUX
geusb-8357	204	5	like	like	VERB
geusb-8357	204	6	to	to	PART
geusb-8357	204	7	acknowledge	acknowledge	VERB
geusb-8357	204	8	innovation	innovation	NOUN
geusb-8357	204	9	fund	fund	PROPN
geusb-8357	204	10	denmark	denmark	NOUN
geusb-8357	204	11	for	for	ADP
geusb-8357	204	12	funding	fund	VERB
geusb-8357	204	13	this	this	DET
geusb-8357	204	14	project	project	NOUN
geusb-8357	204	15	.	.	PUNCT
geusb-8357	205	1	the	the	DET
geusb-8357	205	2	authors	author	NOUN
geusb-8357	205	3	would	would	AUX
geusb-8357	205	4	also	also	ADV
geusb-8357	205	5	like	like	VERB
geusb-8357	205	6	to	to	PART
geusb-8357	205	7	thank	thank	VERB
geusb-8357	205	8	robin	robin	PROPN
geusb-8357	205	9	thibaut	thibaut	PROPN
geusb-8357	205	10	and	and	CCONJ
geusb-8357	205	11	two	two	NUM
geusb-8357	205	12	anonymous	anonymous	ADJ
geusb-8357	205	13	reviewers	reviewer	NOUN
geusb-8357	205	14	for	for	ADP
geusb-8357	205	15	a	a	DET
geusb-8357	205	16	constructive	constructive	ADJ
geusb-8357	205	17	review	review	NOUN
geusb-8357	205	18	process	process	NOUN
geusb-8357	205	19	that	that	PRON
geusb-8357	205	20	improved	improve	VERB
geusb-8357	205	21	the	the	DET
geusb-8357	205	22	quality	quality	NOUN
geusb-8357	205	23	of	of	ADP
geusb-8357	205	24	this	this	DET
geusb-8357	205	25	paper	paper	NOUN
geusb-8357	205	26	.	.	PUNCT
geusb-8357	206	1	and	and	CCONJ
geusb-8357	206	2	a	a	DET
geusb-8357	206	3	special	special	ADJ
geusb-8357	206	4	thanks	thank	NOUN
geusb-8357	206	5	to	to	ADP
geusb-8357	206	6	handling	handling	NOUN
geusb-8357	206	7	editor	editor	NOUN
geusb-8357	206	8	,	,	PUNCT
geusb-8357	206	9	hyojin	hyojin	VERB
geusb-8357	206	10	kim	kim	PROPN
geusb-8357	206	11	.	.	PUNCT
geusb-8357	207	1	additional	additional	ADJ
geusb-8357	207	2	information	information	NOUN
geusb-8357	207	3	funding	funding	NOUN
geusb-8357	207	4	statement	statement	NOUN
geusb-8357	207	5	this	this	DET
geusb-8357	207	6	work	work	NOUN
geusb-8357	207	7	was	be	AUX
geusb-8357	207	8	funded	fund	VERB
geusb-8357	207	9	by	by	ADP
geusb-8357	207	10	the	the	DET
geusb-8357	207	11	innovation	innovation	NOUN
geusb-8357	207	12	fund	fund	PROPN
geusb-8357	207	13	denmark	denmark	PROPN
geusb-8357	207	14	,	,	PUNCT
geusb-8357	207	15	project	project	NOUN
geusb-8357	207	16	9065	9065	NUM
geusb-8357	207	17	-	-	PUNCT
geusb-8357	207	18	00212b	00212b	NUM
geusb-8357	207	19	.	.	PUNCT
geusb-8357	208	1	author	author	NOUN
geusb-8357	208	2	contributions	contribution	VERB
geusb-8357	208	3	md	md	PROPN
geusb-8357	208	4	:	:	PUNCT
geusb-8357	208	5	conceptualisation	conceptualisation	NOUN
geusb-8357	208	6	,	,	PUNCT
geusb-8357	208	7	code	code	NOUN
geusb-8357	208	8	development	development	NOUN
geusb-8357	208	9	,	,	PUNCT
geusb-8357	208	10	investigation	investigation	NOUN
geusb-8357	208	11	,	,	PUNCT
geusb-8357	208	12	methodology	methodology	NOUN
geusb-8357	208	13	,	,	PUNCT
geusb-8357	208	14	validation	validation	NOUN
geusb-8357	208	15	,	,	PUNCT
geusb-8357	208	16	visualisation	visualisation	NOUN
geusb-8357	208	17	,	,	PUNCT
geusb-8357	208	18	writing	writing	NOUN
geusb-8357	208	19	–	–	PUNCT
geusb-8357	208	20	original	original	ADJ
geusb-8357	208	21	draft	draft	NOUN
geusb-8357	208	22	,	,	PUNCT
geusb-8357	208	23	writing	writing	NOUN
geusb-8357	208	24	–	–	PUNCT
geusb-8357	208	25	review	review	NOUN
geusb-8357	208	26	&	&	CCONJ
geusb-8357	208	27	editing	editing	NOUN
geusb-8357	208	28	.	.	PUNCT
geusb-8357	209	1	tv	tv	NOUN
geusb-8357	209	2	:	:	PUNCT
geusb-8357	209	3	conceptualisation	conceptualisation	NOUN
geusb-8357	209	4	,	,	PUNCT
geusb-8357	209	5	methodology	methodology	NOUN
geusb-8357	209	6	,	,	PUNCT
geusb-8357	209	7	validation	validation	NOUN
geusb-8357	209	8	,	,	PUNCT
geusb-8357	209	9	supervision	supervision	NOUN
geusb-8357	209	10	,	,	PUNCT
geusb-8357	209	11	writing	writing	NOUN
geusb-8357	209	12	–	–	PUNCT
geusb-8357	209	13	review	review	NOUN
geusb-8357	209	14	&	&	CCONJ
geusb-8357	209	15	editing	editing	NOUN
geusb-8357	209	16	.	.	PUNCT
geusb-8357	210	1	te	te	ADP
geusb-8357	210	2	:	:	PUNCT
geusb-8357	210	3	code	code	NOUN
geusb-8357	210	4	development	development	NOUN
geusb-8357	210	5	,	,	PUNCT
geusb-8357	210	6	model	model	NOUN
geusb-8357	210	7	creation	creation	NOUN
geusb-8357	210	8	,	,	PUNCT
geusb-8357	210	9	writing	write	VERB
geusb-8357	210	10	–	–	PUNCT
geusb-8357	210	11	review	review	NOUN
geusb-8357	210	12	&	&	CCONJ
geusb-8357	210	13	editing	editing	NOUN
geusb-8357	210	14	.	.	PUNCT
geusb-8357	211	1	th	th	X
geusb-8357	211	2	:	:	PUNCT
geusb-8357	211	3	conceptualisation	conceptualisation	NOUN
geusb-8357	211	4	,	,	PUNCT
geusb-8357	211	5	methodology	methodology	NOUN
geusb-8357	211	6	,	,	PUNCT
geusb-8357	211	7	validation	validation	NOUN
geusb-8357	211	8	,	,	PUNCT
geusb-8357	211	9	supervision	supervision	NOUN
geusb-8357	211	10	,	,	PUNCT
geusb-8357	211	11	funding	funding	NOUN
geusb-8357	211	12	acquisition	acquisition	NOUN
geusb-8357	211	13	,	,	PUNCT
geusb-8357	211	14	writing	write	VERB
geusb-8357	211	15	–	–	PUNCT
geusb-8357	211	16	review	review	NOUN
geusb-8357	211	17	&	&	CCONJ
geusb-8357	211	18	editing	editing	NOUN
geusb-8357	211	19	.	.	PUNCT
geusb-8357	212	1	competing	compete	VERB
geusb-8357	212	2	interests	interest	NOUN
geusb-8357	212	3	tv	tv	NOUN
geusb-8357	212	4	and	and	CCONJ
geusb-8357	212	5	md	md	PROPN
geusb-8357	212	6	were	be	AUX
geusb-8357	212	7	employed	employ	VERB
geusb-8357	212	8	by	by	ADP
geusb-8357	212	9	niras	nira	NOUN
geusb-8357	212	10	.	.	PUNCT
geusb-8357	213	1	the	the	DET
geusb-8357	213	2	remaining	remain	VERB
geusb-8357	213	3	authors	author	NOUN
geusb-8357	213	4	declare	declare	VERB
geusb-8357	213	5	that	that	SCONJ
geusb-8357	213	6	the	the	DET
geusb-8357	213	7	research	research	NOUN
geusb-8357	213	8	was	be	AUX
geusb-8357	213	9	conducted	conduct	VERB
geusb-8357	213	10	in	in	ADP
geusb-8357	213	11	the	the	DET
geusb-8357	213	12	absence	absence	NOUN
geusb-8357	213	13	of	of	ADP
geusb-8357	213	14	any	any	DET
geusb-8357	213	15	commercial	commercial	ADJ
geusb-8357	213	16	or	or	CCONJ
geusb-8357	213	17	financial	financial	ADJ
geusb-8357	213	18	relationship	relationship	NOUN
geusb-8357	213	19	that	that	PRON
geusb-8357	213	20	could	could	AUX
geusb-8357	213	21	be	be	AUX
geusb-8357	213	22	construed	construe	VERB
geusb-8357	213	23	as	as	ADP
geusb-8357	213	24	a	a	DET
geusb-8357	213	25	potential	potential	ADJ
geusb-8357	213	26	conflict	conflict	NOUN
geusb-8357	213	27	of	of	ADP
geusb-8357	213	28	interest	interest	NOUN
geusb-8357	213	29	.	.	PUNCT
geusb-8357	214	1	additional	additional	ADJ
geusb-8357	214	2	files	file	NOUN
geusb-8357	214	3	data	datum	NOUN
geusb-8357	214	4	and	and	CCONJ
geusb-8357	214	5	code	code	NOUN
geusb-8357	214	6	available	available	ADJ
geusb-8357	214	7	at	at	ADP
geusb-8357	214	8	https://github.com/mathiasbusk/	https://github.com/mathiasbusk/	PROPN
geusb-8357	214	9	hydrosim_egebjerg	hydrosim_egebjerg	PROPN
geusb-8357	214	10	.	.	PUNCT
geusb-8357	215	1	references	reference	NOUN
geusb-8357	215	2	abadi	abadi	PROPN
geusb-8357	215	3	,	,	PUNCT
geusb-8357	215	4	m.	m.	NOUN
geusb-8357	215	5	et	et	PROPN
geusb-8357	215	6	al	al	PROPN
geusb-8357	215	7	.	.	PROPN
geusb-8357	215	8	2016	2016	NUM
geusb-8357	215	9	:	:	PUNCT
geusb-8357	216	1	tensorflow	tensorflow	NOUN
geusb-8357	216	2	:	:	PUNCT
geusb-8357	216	3	large	large	ADJ
geusb-8357	216	4	-	-	PUNCT
geusb-8357	216	5	scale	scale	NOUN
geusb-8357	216	6	machine	machine	NOUN
geusb-8357	216	7	learning	learn	VERB
geusb-8357	216	8	on	on	ADP
geusb-8357	216	9	heterogeneous	heterogeneous	ADJ
geusb-8357	216	10	distributed	distribute	VERB
geusb-8357	216	11	systems	system	NOUN
geusb-8357	216	12	.	.	PUNCT
geusb-8357	217	1	arxiv	arxiv	PROPN
geusb-8357	217	2	preprint	preprint	PROPN
geusb-8357	217	3	arxiv:1603.04467	arxiv:1603.04467	ADV
geusb-8357	217	4	.	.	PUNCT
geusb-8357	218	1	https://doi.org/10.48550/arxiv.1603.04467	https://doi.org/10.48550/arxiv.1603.04467	PROPN
geusb-8357	218	2	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	218	3	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	218	4	https://github.com/mathiasbusk/hydrosim_egebjerg	https://github.com/mathiasbusk/hydrosim_egebjerg	NOUN
geusb-8357	218	5	https://github.com/mathiasbusk/hydrosim_egebjerg	https://github.com/mathiasbusk/hydrosim_egebjerg	SYM
geusb-8357	218	6	https://doi.org/10.48550/arxiv.1603.04467	https://doi.org/10.48550/arxiv.1603.04467	PRON
geusb-8357	218	7	dahl	dahl	X
geusb-8357	218	8	et	et	PROPN
geusb-8357	218	9	al	al	PROPN
geusb-8357	218	10	.	.	PROPN
geusb-8357	218	11	2023	2023	NUM
geusb-8357	218	12	:	:	PUNCT
geusb-8357	218	13	geus	geus	NOUN
geusb-8357	218	14	bulletin	bulletin	NOUN
geusb-8357	218	15	53	53	NUM
geusb-8357	218	16	.	.	PUNCT
geusb-8357	218	17	8357	8357	NUM
geusb-8357	218	18	.	.	PUNCT
geusb-8357	219	1	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	219	2	7	7	NUM
geusb-8357	219	3	of	of	ADP
geusb-8357	219	4	7	7	NUM
geusb-8357	219	5	www.geusbul	www.geusbul	NOUN
geusb-8357	219	6	let	let	VERB
geusb-8357	219	7	in.org	in.org	ADJ
geusb-8357	219	8	belitz	belitz	NOUN
geusb-8357	219	9	,	,	PUNCT
geusb-8357	219	10	k.	k.	PROPN
geusb-8357	219	11	&	&	CCONJ
geusb-8357	219	12	stackelberg	stackelberg	PROPN
geusb-8357	219	13	,	,	PUNCT
geusb-8357	219	14	p.e	p.e	PROPN
geusb-8357	219	15	.	.	PROPN
geusb-8357	219	16	2021	2021	NUM
geusb-8357	219	17	:	:	PUNCT
geusb-8357	219	18	evaluation	evaluation	NOUN
geusb-8357	219	19	of	of	ADP
geusb-8357	219	20	six	six	NUM
geusb-8357	219	21	methods	method	NOUN
geusb-8357	219	22	for	for	ADP
geusb-8357	219	23	correcting	correct	VERB
geusb-8357	219	24	bias	bias	NOUN
geusb-8357	219	25	in	in	ADP
geusb-8357	219	26	estimates	estimate	NOUN
geusb-8357	219	27	from	from	ADP
geusb-8357	219	28	ensemble	ensemble	ADJ
geusb-8357	219	29	tree	tree	NOUN
geusb-8357	219	30	machine	machine	NOUN
geusb-8357	219	31	learning	learn	VERB
geusb-8357	219	32	regression	regression	NOUN
geusb-8357	219	33	models	model	NOUN
geusb-8357	219	34	.	.	PUNCT
geusb-8357	220	1	environmental	environmental	ADJ
geusb-8357	220	2	modelling	modelling	NOUN
geusb-8357	220	3	&	&	CCONJ
geusb-8357	220	4	software	software	NOUN
geusb-8357	220	5	139	139	NUM
geusb-8357	220	6	,	,	PUNCT
geusb-8357	220	7	105006	105006	NUM
geusb-8357	220	8	.	.	PUNCT
geusb-8357	221	1	https://doi	https://doi	X
geusb-8357	221	2	.	.	PUNCT
geusb-8357	222	1	org/10.1016	org/10.1016	PROPN
geusb-8357	222	2	/	/	SYM
geusb-8357	222	3	j.envsoft.2021.105006	j.envsoft.2021.105006	NOUN
geusb-8357	222	4	dahl	dahl	PROPN
geusb-8357	222	5	,	,	PUNCT
geusb-8357	222	6	m.b	m.b	PROPN
geusb-8357	222	7	.	.	PROPN
geusb-8357	222	8	,	,	PUNCT
geusb-8357	222	9	vilhelmsen	vilhelmsen	ADJ
geusb-8357	222	10	,	,	PUNCT
geusb-8357	222	11	t.n	t.n	PROPN
geusb-8357	222	12	.	.	PROPN
geusb-8357	222	13	,	,	PUNCT
geusb-8357	222	14	bach	bach	PROPN
geusb-8357	222	15	,	,	PUNCT
geusb-8357	222	16	t.	t.	PROPN
geusb-8357	222	17	&	&	CCONJ
geusb-8357	222	18	hansen	hansen	PROPN
geusb-8357	222	19	,	,	PUNCT
geusb-8357	222	20	t.m	t.m	PROPN
geusb-8357	222	21	.	.	PROPN
geusb-8357	222	22	2023	2023	NUM
geusb-8357	222	23	:	:	PUNCT
geusb-8357	222	24	hydraulic	hydraulic	ADJ
geusb-8357	222	25	head	head	NOUN
geusb-8357	222	26	change	change	NOUN
geusb-8357	222	27	predictions	prediction	NOUN
geusb-8357	222	28	in	in	ADP
geusb-8357	222	29	groundwater	groundwater	NOUN
geusb-8357	222	30	models	model	NOUN
geusb-8357	222	31	using	use	VERB
geusb-8357	222	32	a	a	DET
geusb-8357	222	33	probabilistic	probabilistic	ADJ
geusb-8357	222	34	neural	neural	ADJ
geusb-8357	222	35	network	network	NOUN
geusb-8357	222	36	.	.	PUNCT
geusb-8357	223	1	frontiers	frontier	NOUN
geusb-8357	223	2	in	in	ADP
geusb-8357	223	3	water	water	NOUN
geusb-8357	223	4	5	5	NUM
geusb-8357	223	5	.	.	PUNCT
geusb-8357	224	1	https://doi.org/10.3389/frwa.2023.1028922	https://doi.org/10.3389/frwa.2023.1028922	PROPN
geusb-8357	224	2	dillon	dillon	PROPN
geusb-8357	224	3	,	,	PUNCT
geusb-8357	224	4	j.v	j.v	PROPN
geusb-8357	224	5	.	.	PROPN
geusb-8357	224	6	et	et	PROPN
geusb-8357	224	7	  	  	SPACE
geusb-8357	224	8	al	al	PROPN
geusb-8357	224	9	.	.	PROPN
geusb-8357	224	10	2017	2017	NUM
geusb-8357	224	11	:	:	PUNCT
geusb-8357	224	12	tensorflow	tensorflow	NOUN
geusb-8357	224	13	distributions	distribution	NOUN
geusb-8357	224	14	.	.	PUNCT
geusb-8357	225	1	arxiv	arxiv	PROPN
geusb-8357	225	2	preprint	preprint	PROPN
geusb-8357	225	3	arxiv:1711.10604	arxiv:1711.10604	PROPN
geusb-8357	225	4	.	.	PUNCT
geusb-8357	226	1	https://doi.org/10.48550/arxiv.1711.10604	https://doi.org/10.48550/arxiv.1711.10604	ADJ
geusb-8357	226	2	enemark	enemark	NOUN
geusb-8357	226	3	,	,	PUNCT
geusb-8357	226	4	t.	t.	PROPN
geusb-8357	226	5	,	,	PUNCT
geusb-8357	226	6	andersen	andersen	PROPN
geusb-8357	226	7	,	,	PUNCT
geusb-8357	226	8	l.t	l.t	PROPN
geusb-8357	226	9	.	.	PROPN
geusb-8357	226	10	,	,	PUNCT
geusb-8357	226	11	høyer	høyer	PROPN
geusb-8357	226	12	,	,	PUNCT
geusb-8357	226	13	a.-s	a.-s	PROPN
geusb-8357	226	14	.	.	PROPN
geusb-8357	226	15	,	,	PUNCT
geusb-8357	226	16	jensen	jensen	PROPN
geusb-8357	226	17	,	,	PUNCT
geusb-8357	226	18	k.h	k.h	PROPN
geusb-8357	226	19	.	.	PROPN
geusb-8357	226	20	,	,	PUNCT
geusb-8357	226	21	kidmose	kidmose	PROPN
geusb-8357	226	22	,	,	PUNCT
geusb-8357	226	23	j.	j.	PROPN
geusb-8357	226	24	,	,	PUNCT
geusb-8357	226	25	sandersen	sandersen	NOUN
geusb-8357	226	26	,	,	PUNCT
geusb-8357	226	27	p.b.e	p.b.e	NOUN
geusb-8357	226	28	.	.	PUNCT
geusb-8357	226	29	&	&	CCONJ
geusb-8357	226	30	sonnenborg	sonnenborg	PROPN
geusb-8357	226	31	,	,	PUNCT
geusb-8357	226	32	t.o	t.o	PROPN
geusb-8357	226	33	.	.	PROPN
geusb-8357	226	34	2022	2022	NUM
geusb-8357	226	35	:	:	PUNCT
geusb-8357	226	36	the	the	DET
geusb-8357	226	37	influence	influence	NOUN
geusb-8357	226	38	of	of	ADP
geusb-8357	226	39	layer	layer	NOUN
geusb-8357	226	40	and	and	CCONJ
geusb-8357	226	41	voxel	voxel	PROPN
geusb-8357	226	42	geological	geological	ADJ
geusb-8357	226	43	modelling	modelling	NOUN
geusb-8357	226	44	strategy	strategy	NOUN
geusb-8357	226	45	on	on	ADP
geusb-8357	226	46	groundwater	groundwater	NOUN
geusb-8357	226	47	modelling	modelling	NOUN
geusb-8357	226	48	results	result	NOUN
geusb-8357	226	49	.	.	PUNCT
geusb-8357	227	1	hydrogeology	hydrogeology	NOUN
geusb-8357	227	2	journal	journal	PROPN
geusb-8357	227	3	30(2	30(2	NUM
geusb-8357	227	4	)	)	PUNCT
geusb-8357	227	5	,	,	PUNCT
geusb-8357	227	6	617–635	617–635	NUM
geusb-8357	227	7	.	.	PUNCT
geusb-8357	228	1	https://doi.org/10.1007/s10040-021-02442-9	https://doi.org/10.1007/s10040-021-02442-9	NOUN
geusb-8357	228	2	furtney	furtney	NOUN
geusb-8357	228	3	,	,	PUNCT
geusb-8357	228	4	j.	j.	PROPN
geusb-8357	228	5	2021	2021	NUM
geusb-8357	228	6	:	:	PUNCT
geusb-8357	228	7	scikit	scikit	PROPN
geusb-8357	228	8	-	-	PUNCT
geusb-8357	228	9	fmm	fmm	PROPN
geusb-8357	228	10	:	:	PUNCT
geusb-8357	228	11	the	the	DET
geusb-8357	228	12	fast	fast	ADJ
geusb-8357	228	13	marching	marching	NOUN
geusb-8357	228	14	method	method	NOUN
geusb-8357	228	15	for	for	ADP
geusb-8357	228	16	python	python	PROPN
geusb-8357	228	17	.	.	PUNCT
geusb-8357	229	1	https://github.com/scikit-fmm/scikit-fmm	https://github.com/scikit-fmm/scikit-fmm	PROPN
geusb-8357	229	2	(	(	PUNCT
geusb-8357	229	3	accessed	access	VERB
geusb-8357	229	4	june	june	PROPN
geusb-8357	229	5	2023	2023	NUM
geusb-8357	229	6	)	)	PUNCT
geusb-8357	229	7	.	.	PUNCT
geusb-8357	230	1	gardner	gardner	PROPN
geusb-8357	230	2	,	,	PUNCT
geusb-8357	230	3	m.w	m.w	PROPN
geusb-8357	230	4	.	.	PROPN
geusb-8357	230	5	&	&	CCONJ
geusb-8357	230	6	dorling	dorling	PROPN
geusb-8357	230	7	,	,	PUNCT
geusb-8357	230	8	s.r	s.r	PROPN
geusb-8357	230	9	.	.	PROPN
geusb-8357	230	10	1998	1998	NUM
geusb-8357	230	11	:	:	PUNCT
geusb-8357	230	12	artificial	artificial	ADJ
geusb-8357	230	13	neural	neural	ADJ
geusb-8357	230	14	networks	network	NOUN
geusb-8357	230	15	(	(	PUNCT
geusb-8357	230	16	the	the	DET
geusb-8357	230	17	multilayer	multilayer	PROPN
geusb-8357	230	18	perceptron	perceptron	PROPN
geusb-8357	230	19	)	)	PUNCT
geusb-8357	230	20	–	–	PUNCT
geusb-8357	230	21	a	a	DET
geusb-8357	230	22	review	review	NOUN
geusb-8357	230	23	of	of	ADP
geusb-8357	230	24	applications	application	NOUN
geusb-8357	230	25	in	in	ADP
geusb-8357	230	26	the	the	DET
geusb-8357	230	27	atmospheric	atmospheric	ADJ
geusb-8357	230	28	sciences	science	NOUN
geusb-8357	230	29	.	.	PUNCT
geusb-8357	231	1	atmospheric	atmospheric	ADJ
geusb-8357	231	2	environment	environment	NOUN
geusb-8357	231	3	32	32	NUM
geusb-8357	231	4	,	,	PUNCT
geusb-8357	231	5	2627–2636	2627–2636	NUM
geusb-8357	231	6	.	.	PUNCT
geusb-8357	232	1	https://doi.org/10.1016/	https://doi.org/10.1016/	PROPN
geusb-8357	232	2	s1352	s1352	PROPN
geusb-8357	232	3	-	-	PUNCT
geusb-8357	232	4	2310(97)00447	2310(97)00447	NUM
geusb-8357	232	5	-	-	SYM
geusb-8357	232	6	0	0	NUM
geusb-8357	232	7	gorelick	gorelick	NOUN
geusb-8357	232	8	,	,	PUNCT
geusb-8357	232	9	s.m	s.m	PROPN
geusb-8357	232	10	.	.	PROPN
geusb-8357	232	11	1983	1983	NUM
geusb-8357	232	12	:	:	PUNCT
geusb-8357	233	1	a	a	DET
geusb-8357	233	2	review	review	NOUN
geusb-8357	233	3	of	of	ADP
geusb-8357	233	4	distributed	distribute	VERB
geusb-8357	233	5	parameter	parameter	NOUN
geusb-8357	233	6	groundwater	groundwater	NOUN
geusb-8357	233	7	management	management	NOUN
geusb-8357	233	8	modeling	modeling	NOUN
geusb-8357	233	9	methods	method	NOUN
geusb-8357	233	10	.	.	PUNCT
geusb-8357	234	1	water	water	NOUN
geusb-8357	234	2	resources	resource	NOUN
geusb-8357	234	3	research	research	NOUN
geusb-8357	234	4	19	19	NUM
geusb-8357	234	5	,	,	PUNCT
geusb-8357	234	6	305–319	305–319	NUM
geusb-8357	234	7	.	.	PUNCT
geusb-8357	235	1	https://doi.org/10.1029/wr019i002p00305	https://doi.org/10.1029/wr019i002p00305	PROPN
geusb-8357	235	2	green	green	PROPN
geusb-8357	235	3	,	,	PUNCT
geusb-8357	235	4	t.r	t.r	PROPN
geusb-8357	235	5	.	.	PROPN
geusb-8357	235	6	,	,	PUNCT
geusb-8357	235	7	taniguchi	taniguchi	PROPN
geusb-8357	235	8	,	,	PUNCT
geusb-8357	235	9	m.	m.	NOUN
geusb-8357	235	10	,	,	PUNCT
geusb-8357	235	11	kooi	kooi	PROPN
geusb-8357	235	12	,	,	PUNCT
geusb-8357	235	13	h.	h.	PROPN
geusb-8357	235	14	,	,	PUNCT
geusb-8357	235	15	gurdak	gurdak	PROPN
geusb-8357	235	16	,	,	PUNCT
geusb-8357	235	17	j.j	j.j	PROPN
geusb-8357	235	18	.	.	PROPN
geusb-8357	235	19	,	,	PUNCT
geusb-8357	235	20	allen	allen	PROPN
geusb-8357	235	21	,	,	PUNCT
geusb-8357	235	22	d.m	d.m	PROPN
geusb-8357	235	23	.	.	PROPN
geusb-8357	235	24	,	,	PUNCT
geusb-8357	235	25	hiscock	hiscock	PROPN
geusb-8357	235	26	,	,	PUNCT
geusb-8357	235	27	k.m	k.m	PROPN
geusb-8357	235	28	.	.	PROPN
geusb-8357	235	29	,	,	PUNCT
geusb-8357	235	30	treidel	treidel	PROPN
geusb-8357	235	31	,	,	PUNCT
geusb-8357	235	32	h.	h.	PROPN
geusb-8357	235	33	&	&	CCONJ
geusb-8357	235	34	aureli	aureli	PROPN
geusb-8357	235	35	,	,	PUNCT
geusb-8357	235	36	a.	a.	NOUN
geusb-8357	235	37	2011	2011	NUM
geusb-8357	235	38	:	:	PUNCT
geusb-8357	235	39	beneath	beneath	ADP
geusb-8357	235	40	the	the	DET
geusb-8357	235	41	surface	surface	NOUN
geusb-8357	235	42	of	of	ADP
geusb-8357	235	43	global	global	ADJ
geusb-8357	235	44	change	change	NOUN
geusb-8357	235	45	:	:	PUNCT
geusb-8357	235	46	impacts	impact	NOUN
geusb-8357	235	47	of	of	ADP
geusb-8357	235	48	climate	climate	NOUN
geusb-8357	235	49	change	change	NOUN
geusb-8357	235	50	on	on	ADP
geusb-8357	235	51	groundwater	groundwater	NOUN
geusb-8357	235	52	.	.	PUNCT
geusb-8357	236	1	journal	journal	NOUN
geusb-8357	236	2	of	of	ADP
geusb-8357	236	3	hydrology	hydrology	NOUN
geusb-8357	236	4	405(3	405(3	NUM
geusb-8357	236	5	)	)	PUNCT
geusb-8357	236	6	,	,	PUNCT
geusb-8357	237	1	532–560	532–560	NUM
geusb-8357	237	2	.	.	PUNCT
geusb-8357	238	1	https://doi.org/10.1016/j.jhydrol.2011.05.002	https://doi.org/10.1016/j.jhydrol.2011.05.002	NOUN
geusb-8357	238	2	hadded	hadde	VERB
geusb-8357	238	3	,	,	PUNCT
geusb-8357	238	4	r.	r.	PROPN
geusb-8357	238	5	,	,	PUNCT
geusb-8357	238	6	nouiri	nouiri	PROPN
geusb-8357	238	7	,	,	PUNCT
geusb-8357	238	8	i.	i.	PROPN
geusb-8357	238	9	,	,	PUNCT
geusb-8357	238	10	alshihabi	alshihabi	PROPN
geusb-8357	238	11	,	,	PUNCT
geusb-8357	238	12	o.	o.	PROPN
geusb-8357	238	13	,	,	PUNCT
geusb-8357	238	14	mamann	mamann	PROPN
geusb-8357	238	15	,	,	PUNCT
geusb-8357	238	16	j.	j.	PROPN
geusb-8357	238	17	,	,	PUNCT
geusb-8357	238	18	huber	huber	PROPN
geusb-8357	238	19	,	,	PUNCT
geusb-8357	238	20	m.	m.	NOUN
geusb-8357	238	21	,	,	PUNCT
geusb-8357	238	22	laghouane	laghouane	NOUN
geusb-8357	238	23	,	,	PUNCT
geusb-8357	238	24	a.	a.	NOUN
geusb-8357	238	25	,	,	PUNCT
geusb-8357	238	26	yahiaoui	yahiaoui	PROPN
geusb-8357	238	27	,	,	PUNCT
geusb-8357	238	28	h.	h.	PROPN
geusb-8357	238	29	&	&	CCONJ
geusb-8357	238	30	tarhouni	tarhouni	PROPN
geusb-8357	238	31	,	,	PUNCT
geusb-8357	238	32	j.	j.	PROPN
geusb-8357	238	33	2013	2013	NUM
geusb-8357	238	34	:	:	PUNCT
geusb-8357	238	35	a	a	DET
geusb-8357	238	36	decision	decision	NOUN
geusb-8357	238	37	support	support	NOUN
geusb-8357	238	38	system	system	NOUN
geusb-8357	238	39	to	to	PART
geusb-8357	238	40	manage	manage	VERB
geusb-8357	238	41	the	the	DET
geusb-8357	238	42	groundwater	groundwater	NOUN
geusb-8357	238	43	of	of	ADP
geusb-8357	238	44	the	the	DET
geusb-8357	238	45	zeuss	zeuss	PROPN
geusb-8357	238	46	koutine	koutine	NOUN
geusb-8357	238	47	aquifer	aquifer	NOUN
geusb-8357	238	48	using	use	VERB
geusb-8357	238	49	the	the	DET
geusb-8357	238	50	weap	weap	NOUN
geusb-8357	238	51	-	-	PUNCT
geusb-8357	238	52	modflow	modflow	ADJ
geusb-8357	238	53	framework	framework	NOUN
geusb-8357	238	54	.	.	PUNCT
geusb-8357	239	1	water	water	NOUN
geusb-8357	239	2	resources	resource	NOUN
geusb-8357	239	3	management	management	NOUN
geusb-8357	239	4	27	27	NUM
geusb-8357	239	5	,	,	PUNCT
geusb-8357	239	6	1981–2000	1981–2000	NUM
geusb-8357	239	7	.	.	PUNCT
geusb-8357	240	1	https://doi.org/10.1007/s11269-013-0266-7	https://doi.org/10.1007/s11269-013-0266-7	PROPN
geusb-8357	240	2	harbaugh	harbaugh	PROPN
geusb-8357	240	3	,	,	PUNCT
geusb-8357	240	4	a.w	a.w	PROPN
geusb-8357	240	5	.	.	PROPN
geusb-8357	240	6	2005	2005	NUM
geusb-8357	240	7	:	:	PUNCT
geusb-8357	240	8	modflow-2005	modflow-2005	NOUN
geusb-8357	240	9	,	,	PUNCT
geusb-8357	240	10	the	the	DET
geusb-8357	240	11	u.s	u.s	PROPN
geusb-8357	240	12	.	.	PROPN
geusb-8357	240	13	geological	geological	ADJ
geusb-8357	240	14	survey	survey	NOUN
geusb-8357	240	15	modular	modular	ADJ
geusb-8357	240	16	ground	ground	NOUN
geusb-8357	240	17	-	-	PUNCT
geusb-8357	240	18	water	water	NOUN
geusb-8357	240	19	model	model	NOUN
geusb-8357	240	20	—	—	PUNCT
geusb-8357	240	21	the	the	DET
geusb-8357	240	22	ground	ground	NOUN
geusb-8357	240	23	-	-	PUNCT
geusb-8357	240	24	water	water	NOUN
geusb-8357	240	25	flow	flow	NOUN
geusb-8357	240	26	process	process	NOUN
geusb-8357	240	27	:	:	PUNCT
geusb-8357	240	28	u.s	u.s	PROPN
geusb-8357	240	29	.	.	PROPN
geusb-8357	240	30	geological	geological	ADJ
geusb-8357	240	31	survey	survey	NOUN
geusb-8357	240	32	techniques	technique	NOUN
geusb-8357	240	33	and	and	CCONJ
geusb-8357	240	34	methods	method	NOUN
geusb-8357	240	35	6	6	NUM
geusb-8357	240	36	-	-	PUNCT
geusb-8357	240	37	a16	a16	PROPN
geusb-8357	240	38	.	.	PUNCT
geusb-8357	240	39	usgs	usgs	PROPN
geusb-8357	240	40	.	.	PUNCT
geusb-8357	241	1	https://doi	https://doi	PROPN
geusb-8357	241	2	.	.	PUNCT
geusb-8357	241	3	org/10.3133	org/10.3133	PROPN
geusb-8357	241	4	/	/	SYM
geusb-8357	241	5	tm6a16	tm6a16	NOUN
geusb-8357	241	6	hendrycks	hendryck	NOUN
geusb-8357	241	7	,	,	PUNCT
geusb-8357	241	8	d.	d.	PROPN
geusb-8357	241	9	&	&	CCONJ
geusb-8357	241	10	gimpel	gimpel	PROPN
geusb-8357	241	11	,	,	PUNCT
geusb-8357	241	12	k.	k.	PROPN
geusb-8357	241	13	2016	2016	NUM
geusb-8357	241	14	:	:	PUNCT
geusb-8357	241	15	gaussian	gaussian	ADJ
geusb-8357	241	16	error	error	NOUN
geusb-8357	241	17	linear	linear	NOUN
geusb-8357	241	18	units	unit	NOUN
geusb-8357	241	19	(	(	PUNCT
geusb-8357	241	20	gelus	gelus	PROPN
geusb-8357	241	21	)	)	PUNCT
geusb-8357	241	22	.	.	PUNCT
geusb-8357	242	1	arxiv	arxiv	PROPN
geusb-8357	242	2	preprint	preprint	NOUN
geusb-8357	242	3	arxiv:1606.08415	arxiv:1606.08415	NOUN
geusb-8357	242	4	.	.	PUNCT
geusb-8357	243	1	https://doi.org/10.48550/	https://doi.org/10.48550/	PROPN
geusb-8357	243	2	arxiv.1606.08415	arxiv.1606.08415	ADJ
geusb-8357	243	3	kingma	kingma	PROPN
geusb-8357	243	4	,	,	PUNCT
geusb-8357	243	5	d.p	d.p	PROPN
geusb-8357	243	6	.	.	PROPN
geusb-8357	243	7	&	&	CCONJ
geusb-8357	243	8	ba	ba	PROPN
geusb-8357	243	9	,	,	PUNCT
geusb-8357	243	10	j.	j.	PROPN
geusb-8357	243	11	2014	2014	NUM
geusb-8357	243	12	:	:	PUNCT
geusb-8357	243	13	adam	adam	PROPN
geusb-8357	243	14	:	:	PUNCT
geusb-8357	243	15	a	a	DET
geusb-8357	243	16	method	method	NOUN
geusb-8357	243	17	for	for	ADP
geusb-8357	243	18	stochastic	stochastic	ADJ
geusb-8357	243	19	optimization	optimization	NOUN
geusb-8357	243	20	.	.	PUNCT
geusb-8357	244	1	arxiv	arxiv	PROPN
geusb-8357	244	2	preprint	preprint	VERB
geusb-8357	244	3	arxiv:1412.6980	arxiv:1412.6980	NOUN
geusb-8357	244	4	.	.	PUNCT
geusb-8357	245	1	https://doi.org/10.48550/	https://doi.org/10.48550/	PROPN
geusb-8357	245	2	arxiv.1412.6980	arxiv.1412.6980	PROPN
geusb-8357	245	3	johnson	johnson	PROPN
geusb-8357	245	4	,	,	PUNCT
geusb-8357	245	5	j.m	j.m	PROPN
geusb-8357	245	6	.	.	PROPN
geusb-8357	245	7	&	&	CCONJ
geusb-8357	245	8	khoshgoftaar	khoshgoftaar	PROPN
geusb-8357	245	9	,	,	PUNCT
geusb-8357	245	10	t.m	t.m	PROPN
geusb-8357	245	11	.	.	PROPN
geusb-8357	245	12	2019	2019	NUM
geusb-8357	245	13	:	:	PUNCT
geusb-8357	245	14	survey	survey	NOUN
geusb-8357	245	15	on	on	ADP
geusb-8357	245	16	deep	deep	ADJ
geusb-8357	245	17	learning	learning	NOUN
geusb-8357	245	18	with	with	ADP
geusb-8357	245	19	class	class	NOUN
geusb-8357	245	20	imbalance	imbalance	NOUN
geusb-8357	245	21	.	.	PUNCT
geusb-8357	246	1	journal	journal	NOUN
geusb-8357	246	2	of	of	ADP
geusb-8357	246	3	big	big	ADJ
geusb-8357	246	4	data	datum	NOUN
geusb-8357	246	5	6(1	6(1	NUM
geusb-8357	246	6	)	)	PUNCT
geusb-8357	246	7	,	,	PUNCT
geusb-8357	246	8	27	27	NUM
geusb-8357	246	9	.	.	PUNCT
geusb-8357	247	1	https://doi.org/10.1186/	https://doi.org/10.1186/	PROPN
geusb-8357	247	2	s40537	s40537	PROPN
geusb-8357	247	3	-	-	PUNCT
geusb-8357	247	4	019	019	NUM
geusb-8357	247	5	-	-	PUNCT
geusb-8357	247	6	0192	0192	NUM
geusb-8357	247	7	-	-	SYM
geusb-8357	247	8	5	5	NUM
geusb-8357	247	9	kingma	kingma	NOUN
geusb-8357	247	10	,	,	PUNCT
geusb-8357	247	11	d.p	d.p	PROPN
geusb-8357	247	12	.	.	PROPN
geusb-8357	247	13	&	&	CCONJ
geusb-8357	247	14	ba	ba	PROPN
geusb-8357	247	15	,	,	PUNCT
geusb-8357	247	16	j.	j.	PROPN
geusb-8357	247	17	2014	2014	NUM
geusb-8357	247	18	:	:	PUNCT
geusb-8357	247	19	adam	adam	PROPN
geusb-8357	247	20	:	:	PUNCT
geusb-8357	247	21	a	a	DET
geusb-8357	247	22	method	method	NOUN
geusb-8357	247	23	for	for	ADP
geusb-8357	247	24	stochastic	stochastic	ADJ
geusb-8357	247	25	optimization	optimization	NOUN
geusb-8357	247	26	.	.	PUNCT
geusb-8357	248	1	arxiv	arxiv	PROPN
geusb-8357	248	2	preprint	preprint	VERB
geusb-8357	248	3	arxiv:1412.6980	arxiv:1412.6980	NOUN
geusb-8357	248	4	.	.	PUNCT
geusb-8357	249	1	langevin	langevin	PROPN
geusb-8357	249	2	,	,	PUNCT
geusb-8357	249	3	c.d	c.d	PROPN
geusb-8357	249	4	.	.	PROPN
geusb-8357	249	5	,	,	PUNCT
geusb-8357	249	6	hughes	hughes	PROPN
geusb-8357	249	7	,	,	PUNCT
geusb-8357	249	8	j.d	j.d	PROPN
geusb-8357	249	9	.	.	PROPN
geusb-8357	249	10	,	,	PUNCT
geusb-8357	249	11	banta	banta	PROPN
geusb-8357	249	12	,	,	PUNCT
geusb-8357	249	13	e.r	e.r	PROPN
geusb-8357	249	14	.	.	PROPN
geusb-8357	249	15	,	,	PUNCT
geusb-8357	249	16	niswonger	niswonger	PROPN
geusb-8357	249	17	,	,	PUNCT
geusb-8357	249	18	r.g	r.g	PROPN
geusb-8357	249	19	.	.	PROPN
geusb-8357	249	20	,	,	PUNCT
geusb-8357	249	21	panday	panday	PROPN
geusb-8357	249	22	,	,	PUNCT
geusb-8357	249	23	s.	s.	PROPN
geusb-8357	249	24	&	&	CCONJ
geusb-8357	249	25	provost	provost	PROPN
geusb-8357	249	26	,	,	PUNCT
geusb-8357	249	27	a.m.	a.m.	NOUN
geusb-8357	249	28	2017	2017	NUM
geusb-8357	249	29	:	:	PUNCT
geusb-8357	249	30	documentation	documentation	NOUN
geusb-8357	249	31	for	for	ADP
geusb-8357	249	32	the	the	DET
geusb-8357	249	33	modflow	modflow	ADJ
geusb-8357	249	34	6	6	NUM
geusb-8357	249	35	groundwater	groundwater	NOUN
geusb-8357	249	36	flow	flow	NOUN
geusb-8357	249	37	model	model	NOUN
geusb-8357	249	38	.	.	PUNCT
geusb-8357	250	1	report	report	NOUN
geusb-8357	250	2	.	.	PUNCT
geusb-8357	251	1	u.s	u.s	PROPN
geusb-8357	251	2	.	.	PROPN
geusb-8357	251	3	geological	geological	ADJ
geusb-8357	251	4	survey	survey	NOUN
geusb-8357	251	5	techniques	technique	NOUN
geusb-8357	251	6	and	and	CCONJ
geusb-8357	251	7	methods	method	NOUN
geusb-8357	251	8	6	6	NUM
geusb-8357	251	9	-	-	PUNCT
geusb-8357	251	10	a55	a55	NOUN
geusb-8357	251	11	.	.	PUNCT
geusb-8357	252	1	http://pubs.er.usgs.gov/publication/tm6a55	http://pubs.er.usgs.gov/publication/tm6a55	PROPN
geusb-8357	252	2	(	(	PUNCT
geusb-8357	252	3	accessed	access	VERB
geusb-8357	252	4	june	june	PROPN
geusb-8357	252	5	2023	2023	NUM
geusb-8357	252	6	)	)	PUNCT
geusb-8357	252	7	.	.	PUNCT
geusb-8357	253	1	langevin	langevin	PROPN
geusb-8357	253	2	,	,	PUNCT
geusb-8357	253	3	c.	c.	PROPN
geusb-8357	253	4	,	,	PUNCT
geusb-8357	253	5	hughes	hughes	PROPN
geusb-8357	253	6	,	,	PUNCT
geusb-8357	253	7	j.	j.	PROPN
geusb-8357	253	8	,	,	PUNCT
geusb-8357	253	9	banta	banta	PROPN
geusb-8357	253	10	,	,	PUNCT
geusb-8357	253	11	e.	e.	PROPN
geusb-8357	253	12	,	,	PUNCT
geusb-8357	253	13	provost	provost	NOUN
geusb-8357	253	14	,	,	PUNCT
geusb-8357	253	15	a.	a.	NOUN
geusb-8357	253	16	,	,	PUNCT
geusb-8357	253	17	niswonger	niswonger	PROPN
geusb-8357	253	18	,	,	PUNCT
geusb-8357	253	19	r.	r.	PROPN
geusb-8357	253	20	&	&	CCONJ
geusb-8357	253	21	panday	panday	PROPN
geusb-8357	253	22	,	,	PUNCT
geusb-8357	253	23	s.	s.	PROPN
geusb-8357	253	24	2019	2019	NUM
geusb-8357	253	25	:	:	PUNCT
geusb-8357	253	26	modflow	modflow	ADJ
geusb-8357	253	27	6	6	NUM
geusb-8357	253	28	modular	modular	ADJ
geusb-8357	253	29	hydrologic	hydrologic	NOUN
geusb-8357	253	30	model	model	NOUN
geusb-8357	253	31	version	version	PROPN
geusb-8357	253	32	6.1.0	6.1.0	PROPN
geusb-8357	253	33	.	.	PUNCT
geusb-8357	254	1	u.s	u.s	PROPN
geusb-8357	254	2	.	.	PROPN
geusb-8357	254	3	geological	geological	ADJ
geusb-8357	254	4	survey	survey	NOUN
geusb-8357	254	5	software	software	NOUN
geusb-8357	254	6	.	.	PUNCT
geusb-8357	255	1	https://doi.org/10.5066/	https://doi.org/10.5066/	PROPN
geusb-8357	255	2	f76q1vqv	f76q1vqv	PROPN
geusb-8357	255	3	pisinaras	pisinara	NOUN
geusb-8357	255	4	,	,	PUNCT
geusb-8357	255	5	v.	v.	ADV
geusb-8357	255	6	,	,	PUNCT
geusb-8357	255	7	petalas	petala	NOUN
geusb-8357	255	8	,	,	PUNCT
geusb-8357	255	9	c.	c.	PROPN
geusb-8357	255	10	,	,	PUNCT
geusb-8357	255	11	tsihrintzis	tsihrintzis	PROPN
geusb-8357	255	12	,	,	PUNCT
geusb-8357	255	13	v.a	v.a	PROPN
geusb-8357	255	14	.	.	PROPN
geusb-8357	255	15	&	&	CCONJ
geusb-8357	255	16	zagana	zagana	PROPN
geusb-8357	255	17	,	,	PUNCT
geusb-8357	255	18	e.	e.	PROPN
geusb-8357	255	19	2007	2007	NUM
geusb-8357	255	20	:	:	PUNCT
geusb-8357	255	21	a	a	DET
geusb-8357	255	22	groundwater	groundwater	NOUN
geusb-8357	255	23	flow	flow	NOUN
geusb-8357	255	24	model	model	NOUN
geusb-8357	255	25	for	for	ADP
geusb-8357	255	26	water	water	NOUN
geusb-8357	255	27	resources	resource	NOUN
geusb-8357	255	28	management	management	NOUN
geusb-8357	255	29	in	in	ADP
geusb-8357	255	30	the	the	DET
geusb-8357	255	31	ismarida	ismarida	PROPN
geusb-8357	255	32	plain	plain	NOUN
geusb-8357	255	33	,	,	PUNCT
geusb-8357	255	34	north	north	PROPN
geusb-8357	255	35	greece	greece	PROPN
geusb-8357	255	36	.	.	PUNCT
geusb-8357	256	1	environmental	environmental	ADJ
geusb-8357	256	2	modeling	modeling	NOUN
geusb-8357	256	3	&	&	CCONJ
geusb-8357	256	4	assessment	assessment	NOUN
geusb-8357	256	5	12(2	12(2	NUM
geusb-8357	256	6	)	)	PUNCT
geusb-8357	256	7	,	,	PUNCT
geusb-8357	256	8	75–89	75–89	X
geusb-8357	256	9	.	.	PUNCT
geusb-8357	257	1	https://doi.org/10.1007/s10666-006-9040-z	https://doi.org/10.1007/s10666-006-9040-z	PROPN
geusb-8357	257	2	thibaut	thibaut	PROPN
geusb-8357	257	3	,	,	PUNCT
geusb-8357	257	4	r.	r.	PROPN
geusb-8357	257	5	,	,	PUNCT
geusb-8357	257	6	laloy	laloy	PROPN
geusb-8357	257	7	,	,	PUNCT
geusb-8357	257	8	e.	e.	PROPN
geusb-8357	257	9	&	&	CCONJ
geusb-8357	257	10	hermans	hermans	PROPN
geusb-8357	257	11	,	,	PUNCT
geusb-8357	257	12	t.	t.	NOUN
geusb-8357	257	13	2021	2021	NUM
geusb-8357	257	14	:	:	PUNCT
geusb-8357	257	15	a	a	DET
geusb-8357	257	16	new	new	ADJ
geusb-8357	257	17	framework	framework	NOUN
geusb-8357	257	18	for	for	ADP
geusb-8357	257	19	experimental	experimental	ADJ
geusb-8357	257	20	design	design	NOUN
geusb-8357	257	21	using	use	VERB
geusb-8357	257	22	bayesian	bayesian	NOUN
geusb-8357	257	23	evidential	evidential	ADJ
geusb-8357	257	24	learning	learning	NOUN
geusb-8357	257	25	:	:	PUNCT
geusb-8357	257	26	the	the	DET
geusb-8357	257	27	case	case	NOUN
geusb-8357	257	28	of	of	ADP
geusb-8357	257	29	wellhead	wellhead	NOUN
geusb-8357	257	30	protection	protection	NOUN
geusb-8357	257	31	area	area	NOUN
geusb-8357	257	32	.	.	PUNCT
geusb-8357	258	1	journal	journal	NOUN
geusb-8357	258	2	of	of	ADP
geusb-8357	258	3	hydrology	hydrology	NOUN
geusb-8357	258	4	603	603	NUM
geusb-8357	258	5	,	,	PUNCT
geusb-8357	258	6	126903	126903	NUM
geusb-8357	258	7	.	.	PUNCT
geusb-8357	259	1	https://doi	https://doi	X
geusb-8357	259	2	.	.	PUNCT
geusb-8357	259	3	org/10.1016	org/10.1016	PROPN
geusb-8357	259	4	/	/	SYM
geusb-8357	259	5	j.jhydrol.2021.126903	j.jhydrol.2021.126903	PROPN
geusb-8357	259	6	https://doi.org/10.34194/geusb.v53.8357	https://doi.org/10.34194/geusb.v53.8357	PROPN
geusb-8357	259	7	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8357	259	8	https://doi.org/10.1016/j.envsoft.2021.105006	https://doi.org/10.1016/j.envsoft.2021.105006	ADJ
geusb-8357	259	9	https://doi.org/10.1016/j.envsoft.2021.105006	https://doi.org/10.1016/j.envsoft.2021.105006	NOUN
geusb-8357	259	10	https://doi.org/10.3389/frwa.2023.1028922	https://doi.org/10.3389/frwa.2023.1028922	PROPN
geusb-8357	259	11	https://doi.org/10.48550/arxiv.1711.10604	https://doi.org/10.48550/arxiv.1711.10604	PROPN
geusb-8357	259	12	https://doi.org/10.1007/s10040-021-02442-9	https://doi.org/10.1007/s10040-021-02442-9	VERB
geusb-8357	260	1	https://github.com/scikit-fmm/scikit-fmm	https://github.com/scikit-fmm/scikit-fmm	PROPN
geusb-8357	260	2	https://doi.org/10.1016/s1352-2310(97)00447-0	https://doi.org/10.1016/s1352-2310(97)00447-0	VERB
geusb-8357	260	3	https://doi.org/10.1016/s1352-2310(97)00447-0	https://doi.org/10.1016/s1352-2310(97)00447-0	ADV
geusb-8357	260	4	https://doi.org/10.1029/wr019i002p00305	https://doi.org/10.1029/wr019i002p00305	NOUN
geusb-8357	261	1	https://doi.org/10.1016/j.jhydrol.2011.05.002	https://doi.org/10.1016/j.jhydrol.2011.05.002	NUM
geusb-8357	261	2	https://doi.org/10.1007/s11269-013-0266-7	https://doi.org/10.1007/s11269-013-0266-7	PROPN
geusb-8357	261	3	https://doi.org/10.3133/tm6a16	https://doi.org/10.3133/tm6a16	NOUN
geusb-8357	261	4	https://doi.org/10.3133/tm6a16	https://doi.org/10.3133/tm6a16	NOUN
geusb-8357	262	1	https://doi.org/10.48550/arxiv.1606.08415	https://doi.org/10.48550/arxiv.1606.08415	PROPN
geusb-8357	262	2	https://doi.org/10.48550/arxiv.1606.08415	https://doi.org/10.48550/arxiv.1606.08415	PROPN
geusb-8357	262	3	https://doi.org/10.48550/arxiv.1412.6980	https://doi.org/10.48550/arxiv.1412.6980	PROPN
geusb-8357	262	4	https://doi.org/10.48550/arxiv.1412.6980	https://doi.org/10.48550/arxiv.1412.6980	PROPN
geusb-8357	262	5	https://doi.org/10.1186/s40537-019-0192-5	https://doi.org/10.1186/s40537-019-0192-5	ADJ
geusb-8357	262	6	https://doi.org/10.1186/s40537-019-0192-5	https://doi.org/10.1186/s40537-019-0192-5	NUM
geusb-8357	262	7	http://pubs.er.usgs.gov/publication/tm6a55	http://pubs.er.usgs.gov/publication/tm6a55	NOUN
geusb-8357	262	8	https://doi.org/10.5066/f76q1vqv	https://doi.org/10.5066/f76q1vqv	PROPN
geusb-8357	262	9	https://doi.org/10.5066/f76q1vqv	https://doi.org/10.5066/f76q1vqv	PROPN
geusb-8357	263	1	https://doi.org/10.1007/s10666-006-9040-z	https://doi.org/10.1007/s10666-006-9040-z	PROPN
geusb-8357	263	2	https://doi.org/10.1016/j.jhydrol.2021.126903	https://doi.org/10.1016/j.jhydrol.2021.126903	PROPN
geusb-8357	263	3	https://doi.org/10.1016/j.jhydrol.2021.126903	https://doi.org/10.1016/j.jhydrol.2021.126903	VERB
geusb-8357	263	4	neural	neural	ADJ
geusb-8357	263	5	network	network	NOUN
geusb-8357	263	6	predictions	prediction	NOUN
geusb-8357	263	7	of	of	ADP
geusb-8357	263	8	drawdown	drawdown	NOUN
geusb-8357	263	9	from	from	ADP
geusb-8357	263	10	groundwater	groundwater	NOUN
geusb-8357	263	11	abstraction	abstraction	NOUN
geusb-8357	263	12	in	in	ADP
geusb-8357	263	13	the	the	DET
geusb-8357	263	14	egebjerg	egebjerg	PROPN
geusb-8357	263	15	catchment	catchment	NOUN
geusb-8357	263	16	,	,	PUNCT
geusb-8357	263	17	denma	denma	NOUN
geusb-8357	263	18	1	1	NUM
geusb-8357	263	19	.	.	PUNCT
geusb-8357	264	1	introduction	introduction	NOUN
geusb-8357	264	2	2	2	NUM
geusb-8357	264	3	.	.	PUNCT
geusb-8357	264	4	materials	material	NOUN
geusb-8357	264	5	and	and	CCONJ
geusb-8357	264	6	methods	method	NOUN
geusb-8357	264	7	2.1	2.1	NUM
geusb-8357	264	8	egebjerg	egebjerg	PROPN
geusb-8357	264	9	groundwater	groundwater	NOUN
geusb-8357	264	10	model	model	NOUN
geusb-8357	264	11	2.2	2.2	NUM
geusb-8357	264	12	data	data	NOUN
geusb-8357	264	13	-	-	PUNCT
geusb-8357	264	14	set	set	VERB
geusb-8357	264	15	construction	construction	NOUN
geusb-8357	264	16	2.3	2.3	NUM
geusb-8357	264	17	neural	neural	ADJ
geusb-8357	264	18	network	network	NOUN
geusb-8357	264	19	setup	setup	NOUN
geusb-8357	264	20	3	3	NUM
geusb-8357	264	21	.	.	NOUN
geusb-8357	264	22	results	result	VERB
geusb-8357	264	23	4	4	NUM
geusb-8357	264	24	.	.	PUNCT
geusb-8357	264	25	discussion	discussion	NOUN
geusb-8357	264	26	5	5	NUM
geusb-8357	264	27	.	.	PUNCT
geusb-8357	265	1	conclusions	conclusion	NOUN
geusb-8357	265	2	acknowledgements	acknowledgement	VERB
geusb-8357	265	3	additional	additional	ADJ
geusb-8357	265	4	information	information	NOUN
geusb-8357	265	5	funding	funding	NOUN
geusb-8357	265	6	statement	statement	NOUN
geusb-8357	265	7	author	author	NOUN
geusb-8357	265	8	contributions	contribution	VERB
geusb-8357	265	9	competing	compete	VERB
geusb-8357	265	10	interests	interest	NOUN
geusb-8357	265	11	additional	additional	ADJ
geusb-8357	265	12	files	file	NOUN
geusb-8357	265	13	references	reference	NOUN
geusb-8357	265	14	figures	figure	VERB
geusb-8357	265	15	fig	fig	NOUN
geusb-8357	265	16	.	.	PUNCT
geusb-8357	266	1	1	1	NUM
geusb-8357	266	2	a	a	DET
geusb-8357	266	3	visualisation	visualisation	NOUN
geusb-8357	266	4	of	of	ADP
geusb-8357	266	5	the	the	DET
geusb-8357	266	6	egebjerg	egebjerg	PROPN
geusb-8357	266	7	model	model	VERB
geusb-8357	266	8	boundary	boundary	ADJ
geusb-8357	266	9	conditions	condition	NOUN
geusb-8357	266	10	and	and	CCONJ
geusb-8357	266	11	some	some	PRON
geusb-8357	266	12	of	of	ADP
geusb-8357	266	13	the	the	DET
geusb-8357	266	14	relevant	relevant	ADJ
geusb-8357	266	15	features	feature	NOUN
geusb-8357	266	16	fig	fig	NOUN
geusb-8357	266	17	.	.	PUNCT
geusb-8357	267	1	2	2	NUM
geusb-8357	267	2	the	the	DET
geusb-8357	267	3	performances	performance	NOUN
geusb-8357	267	4	of	of	ADP
geusb-8357	267	5	the	the	DET
geusb-8357	267	6	trained	train	VERB
geusb-8357	267	7	neural	neural	ADJ
geusb-8357	267	8	network	network	NOUN
geusb-8357	267	9	on	on	ADP
geusb-8357	267	10	the	the	DET
geusb-8357	267	11	validation	validation	NOUN
geusb-8357	267	12	data	datum	NOUN
geusb-8357	267	13	set	set	VERB
geusb-8357	267	14	and	and	CCONJ
geusb-8357	267	15	an	an	DET
geusb-8357	267	16	independent	independent	ADJ
geusb-8357	267	17	fig	fig	NOUN
geusb-8357	267	18	.	.	PUNCT
geusb-8357	268	1	3	3	NUM
geusb-8357	268	2	results	result	NOUN
geusb-8357	268	3	and	and	CCONJ
geusb-8357	268	4	comparisons	comparison	NOUN
geusb-8357	268	5	between	between	ADP
geusb-8357	268	6	modflow	modflow	NOUN
geusb-8357	268	7	and	and	CCONJ
geusb-8357	268	8	the	the	DET
geusb-8357	268	9	neural	neural	ADJ
geusb-8357	268	10	network	network	NOUN
geusb-8357	268	11	in	in	ADP
geusb-8357	268	12	two	two	NUM
geusb-8357	268	13	test	test	NOUN
geusb-8357	268	14	cases	case	NOUN
geusb-8357	268	15	.	.	PUNCT
geusb-8357	269	1	a	a	X
geusb-8357	269	2	-	-	PUNCT
geusb-8357	269	3	d	d	NOUN
geusb-8357	269	4	:	:	PUNCT
geusb-8357	269	5	case	case	NOUN
geusb-8357	269	6	1	1	NUM
geusb-8357	269	7	fig	fig	NOUN
geusb-8357	269	8	.	.	PUNCT
geusb-8357	270	1	4	4	NUM
geusb-8357	270	2	drawdown	drawdown	NOUN
geusb-8357	270	3	in	in	ADP
geusb-8357	270	4	multiple	multiple	ADJ
geusb-8357	270	5	layers	layer	NOUN
geusb-8357	270	6	predicted	predict	VERB
geusb-8357	270	7	with	with	ADP
geusb-8357	270	8	the	the	DET
geusb-8357	270	9	neural	neural	ADJ
geusb-8357	270	10	network	network	NOUN
geusb-8357	270	11	with	with	ADP
geusb-8357	270	12	the	the	DET
geusb-8357	270	13	well	well	ADJ
geusb-8357	270	14	setup	setup	NOUN
geusb-8357	270	15	from	from	ADP
geusb-8357	270	16	case	case	NOUN
geusb-8357	270	17	1	1	NUM