id	sid	tid	token	lemma	pos
cana-2912	1	1	communications	communication	NOUN
cana-2912	1	2	on	on	ADP
cana-2912	1	3	applied	apply	VERB
cana-2912	1	4	nonlinear	nonlinear	ADJ
cana-2912	1	5	analysis	analysis	NOUN
cana-2912	1	6	issn	issn	NOUN
cana-2912	1	7	:	:	PUNCT
cana-2912	1	8	1074	1074	NUM
cana-2912	1	9	-	-	PUNCT
cana-2912	1	10	133x	133x	NUM
cana-2912	1	11	vol	vol	NOUN
cana-2912	1	12	32	32	NUM
cana-2912	1	13	no	no	NOUN
cana-2912	1	14	.	.	PUNCT
cana-2912	2	1	5s	5s	NUM
cana-2912	2	2	(	(	PUNCT
cana-2912	2	3	2025	2025	NUM
cana-2912	2	4	)	)	PUNCT
cana-2912	2	5	1	1	NUM
cana-2912	2	6	advanced	advanced	ADJ
cana-2912	2	7	weather	weather	NOUN
cana-2912	2	8	forecasting	forecasting	NOUN
cana-2912	2	9	with	with	ADP
cana-2912	2	10	machine	machine	NOUN
cana-2912	2	11	learning	learning	NOUN
cana-2912	2	12	:	:	PUNCT
cana-2912	2	13	leveraging	leverage	VERB
cana-2912	2	14	meteorological	meteorological	ADJ
cana-2912	2	15	data	datum	NOUN
cana-2912	2	16	for	for	ADP
cana-2912	2	17	improved	improved	ADJ
cana-2912	2	18	predictions	prediction	NOUN
cana-2912	2	19	sunil	sunil	PROPN
cana-2912	2	20	khatri1,2	khatri1,2	PROPN
cana-2912	2	21	*	*	PROPN
cana-2912	2	22	,	,	PUNCT
cana-2912	2	23	rajani	rajani	X
cana-2912	2	24	p.k.3	p.k.3	VERB
cana-2912	2	25	1research	1research	NUM
cana-2912	2	26	scholar	scholar	NOUN
cana-2912	2	27	,	,	PUNCT
cana-2912	2	28	dept	dept	NOUN
cana-2912	2	29	.	.	PROPN
cana-2912	2	30	of	of	ADP
cana-2912	2	31	extc	extc	NOUN
cana-2912	2	32	,	,	PUNCT
cana-2912	2	33	pimpri	pimpri	NOUN
cana-2912	2	34	chinchwad	chinchwad	PROPN
cana-2912	2	35	college	college	PROPN
cana-2912	2	36	of	of	ADP
cana-2912	2	37	engineering	engineering	PROPN
cana-2912	2	38	,	,	PUNCT
cana-2912	2	39	maharashtra	maharashtra	PROPN
cana-2912	2	40	,	,	PUNCT
cana-2912	2	41	india	india	PROPN
cana-2912	2	42	2assistant	2assistant	PROPN
cana-2912	2	43	professor	professor	NOUN
cana-2912	2	44	,	,	PUNCT
cana-2912	2	45	dept	dept	NOUN
cana-2912	2	46	.	.	PROPN
cana-2912	2	47	of	of	ADP
cana-2912	2	48	iot	iot	PROPN
cana-2912	2	49	,	,	PUNCT
cana-2912	2	50	thakur	thakur	PROPN
cana-2912	2	51	college	college	PROPN
cana-2912	2	52	of	of	ADP
cana-2912	2	53	engineering	engineering	NOUN
cana-2912	2	54	and	and	CCONJ
cana-2912	2	55	technology	technology	NOUN
cana-2912	2	56	,	,	PUNCT
cana-2912	2	57	maharashtra	maharashtra	PROPN
cana-2912	2	58	,	,	PUNCT
cana-2912	2	59	india	india	PROPN
cana-2912	2	60	3associate	3associate	NUM
cana-2912	2	61	professor	professor	NOUN
cana-2912	2	62	,	,	PUNCT
cana-2912	2	63	dept	dept	NOUN
cana-2912	2	64	.	.	PROPN
cana-2912	2	65	of	of	ADP
cana-2912	2	66	extc	extc	NOUN
cana-2912	2	67	,	,	PUNCT
cana-2912	2	68	pimpri	pimpri	NOUN
cana-2912	2	69	chinchwad	chinchwad	PROPN
cana-2912	2	70	college	college	PROPN
cana-2912	2	71	of	of	ADP
cana-2912	2	72	engineering	engineering	PROPN
cana-2912	2	73	,	,	PUNCT
cana-2912	2	74	maharashtra	maharashtra	PROPN
cana-2912	2	75	,	,	PUNCT
cana-2912	2	76	india	india	PROPN
cana-2912	2	77	*	*	PUNCT
cana-2912	2	78	corresponding	correspond	VERB
cana-2912	2	79	email	email	NOUN
cana-2912	2	80	:	:	PUNCT
cana-2912	2	81	skhatri1909@gmail.com	skhatri1909@gmail.com	NUM
cana-2912	2	82	article	article	NOUN
cana-2912	2	83	history	history	NOUN
cana-2912	2	84	:	:	PUNCT
cana-2912	2	85	received	receive	VERB
cana-2912	2	86	:	:	PUNCT
cana-2912	2	87	01	01	NUM
cana-2912	2	88	-	-	SYM
cana-2912	2	89	10	10	NUM
cana-2912	2	90	-	-	PUNCT
cana-2912	2	91	2024	2024	NUM
cana-2912	2	92	revised	revise	VERB
cana-2912	2	93	:	:	PUNCT
cana-2912	2	94	22	22	NUM
cana-2912	2	95	-	-	SYM
cana-2912	2	96	11	11	NUM
cana-2912	2	97	-	-	PUNCT
cana-2912	2	98	2024	2024	NUM
cana-2912	2	99	accepted	accept	VERB
cana-2912	2	100	:	:	PUNCT
cana-2912	2	101	02	02	NUM
cana-2912	2	102	-	-	SYM
cana-2912	2	103	12	12	NUM
cana-2912	2	104	-	-	PUNCT
cana-2912	2	105	2024	2024	NUM
cana-2912	2	106	abstract	abstract	NOUN
cana-2912	2	107	:	:	PUNCT
cana-2912	2	108	objectives	objective	NOUN
cana-2912	2	109	:	:	PUNCT
cana-2912	2	110	this	this	DET
cana-2912	2	111	study	study	NOUN
cana-2912	2	112	proposes	propose	VERB
cana-2912	2	113	a	a	DET
cana-2912	2	114	machine	machine	NOUN
cana-2912	2	115	learning	learn	VERB
cana-2912	2	116	approach	approach	NOUN
cana-2912	2	117	for	for	ADP
cana-2912	2	118	improving	improve	VERB
cana-2912	2	119	weather	weather	NOUN
cana-2912	2	120	forecasting	forecasting	NOUN
cana-2912	2	121	accuracy	accuracy	NOUN
cana-2912	2	122	with	with	ADP
cana-2912	2	123	reduced	reduced	ADJ
cana-2912	2	124	resource	resource	NOUN
cana-2912	2	125	requirements	requirement	NOUN
cana-2912	2	126	,	,	PUNCT
cana-2912	2	127	focusing	focus	VERB
cana-2912	2	128	on	on	ADP
cana-2912	2	129	rainfall	rainfall	NOUN
cana-2912	2	130	and	and	CCONJ
cana-2912	2	131	flooding	flooding	NOUN
cana-2912	2	132	predictions	prediction	NOUN
cana-2912	2	133	in	in	ADP
cana-2912	2	134	urban	urban	ADJ
cana-2912	2	135	regions	region	NOUN
cana-2912	2	136	.	.	PUNCT
cana-2912	3	1	methods	method	NOUN
cana-2912	3	2	:	:	PUNCT
cana-2912	3	3	the	the	DET
cana-2912	3	4	study	study	NOUN
cana-2912	3	5	used	use	VERB
cana-2912	3	6	historical	historical	ADJ
cana-2912	3	7	meteorological	meteorological	ADJ
cana-2912	3	8	data	datum	NOUN
cana-2912	3	9	(	(	PUNCT
cana-2912	3	10	2009–2023	2009–2023	NUM
cana-2912	3	11	)	)	PUNCT
cana-2912	3	12	from	from	ADP
cana-2912	3	13	indian	indian	ADJ
cana-2912	3	14	regions	region	NOUN
cana-2912	3	15	,	,	PUNCT
cana-2912	3	16	applying	apply	VERB
cana-2912	3	17	supervised	supervised	ADJ
cana-2912	3	18	learning	learning	NOUN
cana-2912	3	19	models	model	NOUN
cana-2912	3	20	,	,	PUNCT
cana-2912	3	21	including	include	VERB
cana-2912	3	22	regression	regression	NOUN
cana-2912	3	23	and	and	CCONJ
cana-2912	3	24	ensemble	ensemble	ADJ
cana-2912	3	25	methods	method	NOUN
cana-2912	3	26	.	.	PUNCT
cana-2912	4	1	data	datum	NOUN
cana-2912	4	2	preprocessing	preprocessing	NOUN
cana-2912	4	3	steps	step	NOUN
cana-2912	4	4	included	include	VERB
cana-2912	4	5	handling	handle	VERB
cana-2912	4	6	missing	miss	VERB
cana-2912	4	7	values	value	NOUN
cana-2912	4	8	,	,	PUNCT
cana-2912	4	9	outlier	outlier	NOUN
cana-2912	4	10	detection	detection	NOUN
cana-2912	4	11	,	,	PUNCT
cana-2912	4	12	and	and	CCONJ
cana-2912	4	13	normalization	normalization	NOUN
cana-2912	4	14	.	.	PUNCT
cana-2912	5	1	the	the	DET
cana-2912	5	2	models	model	NOUN
cana-2912	5	3	were	be	AUX
cana-2912	5	4	evaluated	evaluate	VERB
cana-2912	5	5	using	use	VERB
cana-2912	5	6	accuracy	accuracy	NOUN
cana-2912	5	7	,	,	PUNCT
cana-2912	5	8	mae	mae	PROPN
cana-2912	5	9	and	and	CCONJ
cana-2912	5	10	mse	mse	PROPN
cana-2912	5	11	value	value	NOUN
cana-2912	5	12	to	to	PART
cana-2912	5	13	determine	determine	VERB
cana-2912	5	14	prediction	prediction	NOUN
cana-2912	5	15	accuracy	accuracy	NOUN
cana-2912	5	16	and	and	CCONJ
cana-2912	5	17	reliability	reliability	NOUN
cana-2912	5	18	.	.	PUNCT
cana-2912	6	1	findings	finding	NOUN
cana-2912	6	2	:	:	PUNCT
cana-2912	6	3	the	the	DET
cana-2912	6	4	results	result	NOUN
cana-2912	6	5	indicate	indicate	VERB
cana-2912	6	6	that	that	SCONJ
cana-2912	6	7	ai	ai	PROPN
cana-2912	6	8	models	model	NOUN
cana-2912	6	9	,	,	PUNCT
cana-2912	6	10	especially	especially	ADV
cana-2912	6	11	lstm	lstm	ADJ
cana-2912	6	12	,	,	PUNCT
cana-2912	6	13	provide	provide	VERB
cana-2912	6	14	significant	significant	ADJ
cana-2912	6	15	accuracy	accuracy	NOUN
cana-2912	6	16	improvements	improvement	NOUN
cana-2912	6	17	with	with	ADP
cana-2912	6	18	80.11	80.11	NUM
cana-2912	6	19	%	%	NOUN
cana-2912	6	20	accuracy	accuracy	NOUN
cana-2912	6	21	.	.	PUNCT
cana-2912	7	1	these	these	DET
cana-2912	7	2	models	model	NOUN
cana-2912	7	3	performed	perform	VERB
cana-2912	7	4	well	well	ADV
cana-2912	7	5	in	in	ADP
cana-2912	7	6	predicting	predict	VERB
cana-2912	7	7	short	short	ADJ
cana-2912	7	8	-	-	PUNCT
cana-2912	7	9	term	term	NOUN
cana-2912	7	10	weather	weather	NOUN
cana-2912	7	11	phenomena	phenomenon	NOUN
cana-2912	7	12	such	such	ADJ
cana-2912	7	13	as	as	ADP
cana-2912	7	14	rainfall	rainfall	NOUN
cana-2912	7	15	in	in	ADP
cana-2912	7	16	urban	urban	ADJ
cana-2912	7	17	flood	flood	NOUN
cana-2912	7	18	-	-	PUNCT
cana-2912	7	19	prone	prone	ADJ
cana-2912	7	20	areas	area	NOUN
cana-2912	7	21	like	like	ADP
cana-2912	7	22	mumbai	mumbai	PROPN
cana-2912	7	23	.	.	PUNCT
cana-2912	8	1	the	the	DET
cana-2912	8	2	method	method	NOUN
cana-2912	8	3	demonstrated	demonstrate	VERB
cana-2912	8	4	the	the	DET
cana-2912	8	5	potential	potential	NOUN
cana-2912	8	6	to	to	PART
cana-2912	8	7	produce	produce	VERB
cana-2912	8	8	reliable	reliable	ADJ
cana-2912	8	9	forecasts	forecast	NOUN
cana-2912	8	10	with	with	ADP
cana-2912	8	11	limited	limited	ADJ
cana-2912	8	12	computational	computational	ADJ
cana-2912	8	13	resources	resource	NOUN
cana-2912	8	14	.	.	PUNCT
cana-2912	9	1	the	the	DET
cana-2912	9	2	findings	finding	NOUN
cana-2912	9	3	complement	complement	VERB
cana-2912	9	4	existing	exist	VERB
cana-2912	9	5	research	research	NOUN
cana-2912	9	6	,	,	PUNCT
cana-2912	9	7	adding	add	VERB
cana-2912	9	8	value	value	NOUN
cana-2912	9	9	by	by	ADP
cana-2912	9	10	showcasing	showcase	VERB
cana-2912	9	11	the	the	DET
cana-2912	9	12	adaptability	adaptability	NOUN
cana-2912	9	13	and	and	CCONJ
cana-2912	9	14	scalability	scalability	NOUN
cana-2912	9	15	of	of	ADP
cana-2912	9	16	resource	resource	NOUN
cana-2912	9	17	-	-	PUNCT
cana-2912	9	18	efficient	efficient	ADJ
cana-2912	9	19	ml	ml	NOUN
cana-2912	9	20	models	model	NOUN
cana-2912	9	21	for	for	ADP
cana-2912	9	22	local	local	ADJ
cana-2912	9	23	meteorological	meteorological	ADJ
cana-2912	9	24	applications	application	NOUN
cana-2912	9	25	.	.	PUNCT
cana-2912	10	1	this	this	DET
cana-2912	10	2	work	work	NOUN
cana-2912	10	3	highlights	highlight	VERB
cana-2912	10	4	the	the	DET
cana-2912	10	5	practical	practical	ADJ
cana-2912	10	6	implications	implication	NOUN
cana-2912	10	7	for	for	ADP
cana-2912	10	8	urban	urban	ADJ
cana-2912	10	9	planning	planning	NOUN
cana-2912	10	10	and	and	CCONJ
cana-2912	10	11	flood	flood	NOUN
cana-2912	10	12	preparedness	preparedness	NOUN
cana-2912	10	13	.	.	PUNCT
cana-2912	11	1	novelty	novelty	NOUN
cana-2912	11	2	:	:	PUNCT
cana-2912	11	3	a	a	DET
cana-2912	11	4	cost	cost	NOUN
cana-2912	11	5	-	-	PUNCT
cana-2912	11	6	effective	effective	ADJ
cana-2912	11	7	machine	machine	NOUN
cana-2912	11	8	learning	learn	VERB
cana-2912	11	9	framework	framework	NOUN
cana-2912	11	10	for	for	ADP
cana-2912	11	11	accurate	accurate	ADJ
cana-2912	11	12	local	local	ADJ
cana-2912	11	13	weather	weather	NOUN
cana-2912	11	14	forecasting	forecasting	NOUN
cana-2912	11	15	,	,	PUNCT
cana-2912	11	16	addressing	address	VERB
cana-2912	11	17	scalability	scalability	NOUN
cana-2912	11	18	and	and	CCONJ
cana-2912	11	19	computational	computational	ADJ
cana-2912	11	20	constraints	constraint	NOUN
cana-2912	11	21	.	.	PUNCT
cana-2912	12	1	keywords	keyword	NOUN
cana-2912	12	2	:	:	PUNCT
cana-2912	12	3	weather	weather	NOUN
cana-2912	12	4	forecasting	forecasting	NOUN
cana-2912	12	5	,	,	PUNCT
cana-2912	12	6	machine	machine	NOUN
cana-2912	12	7	learning	learning	NOUN
cana-2912	12	8	,	,	PUNCT
cana-2912	12	9	meteorological	meteorological	ADJ
cana-2912	12	10	data	datum	NOUN
cana-2912	12	11	,	,	PUNCT
cana-2912	12	12	predictive	predictive	ADJ
cana-2912	12	13	modelling	modelling	NOUN
cana-2912	12	14	,	,	PUNCT
cana-2912	12	15	flood	flood	NOUN
cana-2912	12	16	prediction	prediction	NOUN
cana-2912	12	17	1	1	NUM
cana-2912	12	18	.	.	PUNCT
cana-2912	12	19	introduction	introduction	NOUN
cana-2912	12	20	accurate	accurate	ADJ
cana-2912	12	21	weather	weather	NOUN
cana-2912	12	22	forecasting	forecasting	NOUN
cana-2912	12	23	plays	play	VERB
cana-2912	12	24	a	a	DET
cana-2912	12	25	critical	critical	ADJ
cana-2912	12	26	role	role	NOUN
cana-2912	12	27	in	in	ADP
cana-2912	12	28	various	various	ADJ
cana-2912	12	29	sectors	sector	NOUN
cana-2912	12	30	such	such	ADJ
cana-2912	12	31	as	as	ADP
cana-2912	12	32	agriculture	agriculture	NOUN
cana-2912	12	33	,	,	PUNCT
cana-2912	12	34	transportation	transportation	NOUN
cana-2912	12	35	,	,	PUNCT
cana-2912	12	36	urban	urban	ADJ
cana-2912	12	37	planning	planning	NOUN
cana-2912	12	38	,	,	PUNCT
cana-2912	12	39	and	and	CCONJ
cana-2912	12	40	disaster	disaster	NOUN
cana-2912	12	41	management	management	NOUN
cana-2912	12	42	.	.	PUNCT
cana-2912	13	1	as	as	ADP
cana-2912	13	2	climate	climate	NOUN
cana-2912	13	3	variability	variability	NOUN
cana-2912	13	4	and	and	CCONJ
cana-2912	13	5	extreme	extreme	ADJ
cana-2912	13	6	weather	weather	NOUN
cana-2912	13	7	events	event	NOUN
cana-2912	13	8	continue	continue	VERB
cana-2912	13	9	to	to	PART
cana-2912	13	10	rise	rise	VERB
cana-2912	13	11	,	,	PUNCT
cana-2912	13	12	the	the	DET
cana-2912	13	13	need	need	NOUN
cana-2912	13	14	for	for	ADP
cana-2912	13	15	precise	precise	ADJ
cana-2912	13	16	and	and	CCONJ
cana-2912	13	17	reliable	reliable	ADJ
cana-2912	13	18	forecasting	forecasting	NOUN
cana-2912	13	19	systems	system	NOUN
cana-2912	13	20	has	have	AUX
cana-2912	13	21	become	become	VERB
cana-2912	13	22	more	more	ADV
cana-2912	13	23	important	important	ADJ
cana-2912	13	24	than	than	ADP
cana-2912	13	25	ever	ever	ADV
cana-2912	13	26	.	.	PUNCT
cana-2912	14	1	these	these	DET
cana-2912	14	2	systems	system	NOUN
cana-2912	14	3	enable	enable	VERB
cana-2912	14	4	individuals	individual	NOUN
cana-2912	14	5	and	and	CCONJ
cana-2912	14	6	organizations	organization	NOUN
cana-2912	14	7	to	to	PART
cana-2912	14	8	make	make	VERB
cana-2912	14	9	informed	informed	ADJ
cana-2912	14	10	decisions	decision	NOUN
cana-2912	14	11	,	,	PUNCT
cana-2912	14	12	mitigate	mitigate	VERB
cana-2912	14	13	risks	risk	NOUN
cana-2912	14	14	,	,	PUNCT
cana-2912	14	15	and	and	CCONJ
cana-2912	14	16	plan	plan	VERB
cana-2912	14	17	effectively	effectively	ADV
cana-2912	14	18	for	for	ADP
cana-2912	14	19	future	future	ADJ
cana-2912	14	20	events	event	NOUN
cana-2912	14	21	.	.	PUNCT
cana-2912	15	1	traditional	traditional	ADJ
cana-2912	15	2	weather	weather	NOUN
cana-2912	15	3	prediction	prediction	NOUN
cana-2912	15	4	models	model	NOUN
cana-2912	15	5	rely	rely	VERB
cana-2912	15	6	on	on	ADP
cana-2912	15	7	complex	complex	ADJ
cana-2912	15	8	physical	physical	ADJ
cana-2912	15	9	simulations	simulation	NOUN
cana-2912	15	10	that	that	PRON
cana-2912	15	11	require	require	VERB
cana-2912	15	12	significant	significant	ADJ
cana-2912	15	13	computational	computational	ADJ
cana-2912	15	14	resources	resource	NOUN
cana-2912	15	15	and	and	CCONJ
cana-2912	15	16	extensive	extensive	ADJ
cana-2912	15	17	data	datum	NOUN
cana-2912	15	18	inputs	input	NOUN
cana-2912	15	19	.	.	PUNCT
cana-2912	16	1	despite	despite	SCONJ
cana-2912	16	2	their	their	PRON
cana-2912	16	3	sophistication	sophistication	NOUN
cana-2912	16	4	,	,	PUNCT
cana-2912	16	5	these	these	DET
cana-2912	16	6	models	model	NOUN
cana-2912	16	7	often	often	ADV
cana-2912	16	8	struggle	struggle	VERB
cana-2912	16	9	with	with	ADP
cana-2912	16	10	declining	decline	VERB
cana-2912	16	11	accuracy	accuracy	NOUN
cana-2912	16	12	due	due	ADP
cana-2912	16	13	to	to	ADP
cana-2912	16	14	the	the	DET
cana-2912	16	15	increasing	increase	VERB
cana-2912	16	16	complexity	complexity	NOUN
cana-2912	16	17	of	of	ADP
cana-2912	16	18	atmospheric	atmospheric	ADJ
cana-2912	16	19	conditions	condition	NOUN
cana-2912	16	20	and	and	CCONJ
cana-2912	16	21	inherent	inherent	ADJ
cana-2912	16	22	limitations	limitation	NOUN
cana-2912	16	23	in	in	ADP
cana-2912	16	24	their	their	PRON
cana-2912	16	25	algorithms	algorithm	NOUN
cana-2912	16	26	.	.	PUNCT
cana-2912	17	1	additionally	additionally	ADV
cana-2912	17	2	,	,	PUNCT
cana-2912	17	3	the	the	DET
cana-2912	17	4	high	high	ADJ
cana-2912	17	5	costs	cost	NOUN
cana-2912	17	6	associated	associate	VERB
cana-2912	17	7	with	with	ADP
cana-2912	17	8	running	run	VERB
cana-2912	17	9	these	these	DET
cana-2912	17	10	communications	communication	NOUN
cana-2912	17	11	on	on	ADP
cana-2912	17	12	applied	apply	VERB
cana-2912	17	13	nonlinear	nonlinear	ADJ
cana-2912	17	14	analysis	analysis	NOUN
cana-2912	17	15	issn	issn	NOUN
cana-2912	17	16	:	:	PUNCT
cana-2912	17	17	1074	1074	NUM
cana-2912	17	18	-	-	PUNCT
cana-2912	17	19	133x	133x	NUM
cana-2912	17	20	vol	vol	NOUN
cana-2912	17	21	32	32	NUM
cana-2912	17	22	no	no	NOUN
cana-2912	17	23	.	.	PUNCT
cana-2912	18	1	5s	5s	NUM
cana-2912	18	2	(	(	PUNCT
cana-2912	18	3	2025	2025	NUM
cana-2912	18	4	)	)	PUNCT
cana-2912	18	5	2	2	NUM
cana-2912	18	6	systems	system	NOUN
cana-2912	18	7	on	on	ADP
cana-2912	18	8	advanced	advanced	ADJ
cana-2912	18	9	infrastructure	infrastructure	NOUN
cana-2912	18	10	can	can	AUX
cana-2912	18	11	make	make	VERB
cana-2912	18	12	them	they	PRON
cana-2912	18	13	inaccessible	inaccessible	ADJ
cana-2912	18	14	,	,	PUNCT
cana-2912	18	15	particularly	particularly	ADV
cana-2912	18	16	in	in	ADP
cana-2912	18	17	resource	resource	NOUN
cana-2912	18	18	-	-	PUNCT
cana-2912	18	19	constrained	constrain	VERB
cana-2912	18	20	settings	setting	NOUN
cana-2912	18	21	.	.	PUNCT
cana-2912	19	1	in	in	ADP
cana-2912	19	2	recent	recent	ADJ
cana-2912	19	3	years	year	NOUN
cana-2912	19	4	,	,	PUNCT
cana-2912	19	5	machine	machine	NOUN
cana-2912	19	6	learning	learning	NOUN
cana-2912	19	7	has	have	AUX
cana-2912	19	8	emerged	emerge	VERB
cana-2912	19	9	as	as	ADP
cana-2912	19	10	a	a	DET
cana-2912	19	11	promising	promising	ADJ
cana-2912	19	12	alternative	alternative	NOUN
cana-2912	19	13	for	for	ADP
cana-2912	19	14	weather	weather	NOUN
cana-2912	19	15	prediction	prediction	NOUN
cana-2912	19	16	.	.	PUNCT
cana-2912	20	1	by	by	ADP
cana-2912	20	2	leveraging	leverage	VERB
cana-2912	20	3	historical	historical	ADJ
cana-2912	20	4	meteorological	meteorological	ADJ
cana-2912	20	5	data	datum	NOUN
cana-2912	20	6	,	,	PUNCT
cana-2912	20	7	machine	machine	NOUN
cana-2912	20	8	learning	learning	NOUN
cana-2912	20	9	models	model	NOUN
cana-2912	20	10	can	can	AUX
cana-2912	20	11	identify	identify	VERB
cana-2912	20	12	patterns	pattern	NOUN
cana-2912	20	13	and	and	CCONJ
cana-2912	20	14	trends	trend	NOUN
cana-2912	20	15	that	that	PRON
cana-2912	20	16	traditional	traditional	ADJ
cana-2912	20	17	methods	method	NOUN
cana-2912	20	18	may	may	AUX
cana-2912	20	19	overlook	overlook	VERB
cana-2912	20	20	.	.	PUNCT
cana-2912	21	1	these	these	DET
cana-2912	21	2	models	model	NOUN
cana-2912	21	3	not	not	PART
cana-2912	21	4	only	only	ADV
cana-2912	21	5	provide	provide	VERB
cana-2912	21	6	faster	fast	ADJ
cana-2912	21	7	predictions	prediction	NOUN
cana-2912	21	8	but	but	CCONJ
cana-2912	21	9	also	also	ADV
cana-2912	21	10	reduce	reduce	VERB
cana-2912	21	11	the	the	DET
cana-2912	21	12	computational	computational	ADJ
cana-2912	21	13	burden	burden	NOUN
cana-2912	21	14	,	,	PUNCT
cana-2912	21	15	making	make	VERB
cana-2912	21	16	them	they	PRON
cana-2912	21	17	suitable	suitable	ADJ
cana-2912	21	18	for	for	ADP
cana-2912	21	19	deployment	deployment	NOUN
cana-2912	21	20	on	on	ADP
cana-2912	21	21	low	low	ADJ
cana-2912	21	22	-	-	PUNCT
cana-2912	21	23	cost	cost	NOUN
cana-2912	21	24	systems	system	NOUN
cana-2912	21	25	.	.	PUNCT
cana-2912	22	1	such	such	ADJ
cana-2912	22	2	advancements	advancement	NOUN
cana-2912	22	3	are	be	AUX
cana-2912	22	4	particularly	particularly	ADV
cana-2912	22	5	relevant	relevant	ADJ
cana-2912	22	6	for	for	ADP
cana-2912	22	7	regions	region	NOUN
cana-2912	22	8	like	like	ADP
cana-2912	22	9	india	india	PROPN
cana-2912	22	10	,	,	PUNCT
cana-2912	22	11	where	where	SCONJ
cana-2912	22	12	diverse	diverse	ADJ
cana-2912	22	13	climatic	climatic	ADJ
cana-2912	22	14	conditions	condition	NOUN
cana-2912	22	15	and	and	CCONJ
cana-2912	22	16	frequent	frequent	ADJ
cana-2912	22	17	extreme	extreme	ADJ
cana-2912	22	18	weather	weather	NOUN
cana-2912	22	19	events	event	NOUN
cana-2912	22	20	necessitate	necessitate	ADJ
cana-2912	22	21	localized	localized	ADJ
cana-2912	22	22	and	and	CCONJ
cana-2912	22	23	efficient	efficient	ADJ
cana-2912	22	24	forecasting	forecasting	NOUN
cana-2912	22	25	solutions	solution	NOUN
cana-2912	22	26	.	.	PUNCT
cana-2912	23	1	this	this	DET
cana-2912	23	2	study	study	NOUN
cana-2912	23	3	explores	explore	VERB
cana-2912	23	4	the	the	DET
cana-2912	23	5	application	application	NOUN
cana-2912	23	6	of	of	ADP
cana-2912	23	7	machine	machine	NOUN
cana-2912	23	8	learning	learn	VERB
cana-2912	23	9	techniques	technique	NOUN
cana-2912	23	10	to	to	PART
cana-2912	23	11	predict	predict	VERB
cana-2912	23	12	weather	weather	NOUN
cana-2912	23	13	conditions	condition	NOUN
cana-2912	23	14	,	,	PUNCT
cana-2912	23	15	including	include	VERB
cana-2912	23	16	rainfall	rainfall	NOUN
cana-2912	23	17	and	and	CCONJ
cana-2912	23	18	temperature	temperature	NOUN
cana-2912	23	19	,	,	PUNCT
cana-2912	23	20	with	with	ADP
cana-2912	23	21	a	a	DET
cana-2912	23	22	focus	focus	NOUN
cana-2912	23	23	on	on	ADP
cana-2912	23	24	urban	urban	ADJ
cana-2912	23	25	regions	region	NOUN
cana-2912	23	26	prone	prone	ADJ
cana-2912	23	27	to	to	ADP
cana-2912	23	28	flooding	flooding	NOUN
cana-2912	23	29	.	.	PUNCT
cana-2912	24	1	by	by	ADP
cana-2912	24	2	utilizing	utilize	VERB
cana-2912	24	3	historical	historical	ADJ
cana-2912	24	4	data	datum	NOUN
cana-2912	24	5	from	from	ADP
cana-2912	24	6	multiple	multiple	ADJ
cana-2912	24	7	indian	indian	ADJ
cana-2912	24	8	locations	location	NOUN
cana-2912	24	9	,	,	PUNCT
cana-2912	24	10	the	the	DET
cana-2912	24	11	proposed	propose	VERB
cana-2912	24	12	system	system	NOUN
cana-2912	24	13	demonstrates	demonstrate	VERB
cana-2912	24	14	its	its	PRON
cana-2912	24	15	potential	potential	NOUN
cana-2912	24	16	to	to	PART
cana-2912	24	17	provide	provide	VERB
cana-2912	24	18	accurate	accurate	ADJ
cana-2912	24	19	,	,	PUNCT
cana-2912	24	20	resource	resource	NOUN
cana-2912	24	21	-	-	PUNCT
cana-2912	24	22	efficient	efficient	ADJ
cana-2912	24	23	forecasts	forecast	NOUN
cana-2912	24	24	.	.	PUNCT
cana-2912	25	1	the	the	DET
cana-2912	25	2	findings	finding	NOUN
cana-2912	25	3	of	of	ADP
cana-2912	25	4	this	this	DET
cana-2912	25	5	research	research	NOUN
cana-2912	25	6	highlight	highlight	VERB
cana-2912	25	7	the	the	DET
cana-2912	25	8	transformative	transformative	ADJ
cana-2912	25	9	potential	potential	NOUN
cana-2912	25	10	of	of	ADP
cana-2912	25	11	machine	machine	NOUN
cana-2912	25	12	learning	learning	NOUN
cana-2912	25	13	in	in	ADP
cana-2912	25	14	modern	modern	ADJ
cana-2912	25	15	weather	weather	NOUN
cana-2912	25	16	forecasting	forecasting	NOUN
cana-2912	25	17	,	,	PUNCT
cana-2912	25	18	offering	offer	VERB
cana-2912	25	19	a	a	DET
cana-2912	25	20	practical	practical	ADJ
cana-2912	25	21	and	and	CCONJ
cana-2912	25	22	scalable	scalable	ADJ
cana-2912	25	23	solution	solution	NOUN
cana-2912	25	24	for	for	ADP
cana-2912	25	25	addressing	address	VERB
cana-2912	25	26	challenges	challenge	NOUN
cana-2912	25	27	posed	pose	VERB
cana-2912	25	28	by	by	ADP
cana-2912	25	29	traditional	traditional	ADJ
cana-2912	25	30	methods	method	NOUN
cana-2912	25	31	.	.	PUNCT
cana-2912	26	1	the	the	DET
cana-2912	26	2	key	key	ADJ
cana-2912	26	3	contributions	contribution	NOUN
cana-2912	26	4	of	of	ADP
cana-2912	26	5	this	this	DET
cana-2912	26	6	work	work	NOUN
cana-2912	26	7	include	include	VERB
cana-2912	26	8	:	:	PUNCT
cana-2912	26	9	•	•	X
cana-2912	26	10	implementation	implementation	NOUN
cana-2912	26	11	of	of	ADP
cana-2912	26	12	machine	machine	NOUN
cana-2912	26	13	-	-	PUNCT
cana-2912	26	14	learning	learn	VERB
cana-2912	26	15	algorithms	algorithm	NOUN
cana-2912	26	16	for	for	ADP
cana-2912	26	17	efficient	efficient	ADJ
cana-2912	26	18	and	and	CCONJ
cana-2912	26	19	accurate	accurate	ADJ
cana-2912	26	20	weather	weather	NOUN
cana-2912	26	21	prediction	prediction	NOUN
cana-2912	26	22	.	.	PUNCT
cana-2912	27	1	•	•	NUM
cana-2912	27	2	development	development	NOUN
cana-2912	27	3	of	of	ADP
cana-2912	27	4	a	a	DET
cana-2912	27	5	resource	resource	NOUN
cana-2912	27	6	-	-	PUNCT
cana-2912	27	7	optimized	optimize	VERB
cana-2912	27	8	forecasting	forecasting	NOUN
cana-2912	27	9	framework	framework	NOUN
cana-2912	27	10	that	that	PRON
cana-2912	27	11	can	can	AUX
cana-2912	27	12	operate	operate	VERB
cana-2912	27	13	on	on	ADP
cana-2912	27	14	low	low	ADJ
cana-2912	27	15	-	-	PUNCT
cana-2912	27	16	cost	cost	NOUN
cana-2912	27	17	computational	computational	ADJ
cana-2912	27	18	systems	system	NOUN
cana-2912	27	19	.	.	PUNCT
cana-2912	28	1	•	•	NUM
cana-2912	28	2	comparative	comparative	ADJ
cana-2912	28	3	evaluation	evaluation	NOUN
cana-2912	28	4	of	of	ADP
cana-2912	28	5	different	different	ADJ
cana-2912	28	6	machine	machine	NOUN
cana-2912	28	7	-	-	PUNCT
cana-2912	28	8	learning	learn	VERB
cana-2912	28	9	models	model	NOUN
cana-2912	28	10	to	to	PART
cana-2912	28	11	determine	determine	VERB
cana-2912	28	12	their	their	PRON
cana-2912	28	13	suitability	suitability	NOUN
cana-2912	28	14	for	for	ADP
cana-2912	28	15	predicting	predict	VERB
cana-2912	28	16	specific	specific	ADJ
cana-2912	28	17	meteorological	meteorological	ADJ
cana-2912	28	18	conditions	condition	NOUN
cana-2912	28	19	.	.	PUNCT
cana-2912	29	1	1.1	1.1	NUM
cana-2912	29	2	machine	machine	NOUN
cana-2912	29	3	learning	learn	VERB
cana-2912	29	4	architecture	architecture	NOUN
cana-2912	29	5	fig	fig	NOUN
cana-2912	29	6	1	1	NUM
cana-2912	29	7	.	.	PUNCT
cana-2912	29	8	architecture	architecture	NOUN
cana-2912	29	9	of	of	ADP
cana-2912	29	10	machine	machine	NOUN
cana-2912	29	11	learning	learn	VERB
cana-2912	29	12	the	the	DET
cana-2912	29	13	architecture	architecture	NOUN
cana-2912	29	14	of	of	ADP
cana-2912	29	15	the	the	DET
cana-2912	29	16	machine	machine	NOUN
cana-2912	29	17	learning	learning	NOUN
cana-2912	29	18	model	model	NOUN
cana-2912	29	19	is	be	AUX
cana-2912	29	20	illustrated	illustrate	VERB
cana-2912	29	21	in	in	ADP
cana-2912	29	22	fig	fig	NOUN
cana-2912	29	23	.	.	PUNCT
cana-2912	30	1	1	1	X
cana-2912	30	2	.	.	X
cana-2912	30	3	the	the	DET
cana-2912	30	4	process	process	NOUN
cana-2912	30	5	consists	consist	VERB
cana-2912	30	6	of	of	ADP
cana-2912	30	7	several	several	ADJ
cana-2912	30	8	key	key	ADJ
cana-2912	30	9	stages	stage	NOUN
cana-2912	30	10	to	to	PART
cana-2912	30	11	ensure	ensure	VERB
cana-2912	30	12	accurate	accurate	ADJ
cana-2912	30	13	and	and	CCONJ
cana-2912	30	14	efficient	efficient	ADJ
cana-2912	30	15	weather	weather	NOUN
cana-2912	30	16	predictions	prediction	NOUN
cana-2912	30	17	:	:	PUNCT
cana-2912	30	18	1	1	X
cana-2912	30	19	.	.	PUNCT
cana-2912	30	20	data	datum	NOUN
cana-2912	30	21	collection	collection	NOUN
cana-2912	30	22	:	:	PUNCT
cana-2912	30	23	relevant	relevant	ADJ
cana-2912	30	24	meteorological	meteorological	ADJ
cana-2912	30	25	data	datum	NOUN
cana-2912	30	26	is	be	AUX
cana-2912	30	27	gathered	gather	VERB
cana-2912	30	28	from	from	ADP
cana-2912	30	29	external	external	ADJ
cana-2912	30	30	sources	source	NOUN
cana-2912	30	31	,	,	PUNCT
cana-2912	30	32	focusing	focus	VERB
cana-2912	30	33	on	on	ADP
cana-2912	30	34	historical	historical	ADJ
cana-2912	30	35	weather	weather	NOUN
cana-2912	30	36	records	record	NOUN
cana-2912	30	37	.	.	PUNCT
cana-2912	31	1	this	this	DET
cana-2912	31	2	stage	stage	NOUN
cana-2912	31	3	involves	involve	VERB
cana-2912	31	4	accumulating	accumulate	VERB
cana-2912	31	5	large	large	ADJ
cana-2912	31	6	datasets	dataset	NOUN
cana-2912	31	7	that	that	PRON
cana-2912	31	8	will	will	AUX
cana-2912	31	9	be	be	AUX
cana-2912	31	10	used	use	VERB
cana-2912	31	11	for	for	ADP
cana-2912	31	12	subsequent	subsequent	ADJ
cana-2912	31	13	analysis	analysis	NOUN
cana-2912	31	14	.	.	PUNCT
cana-2912	32	1	2	2	X
cana-2912	32	2	.	.	X
cana-2912	32	3	data	datum	NOUN
cana-2912	32	4	preprocessing	preprocessing	NOUN
cana-2912	32	5	:	:	PUNCT
cana-2912	32	6	in	in	ADP
cana-2912	32	7	this	this	DET
cana-2912	32	8	stage	stage	NOUN
cana-2912	32	9	,	,	PUNCT
cana-2912	32	10	the	the	DET
cana-2912	32	11	collected	collect	VERB
cana-2912	32	12	data	data	NOUN
cana-2912	32	13	undergoes	undergo	VERB
cana-2912	32	14	thorough	thorough	ADJ
cana-2912	32	15	cleaning	cleaning	NOUN
cana-2912	32	16	and	and	CCONJ
cana-2912	32	17	conditioning	conditioning	NOUN
cana-2912	32	18	.	.	PUNCT
cana-2912	33	1	measures	measure	NOUN
cana-2912	33	2	are	be	AUX
cana-2912	33	3	taken	take	VERB
cana-2912	33	4	to	to	PART
cana-2912	33	5	eliminate	eliminate	VERB
cana-2912	33	6	issues	issue	NOUN
cana-2912	33	7	such	such	ADJ
cana-2912	33	8	as	as	ADP
cana-2912	33	9	redundancy	redundancy	NOUN
cana-2912	33	10	,	,	PUNCT
cana-2912	33	11	missing	miss	VERB
cana-2912	33	12	values	value	NOUN
cana-2912	33	13	,	,	PUNCT
cana-2912	33	14	and	and	CCONJ
cana-2912	33	15	inconsistencies	inconsistency	NOUN
cana-2912	33	16	to	to	PART
cana-2912	33	17	ensure	ensure	VERB
cana-2912	33	18	the	the	DET
cana-2912	33	19	quality	quality	NOUN
cana-2912	33	20	and	and	CCONJ
cana-2912	33	21	reliability	reliability	NOUN
cana-2912	33	22	of	of	ADP
cana-2912	33	23	the	the	DET
cana-2912	33	24	data	datum	NOUN
cana-2912	33	25	.	.	PUNCT
cana-2912	34	1	communications	communication	NOUN
cana-2912	34	2	on	on	ADP
cana-2912	34	3	applied	apply	VERB
cana-2912	34	4	nonlinear	nonlinear	ADJ
cana-2912	34	5	analysis	analysis	NOUN
cana-2912	34	6	issn	issn	NOUN
cana-2912	34	7	:	:	PUNCT
cana-2912	34	8	1074	1074	NUM
cana-2912	34	9	-	-	PUNCT
cana-2912	34	10	133x	133x	NUM
cana-2912	34	11	vol	vol	NOUN
cana-2912	34	12	32	32	NUM
cana-2912	34	13	no	no	NOUN
cana-2912	34	14	.	.	PUNCT
cana-2912	35	1	5s	5s	NUM
cana-2912	35	2	(	(	PUNCT
cana-2912	35	3	2025	2025	NUM
cana-2912	35	4	)	)	PUNCT
cana-2912	35	5	3	3	NUM
cana-2912	35	6	3	3	NUM
cana-2912	35	7	.	.	PUNCT
cana-2912	36	1	data	datum	NOUN
cana-2912	36	2	analysis	analysis	NOUN
cana-2912	36	3	:	:	PUNCT
cana-2912	36	4	once	once	SCONJ
cana-2912	36	5	the	the	DET
cana-2912	36	6	data	data	NOUN
cana-2912	36	7	is	be	AUX
cana-2912	36	8	prepared	prepare	VERB
cana-2912	36	9	,	,	PUNCT
cana-2912	36	10	an	an	DET
cana-2912	36	11	analysis	analysis	NOUN
cana-2912	36	12	is	be	AUX
cana-2912	36	13	conducted	conduct	VERB
cana-2912	36	14	to	to	PART
cana-2912	36	15	identify	identify	VERB
cana-2912	36	16	the	the	DET
cana-2912	36	17	key	key	ADJ
cana-2912	36	18	features	feature	NOUN
cana-2912	36	19	or	or	CCONJ
cana-2912	36	20	independent	independent	ADJ
cana-2912	36	21	variables	variable	NOUN
cana-2912	36	22	that	that	PRON
cana-2912	36	23	contribute	contribute	VERB
cana-2912	36	24	to	to	ADP
cana-2912	36	25	weather	weather	NOUN
cana-2912	36	26	predictions	prediction	NOUN
cana-2912	36	27	.	.	PUNCT
cana-2912	37	1	these	these	PRON
cana-2912	37	2	include	include	VERB
cana-2912	37	3	factors	factor	NOUN
cana-2912	37	4	such	such	ADJ
cana-2912	37	5	as	as	ADP
cana-2912	37	6	cloud	cloud	ADJ
cana-2912	37	7	cover	cover	NOUN
cana-2912	37	8	,	,	PUNCT
cana-2912	37	9	wind	wind	NOUN
cana-2912	37	10	chill	chill	NOUN
cana-2912	37	11	,	,	PUNCT
cana-2912	37	12	atmospheric	atmospheric	ADJ
cana-2912	37	13	pressure	pressure	NOUN
cana-2912	37	14	,	,	PUNCT
cana-2912	37	15	and	and	CCONJ
cana-2912	37	16	sunlight	sunlight	NOUN
cana-2912	37	17	hours	hour	NOUN
cana-2912	37	18	,	,	PUNCT
cana-2912	37	19	all	all	PRON
cana-2912	37	20	of	of	ADP
cana-2912	37	21	which	which	PRON
cana-2912	37	22	are	be	AUX
cana-2912	37	23	used	use	VERB
cana-2912	37	24	to	to	PART
cana-2912	37	25	predict	predict	VERB
cana-2912	37	26	the	the	DET
cana-2912	37	27	target	target	NOUN
cana-2912	37	28	variable	variable	NOUN
cana-2912	37	29	,	,	PUNCT
cana-2912	37	30	such	such	ADJ
cana-2912	37	31	as	as	ADP
cana-2912	37	32	temperature	temperature	NOUN
cana-2912	37	33	.	.	PUNCT
cana-2912	38	1	4	4	X
cana-2912	38	2	.	.	X
cana-2912	38	3	model	model	NOUN
cana-2912	38	4	training	training	NOUN
cana-2912	38	5	:	:	PUNCT
cana-2912	38	6	during	during	ADP
cana-2912	38	7	the	the	DET
cana-2912	38	8	model	model	NOUN
cana-2912	38	9	training	training	NOUN
cana-2912	38	10	stage	stage	NOUN
cana-2912	38	11	,	,	PUNCT
cana-2912	38	12	machine	machine	NOUN
cana-2912	38	13	learning	learning	NOUN
cana-2912	38	14	algorithms	algorithm	NOUN
cana-2912	38	15	are	be	AUX
cana-2912	38	16	employed	employ	VERB
cana-2912	38	17	to	to	PART
cana-2912	38	18	fit	fit	VERB
cana-2912	38	19	both	both	CCONJ
cana-2912	38	20	independent	independent	ADJ
cana-2912	38	21	and	and	CCONJ
cana-2912	38	22	dependent	dependent	ADJ
cana-2912	38	23	features	feature	NOUN
cana-2912	38	24	to	to	ADP
cana-2912	38	25	the	the	DET
cana-2912	38	26	model	model	NOUN
cana-2912	38	27	.	.	PUNCT
cana-2912	39	1	the	the	DET
cana-2912	39	2	collected	collect	VERB
cana-2912	39	3	data	datum	NOUN
cana-2912	39	4	is	be	AUX
cana-2912	39	5	used	use	VERB
cana-2912	39	6	to	to	PART
cana-2912	39	7	teach	teach	VERB
cana-2912	39	8	the	the	DET
cana-2912	39	9	model	model	NOUN
cana-2912	39	10	the	the	DET
cana-2912	39	11	underlying	underlie	VERB
cana-2912	39	12	patterns	pattern	NOUN
cana-2912	39	13	,	,	PUNCT
cana-2912	39	14	enabling	enable	VERB
cana-2912	39	15	it	it	PRON
cana-2912	39	16	to	to	PART
cana-2912	39	17	make	make	VERB
cana-2912	39	18	accurate	accurate	ADJ
cana-2912	39	19	predictions	prediction	NOUN
cana-2912	39	20	.	.	PUNCT
cana-2912	40	1	5	5	X
cana-2912	40	2	.	.	PUNCT
cana-2912	40	3	model	model	NOUN
cana-2912	40	4	evaluation	evaluation	NOUN
cana-2912	40	5	:	:	PUNCT
cana-2912	40	6	this	this	DET
cana-2912	40	7	stage	stage	NOUN
cana-2912	40	8	involves	involve	VERB
cana-2912	40	9	assessing	assess	VERB
cana-2912	40	10	the	the	DET
cana-2912	40	11	performance	performance	NOUN
cana-2912	40	12	of	of	ADP
cana-2912	40	13	the	the	DET
cana-2912	40	14	trained	train	VERB
cana-2912	40	15	model	model	NOUN
cana-2912	40	16	using	use	VERB
cana-2912	40	17	various	various	ADJ
cana-2912	40	18	evaluation	evaluation	NOUN
cana-2912	40	19	metrics	metric	NOUN
cana-2912	40	20	.	.	PUNCT
cana-2912	41	1	by	by	ADP
cana-2912	41	2	comparing	compare	VERB
cana-2912	41	3	different	different	ADJ
cana-2912	41	4	model	model	NOUN
cana-2912	41	5	configurations	configuration	NOUN
cana-2912	41	6	,	,	PUNCT
cana-2912	41	7	the	the	DET
cana-2912	41	8	most	most	ADV
cana-2912	41	9	effective	effective	ADJ
cana-2912	41	10	approach	approach	NOUN
cana-2912	41	11	is	be	AUX
cana-2912	41	12	selected	select	VERB
cana-2912	41	13	based	base	VERB
cana-2912	41	14	on	on	ADP
cana-2912	41	15	accuracy	accuracy	NOUN
cana-2912	41	16	and	and	CCONJ
cana-2912	41	17	performance	performance	NOUN
cana-2912	41	18	.	.	PUNCT
cana-2912	42	1	6	6	X
cana-2912	42	2	.	.	X
cana-2912	42	3	prediction	prediction	NOUN
cana-2912	42	4	:	:	PUNCT
cana-2912	42	5	in	in	ADP
cana-2912	42	6	the	the	DET
cana-2912	42	7	prediction	prediction	NOUN
cana-2912	42	8	stage	stage	NOUN
cana-2912	42	9	,	,	PUNCT
cana-2912	42	10	the	the	DET
cana-2912	42	11	model	model	NOUN
cana-2912	42	12	uses	use	VERB
cana-2912	42	13	current	current	ADJ
cana-2912	42	14	meteorological	meteorological	ADJ
cana-2912	42	15	data	datum	NOUN
cana-2912	42	16	as	as	ADP
cana-2912	42	17	input	input	NOUN
cana-2912	42	18	to	to	PART
cana-2912	42	19	generate	generate	VERB
cana-2912	42	20	forecasts	forecast	NOUN
cana-2912	42	21	.	.	PUNCT
cana-2912	43	1	the	the	DET
cana-2912	43	2	predicted	predict	VERB
cana-2912	43	3	weather	weather	NOUN
cana-2912	43	4	conditions	condition	NOUN
cana-2912	43	5	,	,	PUNCT
cana-2912	43	6	such	such	ADJ
cana-2912	43	7	as	as	ADP
cana-2912	43	8	temperature	temperature	NOUN
cana-2912	43	9	and	and	CCONJ
cana-2912	43	10	rainfall	rainfall	NOUN
cana-2912	43	11	,	,	PUNCT
cana-2912	43	12	are	be	AUX
cana-2912	43	13	derived	derive	VERB
cana-2912	43	14	based	base	VERB
cana-2912	43	15	on	on	ADP
cana-2912	43	16	the	the	DET
cana-2912	43	17	accuracy	accuracy	NOUN
cana-2912	43	18	achieved	achieve	VERB
cana-2912	43	19	during	during	ADP
cana-2912	43	20	the	the	DET
cana-2912	43	21	training	training	NOUN
cana-2912	43	22	phase	phase	NOUN
cana-2912	43	23	.	.	PUNCT
cana-2912	44	1	7	7	X
cana-2912	44	2	.	.	X
cana-2912	44	3	display	display	NOUN
cana-2912	44	4	and	and	CCONJ
cana-2912	44	5	output	output	NOUN
cana-2912	44	6	:	:	PUNCT
cana-2912	44	7	finally	finally	ADV
cana-2912	44	8	,	,	PUNCT
cana-2912	44	9	the	the	DET
cana-2912	44	10	forecasted	forecast	VERB
cana-2912	44	11	values	value	NOUN
cana-2912	44	12	are	be	AUX
cana-2912	44	13	presented	present	VERB
cana-2912	44	14	to	to	ADP
cana-2912	44	15	end	end	NOUN
cana-2912	44	16	-	-	PUNCT
cana-2912	44	17	users	user	NOUN
cana-2912	44	18	in	in	ADP
cana-2912	44	19	an	an	DET
cana-2912	44	20	accessible	accessible	ADJ
cana-2912	44	21	format	format	NOUN
cana-2912	44	22	.	.	PUNCT
cana-2912	45	1	the	the	DET
cana-2912	45	2	predicted	predict	VERB
cana-2912	45	3	weather	weather	NOUN
cana-2912	45	4	conditions	condition	NOUN
cana-2912	45	5	are	be	AUX
cana-2912	45	6	displayed	display	VERB
cana-2912	45	7	,	,	PUNCT
cana-2912	45	8	allowing	allow	VERB
cana-2912	45	9	users	user	NOUN
cana-2912	45	10	to	to	PART
cana-2912	45	11	make	make	VERB
cana-2912	45	12	informed	informed	ADJ
cana-2912	45	13	decisions	decision	NOUN
cana-2912	45	14	based	base	VERB
cana-2912	45	15	on	on	ADP
cana-2912	45	16	the	the	DET
cana-2912	45	17	forecasts	forecast	NOUN
cana-2912	45	18	.	.	PUNCT
cana-2912	46	1	1.2	1.2	NUM
cana-2912	46	2	machine	machine	NOUN
cana-2912	46	3	learning	learn	VERB
cana-2912	46	4	algorithms	algorithm	NOUN
cana-2912	46	5	:	:	PUNCT
cana-2912	46	6	machine	machine	NOUN
cana-2912	46	7	learning	learn	VERB
cana-2912	46	8	algorithms	algorithm	NOUN
cana-2912	46	9	can	can	AUX
cana-2912	46	10	be	be	AUX
cana-2912	46	11	grouped	group	VERB
cana-2912	46	12	into	into	ADP
cana-2912	46	13	three	three	NUM
cana-2912	46	14	main	main	ADJ
cana-2912	46	15	types	type	NOUN
cana-2912	46	16	:	:	PUNCT
cana-2912	46	17	supervised	supervised	ADJ
cana-2912	46	18	learning	learning	NOUN
cana-2912	46	19	,	,	PUNCT
cana-2912	46	20	unsupervised	unsupervised	ADJ
cana-2912	46	21	learning	learning	NOUN
cana-2912	46	22	,	,	PUNCT
cana-2912	46	23	and	and	CCONJ
cana-2912	46	24	reinforcement	reinforcement	NOUN
cana-2912	46	25	learning	learning	NOUN
cana-2912	46	26	.	.	PUNCT
cana-2912	47	1	each	each	DET
cana-2912	47	2	type	type	NOUN
cana-2912	47	3	serves	serve	VERB
cana-2912	47	4	specific	specific	ADJ
cana-2912	47	5	purposes	purpose	NOUN
cana-2912	47	6	depending	depend	VERB
cana-2912	47	7	on	on	ADP
cana-2912	47	8	the	the	DET
cana-2912	47	9	data	datum	NOUN
cana-2912	47	10	characteristics	characteristic	NOUN
cana-2912	47	11	and	and	CCONJ
cana-2912	47	12	the	the	DET
cana-2912	47	13	application	application	NOUN
cana-2912	47	14	.	.	PUNCT
cana-2912	48	1	this	this	DET
cana-2912	48	2	work	work	NOUN
cana-2912	48	3	utilizes	utilize	VERB
cana-2912	48	4	supervised	supervise	VERB
cana-2912	48	5	learning	learn	VERB
cana-2912	48	6	to	to	PART
cana-2912	48	7	construct	construct	VERB
cana-2912	48	8	models	model	NOUN
cana-2912	48	9	for	for	ADP
cana-2912	48	10	regression	regression	NOUN
cana-2912	48	11	and	and	CCONJ
cana-2912	48	12	classification	classification	NOUN
cana-2912	48	13	tasks	task	NOUN
cana-2912	48	14	.	.	PUNCT
cana-2912	49	1	1.2.1	1.2.1	NUM
cana-2912	49	2	supervised	supervised	ADJ
cana-2912	49	3	learning	learning	NOUN
cana-2912	49	4	table	table	NOUN
cana-2912	49	5	1	1	NUM
cana-2912	49	6	:	:	PUNCT
cana-2912	49	7	examples	example	NOUN
cana-2912	49	8	of	of	ADP
cana-2912	49	9	algorithms	algorithm	NOUN
cana-2912	49	10	in	in	ADP
cana-2912	49	11	supervised	supervised	ADJ
cana-2912	49	12	learning	learning	NOUN
cana-2912	49	13	:	:	PUNCT
cana-2912	49	14	random	random	ADJ
cana-2912	49	15	forest	forest	NOUN
cana-2912	49	16	,	,	PUNCT
cana-2912	49	17	decision	decision	NOUN
cana-2912	49	18	trees	tree	NOUN
cana-2912	49	19	,	,	PUNCT
cana-2912	49	20	linear	linear	ADJ
cana-2912	49	21	regression	regression	NOUN
cana-2912	49	22	,	,	PUNCT
cana-2912	49	23	logistic	logistic	ADJ
cana-2912	49	24	regression	regression	NOUN
cana-2912	49	25	,	,	PUNCT
cana-2912	49	26	boosting	boost	VERB
cana-2912	49	27	algorithms	algorithm	NOUN
cana-2912	49	28	type	type	VERB
cana-2912	49	29	practical	practical	ADJ
cana-2912	49	30	example	example	NOUN
cana-2912	49	31	neural	neural	ADJ
cana-2912	49	32	network	network	NOUN
cana-2912	49	33	financial	financial	PROPN
cana-2912	49	34	outcome	outcome	NOUN
cana-2912	49	35	prediction	prediction	NOUN
cana-2912	49	36	,	,	PUNCT
cana-2912	49	37	fraud	fraud	NOUN
cana-2912	49	38	detection	detection	NOUN
cana-2912	49	39	classification	classification	NOUN
cana-2912	49	40	&	&	CCONJ
cana-2912	49	41	regression	regression	PROPN
cana-2912	49	42	spam	spam	NOUN
cana-2912	49	43	filtering	filter	VERB
cana-2912	49	44	,	,	PUNCT
cana-2912	49	45	fraud	fraud	NOUN
cana-2912	49	46	detection	detection	NOUN
cana-2912	49	47	decision	decision	NOUN
cana-2912	49	48	tree	tree	NOUN
cana-2912	49	49	risk	risk	NOUN
cana-2912	49	50	management	management	NOUN
cana-2912	49	51	,	,	PUNCT
cana-2912	49	52	threat	threat	NOUN
cana-2912	49	53	detection	detection	NOUN
cana-2912	49	54	supervised	supervise	VERB
cana-2912	49	55	learning	learning	NOUN
cana-2912	49	56	relies	rely	VERB
cana-2912	49	57	on	on	ADP
cana-2912	49	58	labelled	label	VERB
cana-2912	49	59	datasets	dataset	NOUN
cana-2912	49	60	,	,	PUNCT
cana-2912	49	61	where	where	SCONJ
cana-2912	49	62	both	both	DET
cana-2912	49	63	input	input	NOUN
cana-2912	49	64	features	feature	NOUN
cana-2912	49	65	and	and	CCONJ
cana-2912	49	66	corresponding	correspond	VERB
cana-2912	49	67	outputs	output	NOUN
cana-2912	49	68	are	be	AUX
cana-2912	49	69	available	available	ADJ
cana-2912	49	70	.	.	PUNCT
cana-2912	50	1	models	model	NOUN
cana-2912	50	2	are	be	AUX
cana-2912	50	3	trained	train	VERB
cana-2912	50	4	to	to	PART
cana-2912	50	5	map	map	VERB
cana-2912	50	6	inputs	input	NOUN
cana-2912	50	7	to	to	ADP
cana-2912	50	8	outputs	output	NOUN
cana-2912	50	9	by	by	ADP
cana-2912	50	10	minimizing	minimize	VERB
cana-2912	50	11	the	the	DET
cana-2912	50	12	error	error	NOUN
cana-2912	50	13	between	between	ADP
cana-2912	50	14	predicted	predict	VERB
cana-2912	50	15	and	and	CCONJ
cana-2912	50	16	actual	actual	ADJ
cana-2912	50	17	values	value	NOUN
cana-2912	50	18	.	.	PUNCT
cana-2912	51	1	tasks	task	NOUN
cana-2912	51	2	such	such	ADJ
cana-2912	51	3	as	as	ADP
cana-2912	51	4	classification	classification	NOUN
cana-2912	51	5	and	and	CCONJ
cana-2912	51	6	regression	regression	NOUN
cana-2912	51	7	are	be	AUX
cana-2912	51	8	commonly	commonly	ADV
cana-2912	51	9	addressed	address	VERB
cana-2912	51	10	using	use	VERB
cana-2912	51	11	this	this	DET
cana-2912	51	12	method	method	NOUN
cana-2912	51	13	.	.	PUNCT
cana-2912	52	1	communications	communication	NOUN
cana-2912	52	2	on	on	ADP
cana-2912	52	3	applied	apply	VERB
cana-2912	52	4	nonlinear	nonlinear	ADJ
cana-2912	52	5	analysis	analysis	NOUN
cana-2912	52	6	issn	issn	NOUN
cana-2912	52	7	:	:	PUNCT
cana-2912	52	8	1074	1074	NUM
cana-2912	52	9	-	-	PUNCT
cana-2912	52	10	133x	133x	NUM
cana-2912	52	11	vol	vol	NOUN
cana-2912	52	12	32	32	NUM
cana-2912	52	13	no	no	NOUN
cana-2912	52	14	.	.	PUNCT
cana-2912	53	1	5s	5s	NUM
cana-2912	53	2	(	(	PUNCT
cana-2912	53	3	2025	2025	NUM
cana-2912	53	4	)	)	PUNCT
cana-2912	53	5	4	4	NUM
cana-2912	53	6	1.2.2	1.2.2	NUM
cana-2912	53	7	unsupervised	unsupervised	ADJ
cana-2912	53	8	learning	learning	NOUN
cana-2912	53	9	table	table	NOUN
cana-2912	53	10	2	2	NUM
cana-2912	53	11	:	:	PUNCT
cana-2912	53	12	examples	example	NOUN
cana-2912	53	13	of	of	ADP
cana-2912	53	14	algorithms	algorithm	NOUN
cana-2912	53	15	in	in	ADP
cana-2912	53	16	unsupervised	unsupervised	ADJ
cana-2912	53	17	learning	learning	NOUN
cana-2912	53	18	:	:	PUNCT
cana-2912	54	1	k	k	X
cana-2912	54	2	-	-	PUNCT
cana-2912	54	3	means	mean	VERB
cana-2912	54	4	clustering	clustering	NOUN
cana-2912	54	5	,	,	PUNCT
cana-2912	54	6	apriori	apriori	ADV
cana-2912	54	7	algorithm	algorithm	NOUN
cana-2912	54	8	,	,	PUNCT
cana-2912	54	9	adaptive	adaptive	ADJ
cana-2912	54	10	resonance	resonance	NOUN
cana-2912	54	11	theory	theory	NOUN
cana-2912	54	12	,	,	PUNCT
cana-2912	54	13	self	self	NOUN
cana-2912	54	14	-	-	PUNCT
cana-2912	54	15	organizing	organize	VERB
cana-2912	54	16	maps	map	NOUN
cana-2912	54	17	(	(	PUNCT
cana-2912	54	18	som	som	NOUN
cana-2912	54	19	)	)	PUNCT
cana-2912	54	20	.	.	PUNCT
cana-2912	55	1	unsupervised	unsupervised	ADJ
cana-2912	55	2	learning	learning	NOUN
cana-2912	55	3	involves	involve	VERB
cana-2912	55	4	analysing	analyse	VERB
cana-2912	55	5	unlabelled	unlabelled	ADJ
cana-2912	55	6	data	datum	NOUN
cana-2912	55	7	to	to	PART
cana-2912	55	8	identify	identify	VERB
cana-2912	55	9	hidden	hide	VERB
cana-2912	55	10	patterns	pattern	NOUN
cana-2912	55	11	or	or	CCONJ
cana-2912	55	12	structures	structure	NOUN
cana-2912	55	13	.	.	PUNCT
cana-2912	56	1	this	this	DET
cana-2912	56	2	type	type	NOUN
cana-2912	56	3	of	of	ADP
cana-2912	56	4	learning	learning	NOUN
cana-2912	56	5	is	be	AUX
cana-2912	56	6	effective	effective	ADJ
cana-2912	56	7	for	for	ADP
cana-2912	56	8	tasks	task	NOUN
cana-2912	56	9	such	such	ADJ
cana-2912	56	10	as	as	ADP
cana-2912	56	11	clustering	clustering	NOUN
cana-2912	56	12	,	,	PUNCT
cana-2912	56	13	dimensionality	dimensionality	NOUN
cana-2912	56	14	reduction	reduction	NOUN
cana-2912	56	15	,	,	PUNCT
cana-2912	56	16	and	and	CCONJ
cana-2912	56	17	anomaly	anomaly	NOUN
cana-2912	56	18	detection	detection	NOUN
cana-2912	56	19	.	.	PUNCT
cana-2912	57	1	1.2.3	1.2.3	NUM
cana-2912	57	2	reinforcement	reinforcement	NOUN
cana-2912	57	3	learning	learning	NOUN
cana-2912	57	4	reinforcement	reinforcement	NOUN
cana-2912	57	5	learning	learning	NOUN
cana-2912	57	6	focuses	focus	VERB
cana-2912	57	7	on	on	ADP
cana-2912	57	8	training	training	NOUN
cana-2912	57	9	models	model	NOUN
cana-2912	57	10	through	through	ADP
cana-2912	57	11	interaction	interaction	NOUN
cana-2912	57	12	with	with	ADP
cana-2912	57	13	an	an	DET
cana-2912	57	14	environment	environment	NOUN
cana-2912	57	15	.	.	PUNCT
cana-2912	58	1	algorithms	algorithm	NOUN
cana-2912	58	2	learn	learn	VERB
cana-2912	58	3	by	by	ADP
cana-2912	58	4	receiving	receive	VERB
cana-2912	58	5	rewards	reward	NOUN
cana-2912	58	6	for	for	ADP
cana-2912	58	7	favourable	favourable	ADJ
cana-2912	58	8	actions	action	NOUN
cana-2912	58	9	and	and	CCONJ
cana-2912	58	10	penalties	penalty	NOUN
cana-2912	58	11	for	for	ADP
cana-2912	58	12	unfavourable	unfavourable	ADJ
cana-2912	58	13	ones	one	NOUN
cana-2912	58	14	,	,	PUNCT
cana-2912	58	15	gradually	gradually	ADV
cana-2912	58	16	improving	improve	VERB
cana-2912	58	17	decision	decision	NOUN
cana-2912	58	18	-	-	PUNCT
cana-2912	58	19	making	make	VERB
cana-2912	58	20	strategies	strategy	NOUN
cana-2912	58	21	.	.	PUNCT
cana-2912	59	1	example	example	NOUN
cana-2912	59	2	:	:	PUNCT
cana-2912	59	3	markov	markov	NOUN
cana-2912	59	4	decision	decision	NOUN
cana-2912	59	5	process	process	NOUN
cana-2912	59	6	(	(	PUNCT
cana-2912	59	7	mdp	mdp	NOUN
cana-2912	59	8	):	):	PUNCT
cana-2912	59	9	utilized	utilize	VERB
cana-2912	59	10	for	for	ADP
cana-2912	59	11	modelling	model	VERB
cana-2912	59	12	tasks	task	NOUN
cana-2912	59	13	where	where	SCONJ
cana-2912	59	14	outcomes	outcome	NOUN
cana-2912	59	15	are	be	AUX
cana-2912	59	16	influenced	influence	VERB
cana-2912	59	17	by	by	ADP
cana-2912	59	18	both	both	CCONJ
cana-2912	59	19	random	random	ADJ
cana-2912	59	20	factors	factor	NOUN
cana-2912	59	21	and	and	CCONJ
cana-2912	59	22	decision	decision	NOUN
cana-2912	59	23	-	-	PUNCT
cana-2912	59	24	making	make	VERB
cana-2912	59	25	processes	process	NOUN
cana-2912	59	26	.	.	PUNCT
cana-2912	60	1	reinforcement	reinforcement	NOUN
cana-2912	60	2	learning	learning	NOUN
cana-2912	60	3	is	be	AUX
cana-2912	60	4	particularly	particularly	ADV
cana-2912	60	5	suitable	suitable	ADJ
cana-2912	60	6	for	for	ADP
cana-2912	60	7	dynamic	dynamic	ADJ
cana-2912	60	8	and	and	CCONJ
cana-2912	60	9	uncertain	uncertain	ADJ
cana-2912	60	10	environments	environment	NOUN
cana-2912	60	11	,	,	PUNCT
cana-2912	60	12	requiring	require	VERB
cana-2912	60	13	iterative	iterative	NOUN
cana-2912	60	14	updates	update	NOUN
cana-2912	60	15	to	to	PART
cana-2912	60	16	improve	improve	VERB
cana-2912	60	17	strategies	strategy	NOUN
cana-2912	60	18	.	.	PUNCT
cana-2912	61	1	1.2.4	1.2.4	NUM
cana-2912	61	2	challenges	challenge	NOUN
cana-2912	61	3	and	and	CCONJ
cana-2912	61	4	considerations	consideration	NOUN
cana-2912	61	5	applying	apply	VERB
cana-2912	61	6	machine	machine	NOUN
cana-2912	61	7	learning	learning	NOUN
cana-2912	61	8	methods	method	NOUN
cana-2912	61	9	effectively	effectively	ADV
cana-2912	61	10	requires	require	VERB
cana-2912	61	11	addressing	address	VERB
cana-2912	61	12	several	several	ADJ
cana-2912	61	13	key	key	ADJ
cana-2912	61	14	challenges	challenge	NOUN
cana-2912	61	15	:	:	PUNCT
cana-2912	61	16	•	•	ADP
cana-2912	61	17	understanding	understand	VERB
cana-2912	61	18	the	the	DET
cana-2912	61	19	problem	problem	NOUN
cana-2912	61	20	domain	domain	NOUN
cana-2912	61	21	,	,	PUNCT
cana-2912	61	22	including	include	VERB
cana-2912	61	23	constraints	constraint	NOUN
cana-2912	61	24	and	and	CCONJ
cana-2912	61	25	objectives	objective	NOUN
cana-2912	61	26	.	.	PUNCT
cana-2912	62	1	•	•	NUM
cana-2912	62	2	ensuring	ensure	VERB
cana-2912	62	3	data	datum	NOUN
cana-2912	62	4	quality	quality	NOUN
cana-2912	62	5	by	by	ADP
cana-2912	62	6	addressing	address	VERB
cana-2912	62	7	issues	issue	NOUN
cana-2912	62	8	such	such	ADJ
cana-2912	62	9	as	as	ADP
cana-2912	62	10	missing	miss	VERB
cana-2912	62	11	values	value	NOUN
cana-2912	62	12	,	,	PUNCT
cana-2912	62	13	redundancy	redundancy	NOUN
cana-2912	62	14	,	,	PUNCT
cana-2912	62	15	and	and	CCONJ
cana-2912	62	16	inconsistent	inconsistent	ADJ
cana-2912	62	17	variables	variable	NOUN
cana-2912	62	18	.	.	PUNCT
cana-2912	63	1	•	•	NUM
cana-2912	63	2	selecting	select	VERB
cana-2912	63	3	appropriate	appropriate	ADJ
cana-2912	63	4	algorithms	algorithm	NOUN
cana-2912	63	5	and	and	CCONJ
cana-2912	63	6	tuning	tune	VERB
cana-2912	63	7	their	their	PRON
cana-2912	63	8	parameters	parameter	NOUN
cana-2912	63	9	to	to	PART
cana-2912	63	10	optimize	optimize	VERB
cana-2912	63	11	performance	performance	NOUN
cana-2912	63	12	for	for	ADP
cana-2912	63	13	the	the	DET
cana-2912	63	14	specific	specific	ADJ
cana-2912	63	15	task	task	NOUN
cana-2912	63	16	.	.	PUNCT
cana-2912	64	1	2	2	X
cana-2912	64	2	.	.	X
cana-2912	64	3	literature	literature	NOUN
cana-2912	64	4	review	review	VERB
cana-2912	64	5	recent	recent	ADJ
cana-2912	64	6	advancements	advancement	NOUN
cana-2912	64	7	in	in	ADP
cana-2912	64	8	machine	machine	NOUN
cana-2912	64	9	learning	learning	NOUN
cana-2912	64	10	have	have	AUX
cana-2912	64	11	significantly	significantly	ADV
cana-2912	64	12	enhanced	enhance	VERB
cana-2912	64	13	the	the	DET
cana-2912	64	14	accuracy	accuracy	NOUN
cana-2912	64	15	and	and	CCONJ
cana-2912	64	16	efficiency	efficiency	NOUN
cana-2912	64	17	of	of	ADP
cana-2912	64	18	weather	weather	NOUN
cana-2912	64	19	prediction	prediction	NOUN
cana-2912	64	20	systems	system	NOUN
cana-2912	64	21	.	.	PUNCT
cana-2912	65	1	several	several	ADJ
cana-2912	65	2	studies	study	NOUN
cana-2912	65	3	have	have	AUX
cana-2912	65	4	demonstrated	demonstrate	VERB
cana-2912	65	5	the	the	DET
cana-2912	65	6	potential	potential	NOUN
cana-2912	65	7	of	of	ADP
cana-2912	65	8	these	these	DET
cana-2912	65	9	techniques	technique	NOUN
cana-2912	65	10	to	to	PART
cana-2912	65	11	address	address	VERB
cana-2912	65	12	limitations	limitation	NOUN
cana-2912	65	13	in	in	ADP
cana-2912	65	14	traditional	traditional	ADJ
cana-2912	65	15	forecasting	forecasting	NOUN
cana-2912	65	16	methods	method	NOUN
cana-2912	65	17	.	.	PUNCT
cana-2912	66	1	research	research	NOUN
cana-2912	66	2	comparing	compare	VERB
cana-2912	66	3	deep	deep	ADJ
cana-2912	66	4	learning	learning	NOUN
cana-2912	66	5	and	and	CCONJ
cana-2912	66	6	numerical	numerical	PROPN
cana-2912	66	7	weather	weather	PROPN
cana-2912	66	8	prediction	prediction	NOUN
cana-2912	66	9	models	model	NOUN
cana-2912	66	10	underscored	underscore	VERB
cana-2912	66	11	the	the	DET
cana-2912	66	12	strengths	strength	NOUN
cana-2912	66	13	and	and	CCONJ
cana-2912	66	14	weaknesses	weakness	NOUN
cana-2912	66	15	of	of	ADP
cana-2912	66	16	both	both	DET
cana-2912	66	17	approaches	approach	NOUN
cana-2912	66	18	.	.	PUNCT
cana-2912	67	1	deep	deep	ADJ
cana-2912	67	2	learning	learning	NOUN
cana-2912	67	3	demonstrated	demonstrate	VERB
cana-2912	67	4	superior	superior	ADJ
cana-2912	67	5	adaptability	adaptability	NOUN
cana-2912	67	6	to	to	ADP
cana-2912	67	7	complex	complex	ADJ
cana-2912	67	8	,	,	PUNCT
cana-2912	67	9	non	non	ADJ
cana-2912	67	10	-	-	ADJ
cana-2912	67	11	linear	linear	ADJ
cana-2912	67	12	patterns	pattern	NOUN
cana-2912	67	13	in	in	ADP
cana-2912	67	14	atmospheric	atmospheric	ADJ
cana-2912	67	15	data	datum	NOUN
cana-2912	67	16	,	,	PUNCT
cana-2912	67	17	whereas	whereas	SCONJ
cana-2912	67	18	traditional	traditional	ADJ
cana-2912	67	19	numerical	numerical	ADJ
cana-2912	67	20	models	model	NOUN
cana-2912	67	21	provided	provide	VERB
cana-2912	67	22	greater	great	ADJ
cana-2912	67	23	reliability	reliability	NOUN
cana-2912	67	24	in	in	ADP
cana-2912	67	25	structured	structured	ADJ
cana-2912	67	26	forecasting	forecasting	NOUN
cana-2912	67	27	scenarios	scenario	NOUN
cana-2912	67	28	[	[	X
cana-2912	67	29	1	1	NUM
cana-2912	67	30	]	]	PUNCT
cana-2912	67	31	.	.	PUNCT
cana-2912	68	1	another	another	DET
cana-2912	68	2	investigation	investigation	NOUN
cana-2912	68	3	evaluated	evaluate	VERB
cana-2912	68	4	machine	machine	NOUN
cana-2912	68	5	learning	learning	NOUN
cana-2912	68	6	models	model	NOUN
cana-2912	68	7	for	for	ADP
cana-2912	68	8	predicting	predict	VERB
cana-2912	68	9	meteorological	meteorological	ADJ
cana-2912	68	10	variables	variable	NOUN
cana-2912	68	11	such	such	ADJ
cana-2912	68	12	as	as	ADP
cana-2912	68	13	temperature	temperature	NOUN
cana-2912	68	14	,	,	PUNCT
cana-2912	68	15	humidity	humidity	NOUN
cana-2912	68	16	,	,	PUNCT
cana-2912	68	17	and	and	CCONJ
cana-2912	68	18	precipitation	precipitation	NOUN
cana-2912	68	19	in	in	ADP
cana-2912	68	20	indian	indian	ADJ
cana-2912	68	21	regions	region	NOUN
cana-2912	68	22	.	.	PUNCT
cana-2912	69	1	this	this	DET
cana-2912	69	2	study	study	NOUN
cana-2912	69	3	emphasized	emphasize	VERB
cana-2912	69	4	the	the	DET
cana-2912	69	5	importance	importance	NOUN
cana-2912	69	6	type	type	NOUN
cana-2912	69	7	practical	practical	ADJ
cana-2912	69	8	example	example	NOUN
cana-2912	69	9	cluster	cluster	NOUN
cana-2912	69	10	analysis	analysis	NOUN
cana-2912	69	11	detecting	detect	VERB
cana-2912	69	12	fraudulent	fraudulent	ADJ
cana-2912	69	13	transactions	transaction	NOUN
cana-2912	69	14	,	,	PUNCT
cana-2912	69	15	spam	spam	VERB
cana-2912	69	16	filtering	filtering	NOUN
cana-2912	69	17	pattern	pattern	NOUN
cana-2912	69	18	recognition	recognition	NOUN
cana-2912	69	19	object	object	NOUN
cana-2912	69	20	detection	detection	NOUN
cana-2912	69	21	,	,	PUNCT
cana-2912	69	22	people	people	NOUN
cana-2912	69	23	detection	detection	NOUN
cana-2912	69	24	association	association	NOUN
cana-2912	69	25	rule	rule	NOUN
cana-2912	69	26	learning	learn	VERB
cana-2912	69	27	bioinformatics	bioinformatics	NOUN
cana-2912	69	28	,	,	PUNCT
cana-2912	69	29	manufacturing	manufacturing	NOUN
cana-2912	69	30	,	,	PUNCT
cana-2912	69	31	and	and	CCONJ
cana-2912	69	32	assembly	assembly	NOUN
cana-2912	69	33	communications	communication	NOUN
cana-2912	69	34	on	on	ADP
cana-2912	69	35	applied	apply	VERB
cana-2912	69	36	nonlinear	nonlinear	ADJ
cana-2912	69	37	analysis	analysis	NOUN
cana-2912	69	38	issn	issn	NOUN
cana-2912	69	39	:	:	PUNCT
cana-2912	69	40	1074	1074	NUM
cana-2912	69	41	-	-	PUNCT
cana-2912	69	42	133x	133x	NUM
cana-2912	69	43	vol	vol	NOUN
cana-2912	69	44	32	32	NUM
cana-2912	69	45	no	no	NOUN
cana-2912	69	46	.	.	PUNCT
cana-2912	70	1	5s	5s	NUM
cana-2912	70	2	(	(	PUNCT
cana-2912	70	3	2025	2025	NUM
cana-2912	70	4	)	)	PUNCT
cana-2912	70	5	5	5	NUM
cana-2912	70	6	of	of	ADP
cana-2912	70	7	feature	feature	NOUN
cana-2912	70	8	selection	selection	NOUN
cana-2912	70	9	and	and	CCONJ
cana-2912	70	10	data	datum	NOUN
cana-2912	70	11	preprocessing	preprocesse	VERB
cana-2912	70	12	in	in	ADP
cana-2912	70	13	enhancing	enhance	VERB
cana-2912	70	14	model	model	NOUN
cana-2912	70	15	performance	performance	NOUN
cana-2912	70	16	,	,	PUNCT
cana-2912	70	17	offering	offer	VERB
cana-2912	70	18	valuable	valuable	ADJ
cana-2912	70	19	insights	insight	NOUN
cana-2912	70	20	for	for	ADP
cana-2912	70	21	localized	localized	ADJ
cana-2912	70	22	forecasting	forecasting	NOUN
cana-2912	70	23	[	[	X
cana-2912	70	24	2	2	NUM
cana-2912	70	25	]	]	PUNCT
cana-2912	70	26	.	.	PUNCT
cana-2912	71	1	a	a	DET
cana-2912	71	2	comparative	comparative	ADJ
cana-2912	71	3	study	study	NOUN
cana-2912	71	4	on	on	ADP
cana-2912	71	5	decision	decision	NOUN
cana-2912	71	6	trees	tree	NOUN
cana-2912	71	7	,	,	PUNCT
cana-2912	71	8	random	random	ADJ
cana-2912	71	9	forests	forest	NOUN
cana-2912	71	10	,	,	PUNCT
cana-2912	71	11	and	and	CCONJ
cana-2912	71	12	artificial	artificial	ADJ
cana-2912	71	13	neural	neural	ADJ
cana-2912	71	14	networks	network	NOUN
cana-2912	71	15	demonstrated	demonstrate	VERB
cana-2912	71	16	their	their	PRON
cana-2912	71	17	effectiveness	effectiveness	NOUN
cana-2912	71	18	in	in	ADP
cana-2912	71	19	weather	weather	NOUN
cana-2912	71	20	prediction	prediction	NOUN
cana-2912	71	21	.	.	PUNCT
cana-2912	72	1	random	random	ADJ
cana-2912	72	2	forests	forest	NOUN
cana-2912	72	3	and	and	CCONJ
cana-2912	72	4	neural	neural	ADJ
cana-2912	72	5	networks	network	NOUN
cana-2912	72	6	outperformed	outperform	VERB
cana-2912	72	7	decision	decision	NOUN
cana-2912	72	8	trees	tree	NOUN
cana-2912	72	9	in	in	ADP
cana-2912	72	10	terms	term	NOUN
cana-2912	72	11	of	of	ADP
cana-2912	72	12	accuracy	accuracy	NOUN
cana-2912	72	13	and	and	CCONJ
cana-2912	72	14	computational	computational	ADJ
cana-2912	72	15	efficiency	efficiency	NOUN
cana-2912	72	16	,	,	PUNCT
cana-2912	72	17	making	make	VERB
cana-2912	72	18	them	they	PRON
cana-2912	72	19	suitable	suitable	ADJ
cana-2912	72	20	for	for	ADP
cana-2912	72	21	high	high	ADJ
cana-2912	72	22	-	-	PUNCT
cana-2912	72	23	dimensional	dimensional	ADJ
cana-2912	72	24	meteorological	meteorological	ADJ
cana-2912	72	25	datasets	dataset	NOUN
cana-2912	72	26	[	[	X
cana-2912	72	27	3	3	NUM
cana-2912	72	28	]	]	PUNCT
cana-2912	72	29	.	.	PUNCT
cana-2912	73	1	time	time	NOUN
cana-2912	73	2	-	-	PUNCT
cana-2912	73	3	series	series	NOUN
cana-2912	73	4	analysis	analysis	NOUN
cana-2912	73	5	techniques	technique	NOUN
cana-2912	73	6	,	,	PUNCT
cana-2912	73	7	including	include	VERB
cana-2912	73	8	arima	arima	NOUN
cana-2912	73	9	and	and	CCONJ
cana-2912	73	10	exponential	exponential	ADJ
cana-2912	73	11	smoothing	smoothing	NOUN
cana-2912	73	12	,	,	PUNCT
cana-2912	73	13	were	be	AUX
cana-2912	73	14	compared	compare	VERB
cana-2912	73	15	to	to	ADP
cana-2912	73	16	modern	modern	ADJ
cana-2912	73	17	deep	deep	ADJ
cana-2912	73	18	learning	learning	NOUN
cana-2912	73	19	methods	method	NOUN
cana-2912	73	20	.	.	PUNCT
cana-2912	74	1	the	the	DET
cana-2912	74	2	findings	finding	NOUN
cana-2912	74	3	highlighted	highlight	VERB
cana-2912	74	4	the	the	DET
cana-2912	74	5	superiority	superiority	NOUN
cana-2912	74	6	of	of	ADP
cana-2912	74	7	deep	deep	ADJ
cana-2912	74	8	learning	learning	NOUN
cana-2912	74	9	in	in	ADP
cana-2912	74	10	handling	handle	VERB
cana-2912	74	11	long	long	ADJ
cana-2912	74	12	-	-	PUNCT
cana-2912	74	13	term	term	NOUN
cana-2912	74	14	dependencies	dependency	NOUN
cana-2912	74	15	and	and	CCONJ
cana-2912	74	16	irregularities	irregularity	NOUN
cana-2912	74	17	in	in	ADP
cana-2912	74	18	weather	weather	NOUN
cana-2912	74	19	patterns	pattern	NOUN
cana-2912	74	20	,	,	PUNCT
cana-2912	74	21	particularly	particularly	ADV
cana-2912	74	22	when	when	SCONJ
cana-2912	74	23	applied	apply	VERB
cana-2912	74	24	to	to	ADP
cana-2912	74	25	large	large	ADJ
cana-2912	74	26	datasets	dataset	NOUN
cana-2912	74	27	[	[	X
cana-2912	74	28	4	4	NUM
cana-2912	74	29	]	]	PUNCT
cana-2912	74	30	.	.	PUNCT
cana-2912	75	1	the	the	DET
cana-2912	75	2	use	use	NOUN
cana-2912	75	3	of	of	ADP
cana-2912	75	4	clustering	clustering	ADJ
cana-2912	75	5	algorithms	algorithm	NOUN
cana-2912	75	6	combined	combine	VERB
cana-2912	75	7	with	with	ADP
cana-2912	75	8	regression	regression	NOUN
cana-2912	75	9	models	model	NOUN
cana-2912	75	10	was	be	AUX
cana-2912	75	11	investigated	investigate	VERB
cana-2912	75	12	for	for	ADP
cana-2912	75	13	weather	weather	NOUN
cana-2912	75	14	prediction	prediction	NOUN
cana-2912	75	15	.	.	PUNCT
cana-2912	76	1	this	this	DET
cana-2912	76	2	hybrid	hybrid	ADJ
cana-2912	76	3	approach	approach	NOUN
cana-2912	76	4	improved	improve	VERB
cana-2912	76	5	forecasting	forecasting	NOUN
cana-2912	76	6	accuracy	accuracy	NOUN
cana-2912	76	7	by	by	ADP
cana-2912	76	8	identifying	identify	VERB
cana-2912	76	9	distinct	distinct	ADJ
cana-2912	76	10	weather	weather	NOUN
cana-2912	76	11	patterns	pattern	NOUN
cana-2912	76	12	within	within	ADP
cana-2912	76	13	datasets	dataset	NOUN
cana-2912	76	14	before	before	ADP
cana-2912	76	15	applying	apply	VERB
cana-2912	76	16	regression	regression	NOUN
cana-2912	76	17	analysis	analysis	NOUN
cana-2912	76	18	[	[	X
cana-2912	76	19	5	5	NUM
cana-2912	76	20	]	]	PUNCT
cana-2912	76	21	.	.	PUNCT
cana-2912	77	1	research	research	NOUN
cana-2912	77	2	on	on	ADP
cana-2912	77	3	ensemble	ensemble	ADJ
cana-2912	77	4	methods	method	NOUN
cana-2912	77	5	,	,	PUNCT
cana-2912	77	6	such	such	ADJ
cana-2912	77	7	as	as	ADP
cana-2912	77	8	integrating	integrate	VERB
cana-2912	77	9	support	support	NOUN
cana-2912	77	10	vector	vector	NOUN
cana-2912	77	11	regression	regression	NOUN
cana-2912	77	12	and	and	CCONJ
cana-2912	77	13	linear	linear	PROPN
cana-2912	77	14	regression	regression	NOUN
cana-2912	77	15	,	,	PUNCT
cana-2912	77	16	revealed	reveal	VERB
cana-2912	77	17	significant	significant	ADJ
cana-2912	77	18	improvements	improvement	NOUN
cana-2912	77	19	in	in	ADP
cana-2912	77	20	prediction	prediction	NOUN
cana-2912	77	21	accuracy	accuracy	NOUN
cana-2912	77	22	.	.	PUNCT
cana-2912	78	1	ensemble	ensemble	ADJ
cana-2912	78	2	models	model	NOUN
cana-2912	78	3	effectively	effectively	ADV
cana-2912	78	4	reduced	reduce	VERB
cana-2912	78	5	errors	error	NOUN
cana-2912	78	6	by	by	ADP
cana-2912	78	7	leveraging	leverage	VERB
cana-2912	78	8	the	the	DET
cana-2912	78	9	strengths	strength	NOUN
cana-2912	78	10	of	of	ADP
cana-2912	78	11	individual	individual	ADJ
cana-2912	78	12	algorithms	algorithm	NOUN
cana-2912	78	13	,	,	PUNCT
cana-2912	78	14	demonstrating	demonstrate	VERB
cana-2912	78	15	their	their	PRON
cana-2912	78	16	potential	potential	NOUN
cana-2912	78	17	for	for	ADP
cana-2912	78	18	meteorological	meteorological	ADJ
cana-2912	78	19	applications	application	NOUN
cana-2912	78	20	[	[	X
cana-2912	78	21	6	6	NUM
cana-2912	78	22	]	]	PUNCT
cana-2912	78	23	.	.	PUNCT
cana-2912	79	1	the	the	DET
cana-2912	79	2	implementation	implementation	NOUN
cana-2912	79	3	of	of	ADP
cana-2912	79	4	back	back	ADJ
cana-2912	79	5	-	-	PUNCT
cana-2912	79	6	propagation	propagation	NOUN
cana-2912	79	7	algorithms	algorithm	NOUN
cana-2912	79	8	was	be	AUX
cana-2912	79	9	explored	explore	VERB
cana-2912	79	10	for	for	ADP
cana-2912	79	11	weather	weather	NOUN
cana-2912	79	12	classification	classification	NOUN
cana-2912	79	13	and	and	CCONJ
cana-2912	79	14	forecasting	forecasting	NOUN
cana-2912	79	15	.	.	PUNCT
cana-2912	80	1	this	this	DET
cana-2912	80	2	method	method	NOUN
cana-2912	80	3	achieved	achieve	VERB
cana-2912	80	4	moderate	moderate	ADJ
cana-2912	80	5	accuracy	accuracy	NOUN
cana-2912	80	6	levels	level	NOUN
cana-2912	80	7	but	but	CCONJ
cana-2912	80	8	highlighted	highlight	VERB
cana-2912	80	9	the	the	DET
cana-2912	80	10	need	need	NOUN
cana-2912	80	11	for	for	ADP
cana-2912	80	12	further	further	ADJ
cana-2912	80	13	refinement	refinement	NOUN
cana-2912	80	14	to	to	PART
cana-2912	80	15	handle	handle	VERB
cana-2912	80	16	complex	complex	ADJ
cana-2912	80	17	meteorological	meteorological	ADJ
cana-2912	80	18	data	datum	NOUN
cana-2912	80	19	[	[	X
cana-2912	80	20	7	7	NUM
cana-2912	80	21	]	]	PUNCT
cana-2912	80	22	.	.	PUNCT
cana-2912	81	1	a	a	DET
cana-2912	81	2	comparison	comparison	NOUN
cana-2912	81	3	between	between	ADP
cana-2912	81	4	artificial	artificial	ADJ
cana-2912	81	5	neural	neural	ADJ
cana-2912	81	6	networks	network	NOUN
cana-2912	81	7	and	and	CCONJ
cana-2912	81	8	linear	linear	PROPN
cana-2912	81	9	regression	regression	NOUN
cana-2912	81	10	models	model	NOUN
cana-2912	81	11	revealed	reveal	VERB
cana-2912	81	12	that	that	SCONJ
cana-2912	81	13	neural	neural	ADJ
cana-2912	81	14	networks	network	NOUN
cana-2912	81	15	excel	excel	VERB
cana-2912	81	16	in	in	ADP
cana-2912	81	17	modelling	model	VERB
cana-2912	81	18	non	non	ADJ
cana-2912	81	19	-	-	ADJ
cana-2912	81	20	linear	linear	ADJ
cana-2912	81	21	weather	weather	NOUN
cana-2912	81	22	patterns	pattern	NOUN
cana-2912	81	23	,	,	PUNCT
cana-2912	81	24	whereas	whereas	SCONJ
cana-2912	81	25	regression	regression	NOUN
cana-2912	81	26	models	model	NOUN
cana-2912	81	27	remained	remain	VERB
cana-2912	81	28	more	more	ADV
cana-2912	81	29	interpretable	interpretable	ADJ
cana-2912	81	30	for	for	ADP
cana-2912	81	31	simpler	simple	ADJ
cana-2912	81	32	scenarios	scenario	NOUN
cana-2912	81	33	[	[	X
cana-2912	81	34	8	8	NUM
cana-2912	81	35	]	]	PUNCT
cana-2912	81	36	.	.	PUNCT
cana-2912	82	1	an	an	DET
cana-2912	82	2	ensemble	ensemble	ADJ
cana-2912	82	3	approach	approach	NOUN
cana-2912	82	4	combining	combine	VERB
cana-2912	82	5	predictions	prediction	NOUN
cana-2912	82	6	from	from	ADP
cana-2912	82	7	multiple	multiple	ADJ
cana-2912	82	8	regression	regression	NOUN
cana-2912	82	9	models	model	NOUN
cana-2912	82	10	,	,	PUNCT
cana-2912	82	11	including	include	VERB
cana-2912	82	12	support	support	NOUN
cana-2912	82	13	vector	vector	NOUN
cana-2912	82	14	regression	regression	NOUN
cana-2912	82	15	,	,	PUNCT
cana-2912	82	16	was	be	AUX
cana-2912	82	17	shown	show	VERB
cana-2912	82	18	to	to	PART
cana-2912	82	19	enhance	enhance	VERB
cana-2912	82	20	forecasting	forecasting	NOUN
cana-2912	82	21	precision	precision	NOUN
cana-2912	82	22	.	.	PUNCT
cana-2912	83	1	this	this	DET
cana-2912	83	2	methodology	methodology	NOUN
cana-2912	83	3	effectively	effectively	ADV
cana-2912	83	4	minimized	minimize	VERB
cana-2912	83	5	individual	individual	ADJ
cana-2912	83	6	model	model	NOUN
cana-2912	83	7	errors	error	NOUN
cana-2912	83	8	,	,	PUNCT
cana-2912	83	9	making	make	VERB
cana-2912	83	10	it	it	PRON
cana-2912	83	11	a	a	DET
cana-2912	83	12	robust	robust	ADJ
cana-2912	83	13	choice	choice	NOUN
cana-2912	83	14	for	for	ADP
cana-2912	83	15	weather	weather	NOUN
cana-2912	83	16	prediction	prediction	NOUN
cana-2912	83	17	[	[	X
cana-2912	83	18	9	9	NUM
cana-2912	83	19	]	]	PUNCT
cana-2912	83	20	.	.	PUNCT
cana-2912	84	1	research	research	NOUN
cana-2912	84	2	on	on	ADP
cana-2912	84	3	support	support	NOUN
cana-2912	84	4	vector	vector	NOUN
cana-2912	84	5	regression	regression	NOUN
cana-2912	84	6	(	(	PUNCT
cana-2912	84	7	svr	svr	PROPN
cana-2912	84	8	)	)	PUNCT
cana-2912	84	9	models	model	NOUN
cana-2912	84	10	demonstrated	demonstrate	VERB
cana-2912	84	11	their	their	PRON
cana-2912	84	12	ability	ability	NOUN
cana-2912	84	13	to	to	PART
cana-2912	84	14	handle	handle	VERB
cana-2912	84	15	highdimensional	highdimensional	ADJ
cana-2912	84	16	meteorological	meteorological	ADJ
cana-2912	84	17	datasets	dataset	NOUN
cana-2912	84	18	.	.	PUNCT
cana-2912	85	1	the	the	DET
cana-2912	85	2	study	study	NOUN
cana-2912	85	3	highlighted	highlight	VERB
cana-2912	85	4	the	the	DET
cana-2912	85	5	advantages	advantage	NOUN
cana-2912	85	6	of	of	ADP
cana-2912	85	7	svr	svr	PROPN
cana-2912	85	8	in	in	ADP
cana-2912	85	9	achieving	achieve	VERB
cana-2912	85	10	higher	high	ADJ
cana-2912	85	11	accuracy	accuracy	NOUN
cana-2912	85	12	compared	compare	VERB
cana-2912	85	13	to	to	ADP
cana-2912	85	14	traditional	traditional	ADJ
cana-2912	85	15	regression	regression	NOUN
cana-2912	85	16	models	model	NOUN
cana-2912	85	17	[	[	X
cana-2912	85	18	10	10	NUM
cana-2912	85	19	]	]	PUNCT
cana-2912	85	20	.	.	PUNCT
cana-2912	86	1	the	the	DET
cana-2912	86	2	integration	integration	NOUN
cana-2912	86	3	of	of	ADP
cana-2912	86	4	clustering	cluster	VERB
cana-2912	86	5	techniques	technique	NOUN
cana-2912	86	6	with	with	ADP
cana-2912	86	7	regression	regression	NOUN
cana-2912	86	8	models	model	NOUN
cana-2912	86	9	was	be	AUX
cana-2912	86	10	proposed	propose	VERB
cana-2912	86	11	to	to	PART
cana-2912	86	12	improve	improve	VERB
cana-2912	86	13	weather	weather	NOUN
cana-2912	86	14	prediction	prediction	NOUN
cana-2912	86	15	accuracy	accuracy	NOUN
cana-2912	86	16	.	.	PUNCT
cana-2912	87	1	this	this	DET
cana-2912	87	2	approach	approach	NOUN
cana-2912	87	3	identified	identify	VERB
cana-2912	87	4	distinct	distinct	ADJ
cana-2912	87	5	weather	weather	NOUN
cana-2912	87	6	patterns	pattern	NOUN
cana-2912	87	7	within	within	ADP
cana-2912	87	8	datasets	dataset	NOUN
cana-2912	87	9	and	and	CCONJ
cana-2912	87	10	applied	apply	VERB
cana-2912	87	11	targeted	target	VERB
cana-2912	87	12	regression	regression	NOUN
cana-2912	87	13	techniques	technique	NOUN
cana-2912	87	14	,	,	PUNCT
cana-2912	87	15	yielding	yield	VERB
cana-2912	87	16	promising	promising	ADJ
cana-2912	87	17	results	result	NOUN
cana-2912	87	18	[	[	X
cana-2912	87	19	11	11	NUM
cana-2912	87	20	]	]	PUNCT
cana-2912	87	21	.	.	PUNCT
cana-2912	88	1	a	a	DET
cana-2912	88	2	study	study	NOUN
cana-2912	88	3	on	on	ADP
cana-2912	88	4	flood	flood	NOUN
cana-2912	88	5	prediction	prediction	NOUN
cana-2912	88	6	in	in	ADP
cana-2912	88	7	mumbai	mumbai	PROPN
cana-2912	88	8	utilized	utilize	VERB
cana-2912	88	9	various	various	ADJ
cana-2912	88	10	machine	machine	NOUN
cana-2912	88	11	learning	learn	VERB
cana-2912	88	12	techniques	technique	NOUN
cana-2912	88	13	,	,	PUNCT
cana-2912	88	14	including	include	VERB
cana-2912	88	15	logistic	logistic	ADJ
cana-2912	88	16	regression	regression	NOUN
cana-2912	88	17	,	,	PUNCT
cana-2912	88	18	k	k	X
cana-2912	88	19	-	-	PUNCT
cana-2912	88	20	nearest	near	ADJ
cana-2912	88	21	neighbours	neighbour	NOUN
cana-2912	88	22	,	,	PUNCT
cana-2912	88	23	random	random	ADJ
cana-2912	88	24	forest	forest	NOUN
cana-2912	88	25	,	,	PUNCT
cana-2912	88	26	and	and	CCONJ
cana-2912	88	27	gradient	gradient	ADJ
cana-2912	88	28	boosting	boosting	NOUN
cana-2912	88	29	.	.	PUNCT
cana-2912	89	1	the	the	DET
cana-2912	89	2	findings	finding	NOUN
cana-2912	89	3	revealed	reveal	VERB
cana-2912	89	4	that	that	SCONJ
cana-2912	89	5	random	random	ADJ
cana-2912	89	6	forest	forest	NOUN
cana-2912	89	7	and	and	CCONJ
cana-2912	89	8	gradient	gradient	ADJ
cana-2912	89	9	boosting	boosting	NOUN
cana-2912	89	10	outperformed	outperform	VERB
cana-2912	89	11	other	other	ADJ
cana-2912	89	12	methods	method	NOUN
cana-2912	89	13	,	,	PUNCT
cana-2912	89	14	accurately	accurately	ADV
cana-2912	89	15	identifying	identify	VERB
cana-2912	89	16	flood	flood	NOUN
cana-2912	89	17	-	-	PUNCT
cana-2912	89	18	prone	prone	ADJ
cana-2912	89	19	areas	area	NOUN
cana-2912	89	20	and	and	CCONJ
cana-2912	89	21	contributing	contribute	VERB
cana-2912	89	22	to	to	ADP
cana-2912	89	23	disaster	disaster	NOUN
cana-2912	89	24	preparedness	preparedness	NOUN
cana-2912	89	25	efforts	effort	NOUN
cana-2912	89	26	[	[	X
cana-2912	89	27	12	12	NUM
cana-2912	89	28	]	]	PUNCT
cana-2912	89	29	.	.	PUNCT
cana-2912	90	1	multiple	multiple	ADJ
cana-2912	90	2	ml	ml	NOUN
cana-2912	90	3	algorithms	algorithm	NOUN
cana-2912	90	4	were	be	AUX
cana-2912	90	5	examined	examine	VERB
cana-2912	90	6	(	(	PUNCT
cana-2912	90	7	elkhrachy	elkhrachy	NOUN
cana-2912	90	8	et	et	PROPN
cana-2912	90	9	al	al	PROPN
cana-2912	90	10	.	.	PROPN
cana-2912	90	11	,	,	PUNCT
cana-2912	90	12	2022	2022	NUM
cana-2912	90	13	)	)	PUNCT
cana-2912	90	14	for	for	ADP
cana-2912	90	15	their	their	PRON
cana-2912	90	16	potential	potential	NOUN
cana-2912	90	17	to	to	PART
cana-2912	90	18	improve	improve	VERB
cana-2912	90	19	the	the	DET
cana-2912	90	20	accuracy	accuracy	NOUN
cana-2912	90	21	of	of	ADP
cana-2912	90	22	water	water	NOUN
cana-2912	90	23	depth	depth	NOUN
cana-2912	90	24	estimates	estimate	NOUN
cana-2912	90	25	during	during	ADP
cana-2912	90	26	a	a	DET
cana-2912	90	27	flash	flash	NOUN
cana-2912	90	28	flood	flood	NOUN
cana-2912	90	29	incident	incident	NOUN
cana-2912	90	30	in	in	ADP
cana-2912	90	31	new	new	ADJ
cana-2912	90	32	cairo	cairo	PROPN
cana-2912	90	33	city	city	PROPN
cana-2912	90	34	,	,	PUNCT
cana-2912	90	35	egypt	egypt	PROPN
cana-2912	90	36	.	.	PUNCT
cana-2912	91	1	features	feature	VERB
cana-2912	91	2	communications	communication	NOUN
cana-2912	91	3	on	on	ADP
cana-2912	91	4	applied	apply	VERB
cana-2912	91	5	nonlinear	nonlinear	ADJ
cana-2912	91	6	analysis	analysis	NOUN
cana-2912	91	7	issn	issn	NOUN
cana-2912	91	8	:	:	PUNCT
cana-2912	91	9	1074	1074	NUM
cana-2912	91	10	-	-	PUNCT
cana-2912	91	11	133x	133x	NUM
cana-2912	91	12	vol	vol	NOUN
cana-2912	91	13	32	32	NUM
cana-2912	91	14	no	no	NOUN
cana-2912	91	15	.	.	PUNCT
cana-2912	92	1	5s	5s	NUM
cana-2912	92	2	(	(	PUNCT
cana-2912	92	3	2025	2025	NUM
cana-2912	92	4	)	)	PUNCT
cana-2912	92	5	6	6	NUM
cana-2912	92	6	were	be	AUX
cana-2912	92	7	extracted	extract	VERB
cana-2912	92	8	from	from	ADP
cana-2912	92	9	the	the	DET
cana-2912	92	10	sar	sar	PROPN
cana-2912	92	11	data	data	PROPN
cana-2912	92	12	's	's	PART
cana-2912	92	13	backscattering	backscatter	VERB
cana-2912	92	14	spectral	spectral	ADJ
cana-2912	92	15	band	band	NOUN
cana-2912	92	16	and	and	CCONJ
cana-2912	92	17	fed	feed	VERB
cana-2912	92	18	into	into	ADP
cana-2912	92	19	ml	ml	ADP
cana-2912	92	20	algorithms	algorithm	NOUN
cana-2912	92	21	separately	separately	ADV
cana-2912	92	22	.	.	PUNCT
cana-2912	93	1	integrating	integrate	VERB
cana-2912	93	2	several	several	ADJ
cana-2912	93	3	training	training	NOUN
cana-2912	93	4	datasets	dataset	NOUN
cana-2912	93	5	and	and	CCONJ
cana-2912	93	6	machine	machine	NOUN
cana-2912	93	7	learning	learning	NOUN
cana-2912	93	8	algorithms	algorithm	NOUN
cana-2912	93	9	was	be	AUX
cana-2912	93	10	suggested	suggest	VERB
cana-2912	93	11	as	as	ADP
cana-2912	93	12	a	a	DET
cana-2912	93	13	means	means	NOUN
cana-2912	93	14	of	of	ADP
cana-2912	93	15	improving	improve	VERB
cana-2912	93	16	the	the	DET
cana-2912	93	17	accuracy	accuracy	NOUN
cana-2912	93	18	of	of	ADP
cana-2912	93	19	water	water	NOUN
cana-2912	93	20	depth	depth	NOUN
cana-2912	93	21	forecasts	forecast	NOUN
cana-2912	93	22	using	use	VERB
cana-2912	93	23	satellite	satellite	NOUN
cana-2912	93	24	data	datum	NOUN
cana-2912	93	25	in	in	ADP
cana-2912	93	26	order	order	NOUN
cana-2912	93	27	to	to	PART
cana-2912	93	28	construct	construct	VERB
cana-2912	93	29	an	an	DET
cana-2912	93	30	emergency	emergency	NOUN
cana-2912	93	31	plan	plan	NOUN
cana-2912	93	32	in	in	ADP
cana-2912	93	33	the	the	DET
cana-2912	93	34	event	event	NOUN
cana-2912	93	35	of	of	ADP
cana-2912	93	36	floods	flood	NOUN
cana-2912	93	37	[	[	X
cana-2912	93	38	13	13	NUM
cana-2912	93	39	]	]	PUNCT
cana-2912	93	40	.	.	PUNCT
cana-2912	94	1	as	as	SCONJ
cana-2912	94	2	reported	report	VERB
cana-2912	94	3	by	by	ADP
cana-2912	94	4	abdullah	abdullah	PROPN
cana-2912	94	5	et	et	PROPN
cana-2912	94	6	al	al	PROPN
cana-2912	94	7	.	.	PROPN
cana-2912	95	1	(	(	PUNCT
cana-2912	95	2	2021	2021	NUM
cana-2912	95	3	)	)	PUNCT
cana-2912	95	4	,	,	PUNCT
cana-2912	95	5	the	the	DET
cana-2912	95	6	author	author	NOUN
cana-2912	95	7	discovered	discover	VERB
cana-2912	95	8	a	a	DET
cana-2912	95	9	rising	rise	VERB
cana-2912	95	10	pattern	pattern	NOUN
cana-2912	95	11	of	of	ADP
cana-2912	95	12	use	use	NOUN
cana-2912	95	13	for	for	ADP
cana-2912	95	14	multiple	multiple	ADJ
cana-2912	95	15	-	-	PUNCT
cana-2912	95	16	criteria	criterion	NOUN
cana-2912	95	17	decision	decision	NOUN
cana-2912	95	18	analysis	analysis	NOUN
cana-2912	95	19	(	(	PUNCT
cana-2912	95	20	mcda	mcda	NOUN
cana-2912	95	21	)	)	PUNCT
cana-2912	95	22	methods	method	NOUN
cana-2912	95	23	in	in	ADP
cana-2912	95	24	the	the	DET
cana-2912	95	25	management	management	NOUN
cana-2912	95	26	of	of	ADP
cana-2912	95	27	flood	flood	NOUN
cana-2912	95	28	and	and	CCONJ
cana-2912	95	29	drought	drought	NOUN
cana-2912	95	30	events	event	NOUN
cana-2912	95	31	in	in	ADP
cana-2912	95	32	the	the	DET
cana-2912	95	33	twenty	twenty	NUM
cana-2912	95	34	-	-	PUNCT
cana-2912	95	35	first	first	ADJ
cana-2912	95	36	century	century	NOUN
cana-2912	95	37	.	.	PUNCT
cana-2912	96	1	this	this	DET
cana-2912	96	2	work	work	NOUN
cana-2912	96	3	surveyed	survey	VERB
cana-2912	96	4	the	the	DET
cana-2912	96	5	literature	literature	NOUN
cana-2912	96	6	on	on	ADP
cana-2912	96	7	mcda	mcda	ADJ
cana-2912	96	8	methods	method	NOUN
cana-2912	96	9	for	for	ADP
cana-2912	96	10	flood	flood	NOUN
cana-2912	96	11	and	and	CCONJ
cana-2912	96	12	drought	drought	NOUN
cana-2912	96	13	management	management	NOUN
cana-2912	96	14	from	from	ADP
cana-2912	96	15	2000	2000	NUM
cana-2912	96	16	to	to	ADP
cana-2912	96	17	2020	2020	NUM
cana-2912	96	18	,	,	PUNCT
cana-2912	96	19	drawing	draw	VERB
cana-2912	96	20	from	from	ADP
cana-2912	96	21	149	149	NUM
cana-2912	96	22	articles	article	NOUN
cana-2912	96	23	in	in	ADP
cana-2912	96	24	journals	journal	NOUN
cana-2912	96	25	,	,	PUNCT
cana-2912	96	26	conference	conference	NOUN
cana-2912	96	27	proceedings	proceeding	NOUN
cana-2912	96	28	,	,	PUNCT
cana-2912	96	29	and	and	CCONJ
cana-2912	96	30	other	other	ADJ
cana-2912	96	31	scholarly	scholarly	ADJ
cana-2912	96	32	publications	publication	NOUN
cana-2912	96	33	.	.	PUNCT
cana-2912	97	1	decision	decision	NOUN
cana-2912	97	2	-	-	PUNCT
cana-2912	97	3	makers	maker	NOUN
cana-2912	97	4	in	in	ADP
cana-2912	97	5	the	the	DET
cana-2912	97	6	data	data	NOUN
cana-2912	97	7	management	management	NOUN
cana-2912	97	8	platform	platform	NOUN
cana-2912	97	9	(	(	PUNCT
cana-2912	97	10	dmp	dmp	NOUN
cana-2912	97	11	)	)	PUNCT
cana-2912	97	12	have	have	AUX
cana-2912	97	13	shifted	shift	VERB
cana-2912	97	14	their	their	PRON
cana-2912	97	15	attention	attention	NOUN
cana-2912	97	16	to	to	ADP
cana-2912	97	17	flood	flood	NOUN
cana-2912	97	18	occurrences	occurrence	NOUN
cana-2912	97	19	because	because	SCONJ
cana-2912	97	20	of	of	ADP
cana-2912	97	21	their	their	PRON
cana-2912	97	22	significant	significant	ADJ
cana-2912	97	23	consequences	consequence	NOUN
cana-2912	97	24	,	,	PUNCT
cana-2912	97	25	which	which	PRON
cana-2912	97	26	has	have	AUX
cana-2912	97	27	prompted	prompt	VERB
cana-2912	97	28	a	a	DET
cana-2912	97	29	greater	great	ADJ
cana-2912	97	30	number	number	NOUN
cana-2912	97	31	of	of	ADP
cana-2912	97	32	research	research	NOUN
cana-2912	97	33	projects	project	NOUN
cana-2912	97	34	on	on	ADP
cana-2912	97	35	the	the	DET
cana-2912	97	36	topic	topic	NOUN
cana-2912	97	37	[	[	X
cana-2912	97	38	14	14	NUM
cana-2912	97	39	]	]	PUNCT
cana-2912	97	40	.	.	PUNCT
cana-2912	98	1	the	the	DET
cana-2912	98	2	influences	influence	NOUN
cana-2912	98	3	of	of	ADP
cana-2912	98	4	both	both	CCONJ
cana-2912	98	5	the	the	DET
cana-2912	98	6	natural	natural	ADJ
cana-2912	98	7	and	and	CCONJ
cana-2912	98	8	social	social	ADJ
cana-2912	98	9	settings	setting	NOUN
cana-2912	98	10	on	on	ADP
cana-2912	98	11	flood	flood	NOUN
cana-2912	98	12	risk	risk	NOUN
cana-2912	98	13	have	have	AUX
cana-2912	98	14	been	be	AUX
cana-2912	98	15	researched	research	VERB
cana-2912	98	16	(	(	PUNCT
cana-2912	98	17	chen	chen	PROPN
cana-2912	98	18	et	et	PROPN
cana-2912	98	19	al	al	PROPN
cana-2912	98	20	.	.	PROPN
cana-2912	98	21	,	,	PUNCT
cana-2912	98	22	2021	2021	NUM
cana-2912	98	23	)	)	PUNCT
cana-2912	98	24	and	and	CCONJ
cana-2912	98	25	found	find	VERB
cana-2912	98	26	to	to	PART
cana-2912	98	27	make	make	VERB
cana-2912	98	28	the	the	DET
cana-2912	98	29	problem	problem	NOUN
cana-2912	98	30	of	of	ADP
cana-2912	98	31	flood	flood	NOUN
cana-2912	98	32	risk	risk	NOUN
cana-2912	98	33	complicated	complicated	ADJ
cana-2912	98	34	and	and	CCONJ
cana-2912	98	35	difficult	difficult	ADJ
cana-2912	98	36	to	to	PART
cana-2912	98	37	conceptualize	conceptualize	VERB
cana-2912	98	38	.	.	PUNCT
cana-2912	99	1	targeted	target	VERB
cana-2912	99	2	flood	flood	NOUN
cana-2912	99	3	control	control	NOUN
cana-2912	99	4	measures	measure	NOUN
cana-2912	99	5	are	be	AUX
cana-2912	99	6	required	require	VERB
cana-2912	99	7	due	due	ADP
cana-2912	99	8	to	to	ADP
cana-2912	99	9	the	the	DET
cana-2912	99	10	unique	unique	ADJ
cana-2912	99	11	features	feature	NOUN
cana-2912	99	12	of	of	ADP
cana-2912	99	13	each	each	DET
cana-2912	99	14	risk	risk	NOUN
cana-2912	99	15	category	category	NOUN
cana-2912	99	16	.	.	PUNCT
cana-2912	100	1	future	future	ADJ
cana-2912	100	2	efforts	effort	NOUN
cana-2912	100	3	to	to	PART
cana-2912	100	4	stop	stop	VERB
cana-2912	100	5	flooding	flooding	NOUN
cana-2912	100	6	should	should	AUX
cana-2912	100	7	put	put	VERB
cana-2912	100	8	more	more	ADJ
cana-2912	100	9	attention	attention	NOUN
cana-2912	100	10	on	on	ADP
cana-2912	100	11	the	the	DET
cana-2912	100	12	digital	digital	ADJ
cana-2912	100	13	elevation	elevation	NOUN
cana-2912	100	14	model	model	NOUN
cana-2912	100	15	,	,	PUNCT
cana-2912	100	16	m1dp	m1dp	NOUN
cana-2912	100	17	,	,	PUNCT
cana-2912	100	18	and	and	CCONJ
cana-2912	100	19	rd	rd	PROPN
cana-2912	100	20	,	,	PUNCT
cana-2912	100	21	which	which	PRON
cana-2912	100	22	are	be	AUX
cana-2912	100	23	the	the	DET
cana-2912	100	24	three	three	NUM
cana-2912	100	25	most	most	ADV
cana-2912	100	26	important	important	ADJ
cana-2912	100	27	driving	driving	NOUN
cana-2912	100	28	factors	factor	NOUN
cana-2912	100	29	[	[	X
cana-2912	100	30	15	15	NUM
cana-2912	100	31	]	]	PUNCT
cana-2912	100	32	.	.	PUNCT
cana-2912	101	1	kalantar	kalantar	NOUN
cana-2912	101	2	et	et	PROPN
cana-2912	101	3	al	al	PROPN
cana-2912	101	4	.	.	PROPN
cana-2912	101	5	(	(	PUNCT
cana-2912	101	6	2021	2021	NUM
cana-2912	101	7	)	)	PUNCT
cana-2912	101	8	employed	employ	VERB
cana-2912	101	9	anns	ann	NOUN
cana-2912	101	10	,	,	PUNCT
cana-2912	101	11	dlnns	dlnn	NOUN
cana-2912	101	12	,	,	PUNCT
cana-2912	101	13	and	and	CCONJ
cana-2912	101	14	dlnns	dlnn	NOUN
cana-2912	101	15	that	that	PRON
cana-2912	101	16	had	have	AUX
cana-2912	101	17	been	be	AUX
cana-2912	101	18	improved	improve	VERB
cana-2912	101	19	with	with	ADP
cana-2912	101	20	particle	particle	NOUN
cana-2912	101	21	swarm	swarm	NOUN
cana-2912	101	22	optimization	optimization	NOUN
cana-2912	101	23	(	(	PUNCT
cana-2912	101	24	pso	pso	NOUN
cana-2912	101	25	)	)	PUNCT
cana-2912	101	26	to	to	PART
cana-2912	101	27	predict	predict	VERB
cana-2912	101	28	and	and	CCONJ
cana-2912	101	29	estimate	estimate	VERB
cana-2912	101	30	flood	flood	NOUN
cana-2912	101	31	-	-	PUNCT
cana-2912	101	32	prone	prone	ADJ
cana-2912	101	33	areas	area	NOUN
cana-2912	101	34	.	.	PUNCT
cana-2912	102	1	methods	method	NOUN
cana-2912	102	2	such	such	ADJ
cana-2912	102	3	as	as	ADP
cana-2912	102	4	sensitivity	sensitivity	NOUN
cana-2912	102	5	,	,	PUNCT
cana-2912	102	6	specificity	specificity	NOUN
cana-2912	102	7	,	,	PUNCT
cana-2912	102	8	area	area	NOUN
cana-2912	102	9	under	under	ADP
cana-2912	102	10	the	the	DET
cana-2912	102	11	curve	curve	NOUN
cana-2912	102	12	(	(	PUNCT
cana-2912	102	13	auc	auc	NOUN
cana-2912	102	14	)	)	PUNCT
cana-2912	102	15	,	,	PUNCT
cana-2912	102	16	and	and	CCONJ
cana-2912	102	17	the	the	DET
cana-2912	102	18	true	true	ADJ
cana-2912	102	19	skill	skill	NOUN
cana-2912	102	20	statistic	statistic	NOUN
cana-2912	102	21	(	(	PUNCT
cana-2912	102	22	tss	tss	NOUN
cana-2912	102	23	)	)	PUNCT
cana-2912	102	24	were	be	AUX
cana-2912	102	25	employed	employ	VERB
cana-2912	102	26	to	to	PART
cana-2912	102	27	evaluate	evaluate	VERB
cana-2912	102	28	the	the	DET
cana-2912	102	29	performance	performance	NOUN
cana-2912	102	30	of	of	ADP
cana-2912	102	31	the	the	DET
cana-2912	102	32	models	model	NOUN
cana-2912	102	33	.	.	PUNCT
cana-2912	103	1	in	in	ADP
cana-2912	103	2	order	order	NOUN
cana-2912	103	3	to	to	PART
cana-2912	103	4	study	study	VERB
cana-2912	103	5	and	and	CCONJ
cana-2912	103	6	analyse	analyse	NOUN
cana-2912	103	7	complex	complex	ADJ
cana-2912	103	8	occurrences	occurrence	NOUN
cana-2912	103	9	like	like	ADP
cana-2912	103	10	floods	flood	NOUN
cana-2912	103	11	,	,	PUNCT
cana-2912	103	12	large	large	ADJ
cana-2912	103	13	datasets	dataset	NOUN
cana-2912	103	14	from	from	ADP
cana-2912	103	15	disciplines	discipline	NOUN
cana-2912	103	16	like	like	ADP
cana-2912	103	17	remote	remote	ADJ
cana-2912	103	18	sensing	sensing	NOUN
cana-2912	103	19	and	and	CCONJ
cana-2912	103	20	earth	earth	NOUN
cana-2912	103	21	observation	observation	NOUN
cana-2912	103	22	provide	provide	VERB
cana-2912	103	23	an	an	DET
cana-2912	103	24	invaluable	invaluable	ADJ
cana-2912	103	25	starting	starting	NOUN
cana-2912	103	26	point	point	NOUN
cana-2912	103	27	.	.	PUNCT
cana-2912	104	1	it	it	PRON
cana-2912	104	2	appears	appear	VERB
cana-2912	104	3	that	that	SCONJ
cana-2912	104	4	optimization	optimization	NOUN
cana-2912	104	5	and	and	CCONJ
cana-2912	104	6	ml	ml	ADP
cana-2912	104	7	algorithms	algorithm	NOUN
cana-2912	104	8	can	can	AUX
cana-2912	104	9	be	be	AUX
cana-2912	104	10	used	use	VERB
cana-2912	104	11	in	in	ADP
cana-2912	104	12	contexts	context	NOUN
cana-2912	104	13	such	such	ADJ
cana-2912	104	14	as	as	ADP
cana-2912	104	15	crisis	crisis	NOUN
cana-2912	104	16	management	management	NOUN
cana-2912	104	17	and	and	CCONJ
cana-2912	104	18	urban	urban	ADJ
cana-2912	104	19	planning	planning	NOUN
cana-2912	104	20	,	,	PUNCT
cana-2912	104	21	which	which	PRON
cana-2912	104	22	call	call	VERB
cana-2912	104	23	for	for	ADP
cana-2912	104	24	the	the	DET
cana-2912	104	25	rapid	rapid	ADJ
cana-2912	104	26	analysis	analysis	NOUN
cana-2912	104	27	,	,	PUNCT
cana-2912	104	28	visualization	visualization	NOUN
cana-2912	104	29	,	,	PUNCT
cana-2912	104	30	and	and	CCONJ
cana-2912	104	31	information	information	NOUN
cana-2912	104	32	extraction	extraction	NOUN
cana-2912	104	33	from	from	ADP
cana-2912	104	34	enormous	enormous	ADJ
cana-2912	104	35	data	datum	NOUN
cana-2912	104	36	sets	set	NOUN
cana-2912	104	37	[	[	X
cana-2912	104	38	16	16	NUM
cana-2912	104	39	]	]	PUNCT
cana-2912	104	40	.	.	PUNCT
cana-2912	105	1	mane	mane	PROPN
cana-2912	105	2	et	et	PROPN
cana-2912	105	3	al	al	PROPN
cana-2912	105	4	.	.	PROPN
cana-2912	105	5	,	,	PUNCT
cana-2912	105	6	(	(	PUNCT
cana-2912	105	7	2020	2020	NUM
cana-2912	105	8	)	)	PUNCT
cana-2912	105	9	tried	try	VERB
cana-2912	105	10	out	out	ADP
cana-2912	105	11	a	a	DET
cana-2912	105	12	variety	variety	NOUN
cana-2912	105	13	of	of	ADP
cana-2912	105	14	ml	ml	ADP
cana-2912	105	15	algorithms	algorithm	NOUN
cana-2912	105	16	on	on	ADP
cana-2912	105	17	the	the	DET
cana-2912	105	18	available	available	ADJ
cana-2912	105	19	rainfall	rainfall	NOUN
cana-2912	105	20	data	datum	NOUN
cana-2912	105	21	,	,	PUNCT
cana-2912	105	22	including	include	VERB
cana-2912	105	23	svm	svm	PROPN
cana-2912	105	24	,	,	PUNCT
cana-2912	105	25	knn	knn	PROPN
cana-2912	105	26	,	,	PUNCT
cana-2912	105	27	logistic	logistic	ADJ
cana-2912	105	28	regression	regression	NOUN
cana-2912	105	29	,	,	PUNCT
cana-2912	105	30	naive	naive	ADJ
cana-2912	105	31	bayes	bayes	NOUN
cana-2912	105	32	,	,	PUNCT
cana-2912	105	33	and	and	CCONJ
cana-2912	105	34	others	other	NOUN
cana-2912	105	35	.	.	PUNCT
cana-2912	106	1	the	the	DET
cana-2912	106	2	author	author	NOUN
cana-2912	106	3	used	use	VERB
cana-2912	106	4	these	these	DET
cana-2912	106	5	ml	ml	NOUN
cana-2912	106	6	models	model	NOUN
cana-2912	106	7	to	to	PART
cana-2912	106	8	create	create	VERB
cana-2912	106	9	a	a	DET
cana-2912	106	10	comprehensive	comprehensive	ADJ
cana-2912	106	11	flood	flood	NOUN
cana-2912	106	12	prediction	prediction	NOUN
cana-2912	106	13	and	and	CCONJ
cana-2912	106	14	warning	warning	NOUN
cana-2912	106	15	system	system	NOUN
cana-2912	106	16	,	,	PUNCT
cana-2912	106	17	including	include	VERB
cana-2912	106	18	a	a	DET
cana-2912	106	19	website	website	NOUN
cana-2912	106	20	and	and	CCONJ
cana-2912	106	21	an	an	DET
cana-2912	106	22	android	android	PROPN
cana-2912	106	23	application	application	NOUN
cana-2912	106	24	,	,	PUNCT
cana-2912	106	25	to	to	PART
cana-2912	106	26	notify	notify	VERB
cana-2912	106	27	worried	worried	ADJ
cana-2912	106	28	citizens	citizen	NOUN
cana-2912	106	29	and	and	CCONJ
cana-2912	106	30	government	government	NOUN
cana-2912	106	31	officials	official	NOUN
cana-2912	106	32	.	.	PUNCT
cana-2912	107	1	moreover	moreover	ADV
cana-2912	107	2	,	,	PUNCT
cana-2912	107	3	the	the	DET
cana-2912	107	4	system	system	NOUN
cana-2912	107	5	encourages	encourage	VERB
cana-2912	107	6	real	real	ADJ
cana-2912	107	7	-	-	PUNCT
cana-2912	107	8	time	time	NOUN
cana-2912	107	9	monitoring	monitoring	NOUN
cana-2912	107	10	via	via	ADP
cana-2912	107	11	the	the	DET
cana-2912	107	12	built	build	VERB
cana-2912	107	13	-	-	PUNCT
cana-2912	107	14	in	in	ADP
cana-2912	107	15	website	website	NOUN
cana-2912	107	16	as	as	ADP
cana-2912	107	17	a	a	DET
cana-2912	107	18	simple	simple	ADJ
cana-2912	107	19	medium	medium	NOUN
cana-2912	107	20	for	for	ADP
cana-2912	107	21	distributing	distribute	VERB
cana-2912	107	22	data	datum	NOUN
cana-2912	107	23	,	,	PUNCT
cana-2912	107	24	especially	especially	ADV
cana-2912	107	25	in	in	ADP
cana-2912	107	26	outlying	outlying	ADJ
cana-2912	107	27	regions	region	NOUN
cana-2912	107	28	[	[	X
cana-2912	107	29	17	17	NUM
cana-2912	107	30	]	]	PUNCT
cana-2912	107	31	.	.	PUNCT
cana-2912	108	1	the	the	DET
cana-2912	108	2	significance	significance	NOUN
cana-2912	108	3	of	of	ADP
cana-2912	108	4	training	training	NOUN
cana-2912	108	5	data	datum	NOUN
cana-2912	108	6	was	be	AUX
cana-2912	108	7	recognised	recognise	VERB
cana-2912	108	8	in	in	ADP
cana-2912	108	9	a	a	DET
cana-2912	108	10	research	research	NOUN
cana-2912	108	11	study	study	NOUN
cana-2912	108	12	(	(	PUNCT
cana-2912	108	13	katiyar	katiyar	PROPN
cana-2912	108	14	et	et	PROPN
cana-2912	108	15	al	al	PROPN
cana-2912	108	16	.	.	PROPN
cana-2912	108	17	,	,	PUNCT
cana-2912	108	18	2020	2020	NUM
cana-2912	108	19	)	)	PUNCT
cana-2912	108	20	.	.	PUNCT
cana-2912	109	1	due	due	ADP
cana-2912	109	2	to	to	ADP
cana-2912	109	3	the	the	DET
cana-2912	109	4	limitations	limitation	NOUN
cana-2912	109	5	of	of	ADP
cana-2912	109	6	sar	sar	NOUN
cana-2912	109	7	photos	photo	NOUN
cana-2912	109	8	,	,	PUNCT
cana-2912	109	9	such	such	ADJ
cana-2912	109	10	as	as	ADP
cana-2912	109	11	distorted	distorted	ADJ
cana-2912	109	12	geometry	geometry	NOUN
cana-2912	109	13	,	,	PUNCT
cana-2912	109	14	shadow	shadow	NOUN
cana-2912	109	15	regions	region	NOUN
cana-2912	109	16	,	,	PUNCT
cana-2912	109	17	and	and	CCONJ
cana-2912	109	18	uncertainty	uncertainty	NOUN
cana-2912	109	19	in	in	ADP
cana-2912	109	20	the	the	DET
cana-2912	109	21	representation	representation	NOUN
cana-2912	109	22	of	of	ADP
cana-2912	109	23	urban	urban	ADJ
cana-2912	109	24	flood	flood	NOUN
cana-2912	109	25	areas	area	NOUN
cana-2912	109	26	,	,	PUNCT
cana-2912	109	27	the	the	DET
cana-2912	109	28	author	author	NOUN
cana-2912	109	29	suggests	suggest	VERB
cana-2912	109	30	including	include	VERB
cana-2912	109	31	additional	additional	ADJ
cana-2912	109	32	data	datum	NOUN
cana-2912	109	33	in	in	ADP
cana-2912	109	34	addition	addition	NOUN
cana-2912	109	35	to	to	ADP
cana-2912	109	36	sar	sar	NOUN
cana-2912	109	37	images	image	NOUN
cana-2912	109	38	in	in	ADP
cana-2912	109	39	future	future	ADJ
cana-2912	109	40	research	research	NOUN
cana-2912	109	41	.	.	PUNCT
cana-2912	110	1	by	by	ADP
cana-2912	110	2	combining	combine	VERB
cana-2912	110	3	dem	dem	ADJ
cana-2912	110	4	data	datum	NOUN
cana-2912	110	5	,	,	PUNCT
cana-2912	110	6	geometry	geometry	NOUN
cana-2912	110	7	and	and	CCONJ
cana-2912	110	8	shadow	shadow	NOUN
cana-2912	110	9	areas	area	NOUN
cana-2912	110	10	can	can	AUX
cana-2912	110	11	be	be	AUX
cana-2912	110	12	taken	take	VERB
cana-2912	110	13	care	care	NOUN
cana-2912	110	14	of	of	ADP
cana-2912	110	15	.	.	PUNCT
cana-2912	111	1	separating	separate	VERB
cana-2912	111	2	the	the	DET
cana-2912	111	3	flooded	flood	VERB
cana-2912	111	4	paddy	paddy	NOUN
cana-2912	111	5	fields	field	NOUN
cana-2912	111	6	from	from	ADP
cana-2912	111	7	other	other	ADJ
cana-2912	111	8	flooded	flood	VERB
cana-2912	111	9	regions	region	NOUN
cana-2912	111	10	may	may	AUX
cana-2912	111	11	also	also	ADV
cana-2912	111	12	benefit	benefit	VERB
cana-2912	111	13	from	from	ADP
cana-2912	111	14	the	the	DET
cana-2912	111	15	use	use	NOUN
cana-2912	111	16	of	of	ADP
cana-2912	111	17	multitemporal	multitemporal	ADJ
cana-2912	111	18	sar	sar	NOUN
cana-2912	111	19	images	image	NOUN
cana-2912	111	20	and	and	CCONJ
cana-2912	111	21	various	various	ADJ
cana-2912	111	22	polarizations	polarization	NOUN
cana-2912	111	23	.	.	PUNCT
cana-2912	112	1	by	by	ADP
cana-2912	112	2	comparing	compare	VERB
cana-2912	112	3	the	the	DET
cana-2912	112	4	image	image	NOUN
cana-2912	112	5	taken	take	VERB
cana-2912	112	6	before	before	ADP
cana-2912	112	7	the	the	DET
cana-2912	112	8	flood	flood	NOUN
cana-2912	112	9	with	with	ADP
cana-2912	112	10	the	the	DET
cana-2912	112	11	one	one	NOUN
cana-2912	112	12	taken	take	VERB
cana-2912	112	13	during	during	ADP
cana-2912	112	14	the	the	DET
cana-2912	112	15	flood	flood	NOUN
cana-2912	112	16	,	,	PUNCT
cana-2912	112	17	a	a	DET
cana-2912	112	18	change	change	NOUN
cana-2912	112	19	-	-	PUNCT
cana-2912	112	20	detecting	detect	VERB
cana-2912	112	21	mechanism	mechanism	NOUN
cana-2912	112	22	can	can	AUX
cana-2912	112	23	be	be	AUX
cana-2912	112	24	used	use	VERB
cana-2912	112	25	,	,	PUNCT
cana-2912	112	26	greatly	greatly	ADV
cana-2912	112	27	improving	improve	VERB
cana-2912	112	28	the	the	DET
cana-2912	112	29	accuracy	accuracy	NOUN
cana-2912	112	30	of	of	ADP
cana-2912	112	31	the	the	DET
cana-2912	112	32	procedure	procedure	NOUN
cana-2912	112	33	[	[	X
cana-2912	112	34	18	18	NUM
cana-2912	112	35	]	]	PUNCT
cana-2912	112	36	.	.	PUNCT
cana-2912	113	1	the	the	DET
cana-2912	113	2	studies	study	NOUN
cana-2912	113	3	reviewed	review	VERB
cana-2912	113	4	provide	provide	VERB
cana-2912	113	5	a	a	DET
cana-2912	113	6	comprehensive	comprehensive	ADJ
cana-2912	113	7	foundation	foundation	NOUN
cana-2912	113	8	for	for	ADP
cana-2912	113	9	applying	apply	VERB
cana-2912	113	10	machine	machine	NOUN
cana-2912	113	11	learning	learning	NOUN
cana-2912	113	12	to	to	PART
cana-2912	113	13	weather	weather	NOUN
cana-2912	113	14	prediction	prediction	NOUN
cana-2912	113	15	.	.	PUNCT
cana-2912	114	1	however	however	ADV
cana-2912	114	2	,	,	PUNCT
cana-2912	114	3	challenges	challenge	NOUN
cana-2912	114	4	such	such	ADJ
cana-2912	114	5	as	as	ADP
cana-2912	114	6	data	datum	NOUN
cana-2912	114	7	quality	quality	NOUN
cana-2912	114	8	,	,	PUNCT
cana-2912	114	9	feature	feature	NOUN
cana-2912	114	10	selection	selection	NOUN
cana-2912	114	11	,	,	PUNCT
cana-2912	114	12	and	and	CCONJ
cana-2912	114	13	high	high	ADJ
cana-2912	114	14	accuracy	accuracy	NOUN
cana-2912	114	15	remain	remain	VERB
cana-2912	114	16	areas	area	NOUN
cana-2912	114	17	communications	communication	NOUN
cana-2912	114	18	on	on	ADP
cana-2912	114	19	applied	apply	VERB
cana-2912	114	20	nonlinear	nonlinear	ADJ
cana-2912	114	21	analysis	analysis	NOUN
cana-2912	114	22	issn	issn	NOUN
cana-2912	114	23	:	:	PUNCT
cana-2912	114	24	1074	1074	NUM
cana-2912	114	25	-	-	PUNCT
cana-2912	114	26	133x	133x	NUM
cana-2912	114	27	vol	vol	NOUN
cana-2912	114	28	32	32	NUM
cana-2912	114	29	no	no	NOUN
cana-2912	114	30	.	.	PUNCT
cana-2912	115	1	5s	5s	NUM
cana-2912	115	2	(	(	PUNCT
cana-2912	115	3	2025	2025	NUM
cana-2912	115	4	)	)	PUNCT
cana-2912	115	5	7	7	NUM
cana-2912	115	6	for	for	ADP
cana-2912	115	7	ongoing	ongoing	ADJ
cana-2912	115	8	research	research	NOUN
cana-2912	115	9	.	.	PUNCT
cana-2912	116	1	the	the	DET
cana-2912	116	2	integration	integration	NOUN
cana-2912	116	3	of	of	ADP
cana-2912	116	4	robust	robust	ADJ
cana-2912	116	5	algorithms	algorithm	NOUN
cana-2912	116	6	with	with	ADP
cana-2912	116	7	scalable	scalable	ADJ
cana-2912	116	8	systems	system	NOUN
cana-2912	116	9	offers	offer	VERB
cana-2912	116	10	promising	promise	VERB
cana-2912	116	11	directions	direction	NOUN
cana-2912	116	12	for	for	ADP
cana-2912	116	13	future	future	ADJ
cana-2912	116	14	advancements	advancement	NOUN
cana-2912	116	15	in	in	ADP
cana-2912	116	16	the	the	DET
cana-2912	116	17	field	field	NOUN
cana-2912	116	18	.	.	PUNCT
cana-2912	117	1	3	3	X
cana-2912	117	2	.	.	X
cana-2912	117	3	methodology	methodology	NOUN
cana-2912	117	4	fig	fig	NOUN
cana-2912	117	5	2	2	NUM
cana-2912	117	6	:	:	PUNCT
cana-2912	117	7	stages	stage	NOUN
cana-2912	117	8	for	for	ADP
cana-2912	117	9	weather	weather	NOUN
cana-2912	117	10	prediction	prediction	NOUN
cana-2912	117	11	.	.	PUNCT
cana-2912	118	1	the	the	DET
cana-2912	118	2	system	system	NOUN
cana-2912	118	3	operates	operate	VERB
cana-2912	118	4	through	through	ADP
cana-2912	118	5	a	a	DET
cana-2912	118	6	comprehensive	comprehensive	ADJ
cana-2912	118	7	process	process	NOUN
cana-2912	118	8	involving	involve	VERB
cana-2912	118	9	data	datum	NOUN
cana-2912	118	10	collection	collection	NOUN
cana-2912	118	11	,	,	PUNCT
cana-2912	118	12	analysis	analysis	NOUN
cana-2912	118	13	,	,	PUNCT
cana-2912	118	14	and	and	CCONJ
cana-2912	118	15	evaluation	evaluation	NOUN
cana-2912	118	16	to	to	PART
cana-2912	118	17	identify	identify	VERB
cana-2912	118	18	the	the	DET
cana-2912	118	19	most	most	ADV
cana-2912	118	20	precise	precise	ADJ
cana-2912	118	21	prediction	prediction	NOUN
cana-2912	118	22	model	model	NOUN
cana-2912	118	23	as	as	SCONJ
cana-2912	118	24	denoted	denote	VERB
cana-2912	118	25	in	in	ADP
cana-2912	118	26	fig	fig	NOUN
cana-2912	118	27	2	2	NUM
cana-2912	118	28	.	.	PUNCT
cana-2912	119	1	the	the	DET
cana-2912	119	2	initial	initial	ADJ
cana-2912	119	3	phase	phase	NOUN
cana-2912	119	4	focuses	focus	VERB
cana-2912	119	5	on	on	ADP
cana-2912	119	6	gathering	gather	VERB
cana-2912	119	7	historical	historical	ADJ
cana-2912	119	8	weather	weather	NOUN
cana-2912	119	9	data	datum	NOUN
cana-2912	119	10	spanning	span	VERB
cana-2912	119	11	from	from	ADP
cana-2912	119	12	2009	2009	NUM
cana-2912	119	13	to	to	ADP
cana-2912	119	14	2023	2023	NUM
cana-2912	119	15	.	.	PUNCT
cana-2912	120	1	during	during	ADP
cana-2912	120	2	this	this	DET
cana-2912	120	3	phase	phase	NOUN
cana-2912	120	4	,	,	PUNCT
cana-2912	120	5	issues	issue	NOUN
cana-2912	120	6	such	such	ADJ
cana-2912	120	7	as	as	ADP
cana-2912	120	8	redundancies	redundancy	NOUN
cana-2912	120	9	and	and	CCONJ
cana-2912	120	10	missing	miss	VERB
cana-2912	120	11	values	value	NOUN
cana-2912	120	12	in	in	ADP
cana-2912	120	13	the	the	DET
cana-2912	120	14	collected	collect	VERB
cana-2912	120	15	data	datum	NOUN
cana-2912	120	16	are	be	AUX
cana-2912	120	17	systematically	systematically	ADV
cana-2912	120	18	addressed	address	VERB
cana-2912	120	19	to	to	PART
cana-2912	120	20	ensure	ensure	VERB
cana-2912	120	21	data	datum	NOUN
cana-2912	120	22	integrity	integrity	NOUN
cana-2912	120	23	and	and	CCONJ
cana-2912	120	24	reliability	reliability	NOUN
cana-2912	120	25	.	.	PUNCT
cana-2912	121	1	subsequently	subsequently	ADV
cana-2912	121	2	,	,	PUNCT
cana-2912	121	3	the	the	DET
cana-2912	121	4	data	data	NOUN
cana-2912	121	5	is	be	AUX
cana-2912	121	6	analysed	analyse	VERB
cana-2912	121	7	to	to	PART
cana-2912	121	8	identify	identify	VERB
cana-2912	121	9	patterns	pattern	NOUN
cana-2912	121	10	and	and	CCONJ
cana-2912	121	11	trends	trend	NOUN
cana-2912	121	12	that	that	PRON
cana-2912	121	13	inform	inform	VERB
cana-2912	121	14	the	the	DET
cana-2912	121	15	prediction	prediction	NOUN
cana-2912	121	16	model	model	NOUN
cana-2912	121	17	's	's	PART
cana-2912	121	18	characteristics	characteristic	NOUN
cana-2912	121	19	.	.	PUNCT
cana-2912	122	1	key	key	ADJ
cana-2912	122	2	features	feature	NOUN
cana-2912	122	3	in	in	ADP
cana-2912	122	4	the	the	DET
cana-2912	122	5	dataset	dataset	NOUN
cana-2912	122	6	include	include	VERB
cana-2912	122	7	variables	variable	NOUN
cana-2912	122	8	such	such	ADJ
cana-2912	122	9	as	as	ADP
cana-2912	122	10	sunlight	sunlight	NOUN
cana-2912	122	11	hours	hour	NOUN
cana-2912	122	12	,	,	PUNCT
cana-2912	122	13	date	date	NOUN
cana-2912	122	14	-	-	PUNCT
cana-2912	122	15	time	time	NOUN
cana-2912	122	16	,	,	PUNCT
cana-2912	122	17	atmospheric	atmospheric	ADJ
cana-2912	122	18	pressure	pressure	NOUN
cana-2912	122	19	,	,	PUNCT
cana-2912	122	20	precipitation	precipitation	NOUN
cana-2912	122	21	levels	level	NOUN
cana-2912	122	22	,	,	PUNCT
cana-2912	122	23	and	and	CCONJ
cana-2912	122	24	other	other	ADJ
cana-2912	122	25	meteorological	meteorological	ADJ
cana-2912	122	26	parameters	parameter	NOUN
cana-2912	122	27	.	.	PUNCT
cana-2912	123	1	these	these	DET
cana-2912	123	2	features	feature	NOUN
cana-2912	123	3	serve	serve	VERB
cana-2912	123	4	as	as	ADP
cana-2912	123	5	the	the	DET
cana-2912	123	6	foundation	foundation	NOUN
cana-2912	123	7	for	for	ADP
cana-2912	123	8	building	building	NOUN
cana-2912	123	9	and	and	CCONJ
cana-2912	123	10	refining	refine	VERB
cana-2912	123	11	the	the	DET
cana-2912	123	12	model	model	NOUN
cana-2912	123	13	.	.	PUNCT
cana-2912	124	1	the	the	DET
cana-2912	124	2	training	training	NOUN
cana-2912	124	3	phase	phase	NOUN
cana-2912	124	4	utilizes	utilize	VERB
cana-2912	124	5	this	this	DET
cana-2912	124	6	curated	curate	VERB
cana-2912	124	7	dataset	dataset	NOUN
cana-2912	124	8	to	to	PART
cana-2912	124	9	develop	develop	VERB
cana-2912	124	10	machine	machine	NOUN
cana-2912	124	11	learning	learning	NOUN
cana-2912	124	12	models	model	NOUN
cana-2912	124	13	capable	capable	ADJ
cana-2912	124	14	of	of	ADP
cana-2912	124	15	making	make	VERB
cana-2912	124	16	accurate	accurate	ADJ
cana-2912	124	17	predictions	prediction	NOUN
cana-2912	124	18	.	.	PUNCT
cana-2912	125	1	these	these	DET
cana-2912	125	2	models	model	NOUN
cana-2912	125	3	are	be	AUX
cana-2912	125	4	assessed	assess	VERB
cana-2912	125	5	through	through	ADP
cana-2912	125	6	rigorous	rigorous	ADJ
cana-2912	125	7	evaluation	evaluation	NOUN
cana-2912	125	8	processes	process	NOUN
cana-2912	125	9	to	to	PART
cana-2912	125	10	determine	determine	VERB
cana-2912	125	11	the	the	DET
cana-2912	125	12	most	most	ADV
cana-2912	125	13	optimal	optimal	ADJ
cana-2912	125	14	configuration	configuration	NOUN
cana-2912	125	15	.	.	PUNCT
cana-2912	126	1	the	the	DET
cana-2912	126	2	evaluation	evaluation	NOUN
cana-2912	126	3	involves	involve	VERB
cana-2912	126	4	comparing	compare	VERB
cana-2912	126	5	various	various	ADJ
cana-2912	126	6	models	model	NOUN
cana-2912	126	7	based	base	VERB
cana-2912	126	8	on	on	ADP
cana-2912	126	9	performance	performance	NOUN
cana-2912	126	10	metrics	metric	NOUN
cana-2912	126	11	to	to	PART
cana-2912	126	12	ensure	ensure	VERB
cana-2912	126	13	the	the	DET
cana-2912	126	14	highest	high	ADJ
cana-2912	126	15	level	level	NOUN
cana-2912	126	16	of	of	ADP
cana-2912	126	17	prediction	prediction	NOUN
cana-2912	126	18	accuracy	accuracy	NOUN
cana-2912	126	19	.	.	PUNCT
cana-2912	127	1	once	once	ADV
cana-2912	127	2	finalized	finalize	VERB
cana-2912	127	3	,	,	PUNCT
cana-2912	127	4	the	the	DET
cana-2912	127	5	model	model	NOUN
cana-2912	127	6	predicts	predict	VERB
cana-2912	127	7	weather	weather	NOUN
cana-2912	127	8	parameters	parameter	NOUN
cana-2912	127	9	such	such	ADJ
cana-2912	127	10	as	as	ADP
cana-2912	127	11	average	average	ADJ
cana-2912	127	12	,	,	PUNCT
cana-2912	127	13	minimum	minimum	ADJ
cana-2912	127	14	,	,	PUNCT
cana-2912	127	15	and	and	CCONJ
cana-2912	127	16	maximum	maximum	ADJ
cana-2912	127	17	temperatures	temperature	NOUN
cana-2912	127	18	using	use	VERB
cana-2912	127	19	current	current	ADJ
cana-2912	127	20	independent	independent	ADJ
cana-2912	127	21	input	input	NOUN
cana-2912	127	22	data	datum	NOUN
cana-2912	127	23	.	.	PUNCT
cana-2912	128	1	these	these	DET
cana-2912	128	2	predictions	prediction	NOUN
cana-2912	128	3	are	be	AUX
cana-2912	128	4	then	then	ADV
cana-2912	128	5	transmitted	transmit	VERB
cana-2912	128	6	to	to	ADP
cana-2912	128	7	the	the	DET
cana-2912	128	8	enduser	enduser	NOUN
cana-2912	128	9	via	via	ADP
cana-2912	128	10	a	a	DET
cana-2912	128	11	user	user	NOUN
cana-2912	128	12	-	-	PUNCT
cana-2912	128	13	friendly	friendly	ADJ
cana-2912	128	14	interface	interface	NOUN
cana-2912	128	15	,	,	PUNCT
cana-2912	128	16	ensuring	ensure	VERB
cana-2912	128	17	practical	practical	ADJ
cana-2912	128	18	applicability	applicability	NOUN
cana-2912	128	19	for	for	ADP
cana-2912	128	20	real	real	ADJ
cana-2912	128	21	-	-	PUNCT
cana-2912	128	22	world	world	NOUN
cana-2912	128	23	use	use	NOUN
cana-2912	128	24	cases	case	NOUN
cana-2912	128	25	.	.	PUNCT
cana-2912	129	1	3.1	3.1	NUM
cana-2912	129	2	flowchart	flowchart	PROPN
cana-2912	129	3	fig	fig	NOUN
cana-2912	129	4	3	3	NUM
cana-2912	129	5	:	:	PUNCT
cana-2912	129	6	flowchart	flowchart	NOUN
cana-2912	129	7	for	for	ADP
cana-2912	129	8	weather	weather	NOUN
cana-2912	129	9	prediction	prediction	NOUN
cana-2912	129	10	.	.	PUNCT
cana-2912	130	1	communications	communication	NOUN
cana-2912	130	2	on	on	ADP
cana-2912	130	3	applied	apply	VERB
cana-2912	130	4	nonlinear	nonlinear	ADJ
cana-2912	130	5	analysis	analysis	NOUN
cana-2912	130	6	issn	issn	NOUN
cana-2912	130	7	:	:	PUNCT
cana-2912	130	8	1074	1074	NUM
cana-2912	130	9	-	-	PUNCT
cana-2912	130	10	133x	133x	NUM
cana-2912	130	11	vol	vol	NOUN
cana-2912	130	12	32	32	NUM
cana-2912	130	13	no	no	NOUN
cana-2912	130	14	.	.	PUNCT
cana-2912	131	1	5s	5s	NUM
cana-2912	131	2	(	(	PUNCT
cana-2912	131	3	2025	2025	NUM
cana-2912	131	4	)	)	PUNCT
cana-2912	131	5	8	8	NUM
cana-2912	131	6	the	the	DET
cana-2912	131	7	flow	flow	NOUN
cana-2912	131	8	of	of	ADP
cana-2912	131	9	the	the	DET
cana-2912	131	10	system	system	NOUN
cana-2912	131	11	,	,	PUNCT
cana-2912	131	12	as	as	SCONJ
cana-2912	131	13	illustrated	illustrate	VERB
cana-2912	131	14	in	in	ADP
cana-2912	131	15	fig	fig	NOUN
cana-2912	131	16	.	.	PUNCT
cana-2912	132	1	3	3	NUM
cana-2912	132	2	,	,	PUNCT
cana-2912	132	3	begins	begin	VERB
cana-2912	132	4	with	with	ADP
cana-2912	132	5	the	the	DET
cana-2912	132	6	selection	selection	NOUN
cana-2912	132	7	of	of	ADP
cana-2912	132	8	regressors	regressor	NOUN
cana-2912	132	9	and	and	CCONJ
cana-2912	132	10	identifying	identify	VERB
cana-2912	132	11	the	the	DET
cana-2912	132	12	most	most	ADV
cana-2912	132	13	suitable	suitable	ADJ
cana-2912	132	14	model	model	NOUN
cana-2912	132	15	from	from	ADP
cana-2912	132	16	the	the	DET
cana-2912	132	17	available	available	ADJ
cana-2912	132	18	set	set	NOUN
cana-2912	132	19	.	.	PUNCT
cana-2912	133	1	the	the	DET
cana-2912	133	2	selected	select	VERB
cana-2912	133	3	regressors	regressor	NOUN
cana-2912	133	4	are	be	AUX
cana-2912	133	5	applied	apply	VERB
cana-2912	133	6	to	to	ADP
cana-2912	133	7	the	the	DET
cana-2912	133	8	collected	collect	VERB
cana-2912	133	9	data	datum	NOUN
cana-2912	133	10	for	for	ADP
cana-2912	133	11	model	model	NOUN
cana-2912	133	12	training	training	NOUN
cana-2912	133	13	and	and	CCONJ
cana-2912	133	14	accuracy	accuracy	NOUN
cana-2912	133	15	testing	testing	NOUN
cana-2912	133	16	.	.	PUNCT
cana-2912	134	1	model	model	NOUN
cana-2912	134	2	accuracy	accuracy	NOUN
cana-2912	134	3	serves	serve	VERB
cana-2912	134	4	as	as	ADP
cana-2912	134	5	a	a	DET
cana-2912	134	6	critical	critical	ADJ
cana-2912	134	7	parameter	parameter	NOUN
cana-2912	134	8	for	for	ADP
cana-2912	134	9	evaluating	evaluate	VERB
cana-2912	134	10	performance	performance	NOUN
cana-2912	134	11	,	,	PUNCT
cana-2912	134	12	guiding	guide	VERB
cana-2912	134	13	the	the	DET
cana-2912	134	14	selection	selection	NOUN
cana-2912	134	15	of	of	ADP
cana-2912	134	16	the	the	DET
cana-2912	134	17	optimal	optimal	ADJ
cana-2912	134	18	regressor	regressor	NOUN
cana-2912	134	19	that	that	PRON
cana-2912	134	20	offers	offer	VERB
cana-2912	134	21	the	the	DET
cana-2912	134	22	best	good	ADJ
cana-2912	134	23	combination	combination	NOUN
cana-2912	134	24	of	of	ADP
cana-2912	134	25	accuracy	accuracy	NOUN
cana-2912	134	26	and	and	CCONJ
cana-2912	134	27	computational	computational	ADJ
cana-2912	134	28	efficiency	efficiency	NOUN
cana-2912	134	29	.	.	PUNCT
cana-2912	135	1	once	once	ADV
cana-2912	135	2	the	the	DET
cana-2912	135	3	most	most	ADV
cana-2912	135	4	suitable	suitable	ADJ
cana-2912	135	5	model	model	NOUN
cana-2912	135	6	is	be	AUX
cana-2912	135	7	identified	identify	VERB
cana-2912	135	8	,	,	PUNCT
cana-2912	135	9	it	it	PRON
cana-2912	135	10	is	be	AUX
cana-2912	135	11	utilized	utilize	VERB
cana-2912	135	12	to	to	PART
cana-2912	135	13	predict	predict	VERB
cana-2912	135	14	the	the	DET
cana-2912	135	15	average	average	ADJ
cana-2912	135	16	,	,	PUNCT
cana-2912	135	17	minimum	minimum	ADJ
cana-2912	135	18	,	,	PUNCT
cana-2912	135	19	and	and	CCONJ
cana-2912	135	20	maximum	maximum	ADJ
cana-2912	135	21	temperatures	temperature	NOUN
cana-2912	135	22	for	for	ADP
cana-2912	135	23	the	the	DET
cana-2912	135	24	next	next	ADJ
cana-2912	135	25	five	five	NUM
cana-2912	135	26	days	day	NOUN
cana-2912	135	27	based	base	VERB
cana-2912	135	28	on	on	ADP
cana-2912	135	29	the	the	DET
cana-2912	135	30	provided	provide	VERB
cana-2912	135	31	independent	independent	ADJ
cana-2912	135	32	input	input	NOUN
cana-2912	135	33	values	value	NOUN
cana-2912	135	34	.	.	PUNCT
cana-2912	136	1	the	the	DET
cana-2912	136	2	process	process	NOUN
cana-2912	136	3	incorporates	incorporate	VERB
cana-2912	136	4	location	location	NOUN
cana-2912	136	5	-	-	PUNCT
cana-2912	136	6	specific	specific	ADJ
cana-2912	136	7	data	datum	NOUN
cana-2912	136	8	and	and	CCONJ
cana-2912	136	9	relevant	relevant	ADJ
cana-2912	136	10	meteorological	meteorological	ADJ
cana-2912	136	11	variables	variable	NOUN
cana-2912	136	12	,	,	PUNCT
cana-2912	136	13	which	which	PRON
cana-2912	136	14	are	be	AUX
cana-2912	136	15	fitted	fit	VERB
cana-2912	136	16	into	into	ADP
cana-2912	136	17	the	the	DET
cana-2912	136	18	model	model	NOUN
cana-2912	136	19	to	to	PART
cana-2912	136	20	generate	generate	VERB
cana-2912	136	21	accurate	accurate	ADJ
cana-2912	136	22	predictions	prediction	NOUN
cana-2912	136	23	.	.	PUNCT
cana-2912	137	1	the	the	DET
cana-2912	137	2	predicted	predict	VERB
cana-2912	137	3	values	value	NOUN
cana-2912	137	4	are	be	AUX
cana-2912	137	5	subsequently	subsequently	ADV
cana-2912	137	6	displayed	display	VERB
cana-2912	137	7	through	through	ADP
cana-2912	137	8	a	a	DET
cana-2912	137	9	user	user	NOUN
cana-2912	137	10	-	-	PUNCT
cana-2912	137	11	friendly	friendly	ADJ
cana-2912	137	12	interface	interface	NOUN
cana-2912	137	13	,	,	PUNCT
cana-2912	137	14	enabling	enable	VERB
cana-2912	137	15	end	end	NOUN
cana-2912	137	16	-	-	PUNCT
cana-2912	137	17	users	user	NOUN
cana-2912	137	18	to	to	PART
cana-2912	137	19	access	access	VERB
cana-2912	137	20	the	the	DET
cana-2912	137	21	forecasted	forecast	VERB
cana-2912	137	22	weather	weather	NOUN
cana-2912	137	23	conditions	condition	NOUN
cana-2912	137	24	with	with	ADP
cana-2912	137	25	ease	ease	NOUN
cana-2912	137	26	.	.	PUNCT
cana-2912	138	1	this	this	DET
cana-2912	138	2	streamlined	streamlined	ADJ
cana-2912	138	3	approach	approach	NOUN
cana-2912	138	4	ensures	ensure	VERB
cana-2912	138	5	that	that	SCONJ
cana-2912	138	6	predictions	prediction	NOUN
cana-2912	138	7	are	be	AUX
cana-2912	138	8	both	both	CCONJ
cana-2912	138	9	accurate	accurate	ADJ
cana-2912	138	10	and	and	CCONJ
cana-2912	138	11	accessible	accessible	ADJ
cana-2912	138	12	,	,	PUNCT
cana-2912	138	13	enhancing	enhance	VERB
cana-2912	138	14	practical	practical	ADJ
cana-2912	138	15	applicability	applicability	NOUN
cana-2912	138	16	.	.	PUNCT
cana-2912	139	1	3.2	3.2	NUM
cana-2912	139	2	data	datum	NOUN
cana-2912	139	3	analysis	analysis	NOUN
cana-2912	139	4	for	for	ADP
cana-2912	139	5	weather	weather	NOUN
cana-2912	139	6	prediction	prediction	NOUN
cana-2912	139	7	fig	fig	NOUN
cana-2912	139	8	4	4	NUM
cana-2912	139	9	:	:	PUNCT
cana-2912	139	10	data	datum	NOUN
cana-2912	139	11	for	for	ADP
cana-2912	139	12	the	the	DET
cana-2912	139	13	month	month	NOUN
cana-2912	139	14	of	of	ADP
cana-2912	139	15	september	september	PROPN
cana-2912	139	16	2023	2023	NUM
cana-2912	139	17	for	for	ADP
cana-2912	139	18	temperature	temperature	NOUN
cana-2912	139	19	,	,	PUNCT
cana-2912	139	20	precipitation	precipitation	NOUN
cana-2912	139	21	,	,	PUNCT
cana-2912	139	22	wind	wind	NOUN
cana-2912	139	23	for	for	ADP
cana-2912	139	24	mumbai	mumbai	NOUN
cana-2912	139	25	throughout	throughout	ADP
cana-2912	139	26	september	september	PROPN
cana-2912	139	27	,	,	PUNCT
cana-2912	139	28	temperature	temperature	NOUN
cana-2912	139	29	variations	variation	NOUN
cana-2912	139	30	were	be	AUX
cana-2912	139	31	observed	observe	VERB
cana-2912	139	32	,	,	PUNCT
cana-2912	139	33	with	with	ADP
cana-2912	139	34	significant	significant	ADJ
cana-2912	139	35	highs	high	NOUN
cana-2912	139	36	and	and	CCONJ
cana-2912	139	37	lows	low	NOUN
cana-2912	139	38	.	.	PUNCT
cana-2912	140	1	maximum	maximum	ADJ
cana-2912	140	2	temperatures	temperature	NOUN
cana-2912	140	3	ranged	range	VERB
cana-2912	140	4	from	from	ADP
cana-2912	140	5	82	82	NUM
cana-2912	140	6	°	°	NOUN
cana-2912	140	7	f	f	NOUN
cana-2912	140	8	to	to	ADP
cana-2912	140	9	99	99	NUM
cana-2912	140	10	°	°	NOUN
cana-2912	140	11	f	f	PROPN
cana-2912	140	12	,	,	PUNCT
cana-2912	140	13	with	with	ADP
cana-2912	140	14	the	the	DET
cana-2912	140	15	peak	peak	NOUN
cana-2912	140	16	of	of	ADP
cana-2912	140	17	99	99	NUM
cana-2912	140	18	°	°	NOUN
cana-2912	140	19	f	f	NOUN
cana-2912	140	20	recorded	record	VERB
cana-2912	140	21	on	on	ADP
cana-2912	140	22	the	the	DET
cana-2912	140	23	5th	5th	ADJ
cana-2912	140	24	and	and	CCONJ
cana-2912	140	25	7th	7th	NOUN
cana-2912	140	26	.	.	PUNCT
cana-2912	141	1	the	the	DET
cana-2912	141	2	average	average	ADJ
cana-2912	141	3	temperature	temperature	NOUN
cana-2912	141	4	for	for	ADP
cana-2912	141	5	the	the	DET
cana-2912	141	6	month	month	NOUN
cana-2912	141	7	was	be	AUX
cana-2912	141	8	88.6	88.6	NUM
cana-2912	141	9	°	°	NOUN
cana-2912	141	10	f	f	PROPN
cana-2912	141	11	,	,	PUNCT
cana-2912	141	12	reflecting	reflect	VERB
cana-2912	141	13	relatively	relatively	ADV
cana-2912	141	14	warm	warm	ADJ
cana-2912	141	15	conditions	condition	NOUN
cana-2912	141	16	.	.	PUNCT
cana-2912	142	1	minimum	minimum	ADJ
cana-2912	142	2	temperatures	temperature	NOUN
cana-2912	142	3	fluctuated	fluctuate	VERB
cana-2912	142	4	between	between	ADP
cana-2912	142	5	74	74	NUM
cana-2912	142	6	°	°	NUM
cana-2912	142	7	f	f	NOUN
cana-2912	142	8	and	and	CCONJ
cana-2912	142	9	85	85	NUM
cana-2912	142	10	°	°	NOUN
cana-2912	142	11	f	f	NOUN
cana-2912	142	12	,	,	PUNCT
cana-2912	142	13	with	with	ADP
cana-2912	142	14	the	the	DET
cana-2912	142	15	lowest	low	ADJ
cana-2912	142	16	temperature	temperature	NOUN
cana-2912	142	17	of	of	ADP
cana-2912	142	18	74	74	NUM
cana-2912	142	19	°	°	SYM
cana-2912	142	20	f	f	NOUN
cana-2912	142	21	recorded	record	VERB
cana-2912	142	22	on	on	ADP
cana-2912	142	23	multiple	multiple	ADJ
cana-2912	142	24	occasions	occasion	NOUN
cana-2912	142	25	.	.	PUNCT
cana-2912	143	1	the	the	DET
cana-2912	143	2	month	month	NOUN
cana-2912	143	3	commenced	commence	VERB
cana-2912	143	4	with	with	ADP
cana-2912	143	5	temperatures	temperature	NOUN
cana-2912	143	6	ranging	range	VERB
cana-2912	143	7	from	from	ADP
cana-2912	143	8	83	83	NUM
cana-2912	143	9	°	°	NOUN
cana-2912	143	10	f	f	NOUN
cana-2912	143	11	to	to	ADP
cana-2912	143	12	89	89	NUM
cana-2912	143	13	°	°	NOUN
cana-2912	143	14	f	f	NOUN
cana-2912	143	15	,	,	PUNCT
cana-2912	143	16	gradually	gradually	ADV
cana-2912	143	17	rising	rise	VERB
cana-2912	143	18	to	to	ADP
cana-2912	143	19	a	a	DET
cana-2912	143	20	mid	mid	ADJ
cana-2912	143	21	-	-	ADJ
cana-2912	143	22	month	month	NOUN
cana-2912	143	23	peak	peak	NOUN
cana-2912	143	24	of	of	ADP
cana-2912	143	25	97	97	NUM
cana-2912	143	26	°	°	NOUN
cana-2912	143	27	f	f	NOUN
cana-2912	143	28	to	to	ADP
cana-2912	143	29	99	99	NUM
cana-2912	143	30	°	°	NOUN
cana-2912	143	31	f	f	PROPN
cana-2912	143	32	over	over	ADP
cana-2912	143	33	several	several	ADJ
cana-2912	143	34	days	day	NOUN
cana-2912	143	35	.	.	PUNCT
cana-2912	144	1	as	as	SCONJ
cana-2912	144	2	september	september	PROPN
cana-2912	144	3	progressed	progress	VERB
cana-2912	144	4	,	,	PUNCT
cana-2912	144	5	temperatures	temperature	NOUN
cana-2912	144	6	began	begin	VERB
cana-2912	144	7	to	to	PART
cana-2912	144	8	decline	decline	VERB
cana-2912	144	9	,	,	PUNCT
cana-2912	144	10	with	with	ADP
cana-2912	144	11	the	the	DET
cana-2912	144	12	lowest	low	ADJ
cana-2912	144	13	values	value	NOUN
cana-2912	144	14	of	of	ADP
cana-2912	144	15	74	74	NUM
cana-2912	144	16	°	°	NOUN
cana-2912	144	17	f	f	NOUN
cana-2912	144	18	to	to	ADP
cana-2912	144	19	78	78	NUM
cana-2912	145	1	°	°	PRON
cana-2912	145	2	f	f	NOUN
cana-2912	145	3	occurring	occur	VERB
cana-2912	145	4	towards	towards	ADP
cana-2912	145	5	the	the	DET
cana-2912	145	6	month	month	NOUN
cana-2912	145	7	's	's	PART
cana-2912	145	8	end	end	NOUN
cana-2912	146	1	.	.	PUNCT
cana-2912	147	1	these	these	DET
cana-2912	147	2	trends	trend	NOUN
cana-2912	147	3	reflect	reflect	VERB
cana-2912	147	4	the	the	DET
cana-2912	147	5	transition	transition	NOUN
cana-2912	147	6	from	from	ADP
cana-2912	147	7	late	late	ADJ
cana-2912	147	8	summer	summer	NOUN
cana-2912	147	9	to	to	ADP
cana-2912	147	10	early	early	ADJ
cana-2912	147	11	autumn	autumn	NOUN
cana-2912	147	12	,	,	PUNCT
cana-2912	147	13	characterized	characterize	VERB
cana-2912	147	14	by	by	ADP
cana-2912	147	15	residual	residual	ADJ
cana-2912	147	16	summer	summer	NOUN
cana-2912	147	17	heat	heat	NOUN
cana-2912	147	18	giving	give	VERB
cana-2912	147	19	way	way	NOUN
cana-2912	147	20	to	to	ADP
cana-2912	147	21	cooler	cooler	ADJ
cana-2912	147	22	fall	fall	NOUN
cana-2912	147	23	conditions	condition	NOUN
cana-2912	147	24	.	.	PUNCT
cana-2912	148	1	precipitation	precipitation	NOUN
cana-2912	148	2	data	datum	NOUN
cana-2912	148	3	for	for	ADP
cana-2912	148	4	september	september	PROPN
cana-2912	148	5	indicates	indicate	VERB
cana-2912	148	6	varied	varied	ADJ
cana-2912	148	7	rainfall	rainfall	NOUN
cana-2912	148	8	levels	level	NOUN
cana-2912	148	9	.	.	PUNCT
cana-2912	149	1	the	the	DET
cana-2912	149	2	initial	initial	ADJ
cana-2912	149	3	eight	eight	NUM
cana-2912	149	4	days	day	NOUN
cana-2912	149	5	were	be	AUX
cana-2912	149	6	marked	mark	VERB
cana-2912	149	7	by	by	ADP
cana-2912	149	8	dry	dry	ADJ
cana-2912	149	9	conditions	condition	NOUN
cana-2912	149	10	,	,	PUNCT
cana-2912	149	11	with	with	ADP
cana-2912	149	12	no	no	DET
cana-2912	149	13	measurable	measurable	ADJ
cana-2912	149	14	rainfall	rainfall	NOUN
cana-2912	149	15	.	.	PUNCT
cana-2912	150	1	on	on	ADP
cana-2912	150	2	the	the	DET
cana-2912	150	3	9th	9th	NOUN
cana-2912	150	4	,	,	PUNCT
cana-2912	150	5	precipitation	precipitation	NOUN
cana-2912	150	6	increased	increase	VERB
cana-2912	150	7	to	to	ADP
cana-2912	150	8	0.39	0.39	NUM
cana-2912	150	9	inches	inch	NOUN
cana-2912	150	10	,	,	PUNCT
cana-2912	150	11	followed	follow	VERB
cana-2912	150	12	by	by	ADP
cana-2912	150	13	a	a	DET
cana-2912	150	14	significant	significant	ADJ
cana-2912	150	15	spike	spike	NOUN
cana-2912	150	16	of	of	ADP
cana-2912	150	17	1.54	1.54	NUM
cana-2912	150	18	inches	inch	NOUN
cana-2912	150	19	on	on	ADP
cana-2912	150	20	the	the	DET
cana-2912	150	21	10th	10th	NOUN
cana-2912	150	22	.	.	PUNCT
cana-2912	151	1	the	the	DET
cana-2912	151	2	remainder	remainder	NOUN
cana-2912	151	3	of	of	ADP
cana-2912	151	4	the	the	DET
cana-2912	151	5	month	month	NOUN
cana-2912	151	6	featured	feature	VERB
cana-2912	151	7	minor	minor	ADJ
cana-2912	151	8	rainfall	rainfall	NOUN
cana-2912	151	9	events	event	NOUN
cana-2912	151	10	,	,	PUNCT
cana-2912	151	11	including	include	VERB
cana-2912	151	12	0.04	0.04	NUM
cana-2912	151	13	inches	inch	NOUN
cana-2912	151	14	on	on	ADP
cana-2912	151	15	the	the	DET
cana-2912	151	16	11th	11th	NOUN
cana-2912	151	17	,	,	PUNCT
cana-2912	151	18	0.16	0.16	NUM
cana-2912	151	19	inches	inch	NOUN
cana-2912	151	20	on	on	ADP
cana-2912	151	21	the	the	DET
cana-2912	151	22	15th	15th	NOUN
cana-2912	151	23	,	,	PUNCT
cana-2912	151	24	and	and	CCONJ
cana-2912	151	25	another	another	DET
cana-2912	151	26	0.04	0.04	NUM
cana-2912	151	27	inches	inch	NOUN
cana-2912	151	28	on	on	ADP
cana-2912	151	29	the	the	DET
cana-2912	151	30	16th	16th	NOUN
cana-2912	151	31	.	.	PUNCT
cana-2912	152	1	later	later	ADV
cana-2912	152	2	in	in	ADP
cana-2912	152	3	the	the	DET
cana-2912	152	4	month	month	NOUN
cana-2912	152	5	,	,	PUNCT
cana-2912	152	6	precipitation	precipitation	NOUN
cana-2912	152	7	increased	increase	VERB
cana-2912	152	8	notably	notably	ADV
cana-2912	152	9	to	to	ADP
cana-2912	152	10	0.59	0.59	NUM
cana-2912	152	11	inches	inch	NOUN
cana-2912	152	12	on	on	ADP
cana-2912	152	13	the	the	DET
cana-2912	152	14	23rd	23rd	NOUN
cana-2912	152	15	.	.	PUNCT
cana-2912	153	1	however	however	ADV
cana-2912	153	2	,	,	PUNCT
cana-2912	153	3	the	the	DET
cana-2912	153	4	majority	majority	NOUN
cana-2912	153	5	of	of	ADP
cana-2912	153	6	days	day	NOUN
cana-2912	153	7	after	after	SCONJ
cana-2912	153	8	this	this	PRON
cana-2912	153	9	remained	remain	VERB
cana-2912	153	10	dry	dry	ADJ
cana-2912	153	11	,	,	PUNCT
cana-2912	153	12	resulting	result	VERB
cana-2912	153	13	in	in	ADP
cana-2912	153	14	a	a	DET
cana-2912	153	15	total	total	ADJ
cana-2912	153	16	monthly	monthly	ADJ
cana-2912	153	17	precipitation	precipitation	NOUN
cana-2912	153	18	of	of	ADP
cana-2912	153	19	3.27	3.27	NUM
cana-2912	153	20	inches	inch	NOUN
cana-2912	153	21	.	.	PUNCT
cana-2912	154	1	the	the	DET
cana-2912	154	2	midmonth	midmonth	ADJ
cana-2912	154	3	rainfall	rainfall	NOUN
cana-2912	154	4	significantly	significantly	ADV
cana-2912	154	5	influenced	influence	VERB
cana-2912	154	6	the	the	DET
cana-2912	154	7	overall	overall	ADJ
cana-2912	154	8	weather	weather	NOUN
cana-2912	154	9	and	and	CCONJ
cana-2912	154	10	water	water	NOUN
cana-2912	154	11	availability	availability	NOUN
cana-2912	154	12	during	during	ADP
cana-2912	154	13	september	september	PROPN
cana-2912	154	14	.	.	PUNCT
cana-2912	155	1	communications	communication	NOUN
cana-2912	155	2	on	on	ADP
cana-2912	155	3	applied	apply	VERB
cana-2912	155	4	nonlinear	nonlinear	ADJ
cana-2912	155	5	analysis	analysis	NOUN
cana-2912	155	6	issn	issn	NOUN
cana-2912	155	7	:	:	PUNCT
cana-2912	155	8	1074	1074	NUM
cana-2912	155	9	-	-	PUNCT
cana-2912	155	10	133x	133x	NUM
cana-2912	155	11	vol	vol	NOUN
cana-2912	155	12	32	32	NUM
cana-2912	155	13	no	no	NOUN
cana-2912	155	14	.	.	PUNCT
cana-2912	156	1	5s	5s	NUM
cana-2912	156	2	(	(	PUNCT
cana-2912	156	3	2025	2025	NUM
cana-2912	156	4	)	)	PUNCT
cana-2912	156	5	9	9	NUM
cana-2912	156	6	wind	wind	NOUN
cana-2912	156	7	speed	speed	NOUN
cana-2912	156	8	data	datum	NOUN
cana-2912	156	9	for	for	ADP
cana-2912	156	10	the	the	DET
cana-2912	156	11	month	month	NOUN
cana-2912	156	12	highlights	highlight	NOUN
cana-2912	156	13	fluctuations	fluctuation	NOUN
cana-2912	156	14	in	in	ADP
cana-2912	156	15	intensity	intensity	NOUN
cana-2912	156	16	.	.	PUNCT
cana-2912	157	1	maximum	maximum	ADJ
cana-2912	157	2	wind	wind	NOUN
cana-2912	157	3	speeds	speed	NOUN
cana-2912	157	4	ranged	range	VERB
cana-2912	157	5	from	from	ADP
cana-2912	157	6	3	3	NUM
cana-2912	157	7	mph	mph	NOUN
cana-2912	157	8	to	to	ADP
cana-2912	157	9	16	16	NUM
cana-2912	157	10	mph	mph	NOUN
cana-2912	157	11	,	,	PUNCT
cana-2912	157	12	with	with	ADP
cana-2912	157	13	the	the	DET
cana-2912	157	14	highest	high	ADJ
cana-2912	157	15	speed	speed	NOUN
cana-2912	157	16	of	of	ADP
cana-2912	157	17	16	16	NUM
cana-2912	157	18	mph	mph	NOUN
cana-2912	157	19	recorded	record	VERB
cana-2912	157	20	on	on	ADP
cana-2912	157	21	the	the	DET
cana-2912	157	22	8th	8th	NOUN
cana-2912	157	23	.	.	PUNCT
cana-2912	158	1	the	the	DET
cana-2912	158	2	average	average	ADJ
cana-2912	158	3	wind	wind	NOUN
cana-2912	158	4	speed	speed	NOUN
cana-2912	158	5	for	for	ADP
cana-2912	158	6	september	september	PROPN
cana-2912	158	7	was	be	AUX
cana-2912	158	8	4.1	4.1	NUM
cana-2912	158	9	mph	mph	NOUN
cana-2912	158	10	,	,	PUNCT
cana-2912	158	11	indicating	indicate	VERB
cana-2912	158	12	generally	generally	ADV
cana-2912	158	13	moderate	moderate	ADJ
cana-2912	158	14	conditions	condition	NOUN
cana-2912	158	15	.	.	PUNCT
cana-2912	159	1	some	some	DET
cana-2912	159	2	days	day	NOUN
cana-2912	159	3	recorded	record	VERB
cana-2912	159	4	minimal	minimal	ADJ
cana-2912	159	5	wind	wind	NOUN
cana-2912	159	6	activity	activity	NOUN
cana-2912	159	7	,	,	PUNCT
cana-2912	159	8	including	include	VERB
cana-2912	159	9	calm	calm	ADJ
cana-2912	159	10	conditions	condition	NOUN
cana-2912	159	11	(	(	PUNCT
cana-2912	159	12	0	0	NUM
cana-2912	159	13	mph	mph	NOUN
cana-2912	159	14	)	)	PUNCT
cana-2912	159	15	at	at	ADP
cana-2912	159	16	the	the	DET
cana-2912	159	17	start	start	NOUN
cana-2912	159	18	and	and	CCONJ
cana-2912	159	19	end	end	NOUN
cana-2912	159	20	of	of	ADP
cana-2912	159	21	the	the	DET
cana-2912	159	22	month	month	NOUN
cana-2912	159	23	.	.	PUNCT
cana-2912	160	1	wind	wind	NOUN
cana-2912	160	2	intensity	intensity	NOUN
cana-2912	160	3	increased	increase	VERB
cana-2912	160	4	gradually	gradually	ADV
cana-2912	160	5	during	during	ADP
cana-2912	160	6	the	the	DET
cana-2912	160	7	month	month	NOUN
cana-2912	160	8	,	,	PUNCT
cana-2912	160	9	with	with	ADP
cana-2912	160	10	sporadic	sporadic	ADJ
cana-2912	160	11	peaks	peak	NOUN
cana-2912	160	12	,	,	PUNCT
cana-2912	160	13	such	such	ADJ
cana-2912	160	14	as	as	ADP
cana-2912	160	15	the	the	DET
cana-2912	160	16	16	16	NUM
cana-2912	160	17	mph	mph	NOUN
cana-2912	160	18	recorded	record	VERB
cana-2912	160	19	on	on	ADP
cana-2912	160	20	the	the	DET
cana-2912	160	21	8th	8th	NOUN
cana-2912	160	22	.	.	PUNCT
cana-2912	161	1	towards	towards	ADP
cana-2912	161	2	the	the	DET
cana-2912	161	3	latter	latter	ADJ
cana-2912	161	4	part	part	NOUN
cana-2912	161	5	of	of	ADP
cana-2912	161	6	september	september	PROPN
cana-2912	161	7	,	,	PUNCT
cana-2912	161	8	wind	wind	NOUN
cana-2912	161	9	speeds	speed	NOUN
cana-2912	161	10	diminished	diminish	VERB
cana-2912	161	11	,	,	PUNCT
cana-2912	161	12	with	with	ADP
cana-2912	161	13	several	several	ADJ
cana-2912	161	14	days	day	NOUN
cana-2912	161	15	experiencing	experience	VERB
cana-2912	161	16	little	little	ADJ
cana-2912	161	17	to	to	ADP
cana-2912	161	18	no	no	DET
cana-2912	161	19	wind	wind	NOUN
cana-2912	161	20	,	,	PUNCT
cana-2912	161	21	reflecting	reflect	VERB
cana-2912	161	22	calmer	calm	ADJ
cana-2912	161	23	conditions	condition	NOUN
cana-2912	161	24	.	.	PUNCT
cana-2912	162	1	these	these	DET
cana-2912	162	2	wind	wind	NOUN
cana-2912	162	3	speed	speed	NOUN
cana-2912	162	4	variations	variation	NOUN
cana-2912	162	5	indicate	indicate	VERB
cana-2912	162	6	shifts	shift	NOUN
cana-2912	162	7	in	in	ADP
cana-2912	162	8	atmospheric	atmospheric	ADJ
cana-2912	162	9	dynamics	dynamic	NOUN
cana-2912	162	10	typical	typical	ADJ
cana-2912	162	11	of	of	ADP
cana-2912	162	12	the	the	DET
cana-2912	162	13	late	late	ADJ
cana-2912	162	14	summer	summer	NOUN
cana-2912	162	15	to	to	ADP
cana-2912	162	16	early	early	ADJ
cana-2912	162	17	autumn	autumn	NOUN
cana-2912	162	18	transition	transition	NOUN
cana-2912	162	19	,	,	PUNCT
cana-2912	162	20	driven	drive	VERB
cana-2912	162	21	by	by	ADP
cana-2912	162	22	changes	change	NOUN
cana-2912	162	23	in	in	ADP
cana-2912	162	24	air	air	NOUN
cana-2912	162	25	pressure	pressure	NOUN
cana-2912	162	26	and	and	CCONJ
cana-2912	162	27	prevailing	prevail	VERB
cana-2912	162	28	weather	weather	NOUN
cana-2912	162	29	systems	system	NOUN
cana-2912	162	30	.	.	PUNCT
cana-2912	163	1	3.3	3.3	NUM
cana-2912	163	2	working	working	NOUN
cana-2912	163	3	methodology	methodology	NOUN
cana-2912	163	4	the	the	DET
cana-2912	163	5	system	system	NOUN
cana-2912	163	6	can	can	AUX
cana-2912	163	7	be	be	AUX
cana-2912	163	8	divided	divide	VERB
cana-2912	163	9	into	into	ADP
cana-2912	163	10	three	three	NUM
cana-2912	163	11	parts	part	NOUN
cana-2912	163	12	as	as	SCONJ
cana-2912	163	13	given	give	VERB
cana-2912	163	14	below	below	ADP
cana-2912	163	15	:	:	PUNCT
cana-2912	163	16	3.3.1	3.3.1	NUM
cana-2912	163	17	the	the	DET
cana-2912	163	18	prediction	prediction	NOUN
cana-2912	163	19	model	model	NOUN
cana-2912	163	20	the	the	DET
cana-2912	163	21	prediction	prediction	NOUN
cana-2912	163	22	model	model	NOUN
cana-2912	163	23	forecasts	forecast	VERB
cana-2912	163	24	the	the	DET
cana-2912	163	25	temperature	temperature	NOUN
cana-2912	163	26	for	for	ADP
cana-2912	163	27	the	the	DET
cana-2912	163	28	next	next	ADJ
cana-2912	163	29	five	five	NUM
cana-2912	163	30	days	day	NOUN
cana-2912	163	31	.	.	PUNCT
cana-2912	164	1	similar	similar	ADJ
cana-2912	164	2	to	to	ADP
cana-2912	164	3	other	other	ADJ
cana-2912	164	4	machine	machine	NOUN
cana-2912	164	5	learning	learning	NOUN
cana-2912	164	6	models	model	NOUN
cana-2912	164	7	,	,	PUNCT
cana-2912	164	8	the	the	DET
cana-2912	164	9	process	process	NOUN
cana-2912	164	10	begins	begin	VERB
cana-2912	164	11	with	with	ADP
cana-2912	164	12	dataset	dataset	ADJ
cana-2912	164	13	selection	selection	NOUN
cana-2912	164	14	.	.	PUNCT
cana-2912	165	1	the	the	DET
cana-2912	165	2	datasets	dataset	NOUN
cana-2912	165	3	,	,	PUNCT
cana-2912	165	4	sourced	source	VERB
cana-2912	165	5	from	from	ADP
cana-2912	165	6	kaggle	kaggle	PROPN
cana-2912	165	7	,	,	PUNCT
cana-2912	165	8	cover	cover	VERB
cana-2912	165	9	various	various	ADJ
cana-2912	165	10	locations	location	NOUN
cana-2912	165	11	such	such	ADJ
cana-2912	165	12	as	as	ADP
cana-2912	165	13	jaipur	jaipur	NOUN
cana-2912	165	14	,	,	PUNCT
cana-2912	165	15	mumbai	mumbai	PROPN
cana-2912	165	16	,	,	PUNCT
cana-2912	165	17	pune	pune	NOUN
cana-2912	165	18	,	,	PUNCT
cana-2912	165	19	bengaluru	bengaluru	PROPN
cana-2912	165	20	,	,	PUNCT
cana-2912	165	21	hyderabad	hyderabad	PROPN
cana-2912	165	22	,	,	PUNCT
cana-2912	165	23	and	and	CCONJ
cana-2912	165	24	others	other	NOUN
cana-2912	165	25	.	.	PUNCT
cana-2912	166	1	python	python	NOUN
cana-2912	166	2	libraries	library	NOUN
cana-2912	166	3	such	such	ADJ
cana-2912	166	4	as	as	ADP
cana-2912	166	5	matplotlib	matplotlib	PROPN
cana-2912	166	6	,	,	PUNCT
cana-2912	166	7	pandas	panda	NOUN
cana-2912	166	8	,	,	PUNCT
cana-2912	166	9	and	and	CCONJ
cana-2912	166	10	numpy	numpy	NOUN
cana-2912	166	11	are	be	AUX
cana-2912	166	12	utilized	utilize	VERB
cana-2912	166	13	for	for	ADP
cana-2912	166	14	data	data	NOUN
cana-2912	166	15	manipulation	manipulation	NOUN
cana-2912	166	16	and	and	CCONJ
cana-2912	166	17	visualization	visualization	NOUN
cana-2912	166	18	.	.	PUNCT
cana-2912	167	1	a	a	DET
cana-2912	167	2	pre	pre	ADJ
cana-2912	167	3	-	-	ADJ
cana-2912	167	4	processed	processed	ADJ
cana-2912	167	5	dataset	dataset	NOUN
cana-2912	167	6	is	be	AUX
cana-2912	167	7	employed	employ	VERB
cana-2912	167	8	in	in	ADP
cana-2912	167	9	conjunction	conjunction	NOUN
cana-2912	167	10	with	with	ADP
cana-2912	167	11	the	the	DET
cana-2912	167	12	scikit	scikit	NOUN
cana-2912	167	13	-	-	PUNCT
cana-2912	167	14	learn	learn	VERB
cana-2912	167	15	library	library	NOUN
cana-2912	167	16	to	to	PART
cana-2912	167	17	develop	develop	VERB
cana-2912	167	18	the	the	DET
cana-2912	167	19	prediction	prediction	NOUN
cana-2912	167	20	model	model	NOUN
cana-2912	167	21	.	.	PUNCT
cana-2912	168	1	the	the	DET
cana-2912	168	2	model	model	NOUN
cana-2912	168	3	is	be	AUX
cana-2912	168	4	designed	design	VERB
cana-2912	168	5	to	to	PART
cana-2912	168	6	handle	handle	VERB
cana-2912	168	7	independent	independent	ADJ
cana-2912	168	8	values	value	NOUN
cana-2912	168	9	and	and	CCONJ
cana-2912	168	10	predict	predict	VERB
cana-2912	168	11	the	the	DET
cana-2912	168	12	target	target	NOUN
cana-2912	168	13	variable	variable	NOUN
cana-2912	168	14	,	,	PUNCT
cana-2912	168	15	temperature	temperature	NOUN
cana-2912	168	16	.	.	PUNCT
cana-2912	169	1	linear	linear	ADJ
cana-2912	169	2	regression	regression	NOUN
cana-2912	169	3	is	be	AUX
cana-2912	169	4	used	use	VERB
cana-2912	169	5	to	to	PART
cana-2912	169	6	fit	fit	VERB
cana-2912	169	7	the	the	DET
cana-2912	169	8	independent	independent	ADJ
cana-2912	169	9	variables	variable	NOUN
cana-2912	169	10	,	,	PUNCT
cana-2912	169	11	which	which	PRON
cana-2912	169	12	include	include	VERB
cana-2912	169	13	factors	factor	NOUN
cana-2912	169	14	such	such	ADJ
cana-2912	169	15	as	as	ADP
cana-2912	169	16	date	date	NOUN
cana-2912	169	17	/	/	SYM
cana-2912	169	18	time	time	NOUN
cana-2912	169	19	,	,	PUNCT
cana-2912	169	20	maximum	maximum	ADJ
cana-2912	169	21	temperature	temperature	NOUN
cana-2912	169	22	,	,	PUNCT
cana-2912	169	23	minimum	minimum	ADJ
cana-2912	169	24	temperature	temperature	NOUN
cana-2912	169	25	,	,	PUNCT
cana-2912	169	26	solar	solar	ADJ
cana-2912	169	27	hours	hour	NOUN
cana-2912	169	28	,	,	PUNCT
cana-2912	169	29	uv	uv	NOUN
cana-2912	169	30	index	index	NOUN
cana-2912	169	31	,	,	PUNCT
cana-2912	169	32	sunrise	sunrise	NOUN
cana-2912	169	33	and	and	CCONJ
cana-2912	169	34	sunset	sunset	NOUN
cana-2912	169	35	times	time	NOUN
cana-2912	169	36	,	,	PUNCT
cana-2912	169	37	pressure	pressure	NOUN
cana-2912	169	38	,	,	PUNCT
cana-2912	169	39	humidity	humidity	NOUN
cana-2912	169	40	,	,	PUNCT
cana-2912	169	41	heat	heat	NOUN
cana-2912	169	42	index	index	NOUN
cana-2912	169	43	,	,	PUNCT
cana-2912	169	44	and	and	CCONJ
cana-2912	169	45	wind	wind	NOUN
cana-2912	169	46	-	-	PUNCT
cana-2912	169	47	chill	chill	NOUN
cana-2912	169	48	.	.	PUNCT
cana-2912	170	1	the	the	DET
cana-2912	170	2	dataset	dataset	NOUN
cana-2912	170	3	typically	typically	ADV
cana-2912	170	4	consists	consist	VERB
cana-2912	170	5	of	of	ADP
cana-2912	170	6	25	25	NUM
cana-2912	170	7	columns	column	NOUN
cana-2912	170	8	and	and	CCONJ
cana-2912	170	9	approximately	approximately	ADV
cana-2912	170	10	96,000	96,000	NUM
cana-2912	170	11	entries	entry	NOUN
cana-2912	170	12	,	,	PUNCT
cana-2912	170	13	spanning	span	VERB
cana-2912	170	14	hourly	hourly	ADJ
cana-2912	170	15	temperature	temperature	NOUN
cana-2912	170	16	records	record	NOUN
cana-2912	170	17	from	from	ADP
cana-2912	170	18	2009	2009	NUM
cana-2912	170	19	to	to	ADP
cana-2912	170	20	2023	2023	NUM
cana-2912	170	21	.	.	PUNCT
cana-2912	171	1	a	a	DET
cana-2912	171	2	data	data	NOUN
cana-2912	171	3	visualization	visualization	NOUN
cana-2912	171	4	tool	tool	NOUN
cana-2912	171	5	is	be	AUX
cana-2912	171	6	used	use	VERB
cana-2912	171	7	to	to	PART
cana-2912	171	8	show	show	VERB
cana-2912	171	9	that	that	SCONJ
cana-2912	171	10	measurements	measurement	NOUN
cana-2912	171	11	are	be	AUX
cana-2912	171	12	taken	take	VERB
cana-2912	171	13	across	across	ADP
cana-2912	171	14	a	a	DET
cana-2912	171	15	range	range	NOUN
cana-2912	171	16	of	of	ADP
cana-2912	171	17	latitude	latitude	NOUN
cana-2912	171	18	and	and	CCONJ
cana-2912	171	19	longitude	longitude	ADJ
cana-2912	171	20	values	value	NOUN
cana-2912	171	21	for	for	ADP
cana-2912	171	22	each	each	DET
cana-2912	171	23	date	date	NOUN
cana-2912	171	24	.	.	PUNCT
cana-2912	172	1	the	the	DET
cana-2912	172	2	model	model	NOUN
cana-2912	172	3	's	's	PART
cana-2912	172	4	performance	performance	NOUN
cana-2912	172	5	is	be	AUX
cana-2912	172	6	evaluated	evaluate	VERB
cana-2912	172	7	using	use	VERB
cana-2912	172	8	metrics	metric	NOUN
cana-2912	172	9	such	such	ADJ
cana-2912	172	10	as	as	ADP
cana-2912	172	11	mean	mean	NOUN
cana-2912	172	12	absolute	absolute	ADJ
cana-2912	172	13	error	error	NOUN
cana-2912	172	14	(	(	PUNCT
cana-2912	172	15	mae	mae	PROPN
cana-2912	172	16	)	)	PUNCT
cana-2912	172	17	and	and	CCONJ
cana-2912	172	18	mean	mean	VERB
cana-2912	172	19	squared	square	VERB
cana-2912	172	20	error	error	NOUN
cana-2912	172	21	(	(	PUNCT
cana-2912	172	22	mse	mse	NOUN
cana-2912	172	23	)	)	PUNCT
cana-2912	172	24	from	from	ADP
cana-2912	172	25	the	the	DET
cana-2912	172	26	scikit	scikit	NOUN
cana-2912	172	27	-	-	PUNCT
cana-2912	172	28	learn	learn	NOUN
cana-2912	172	29	library	library	NOUN
cana-2912	172	30	.	.	PUNCT
cana-2912	173	1	the	the	DET
cana-2912	173	2	following	follow	VERB
cana-2912	173	3	methodology	methodology	NOUN
cana-2912	173	4	outlines	outline	VERB
cana-2912	173	5	the	the	DET
cana-2912	173	6	steps	step	NOUN
cana-2912	173	7	for	for	ADP
cana-2912	173	8	weather	weather	NOUN
cana-2912	173	9	prediction	prediction	NOUN
cana-2912	173	10	using	use	VERB
cana-2912	173	11	machine	machine	NOUN
cana-2912	173	12	learning	learning	NOUN
cana-2912	173	13	models	model	NOUN
cana-2912	173	14	:	:	PUNCT
cana-2912	173	15	1	1	X
cana-2912	173	16	.	.	PUNCT
cana-2912	173	17	data	datum	NOUN
cana-2912	173	18	collection	collection	NOUN
cana-2912	173	19	:	:	PUNCT
cana-2912	173	20	the	the	DET
cana-2912	173	21	required	require	VERB
cana-2912	173	22	dataset	dataset	NOUN
cana-2912	173	23	is	be	AUX
cana-2912	173	24	gathered	gather	VERB
cana-2912	173	25	,	,	PUNCT
cana-2912	173	26	containing	contain	VERB
cana-2912	173	27	parameters	parameter	NOUN
cana-2912	173	28	such	such	ADJ
cana-2912	173	29	as	as	ADP
cana-2912	173	30	minimum	minimum	ADJ
cana-2912	173	31	and	and	CCONJ
cana-2912	173	32	maximum	maximum	ADJ
cana-2912	173	33	temperature	temperature	NOUN
cana-2912	173	34	,	,	PUNCT
cana-2912	173	35	sun	sun	NOUN
cana-2912	173	36	hours	hour	NOUN
cana-2912	173	37	,	,	PUNCT
cana-2912	173	38	actual	actual	ADJ
cana-2912	173	39	temperature	temperature	NOUN
cana-2912	173	40	,	,	PUNCT
cana-2912	173	41	cloud	cloud	NOUN
cana-2912	173	42	cover	cover	NOUN
cana-2912	173	43	,	,	PUNCT
cana-2912	173	44	wind	wind	NOUN
cana-2912	173	45	speed	speed	NOUN
cana-2912	173	46	,	,	PUNCT
cana-2912	173	47	heat	heat	NOUN
cana-2912	173	48	index	index	NOUN
cana-2912	173	49	,	,	PUNCT
cana-2912	173	50	humidity	humidity	NOUN
cana-2912	173	51	,	,	PUNCT
cana-2912	173	52	precipitation	precipitation	NOUN
cana-2912	173	53	,	,	PUNCT
cana-2912	173	54	wind	wind	NOUN
cana-2912	173	55	direction	direction	NOUN
cana-2912	173	56	,	,	PUNCT
cana-2912	173	57	sunrise	sunrise	NOUN
cana-2912	173	58	,	,	PUNCT
cana-2912	173	59	sunset	sunset	NOUN
cana-2912	173	60	,	,	PUNCT
cana-2912	173	61	and	and	CCONJ
cana-2912	173	62	uv	uv	NOUN
cana-2912	173	63	index	index	NOUN
cana-2912	173	64	.	.	PUNCT
cana-2912	174	1	data	datum	NOUN
cana-2912	174	2	is	be	AUX
cana-2912	174	3	collected	collect	VERB
cana-2912	174	4	from	from	ADP
cana-2912	174	5	january	january	PROPN
cana-2912	174	6	2009	2009	NUM
cana-2912	174	7	to	to	ADP
cana-2912	174	8	december	december	PROPN
cana-2912	174	9	2023	2023	NUM
cana-2912	174	10	on	on	ADP
cana-2912	174	11	an	an	DET
cana-2912	174	12	hourly	hourly	ADJ
cana-2912	174	13	basis	basis	NOUN
cana-2912	174	14	.	.	PUNCT
cana-2912	175	1	2	2	X
cana-2912	175	2	.	.	X
cana-2912	175	3	data	datum	NOUN
cana-2912	175	4	preprocessing	preprocessing	NOUN
cana-2912	175	5	:	:	PUNCT
cana-2912	175	6	the	the	DET
cana-2912	175	7	dataset	dataset	NOUN
cana-2912	175	8	undergoes	undergoe	NOUN
cana-2912	175	9	preprocessing	preprocesse	VERB
cana-2912	175	10	to	to	PART
cana-2912	175	11	ensure	ensure	VERB
cana-2912	175	12	it	it	PRON
cana-2912	175	13	is	be	AUX
cana-2912	175	14	clean	clean	ADJ
cana-2912	175	15	and	and	CCONJ
cana-2912	175	16	ready	ready	ADJ
cana-2912	175	17	for	for	ADP
cana-2912	175	18	analysis	analysis	NOUN
cana-2912	175	19	.	.	PUNCT
cana-2912	176	1	this	this	DET
cana-2912	176	2	step	step	NOUN
cana-2912	176	3	involves	involve	VERB
cana-2912	176	4	checking	check	VERB
cana-2912	176	5	for	for	ADP
cana-2912	176	6	missing	miss	VERB
cana-2912	176	7	values	value	NOUN
cana-2912	176	8	,	,	PUNCT
cana-2912	176	9	removing	remove	VERB
cana-2912	176	10	outliers	outlier	NOUN
cana-2912	176	11	,	,	PUNCT
cana-2912	176	12	and	and	CCONJ
cana-2912	176	13	normalizing	normalize	VERB
cana-2912	176	14	the	the	DET
cana-2912	176	15	data	datum	NOUN
cana-2912	176	16	as	as	SCONJ
cana-2912	176	17	needed	need	VERB
cana-2912	176	18	.	.	PUNCT
cana-2912	177	1	3	3	X
cana-2912	177	2	.	.	X
cana-2912	177	3	feature	feature	NOUN
cana-2912	177	4	selection	selection	NOUN
cana-2912	177	5	:	:	PUNCT
cana-2912	177	6	relevant	relevant	ADJ
cana-2912	177	7	features	feature	NOUN
cana-2912	177	8	are	be	AUX
cana-2912	177	9	selected	select	VERB
cana-2912	177	10	from	from	ADP
cana-2912	177	11	the	the	DET
cana-2912	177	12	pre	pre	ADJ
cana-2912	177	13	-	-	ADJ
cana-2912	177	14	processed	processed	ADJ
cana-2912	177	15	dataset	dataset	NOUN
cana-2912	177	16	.	.	PUNCT
cana-2912	178	1	feature	feature	NOUN
cana-2912	178	2	selection	selection	NOUN
cana-2912	178	3	is	be	AUX
cana-2912	178	4	performed	perform	VERB
cana-2912	178	5	using	use	VERB
cana-2912	178	6	techniques	technique	NOUN
cana-2912	178	7	such	such	ADJ
cana-2912	178	8	as	as	ADP
cana-2912	178	9	correlation	correlation	NOUN
cana-2912	178	10	analysis	analysis	NOUN
cana-2912	178	11	,	,	PUNCT
cana-2912	178	12	mutual	mutual	ADJ
cana-2912	178	13	information	information	NOUN
cana-2912	178	14	,	,	PUNCT
cana-2912	178	15	and	and	CCONJ
cana-2912	178	16	recursive	recursive	ADJ
cana-2912	178	17	feature	feature	NOUN
cana-2912	178	18	elimination	elimination	NOUN
cana-2912	178	19	to	to	PART
cana-2912	178	20	improve	improve	VERB
cana-2912	178	21	model	model	NOUN
cana-2912	178	22	accuracy	accuracy	NOUN
cana-2912	178	23	.	.	PUNCT
cana-2912	179	1	communications	communication	NOUN
cana-2912	179	2	on	on	ADP
cana-2912	179	3	applied	apply	VERB
cana-2912	179	4	nonlinear	nonlinear	ADJ
cana-2912	179	5	analysis	analysis	NOUN
cana-2912	179	6	issn	issn	NOUN
cana-2912	179	7	:	:	PUNCT
cana-2912	179	8	1074	1074	NUM
cana-2912	179	9	-	-	PUNCT
cana-2912	179	10	133x	133x	NUM
cana-2912	179	11	vol	vol	NOUN
cana-2912	179	12	32	32	NUM
cana-2912	179	13	no	no	NOUN
cana-2912	179	14	.	.	PUNCT
cana-2912	180	1	5s	5s	NUM
cana-2912	180	2	(	(	PUNCT
cana-2912	180	3	2025	2025	NUM
cana-2912	180	4	)	)	PUNCT
cana-2912	180	5	10	10	NUM
cana-2912	180	6	4	4	NUM
cana-2912	180	7	.	.	PUNCT
cana-2912	180	8	model	model	NOUN
cana-2912	180	9	training	training	NOUN
cana-2912	180	10	:	:	PUNCT
cana-2912	180	11	various	various	ADJ
cana-2912	180	12	machine	machine	NOUN
cana-2912	180	13	learning	learning	NOUN
cana-2912	180	14	models	model	NOUN
cana-2912	180	15	,	,	PUNCT
cana-2912	180	16	including	include	VERB
cana-2912	180	17	decision	decision	NOUN
cana-2912	180	18	trees	tree	NOUN
cana-2912	180	19	,	,	PUNCT
cana-2912	180	20	random	random	ADJ
cana-2912	180	21	forests	forest	NOUN
cana-2912	180	22	,	,	PUNCT
cana-2912	180	23	linear	linear	ADJ
cana-2912	180	24	regression	regression	NOUN
cana-2912	180	25	,	,	PUNCT
cana-2912	180	26	and	and	CCONJ
cana-2912	180	27	artificial	artificial	ADJ
cana-2912	180	28	neural	neural	ADJ
cana-2912	180	29	networks	network	NOUN
cana-2912	180	30	,	,	PUNCT
cana-2912	180	31	are	be	AUX
cana-2912	180	32	trained	train	VERB
cana-2912	180	33	on	on	ADP
cana-2912	180	34	the	the	DET
cana-2912	180	35	pre	pre	ADJ
cana-2912	180	36	-	-	ADJ
cana-2912	180	37	processed	processed	ADJ
cana-2912	180	38	dataset	dataset	NOUN
cana-2912	180	39	.	.	PUNCT
cana-2912	181	1	hyperparameter	hyperparameter	NOUN
cana-2912	181	2	tuning	tuning	NOUN
cana-2912	181	3	is	be	AUX
cana-2912	181	4	performed	perform	VERB
cana-2912	181	5	to	to	PART
cana-2912	181	6	identify	identify	VERB
cana-2912	181	7	the	the	DET
cana-2912	181	8	optimal	optimal	ADJ
cana-2912	181	9	model	model	NOUN
cana-2912	181	10	for	for	ADP
cana-2912	181	11	weather	weather	NOUN
cana-2912	181	12	forecasting	forecasting	NOUN
cana-2912	181	13	.	.	PUNCT
cana-2912	182	1	5	5	X
cana-2912	182	2	.	.	PUNCT
cana-2912	182	3	model	model	NOUN
cana-2912	182	4	evaluation	evaluation	NOUN
cana-2912	182	5	:	:	PUNCT
cana-2912	182	6	the	the	DET
cana-2912	182	7	trained	train	VERB
cana-2912	182	8	models	model	NOUN
cana-2912	182	9	are	be	AUX
cana-2912	182	10	evaluated	evaluate	VERB
cana-2912	182	11	using	use	VERB
cana-2912	182	12	performance	performance	NOUN
cana-2912	182	13	metrics	metric	NOUN
cana-2912	182	14	such	such	ADJ
cana-2912	182	15	as	as	ADP
cana-2912	182	16	mae	mae	PROPN
cana-2912	182	17	,	,	PUNCT
cana-2912	182	18	mse	mse	PROPN
cana-2912	182	19	,	,	PUNCT
cana-2912	182	20	and	and	CCONJ
cana-2912	182	21	the	the	DET
cana-2912	182	22	coefficient	coefficient	NOUN
cana-2912	182	23	of	of	ADP
cana-2912	182	24	determination	determination	NOUN
cana-2912	182	25	(	(	PUNCT
cana-2912	182	26	r²	r²	NOUN
cana-2912	182	27	)	)	PUNCT
cana-2912	182	28	.	.	PUNCT
cana-2912	183	1	the	the	DET
cana-2912	183	2	models	model	NOUN
cana-2912	183	3	are	be	AUX
cana-2912	183	4	compared	compare	VERB
cana-2912	183	5	to	to	PART
cana-2912	183	6	determine	determine	VERB
cana-2912	183	7	the	the	DET
cana-2912	183	8	best	good	ADJ
cana-2912	183	9	model	model	NOUN
cana-2912	183	10	for	for	ADP
cana-2912	183	11	accurate	accurate	ADJ
cana-2912	183	12	weather	weather	NOUN
cana-2912	183	13	prediction	prediction	NOUN
cana-2912	183	14	.	.	PUNCT
cana-2912	184	1	6	6	NUM
cana-2912	184	2	.	.	PUNCT
cana-2912	184	3	model	model	NOUN
cana-2912	184	4	deployment	deployment	NOUN
cana-2912	184	5	:	:	PUNCT
cana-2912	184	6	once	once	SCONJ
cana-2912	184	7	the	the	DET
cana-2912	184	8	best	good	ADJ
cana-2912	184	9	model	model	NOUN
cana-2912	184	10	is	be	AUX
cana-2912	184	11	identified	identify	VERB
cana-2912	184	12	,	,	PUNCT
cana-2912	184	13	it	it	PRON
cana-2912	184	14	is	be	AUX
cana-2912	184	15	deployed	deploy	VERB
cana-2912	184	16	for	for	ADP
cana-2912	184	17	making	make	VERB
cana-2912	184	18	predictions	prediction	NOUN
cana-2912	184	19	on	on	ADP
cana-2912	184	20	future	future	ADJ
cana-2912	184	21	weather	weather	NOUN
cana-2912	184	22	conditions	condition	NOUN
cana-2912	184	23	.	.	PUNCT
cana-2912	185	1	the	the	DET
cana-2912	185	2	model	model	NOUN
cana-2912	185	3	is	be	AUX
cana-2912	185	4	integrated	integrate	VERB
cana-2912	185	5	with	with	ADP
cana-2912	185	6	a	a	DET
cana-2912	185	7	user	user	NOUN
cana-2912	185	8	-	-	PUNCT
cana-2912	185	9	friendly	friendly	ADJ
cana-2912	185	10	interface	interface	NOUN
cana-2912	185	11	for	for	ADP
cana-2912	185	12	easy	easy	ADJ
cana-2912	185	13	access	access	NOUN
cana-2912	185	14	to	to	ADP
cana-2912	185	15	predictions	prediction	NOUN
cana-2912	185	16	.	.	PUNCT
cana-2912	186	1	7	7	X
cana-2912	186	2	.	.	NOUN
cana-2912	186	3	results	result	VERB
cana-2912	186	4	analysis	analysis	NOUN
cana-2912	186	5	:	:	PUNCT
cana-2912	186	6	the	the	DET
cana-2912	186	7	deployed	deploy	VERB
cana-2912	186	8	model	model	NOUN
cana-2912	186	9	's	's	PART
cana-2912	186	10	performance	performance	NOUN
cana-2912	186	11	is	be	AUX
cana-2912	186	12	analysed	analyse	VERB
cana-2912	186	13	and	and	CCONJ
cana-2912	186	14	compared	compare	VERB
cana-2912	186	15	with	with	ADP
cana-2912	186	16	other	other	ADJ
cana-2912	186	17	existing	exist	VERB
cana-2912	186	18	models	model	NOUN
cana-2912	186	19	.	.	PUNCT
cana-2912	187	1	the	the	DET
cana-2912	187	2	evaluation	evaluation	NOUN
cana-2912	187	3	is	be	AUX
cana-2912	187	4	based	base	VERB
cana-2912	187	5	on	on	ADP
cana-2912	187	6	accuracy	accuracy	NOUN
cana-2912	187	7	,	,	PUNCT
cana-2912	187	8	reliability	reliability	NOUN
cana-2912	187	9	,	,	PUNCT
cana-2912	187	10	and	and	CCONJ
cana-2912	187	11	usefulness	usefulness	NOUN
cana-2912	187	12	in	in	ADP
cana-2912	187	13	predicting	predict	VERB
cana-2912	187	14	weather	weather	NOUN
cana-2912	187	15	conditions	condition	NOUN
cana-2912	187	16	.	.	PUNCT
cana-2912	188	1	this	this	DET
cana-2912	188	2	structured	structured	ADJ
cana-2912	188	3	methodology	methodology	NOUN
cana-2912	188	4	ensures	ensure	VERB
cana-2912	188	5	the	the	DET
cana-2912	188	6	development	development	NOUN
cana-2912	188	7	of	of	ADP
cana-2912	188	8	a	a	DET
cana-2912	188	9	robust	robust	ADJ
cana-2912	188	10	and	and	CCONJ
cana-2912	188	11	accurate	accurate	ADJ
cana-2912	188	12	machine	machine	NOUN
cana-2912	188	13	learning	learning	NOUN
cana-2912	188	14	model	model	NOUN
cana-2912	188	15	for	for	ADP
cana-2912	188	16	weather	weather	NOUN
cana-2912	188	17	prediction	prediction	NOUN
cana-2912	188	18	.	.	PUNCT
cana-2912	189	1	3.3.2	3.3.2	NUM
cana-2912	189	2	the	the	DET
cana-2912	189	3	backend	backend	NOUN
cana-2912	189	4	the	the	DET
cana-2912	189	5	backend	backend	NOUN
cana-2912	189	6	of	of	ADP
cana-2912	189	7	the	the	DET
cana-2912	189	8	system	system	NOUN
cana-2912	189	9	is	be	AUX
cana-2912	189	10	implemented	implement	VERB
cana-2912	189	11	using	use	VERB
cana-2912	189	12	the	the	DET
cana-2912	189	13	python	python	NOUN
cana-2912	189	14	flask	flask	NOUN
cana-2912	189	15	module	module	NOUN
cana-2912	189	16	to	to	PART
cana-2912	189	17	create	create	VERB
cana-2912	189	18	a	a	DET
cana-2912	189	19	web	web	NOUN
cana-2912	189	20	framework	framework	NOUN
cana-2912	189	21	for	for	ADP
cana-2912	189	22	weather	weather	NOUN
cana-2912	189	23	prediction	prediction	NOUN
cana-2912	189	24	.	.	PUNCT
cana-2912	190	1	a	a	DET
cana-2912	190	2	local	local	ADJ
cana-2912	190	3	host	host	NOUN
cana-2912	190	4	application	application	NOUN
cana-2912	190	5	is	be	AUX
cana-2912	190	6	developed	develop	VERB
cana-2912	190	7	to	to	PART
cana-2912	190	8	take	take	VERB
cana-2912	190	9	user	user	NOUN
cana-2912	190	10	input	input	NOUN
cana-2912	190	11	,	,	PUNCT
cana-2912	190	12	including	include	VERB
cana-2912	190	13	the	the	DET
cana-2912	190	14	city	city	NOUN
cana-2912	190	15	for	for	ADP
cana-2912	190	16	which	which	PRON
cana-2912	190	17	weather	weather	NOUN
cana-2912	190	18	predictions	prediction	NOUN
cana-2912	190	19	are	be	AUX
cana-2912	190	20	required	require	VERB
cana-2912	190	21	.	.	PUNCT
cana-2912	191	1	the	the	DET
cana-2912	191	2	corresponding	correspond	VERB
cana-2912	191	3	latitude	latitude	NOUN
cana-2912	191	4	and	and	CCONJ
cana-2912	191	5	longitude	longitude	ADJ
cana-2912	191	6	values	value	NOUN
cana-2912	191	7	for	for	ADP
cana-2912	191	8	the	the	DET
cana-2912	191	9	selected	select	VERB
cana-2912	191	10	city	city	NOUN
cana-2912	191	11	are	be	AUX
cana-2912	191	12	retrieved	retrieve	VERB
cana-2912	191	13	.	.	PUNCT
cana-2912	192	1	once	once	SCONJ
cana-2912	192	2	the	the	DET
cana-2912	192	3	form	form	NOUN
cana-2912	192	4	is	be	AUX
cana-2912	192	5	submitted	submit	VERB
cana-2912	192	6	,	,	PUNCT
cana-2912	192	7	the	the	DET
cana-2912	192	8	model	model	NOUN
cana-2912	192	9	receives	receive	VERB
cana-2912	192	10	the	the	DET
cana-2912	192	11	city	city	NOUN
cana-2912	192	12	-	-	PUNCT
cana-2912	192	13	specific	specific	ADJ
cana-2912	192	14	data	datum	NOUN
cana-2912	192	15	and	and	CCONJ
cana-2912	192	16	predicts	predict	VERB
cana-2912	192	17	the	the	DET
cana-2912	192	18	temperature	temperature	NOUN
cana-2912	192	19	for	for	ADP
cana-2912	192	20	the	the	DET
cana-2912	192	21	next	next	ADJ
cana-2912	192	22	five	five	NUM
cana-2912	192	23	days	day	NOUN
cana-2912	192	24	.	.	PUNCT
cana-2912	193	1	the	the	DET
cana-2912	193	2	predicted	predict	VERB
cana-2912	193	3	maximum	maximum	ADJ
cana-2912	193	4	temperature	temperature	NOUN
cana-2912	193	5	,	,	PUNCT
cana-2912	193	6	minimum	minimum	ADJ
cana-2912	193	7	temperature	temperature	NOUN
cana-2912	193	8	,	,	PUNCT
cana-2912	193	9	and	and	CCONJ
cana-2912	193	10	rainfall	rainfall	NOUN
cana-2912	193	11	quantity	quantity	NOUN
cana-2912	193	12	are	be	AUX
cana-2912	193	13	then	then	ADV
cana-2912	193	14	passed	pass	VERB
cana-2912	193	15	to	to	ADP
cana-2912	193	16	the	the	DET
cana-2912	193	17	frontend	frontend	NOUN
cana-2912	193	18	for	for	ADP
cana-2912	193	19	display	display	NOUN
cana-2912	193	20	.	.	PUNCT
cana-2912	194	1	the	the	DET
cana-2912	194	2	data	datum	NOUN
cana-2912	194	3	is	be	AUX
cana-2912	194	4	rendered	render	VERB
cana-2912	194	5	on	on	ADP
cana-2912	194	6	the	the	DET
cana-2912	194	7	webpage	webpage	NOUN
cana-2912	194	8	using	use	VERB
cana-2912	194	9	jinja	jinja	ADJ
cana-2912	194	10	templating	templating	NOUN
cana-2912	194	11	.	.	PUNCT
cana-2912	195	1	3.3.3	3.3.3	PUNCT
cana-2912	195	2	the	the	DET
cana-2912	195	3	frontend	frontend	NOUN
cana-2912	195	4	the	the	DET
cana-2912	195	5	frontend	frontend	NOUN
cana-2912	195	6	is	be	AUX
cana-2912	195	7	designed	design	VERB
cana-2912	195	8	using	use	VERB
cana-2912	195	9	html	html	NOUN
cana-2912	195	10	,	,	PUNCT
cana-2912	195	11	css	css	PROPN
cana-2912	195	12	,	,	PUNCT
cana-2912	195	13	and	and	CCONJ
cana-2912	195	14	jinja	jinja	PROPN
cana-2912	195	15	templating	templating	NOUN
cana-2912	195	16	.	.	PUNCT
cana-2912	196	1	the	the	DET
cana-2912	196	2	interface	interface	NOUN
cana-2912	196	3	consists	consist	VERB
cana-2912	196	4	of	of	ADP
cana-2912	196	5	a	a	DET
cana-2912	196	6	form	form	NOUN
cana-2912	196	7	with	with	ADP
cana-2912	196	8	a	a	DET
cana-2912	196	9	dropdown	dropdown	ADJ
cana-2912	196	10	menu	menu	NOUN
cana-2912	196	11	for	for	ADP
cana-2912	196	12	selecting	select	VERB
cana-2912	196	13	cities	city	NOUN
cana-2912	196	14	.	.	PUNCT
cana-2912	197	1	after	after	SCONJ
cana-2912	197	2	the	the	DET
cana-2912	197	3	user	user	NOUN
cana-2912	197	4	selects	select	VERB
cana-2912	197	5	a	a	DET
cana-2912	197	6	city	city	NOUN
cana-2912	197	7	,	,	PUNCT
cana-2912	197	8	the	the	DET
cana-2912	197	9	‘	'	PUNCT
cana-2912	197	10	predict	predict	NOUN
cana-2912	197	11	’	'	PUNCT
cana-2912	197	12	button	button	NOUN
cana-2912	197	13	triggers	trigger	NOUN
cana-2912	197	14	a	a	DET
cana-2912	197	15	post	post	NOUN
cana-2912	197	16	request	request	NOUN
cana-2912	197	17	to	to	PART
cana-2912	197	18	activate	activate	VERB
cana-2912	197	19	the	the	DET
cana-2912	197	20	backend	backend	NOUN
cana-2912	197	21	,	,	PUNCT
cana-2912	197	22	which	which	PRON
cana-2912	197	23	performs	perform	VERB
cana-2912	197	24	the	the	DET
cana-2912	197	25	necessary	necessary	ADJ
cana-2912	197	26	functions	function	NOUN
cana-2912	197	27	to	to	PART
cana-2912	197	28	predict	predict	VERB
cana-2912	197	29	the	the	DET
cana-2912	197	30	temperature	temperature	NOUN
cana-2912	197	31	and	and	CCONJ
cana-2912	197	32	rainfall	rainfall	NOUN
cana-2912	197	33	for	for	ADP
cana-2912	197	34	the	the	DET
cana-2912	197	35	next	next	ADJ
cana-2912	197	36	five	five	NUM
cana-2912	197	37	days	day	NOUN
cana-2912	197	38	.	.	PUNCT
cana-2912	198	1	the	the	DET
cana-2912	198	2	predictions	prediction	NOUN
cana-2912	198	3	,	,	PUNCT
cana-2912	198	4	including	include	VERB
cana-2912	198	5	the	the	DET
cana-2912	198	6	maximum	maximum	ADJ
cana-2912	198	7	and	and	CCONJ
cana-2912	198	8	minimum	minimum	ADJ
cana-2912	198	9	temperatures	temperature	NOUN
cana-2912	198	10	,	,	PUNCT
cana-2912	198	11	are	be	AUX
cana-2912	198	12	passed	pass	VERB
cana-2912	198	13	from	from	ADP
cana-2912	198	14	the	the	DET
cana-2912	198	15	backend	backend	NOUN
cana-2912	198	16	to	to	ADP
cana-2912	198	17	the	the	DET
cana-2912	198	18	frontend	frontend	NOUN
cana-2912	198	19	and	and	CCONJ
cana-2912	198	20	displayed	display	VERB
cana-2912	198	21	using	use	VERB
cana-2912	198	22	html	html	NOUN
cana-2912	198	23	cards	card	NOUN
cana-2912	198	24	.	.	PUNCT
cana-2912	199	1	these	these	DET
cana-2912	199	2	cards	card	NOUN
cana-2912	199	3	show	show	VERB
cana-2912	199	4	the	the	DET
cana-2912	199	5	weather	weather	NOUN
cana-2912	199	6	forecast	forecast	NOUN
cana-2912	199	7	for	for	ADP
cana-2912	199	8	each	each	DET
cana-2912	199	9	day	day	NOUN
cana-2912	199	10	.	.	PUNCT
cana-2912	200	1	the	the	DET
cana-2912	200	2	average	average	ADJ
cana-2912	200	3	temperature	temperature	NOUN
cana-2912	200	4	is	be	AUX
cana-2912	200	5	calculated	calculate	VERB
cana-2912	200	6	from	from	ADP
cana-2912	200	7	the	the	DET
cana-2912	200	8	maximum	maximum	ADJ
cana-2912	200	9	and	and	CCONJ
cana-2912	200	10	minimum	minimum	ADJ
cana-2912	200	11	temperatures	temperature	NOUN
cana-2912	200	12	,	,	PUNCT
cana-2912	200	13	while	while	SCONJ
cana-2912	200	14	the	the	DET
cana-2912	200	15	rainfall	rainfall	NOUN
cana-2912	200	16	prediction	prediction	NOUN
cana-2912	200	17	determines	determine	VERB
cana-2912	200	18	whether	whether	SCONJ
cana-2912	200	19	rain	rain	NOUN
cana-2912	200	20	is	be	AUX
cana-2912	200	21	expected	expect	VERB
cana-2912	200	22	.	.	PUNCT
cana-2912	201	1	if	if	SCONJ
cana-2912	201	2	rain	rain	NOUN
cana-2912	201	3	is	be	AUX
cana-2912	201	4	forecasted	forecast	VERB
cana-2912	201	5	,	,	PUNCT
cana-2912	201	6	the	the	DET
cana-2912	201	7	model	model	NOUN
cana-2912	201	8	further	far	ADV
cana-2912	201	9	classifies	classify	VERB
cana-2912	201	10	it	it	PRON
cana-2912	201	11	as	as	ADP
cana-2912	201	12	a	a	DET
cana-2912	201	13	drizzle	drizzle	NOUN
cana-2912	201	14	or	or	CCONJ
cana-2912	201	15	a	a	DET
cana-2912	201	16	thunderstorm	thunderstorm	NOUN
cana-2912	201	17	.	.	PUNCT
cana-2912	202	1	icons	icon	NOUN
cana-2912	202	2	representing	represent	VERB
cana-2912	202	3	four	four	NUM
cana-2912	202	4	weather	weather	NOUN
cana-2912	202	5	possibilities	possibility	NOUN
cana-2912	202	6	—	—	PUNCT
cana-2912	202	7	sunny	sunny	ADJ
cana-2912	202	8	,	,	PUNCT
cana-2912	202	9	cloudy	cloudy	ADJ
cana-2912	202	10	,	,	PUNCT
cana-2912	202	11	drizzle	drizzle	NOUN
cana-2912	202	12	,	,	PUNCT
cana-2912	202	13	and	and	CCONJ
cana-2912	202	14	thunderstorm	thunderstorm	NOUN
cana-2912	202	15	—	—	PUNCT
cana-2912	202	16	are	be	AUX
cana-2912	202	17	displayed	display	VERB
cana-2912	202	18	for	for	ADP
cana-2912	202	19	each	each	DET
cana-2912	202	20	prediction	prediction	NOUN
cana-2912	202	21	,	,	PUNCT
cana-2912	202	22	providing	provide	VERB
cana-2912	202	23	a	a	DET
cana-2912	202	24	visual	visual	ADJ
cana-2912	202	25	representation	representation	NOUN
cana-2912	202	26	of	of	ADP
cana-2912	202	27	the	the	DET
cana-2912	202	28	forecast	forecast	NOUN
cana-2912	202	29	.	.	PUNCT
cana-2912	203	1	users	user	NOUN
cana-2912	203	2	can	can	AUX
cana-2912	203	3	then	then	ADV
cana-2912	203	4	select	select	VERB
cana-2912	203	5	any	any	DET
cana-2912	203	6	other	other	ADJ
cana-2912	203	7	city	city	NOUN
cana-2912	203	8	to	to	PART
cana-2912	203	9	generate	generate	VERB
cana-2912	203	10	further	further	ADJ
cana-2912	203	11	predictions	prediction	NOUN
cana-2912	203	12	.	.	PUNCT
cana-2912	204	1	communications	communication	NOUN
cana-2912	204	2	on	on	ADP
cana-2912	204	3	applied	apply	VERB
cana-2912	204	4	nonlinear	nonlinear	ADJ
cana-2912	204	5	analysis	analysis	NOUN
cana-2912	204	6	issn	issn	NOUN
cana-2912	204	7	:	:	PUNCT
cana-2912	204	8	1074	1074	NUM
cana-2912	204	9	-	-	PUNCT
cana-2912	204	10	133x	133x	NUM
cana-2912	204	11	vol	vol	NOUN
cana-2912	204	12	32	32	NUM
cana-2912	204	13	no	no	NOUN
cana-2912	204	14	.	.	PUNCT
cana-2912	205	1	5s	5s	NUM
cana-2912	205	2	(	(	PUNCT
cana-2912	205	3	2025	2025	NUM
cana-2912	205	4	)	)	PUNCT
cana-2912	205	5	11	11	NUM
cana-2912	205	6	3.4	3.4	NUM
cana-2912	205	7	mathematical	mathematical	NOUN
cana-2912	205	8	modelling	model	VERB
cana-2912	205	9	a	a	DET
cana-2912	205	10	dataset	dataset	NOUN
cana-2912	205	11	containing	contain	VERB
cana-2912	205	12	historical	historical	ADJ
cana-2912	205	13	weather	weather	NOUN
cana-2912	205	14	data	datum	NOUN
cana-2912	205	15	,	,	PUNCT
cana-2912	205	16	including	include	VERB
cana-2912	205	17	parameters	parameter	NOUN
cana-2912	205	18	such	such	ADJ
cana-2912	205	19	as	as	ADP
cana-2912	205	20	temperature	temperature	NOUN
cana-2912	205	21	,	,	PUNCT
cana-2912	205	22	precipitation	precipitation	NOUN
cana-2912	205	23	,	,	PUNCT
cana-2912	205	24	wind	wind	NOUN
cana-2912	205	25	speed	speed	NOUN
cana-2912	205	26	,	,	PUNCT
cana-2912	205	27	and	and	CCONJ
cana-2912	205	28	other	other	ADJ
cana-2912	205	29	relevant	relevant	ADJ
cana-2912	205	30	meteorological	meteorological	ADJ
cana-2912	205	31	variables	variable	NOUN
cana-2912	205	32	,	,	PUNCT
cana-2912	205	33	is	be	AUX
cana-2912	205	34	used	use	VERB
cana-2912	205	35	for	for	ADP
cana-2912	205	36	model	model	NOUN
cana-2912	205	37	development	development	NOUN
cana-2912	205	38	.	.	PUNCT
cana-2912	206	1	the	the	DET
cana-2912	206	2	model	model	NOUN
cana-2912	206	3	equation	equation	NOUN
cana-2912	206	4	is	be	AUX
cana-2912	206	5	expressed	express	VERB
cana-2912	206	6	as	as	SCONJ
cana-2912	206	7	follows	follow	VERB
cana-2912	206	8	:	:	PUNCT
cana-2912	206	9	temperature	temperature	NOUN
cana-2912	206	10	=	=	NOUN
cana-2912	206	11	β0+β1×humidity+β2×wind	β0+β1×humidity+β2×wind	NOUN
cana-2912	206	12	speed+β3×cloud	speed+β3×cloud	ADJ
cana-2912	206	13	cover+ε	cover+ε	NOUN
cana-2912	206	14	(	(	PUNCT
cana-2912	206	15	1	1	NUM
cana-2912	206	16	)	)	PUNCT
cana-2912	206	17	where	where	SCONJ
cana-2912	206	18	:	:	PUNCT
cana-2912	206	19	temperature	temperature	NOUN
cana-2912	206	20	is	be	AUX
cana-2912	206	21	the	the	DET
cana-2912	206	22	predicted	predict	VERB
cana-2912	206	23	temperature	temperature	NOUN
cana-2912	206	24	.	.	PUNCT
cana-2912	207	1	β0	β0	PROPN
cana-2912	207	2	is	be	AUX
cana-2912	207	3	the	the	DET
cana-2912	207	4	intercept	intercept	NOUN
cana-2912	207	5	of	of	ADP
cana-2912	207	6	the	the	DET
cana-2912	207	7	regression	regression	NOUN
cana-2912	207	8	model	model	NOUN
cana-2912	207	9	.	.	PUNCT
cana-2912	208	1	β1	β1	NOUN
cana-2912	208	2	,	,	PUNCT
cana-2912	208	3	β2	β2	NOUN
cana-2912	208	4	,	,	PUNCT
cana-2912	208	5	and	and	CCONJ
cana-2912	208	6	β3	β3	VERB
cana-2912	208	7	are	be	AUX
cana-2912	208	8	coefficients	coefficient	NOUN
cana-2912	208	9	for	for	ADP
cana-2912	208	10	the	the	DET
cana-2912	208	11	predictor	predictor	NOUN
cana-2912	208	12	variables	variable	NOUN
cana-2912	208	13	(	(	PUNCT
cana-2912	208	14	humidity	humidity	NOUN
cana-2912	208	15	,	,	PUNCT
cana-2912	208	16	wind	wind	NOUN
cana-2912	208	17	speed	speed	NOUN
cana-2912	208	18	,	,	PUNCT
cana-2912	208	19	cloud	cloud	ADJ
cana-2912	208	20	cover	cover	NOUN
cana-2912	208	21	)	)	PUNCT
cana-2912	208	22	.	.	PUNCT
cana-2912	209	1	humidity	humidity	NOUN
cana-2912	209	2	is	be	AUX
cana-2912	209	3	the	the	DET
cana-2912	209	4	humidity	humidity	NOUN
cana-2912	209	5	level	level	NOUN
cana-2912	209	6	.	.	PUNCT
cana-2912	210	1	wind	wind	NOUN
cana-2912	210	2	speed	speed	NOUN
cana-2912	210	3	is	be	AUX
cana-2912	210	4	the	the	DET
cana-2912	210	5	wind	wind	NOUN
cana-2912	210	6	speed	speed	NOUN
cana-2912	210	7	.	.	PUNCT
cana-2912	211	1	cloud	cloud	NOUN
cana-2912	211	2	cover	cover	NOUN
cana-2912	211	3	is	be	AUX
cana-2912	211	4	the	the	DET
cana-2912	211	5	extent	extent	NOUN
cana-2912	211	6	of	of	ADP
cana-2912	211	7	cloud	cloud	ADJ
cana-2912	211	8	cover	cover	NOUN
cana-2912	211	9	.	.	PUNCT
cana-2912	212	1	ε	ε	PROPN
cana-2912	212	2	is	be	AUX
cana-2912	212	3	the	the	DET
cana-2912	212	4	error	error	NOUN
cana-2912	212	5	term	term	NOUN
cana-2912	212	6	,	,	PUNCT
cana-2912	212	7	accounting	account	VERB
cana-2912	212	8	for	for	ADP
cana-2912	212	9	unexplained	unexplained	ADJ
cana-2912	212	10	variability	variability	NOUN
cana-2912	212	11	.	.	PUNCT
cana-2912	213	1	to	to	PART
cana-2912	213	2	develop	develop	VERB
cana-2912	213	3	this	this	DET
cana-2912	213	4	model	model	NOUN
cana-2912	213	5	,	,	PUNCT
cana-2912	213	6	a	a	DET
cana-2912	213	7	dataset	dataset	NOUN
cana-2912	213	8	containing	contain	VERB
cana-2912	213	9	historical	historical	ADJ
cana-2912	213	10	values	value	NOUN
cana-2912	213	11	of	of	ADP
cana-2912	213	12	temperature	temperature	NOUN
cana-2912	213	13	,	,	PUNCT
cana-2912	213	14	humidity	humidity	NOUN
cana-2912	213	15	,	,	PUNCT
cana-2912	213	16	wind	wind	NOUN
cana-2912	213	17	speed	speed	NOUN
cana-2912	213	18	,	,	PUNCT
cana-2912	213	19	and	and	CCONJ
cana-2912	213	20	cloud	cloud	NOUN
cana-2912	213	21	cover	cover	NOUN
cana-2912	213	22	is	be	AUX
cana-2912	213	23	utilized	utilize	VERB
cana-2912	213	24	.	.	PUNCT
cana-2912	214	1	multiple	multiple	ADJ
cana-2912	214	2	linear	linear	PROPN
cana-2912	214	3	regression	regression	NOUN
cana-2912	214	4	analysis	analysis	NOUN
cana-2912	214	5	is	be	AUX
cana-2912	214	6	performed	perform	VERB
cana-2912	214	7	using	use	VERB
cana-2912	214	8	statistical	statistical	ADJ
cana-2912	214	9	software	software	NOUN
cana-2912	214	10	or	or	CCONJ
cana-2912	214	11	programming	programming	NOUN
cana-2912	214	12	libraries	library	NOUN
cana-2912	214	13	to	to	PART
cana-2912	214	14	estimate	estimate	VERB
cana-2912	214	15	the	the	DET
cana-2912	214	16	coefficients	coefficient	NOUN
cana-2912	214	17	(	(	PUNCT
cana-2912	214	18	β0	β0	NOUN
cana-2912	214	19	,	,	PUNCT
cana-2912	214	20	β1	β1	PROPN
cana-2912	214	21	,	,	PUNCT
cana-2912	214	22	β2	β2	NOUN
cana-2912	214	23	,	,	PUNCT
cana-2912	214	24	and	and	CCONJ
cana-2912	214	25	β3	β3	ADJ
cana-2912	214	26	)	)	PUNCT
cana-2912	214	27	that	that	PRON
cana-2912	214	28	best	well	ADV
cana-2912	214	29	fit	fit	VERB
cana-2912	214	30	the	the	DET
cana-2912	214	31	data	datum	NOUN
cana-2912	214	32	.	.	PUNCT
cana-2912	215	1	let	let	VERB
cana-2912	215	2	y	y	PRON
cana-2912	215	3	represent	represent	VERB
cana-2912	215	4	the	the	DET
cana-2912	215	5	dependent	dependent	ADJ
cana-2912	215	6	variable	variable	NOUN
cana-2912	215	7	(	(	PUNCT
cana-2912	215	8	the	the	DET
cana-2912	215	9	target	target	NOUN
cana-2912	215	10	to	to	PART
cana-2912	215	11	be	be	AUX
cana-2912	215	12	predicted	predict	VERB
cana-2912	215	13	)	)	PUNCT
cana-2912	215	14	,	,	PUNCT
cana-2912	215	15	and	and	CCONJ
cana-2912	215	16	x	x	PRON
cana-2912	215	17	represent	represent	VERB
cana-2912	215	18	the	the	DET
cana-2912	215	19	independent	independent	ADJ
cana-2912	215	20	variable	variable	NOUN
cana-2912	215	21	(	(	PUNCT
cana-2912	215	22	the	the	DET
cana-2912	215	23	predictor	predictor	NOUN
cana-2912	215	24	)	)	PUNCT
cana-2912	215	25	.	.	PUNCT
cana-2912	216	1	the	the	DET
cana-2912	216	2	model	model	NOUN
cana-2912	216	3	assumes	assume	VERB
cana-2912	216	4	a	a	DET
cana-2912	216	5	linear	linear	ADJ
cana-2912	216	6	relationship	relationship	NOUN
cana-2912	216	7	between	between	ADP
cana-2912	216	8	y	y	PROPN
cana-2912	216	9	and	and	CCONJ
cana-2912	216	10	x	x	PROPN
cana-2912	216	11	,	,	PUNCT
cana-2912	216	12	which	which	PRON
cana-2912	216	13	is	be	AUX
cana-2912	216	14	represented	represent	VERB
cana-2912	216	15	as	as	ADP
cana-2912	216	16	:	:	PUNCT
cana-2912	216	17	y	y	PROPN
cana-2912	216	18	=	=	NOUN
cana-2912	216	19	β0+β1x+ε	β0+β1x+ε	SYM
cana-2912	216	20	(	(	PUNCT
cana-2912	216	21	2	2	NUM
cana-2912	216	22	)	)	PUNCT
cana-2912	216	23	where	where	SCONJ
cana-2912	216	24	:	:	PUNCT
cana-2912	216	25	y	y	PROPN
cana-2912	216	26	is	be	AUX
cana-2912	216	27	the	the	DET
cana-2912	216	28	dependent	dependent	ADJ
cana-2912	216	29	variable	variable	NOUN
cana-2912	216	30	(	(	PUNCT
cana-2912	216	31	the	the	DET
cana-2912	216	32	value	value	NOUN
cana-2912	216	33	you	you	PRON
cana-2912	216	34	want	want	VERB
cana-2912	216	35	to	to	PART
cana-2912	216	36	predict	predict	VERB
cana-2912	216	37	)	)	PUNCT
cana-2912	216	38	.	.	PUNCT
cana-2912	217	1	x	x	PUNCT
cana-2912	217	2	is	be	AUX
cana-2912	217	3	the	the	DET
cana-2912	217	4	independent	independent	ADJ
cana-2912	217	5	variable	variable	NOUN
cana-2912	217	6	(	(	PUNCT
cana-2912	217	7	the	the	DET
cana-2912	217	8	input	input	NOUN
cana-2912	217	9	used	use	VERB
cana-2912	217	10	for	for	ADP
cana-2912	217	11	prediction	prediction	NOUN
cana-2912	217	12	)	)	PUNCT
cana-2912	217	13	.	.	PUNCT
cana-2912	218	1	β0	β0	PROPN
cana-2912	218	2	is	be	AUX
cana-2912	218	3	the	the	DET
cana-2912	218	4	intercept	intercept	NOUN
cana-2912	218	5	of	of	ADP
cana-2912	218	6	the	the	DET
cana-2912	218	7	regression	regression	NOUN
cana-2912	218	8	line	line	NOUN
cana-2912	218	9	,	,	PUNCT
cana-2912	218	10	representing	represent	VERB
cana-2912	218	11	the	the	DET
cana-2912	218	12	value	value	NOUN
cana-2912	218	13	of	of	ADP
cana-2912	218	14	y	y	PRON
cana-2912	218	15	when	when	SCONJ
cana-2912	218	16	x	x	PRON
cana-2912	218	17	is	be	AUX
cana-2912	218	18	0	0	NUM
cana-2912	218	19	.	.	PUNCT
cana-2912	219	1	β1	β1	PROPN
cana-2912	219	2	is	be	AUX
cana-2912	219	3	the	the	DET
cana-2912	219	4	slope	slope	NOUN
cana-2912	219	5	of	of	ADP
cana-2912	219	6	the	the	DET
cana-2912	219	7	regression	regression	NOUN
cana-2912	219	8	line	line	NOUN
cana-2912	219	9	,	,	PUNCT
cana-2912	219	10	representing	represent	VERB
cana-2912	219	11	the	the	DET
cana-2912	219	12	change	change	NOUN
cana-2912	219	13	in	in	ADP
cana-2912	219	14	y	y	PROPN
cana-2912	219	15	for	for	ADP
cana-2912	219	16	a	a	DET
cana-2912	219	17	unit	unit	NOUN
cana-2912	219	18	change	change	NOUN
cana-2912	219	19	in	in	ADP
cana-2912	219	20	x.	x.	PROPN
cana-2912	219	21	ε	ε	PROPN
cana-2912	219	22	is	be	AUX
cana-2912	219	23	the	the	DET
cana-2912	219	24	error	error	NOUN
cana-2912	219	25	term	term	NOUN
cana-2912	219	26	,	,	PUNCT
cana-2912	219	27	accounting	account	VERB
cana-2912	219	28	for	for	ADP
cana-2912	219	29	the	the	DET
cana-2912	219	30	variability	variability	NOUN
cana-2912	219	31	that	that	PRON
cana-2912	219	32	is	be	AUX
cana-2912	219	33	not	not	PART
cana-2912	219	34	explained	explain	VERB
cana-2912	219	35	by	by	ADP
cana-2912	219	36	the	the	DET
cana-2912	219	37	linear	linear	PROPN
cana-2912	219	38	relationship	relationship	NOUN
cana-2912	219	39	.	.	PUNCT
cana-2912	220	1	in	in	ADP
cana-2912	220	2	the	the	DET
cana-2912	220	3	case	case	NOUN
cana-2912	220	4	of	of	ADP
cana-2912	220	5	multiple	multiple	ADJ
cana-2912	220	6	linear	linear	ADJ
cana-2912	220	7	regression	regression	NOUN
cana-2912	220	8	,	,	PUNCT
cana-2912	220	9	where	where	SCONJ
cana-2912	220	10	there	there	PRON
cana-2912	220	11	are	be	VERB
cana-2912	220	12	multiple	multiple	ADJ
cana-2912	220	13	independent	independent	ADJ
cana-2912	220	14	variables	variable	NOUN
cana-2912	220	15	(	(	PUNCT
cana-2912	220	16	x1	x1	PROPN
cana-2912	220	17	,	,	PUNCT
cana-2912	220	18	x2	x2	PROPN
cana-2912	220	19	,	,	PUNCT
cana-2912	220	20	etc	etc	X
cana-2912	220	21	.	.	X
cana-2912	220	22	)	)	PUNCT
cana-2912	220	23	,	,	PUNCT
cana-2912	220	24	the	the	DET
cana-2912	220	25	model	model	NOUN
cana-2912	220	26	equation	equation	NOUN
cana-2912	220	27	is	be	AUX
cana-2912	220	28	extended	extend	VERB
cana-2912	220	29	as	as	SCONJ
cana-2912	220	30	follows	follow	VERB
cana-2912	220	31	:	:	PUNCT
cana-2912	220	32	y	y	PROPN
cana-2912	220	33	=	=	ADJ
cana-2912	220	34	β0+β1x1+β2x2+	β0+β1x1+β2x2+	NOUN
cana-2912	220	35	…	…	SYM
cana-2912	220	36	+βpxp+ε	+βpxp+ε	X
cana-2912	220	37	(	(	PUNCT
cana-2912	220	38	3	3	NUM
cana-2912	220	39	)	)	PUNCT
cana-2912	220	40	where	where	SCONJ
cana-2912	220	41	,	,	PUNCT
cana-2912	220	42	p	p	PRON
cana-2912	220	43	is	be	AUX
cana-2912	220	44	the	the	DET
cana-2912	220	45	number	number	NOUN
cana-2912	220	46	of	of	ADP
cana-2912	220	47	independent	independent	ADJ
cana-2912	220	48	variables	variable	NOUN
cana-2912	220	49	.	.	PUNCT
cana-2912	221	1	root	root	NOUN
cana-2912	221	2	mean	mean	VERB
cana-2912	221	3	squared	square	VERB
cana-2912	221	4	error	error	NOUN
cana-2912	221	5	(	(	PUNCT
cana-2912	221	6	rmse	rmse	NOUN
cana-2912	221	7	)	)	PUNCT
cana-2912	221	8	is	be	AUX
cana-2912	221	9	used	use	VERB
cana-2912	221	10	as	as	ADP
cana-2912	221	11	the	the	DET
cana-2912	221	12	performance	performance	NOUN
cana-2912	221	13	metric	metric	ADJ
cana-2912	221	14	to	to	PART
cana-2912	221	15	evaluate	evaluate	VERB
cana-2912	221	16	the	the	DET
cana-2912	221	17	machine	machine	NOUN
cana-2912	221	18	learning	learning	NOUN
cana-2912	221	19	model	model	NOUN
cana-2912	221	20	.	.	PUNCT
cana-2912	222	1	the	the	DET
cana-2912	222	2	formula	formula	NOUN
cana-2912	222	3	for	for	ADP
cana-2912	222	4	calculating	calculate	VERB
cana-2912	222	5	rmse	rmse	NOUN
cana-2912	222	6	is	be	AUX
cana-2912	222	7	as	as	SCONJ
cana-2912	222	8	follows	follow	VERB
cana-2912	222	9	:	:	PUNCT
cana-2912	222	10	communications	communication	NOUN
cana-2912	222	11	on	on	ADP
cana-2912	222	12	applied	apply	VERB
cana-2912	222	13	nonlinear	nonlinear	ADJ
cana-2912	222	14	analysis	analysis	NOUN
cana-2912	222	15	issn	issn	NOUN
cana-2912	222	16	:	:	PUNCT
cana-2912	222	17	1074	1074	NUM
cana-2912	222	18	-	-	PUNCT
cana-2912	222	19	133x	133x	NUM
cana-2912	222	20	vol	vol	NOUN
cana-2912	222	21	32	32	NUM
cana-2912	222	22	no	no	NOUN
cana-2912	222	23	.	.	PUNCT
cana-2912	223	1	5s	5s	NUM
cana-2912	223	2	(	(	PUNCT
cana-2912	223	3	2025	2025	NUM
cana-2912	223	4	)	)	PUNCT
cana-2912	223	5	12	12	NUM
cana-2912	223	6	rmse	rmse	NOUN
cana-2912	223	7	=	=	SYM
cana-2912	223	8	√	√	INTJ
cana-2912	223	9	∑	∑	PROPN
cana-2912	223	10	(	(	PUNCT
cana-2912	223	11	�	�	PROPN
cana-2912	223	12	̃	̃	NOUN
cana-2912	223	13	�	�	PROPN
cana-2912	223	14	𝒕−𝒚𝒕)𝟐𝒏	𝒕−𝒚𝒕)𝟐𝒏	NOUN
cana-2912	223	15	𝒕=𝟏	𝒕=𝟏	X
cana-2912	223	16	𝒏	𝒏	PROPN
cana-2912	223	17	(	(	PUNCT
cana-2912	223	18	4	4	NUM
cana-2912	223	19	)	)	PUNCT
cana-2912	223	20	where	where	SCONJ
cana-2912	223	21	n	n	PRON
cana-2912	223	22	is	be	AUX
cana-2912	223	23	the	the	DET
cana-2912	223	24	test	test	NOUN
cana-2912	223	25	ỹ	ỹ	PROPN
cana-2912	223	26	t	t	NOUN
cana-2912	223	27	is	be	AUX
cana-2912	223	28	the	the	DET
cana-2912	223	29	predicted	predict	VERB
cana-2912	223	30	parameter	parameter	NOUN
cana-2912	223	31	&	&	CCONJ
cana-2912	223	32	y	y	PROPN
cana-2912	223	33	t	t	PROPN
cana-2912	223	34	is	be	AUX
cana-2912	223	35	the	the	DET
cana-2912	223	36	actual	actual	ADJ
cana-2912	223	37	parameter	parameter	NOUN
cana-2912	223	38	,	,	PUNCT
cana-2912	223	39	respectively	respectively	ADV
cana-2912	223	40	.	.	PUNCT
cana-2912	224	1	it	it	PRON
cana-2912	224	2	is	be	AUX
cana-2912	224	3	important	important	ADJ
cana-2912	224	4	to	to	PART
cana-2912	224	5	recognize	recognize	VERB
cana-2912	224	6	that	that	SCONJ
cana-2912	224	7	while	while	SCONJ
cana-2912	224	8	this	this	DET
cana-2912	224	9	model	model	NOUN
cana-2912	224	10	captures	capture	VERB
cana-2912	224	11	the	the	DET
cana-2912	224	12	relationships	relationship	NOUN
cana-2912	224	13	between	between	ADP
cana-2912	224	14	temperature	temperature	NOUN
cana-2912	224	15	and	and	CCONJ
cana-2912	224	16	the	the	DET
cana-2912	224	17	three	three	NUM
cana-2912	224	18	predictor	predictor	NOUN
cana-2912	224	19	variables	variable	NOUN
cana-2912	224	20	,	,	PUNCT
cana-2912	224	21	real	real	ADJ
cana-2912	224	22	-	-	PUNCT
cana-2912	224	23	world	world	NOUN
cana-2912	224	24	weather	weather	NOUN
cana-2912	224	25	prediction	prediction	NOUN
cana-2912	224	26	models	model	NOUN
cana-2912	224	27	are	be	AUX
cana-2912	224	28	considerably	considerably	ADV
cana-2912	224	29	more	more	ADV
cana-2912	224	30	complex	complex	ADJ
cana-2912	224	31	.	.	PUNCT
cana-2912	225	1	these	these	DET
cana-2912	225	2	models	model	NOUN
cana-2912	225	3	typically	typically	ADV
cana-2912	225	4	incorporate	incorporate	VERB
cana-2912	225	5	a	a	DET
cana-2912	225	6	larger	large	ADJ
cana-2912	225	7	set	set	NOUN
cana-2912	225	8	of	of	ADP
cana-2912	225	9	predictor	predictor	NOUN
cana-2912	225	10	variables	variable	NOUN
cana-2912	225	11	,	,	PUNCT
cana-2912	225	12	account	account	VERB
cana-2912	225	13	for	for	ADP
cana-2912	225	14	intricate	intricate	ADJ
cana-2912	225	15	interdependencies	interdependency	NOUN
cana-2912	225	16	and	and	CCONJ
cana-2912	225	17	interactions	interaction	NOUN
cana-2912	225	18	among	among	ADP
cana-2912	225	19	variables	variable	NOUN
cana-2912	225	20	,	,	PUNCT
cana-2912	225	21	and	and	CCONJ
cana-2912	225	22	employ	employ	VERB
cana-2912	225	23	advanced	advanced	ADJ
cana-2912	225	24	techniques	technique	NOUN
cana-2912	225	25	such	such	ADJ
cana-2912	225	26	as	as	ADP
cana-2912	225	27	feature	feature	NOUN
cana-2912	225	28	engineering	engineering	NOUN
cana-2912	225	29	and	and	CCONJ
cana-2912	225	30	regularization	regularization	NOUN
cana-2912	225	31	to	to	PART
cana-2912	225	32	mitigate	mitigate	VERB
cana-2912	225	33	overfitting	overfitting	NOUN
cana-2912	225	34	.	.	PUNCT
cana-2912	226	1	additionally	additionally	ADV
cana-2912	226	2	,	,	PUNCT
cana-2912	226	3	weather	weather	NOUN
cana-2912	226	4	prediction	prediction	NOUN
cana-2912	226	5	models	model	NOUN
cana-2912	226	6	are	be	AUX
cana-2912	226	7	often	often	ADV
cana-2912	226	8	updated	update	VERB
cana-2912	226	9	and	and	CCONJ
cana-2912	226	10	refined	refine	VERB
cana-2912	226	11	based	base	VERB
cana-2912	226	12	on	on	ADP
cana-2912	226	13	new	new	ADJ
cana-2912	226	14	data	datum	NOUN
cana-2912	226	15	and	and	CCONJ
cana-2912	226	16	improved	improve	VERB
cana-2912	226	17	understanding	understanding	NOUN
cana-2912	226	18	of	of	ADP
cana-2912	226	19	atmospheric	atmospheric	ADJ
cana-2912	226	20	phenomena	phenomenon	NOUN
cana-2912	226	21	.	.	PUNCT
cana-2912	227	1	operational	operational	ADJ
cana-2912	227	2	weather	weather	NOUN
cana-2912	227	3	prediction	prediction	NOUN
cana-2912	227	4	requires	require	VERB
cana-2912	227	5	the	the	DET
cana-2912	227	6	use	use	NOUN
cana-2912	227	7	of	of	ADP
cana-2912	227	8	sophisticated	sophisticated	ADJ
cana-2912	227	9	mathematical	mathematical	ADJ
cana-2912	227	10	models	model	NOUN
cana-2912	227	11	that	that	PRON
cana-2912	227	12	include	include	VERB
cana-2912	227	13	advanced	advanced	ADJ
cana-2912	227	14	numerical	numerical	ADJ
cana-2912	227	15	simulations	simulation	NOUN
cana-2912	227	16	and	and	CCONJ
cana-2912	227	17	data	datum	NOUN
cana-2912	227	18	assimilation	assimilation	NOUN
cana-2912	227	19	methods	method	NOUN
cana-2912	227	20	,	,	PUNCT
cana-2912	227	21	extending	extend	VERB
cana-2912	227	22	beyond	beyond	ADP
cana-2912	227	23	the	the	DET
cana-2912	227	24	capabilities	capability	NOUN
cana-2912	227	25	of	of	ADP
cana-2912	227	26	basic	basic	ADJ
cana-2912	227	27	regression	regression	NOUN
cana-2912	227	28	models	model	NOUN
cana-2912	227	29	.	.	PUNCT
cana-2912	228	1	4	4	X
cana-2912	228	2	.	.	X
cana-2912	228	3	results	result	NOUN
cana-2912	228	4	&	&	CCONJ
cana-2912	228	5	discussion	discussion	VERB
cana-2912	228	6	the	the	DET
cana-2912	228	7	objective	objective	NOUN
cana-2912	228	8	of	of	ADP
cana-2912	228	9	developing	develop	VERB
cana-2912	228	10	a	a	DET
cana-2912	228	11	prediction	prediction	NOUN
cana-2912	228	12	model	model	NOUN
cana-2912	228	13	and	and	CCONJ
cana-2912	228	14	integrating	integrate	VERB
cana-2912	228	15	it	it	PRON
cana-2912	228	16	into	into	ADP
cana-2912	228	17	a	a	DET
cana-2912	228	18	website	website	NOUN
cana-2912	228	19	with	with	ADP
cana-2912	228	20	an	an	DET
cana-2912	228	21	intuitive	intuitive	ADJ
cana-2912	228	22	user	user	NOUN
cana-2912	228	23	interface	interface	NOUN
cana-2912	228	24	was	be	AUX
cana-2912	228	25	successfully	successfully	ADV
cana-2912	228	26	achieved	achieve	VERB
cana-2912	228	27	.	.	PUNCT
cana-2912	229	1	the	the	DET
cana-2912	229	2	model	model	NOUN
cana-2912	229	3	forecasts	forecast	VERB
cana-2912	229	4	the	the	DET
cana-2912	229	5	maximum	maximum	ADJ
cana-2912	229	6	and	and	CCONJ
cana-2912	229	7	minimum	minimum	ADJ
cana-2912	229	8	temperatures	temperature	NOUN
cana-2912	229	9	,	,	PUNCT
cana-2912	229	10	as	as	ADV
cana-2912	229	11	well	well	ADV
cana-2912	229	12	as	as	ADP
cana-2912	229	13	the	the	DET
cana-2912	229	14	probability	probability	NOUN
cana-2912	229	15	of	of	ADP
cana-2912	229	16	precipitation	precipitation	NOUN
cana-2912	229	17	,	,	PUNCT
cana-2912	229	18	for	for	ADP
cana-2912	229	19	a	a	DET
cana-2912	229	20	specified	specified	ADJ
cana-2912	229	21	number	number	NOUN
cana-2912	229	22	of	of	ADP
cana-2912	229	23	days	day	NOUN
cana-2912	229	24	based	base	VERB
cana-2912	229	25	on	on	ADP
cana-2912	229	26	current	current	ADJ
cana-2912	229	27	data	datum	NOUN
cana-2912	229	28	.	.	PUNCT
cana-2912	230	1	the	the	DET
cana-2912	230	2	model	model	NOUN
cana-2912	230	3	’s	’s	PART
cana-2912	230	4	effectiveness	effectiveness	NOUN
cana-2912	230	5	was	be	AUX
cana-2912	230	6	evaluated	evaluate	VERB
cana-2912	230	7	over	over	ADP
cana-2912	230	8	a	a	DET
cana-2912	230	9	five	five	NUM
cana-2912	230	10	-	-	PUNCT
cana-2912	230	11	day	day	NOUN
cana-2912	230	12	period	period	NOUN
cana-2912	230	13	,	,	PUNCT
cana-2912	230	14	with	with	ADP
cana-2912	230	15	a	a	DET
cana-2912	230	16	deviation	deviation	NOUN
cana-2912	230	17	of	of	ADP
cana-2912	230	18	approximately	approximately	ADV
cana-2912	230	19	0.5	0.5	NUM
cana-2912	230	20	,	,	PUNCT
cana-2912	230	21	demonstrating	demonstrate	VERB
cana-2912	230	22	a	a	DET
cana-2912	230	23	high	high	ADJ
cana-2912	230	24	level	level	NOUN
cana-2912	230	25	of	of	ADP
cana-2912	230	26	precision	precision	NOUN
cana-2912	230	27	.	.	PUNCT
cana-2912	231	1	to	to	PART
cana-2912	231	2	achieve	achieve	VERB
cana-2912	231	3	optimal	optimal	ADJ
cana-2912	231	4	accuracy	accuracy	NOUN
cana-2912	231	5	,	,	PUNCT
cana-2912	231	6	linear	linear	ADJ
cana-2912	231	7	regression	regression	NOUN
cana-2912	231	8	was	be	AUX
cana-2912	231	9	chosen	choose	VERB
cana-2912	231	10	,	,	PUNCT
cana-2912	231	11	utilizing	utilize	VERB
cana-2912	231	12	conventional	conventional	ADJ
cana-2912	231	13	coding	code	VERB
cana-2912	231	14	practices	practice	NOUN
cana-2912	231	15	and	and	CCONJ
cana-2912	231	16	a	a	DET
cana-2912	231	17	limited	limited	ADJ
cana-2912	231	18	dataset	dataset	NOUN
cana-2912	231	19	.	.	PUNCT
cana-2912	232	1	while	while	SCONJ
cana-2912	232	2	other	other	ADJ
cana-2912	232	3	models	model	NOUN
cana-2912	232	4	,	,	PUNCT
cana-2912	232	5	such	such	ADJ
cana-2912	232	6	as	as	ADP
cana-2912	232	7	random	random	ADJ
cana-2912	232	8	forest	forest	NOUN
cana-2912	232	9	and	and	CCONJ
cana-2912	232	10	decision	decision	NOUN
cana-2912	232	11	tree	tree	NOUN
cana-2912	232	12	regression	regression	NOUN
cana-2912	232	13	,	,	PUNCT
cana-2912	232	14	are	be	AUX
cana-2912	232	15	capable	capable	ADJ
cana-2912	232	16	of	of	ADP
cana-2912	232	17	making	make	VERB
cana-2912	232	18	predictions	prediction	NOUN
cana-2912	232	19	,	,	PUNCT
cana-2912	232	20	a	a	DET
cana-2912	232	21	thorough	thorough	ADJ
cana-2912	232	22	evaluation	evaluation	NOUN
cana-2912	232	23	led	lead	VERB
cana-2912	232	24	to	to	ADP
cana-2912	232	25	the	the	DET
cana-2912	232	26	selection	selection	NOUN
cana-2912	232	27	of	of	ADP
cana-2912	232	28	linear	linear	PROPN
cana-2912	232	29	regression	regression	NOUN
cana-2912	232	30	.	.	PUNCT
cana-2912	233	1	the	the	DET
cana-2912	233	2	user	user	NOUN
cana-2912	233	3	-	-	PUNCT
cana-2912	233	4	friendly	friendly	ADJ
cana-2912	233	5	interface	interface	NOUN
cana-2912	233	6	was	be	AUX
cana-2912	233	7	developed	develop	VERB
cana-2912	233	8	using	use	VERB
cana-2912	233	9	python	python	NOUN
cana-2912	233	10	,	,	PUNCT
cana-2912	233	11	with	with	ADP
cana-2912	233	12	html	html	NOUN
cana-2912	233	13	,	,	PUNCT
cana-2912	233	14	css	css	PROPN
cana-2912	233	15	,	,	PUNCT
cana-2912	233	16	and	and	CCONJ
cana-2912	233	17	jinja2	jinja2	PROPN
cana-2912	233	18	employed	employ	VERB
cana-2912	233	19	to	to	PART
cana-2912	233	20	create	create	VERB
cana-2912	233	21	an	an	DET
cana-2912	233	22	aesthetically	aesthetically	ADV
cana-2912	233	23	pleasing	pleasing	ADJ
cana-2912	233	24	design	design	NOUN
cana-2912	233	25	as	as	SCONJ
cana-2912	233	26	denoted	denote	VERB
cana-2912	233	27	in	in	ADP
cana-2912	233	28	fig	fig	NOUN
cana-2912	233	29	5	5	NUM
cana-2912	233	30	,	,	PUNCT
cana-2912	233	31	fig	fig	NOUN
cana-2912	233	32	6	6	NUM
cana-2912	233	33	and	and	CCONJ
cana-2912	233	34	fig	fig	NOUN
cana-2912	233	35	7	7	NUM
cana-2912	233	36	.	.	PUNCT
cana-2912	233	37	fig	fig	NOUN
cana-2912	233	38	5	5	NUM
cana-2912	233	39	:	:	PUNCT
cana-2912	233	40	home	home	NOUN
cana-2912	233	41	page	page	NOUN
cana-2912	233	42	fig	fig	NOUN
cana-2912	233	43	6	6	NUM
cana-2912	233	44	:	:	PUNCT
cana-2912	233	45	prediction	prediction	NOUN
cana-2912	233	46	page	page	NOUN
cana-2912	233	47	1	1	NUM
cana-2912	233	48	fig	fig	NOUN
cana-2912	233	49	7	7	NUM
cana-2912	233	50	:	:	PUNCT
cana-2912	233	51	prediction	prediction	NOUN
cana-2912	233	52	page	page	NOUN
cana-2912	233	53	2	2	NUM
cana-2912	233	54	communications	communication	NOUN
cana-2912	233	55	on	on	ADP
cana-2912	233	56	applied	apply	VERB
cana-2912	233	57	nonlinear	nonlinear	ADJ
cana-2912	233	58	analysis	analysis	NOUN
cana-2912	233	59	issn	issn	NOUN
cana-2912	233	60	:	:	PUNCT
cana-2912	233	61	1074	1074	NUM
cana-2912	233	62	-	-	PUNCT
cana-2912	233	63	133x	133x	NUM
cana-2912	233	64	vol	vol	NOUN
cana-2912	233	65	32	32	NUM
cana-2912	233	66	no	no	NOUN
cana-2912	233	67	.	.	PUNCT
cana-2912	234	1	5s	5s	NUM
cana-2912	234	2	(	(	PUNCT
cana-2912	234	3	2025	2025	NUM
cana-2912	234	4	)	)	PUNCT
cana-2912	234	5	13	13	NUM
cana-2912	234	6	4.1	4.1	NUM
cana-2912	234	7	performance	performance	NOUN
cana-2912	234	8	comparison	comparison	NOUN
cana-2912	234	9	of	of	ADP
cana-2912	234	10	models	model	NOUN
cana-2912	234	11	the	the	DET
cana-2912	234	12	performance	performance	NOUN
cana-2912	234	13	of	of	ADP
cana-2912	234	14	the	the	DET
cana-2912	234	15	models	model	NOUN
cana-2912	234	16	was	be	AUX
cana-2912	234	17	compared	compare	VERB
cana-2912	234	18	by	by	ADP
cana-2912	234	19	observing	observe	VERB
cana-2912	234	20	the	the	DET
cana-2912	234	21	trend	trend	NOUN
cana-2912	234	22	in	in	ADP
cana-2912	234	23	the	the	DET
cana-2912	234	24	root	root	NOUN
cana-2912	234	25	mean	mean	VERB
cana-2912	234	26	squared	square	VERB
cana-2912	234	27	error	error	NOUN
cana-2912	234	28	(	(	PUNCT
cana-2912	234	29	rmse	rmse	NOUN
cana-2912	234	30	)	)	PUNCT
cana-2912	234	31	on	on	ADP
cana-2912	234	32	the	the	DET
cana-2912	234	33	test	test	NOUN
cana-2912	234	34	data	datum	NOUN
cana-2912	234	35	.	.	PUNCT
cana-2912	235	1	initially	initially	ADV
cana-2912	235	2	,	,	PUNCT
cana-2912	235	3	the	the	DET
cana-2912	235	4	rmse	rmse	NOUN
cana-2912	235	5	was	be	AUX
cana-2912	235	6	high	high	ADJ
cana-2912	235	7	,	,	PUNCT
cana-2912	235	8	when	when	SCONJ
cana-2912	235	9	only	only	ADV
cana-2912	235	10	data	datum	NOUN
cana-2912	235	11	from	from	ADP
cana-2912	235	12	northern	northern	ADJ
cana-2912	235	13	india	india	PROPN
cana-2912	235	14	was	be	AUX
cana-2912	235	15	used	use	VERB
cana-2912	235	16	.	.	PUNCT
cana-2912	236	1	however	however	ADV
cana-2912	236	2	,	,	PUNCT
cana-2912	236	3	accuracy	accuracy	NOUN
cana-2912	236	4	improved	improve	VERB
cana-2912	236	5	as	as	SCONJ
cana-2912	236	6	data	datum	NOUN
cana-2912	236	7	from	from	ADP
cana-2912	236	8	multiple	multiple	ADJ
cana-2912	236	9	years	year	NOUN
cana-2912	236	10	were	be	AUX
cana-2912	236	11	incorporated	incorporate	VERB
cana-2912	236	12	.	.	PUNCT
cana-2912	237	1	the	the	DET
cana-2912	237	2	rmse	rmse	NOUN
cana-2912	237	3	continued	continue	VERB
cana-2912	237	4	to	to	PART
cana-2912	237	5	decrease	decrease	VERB
cana-2912	237	6	as	as	SCONJ
cana-2912	237	7	additional	additional	ADJ
cana-2912	237	8	latitudes	latitude	NOUN
cana-2912	237	9	were	be	AUX
cana-2912	237	10	included	include	VERB
cana-2912	237	11	.	.	PUNCT
cana-2912	238	1	the	the	DET
cana-2912	238	2	lowest	low	ADJ
cana-2912	238	3	rmse	rmse	NOUN
cana-2912	238	4	,	,	PUNCT
cana-2912	238	5	when	when	SCONJ
cana-2912	238	6	only	only	ADV
cana-2912	238	7	northern	northern	ADJ
cana-2912	238	8	india	india	PROPN
cana-2912	238	9	data	datum	NOUN
cana-2912	238	10	was	be	AUX
cana-2912	238	11	used	use	VERB
cana-2912	238	12	,	,	PUNCT
cana-2912	238	13	was	be	AUX
cana-2912	238	14	achieved	achieve	VERB
cana-2912	238	15	when	when	SCONJ
cana-2912	238	16	full	full	ADJ
cana-2912	238	17	geographic	geographic	ADJ
cana-2912	238	18	coverage	coverage	NOUN
cana-2912	238	19	was	be	AUX
cana-2912	238	20	attained	attain	VERB
cana-2912	238	21	.	.	PUNCT
cana-2912	239	1	the	the	DET
cana-2912	239	2	rmse	rmse	NOUN
cana-2912	239	3	was	be	AUX
cana-2912	239	4	calculated	calculate	VERB
cana-2912	239	5	on	on	ADP
cana-2912	239	6	test	test	NOUN
cana-2912	239	7	data	datum	NOUN
cana-2912	239	8	with	with	ADP
cana-2912	239	9	progressively	progressively	ADV
cana-2912	239	10	increasing	increase	VERB
cana-2912	239	11	amounts	amount	NOUN
cana-2912	239	12	of	of	ADP
cana-2912	239	13	training	training	NOUN
cana-2912	239	14	data	datum	NOUN
cana-2912	239	15	from	from	ADP
cana-2912	239	16	across	across	ADP
cana-2912	239	17	the	the	DET
cana-2912	239	18	country	country	NOUN
cana-2912	239	19	.	.	PUNCT
cana-2912	240	1	when	when	SCONJ
cana-2912	240	2	only	only	ADV
cana-2912	240	3	one	one	NUM
cana-2912	240	4	year	year	NOUN
cana-2912	240	5	of	of	ADP
cana-2912	240	6	data	datum	NOUN
cana-2912	240	7	was	be	AUX
cana-2912	240	8	used	use	VERB
cana-2912	240	9	,	,	PUNCT
cana-2912	240	10	the	the	DET
cana-2912	240	11	rmse	rmse	NOUN
cana-2912	240	12	was	be	AUX
cana-2912	240	13	high	high	ADJ
cana-2912	240	14	,	,	PUNCT
cana-2912	240	15	but	but	CCONJ
cana-2912	240	16	it	it	PRON
cana-2912	240	17	decreased	decrease	VERB
cana-2912	240	18	as	as	SCONJ
cana-2912	240	19	more	more	ADJ
cana-2912	240	20	years	year	NOUN
cana-2912	240	21	were	be	AUX
cana-2912	240	22	added	add	VERB
cana-2912	240	23	with	with	ADP
cana-2912	240	24	eight	eight	NUM
cana-2912	240	25	years	year	NOUN
cana-2912	240	26	of	of	ADP
cana-2912	240	27	data	datum	NOUN
cana-2912	240	28	.	.	PUNCT
cana-2912	241	1	however	however	ADV
cana-2912	241	2	,	,	PUNCT
cana-2912	241	3	the	the	DET
cana-2912	241	4	rmse	rmse	NOUN
cana-2912	241	5	increased	increase	VERB
cana-2912	241	6	again	again	ADV
cana-2912	241	7	when	when	SCONJ
cana-2912	241	8	more	more	ADJ
cana-2912	241	9	years	year	NOUN
cana-2912	241	10	were	be	AUX
cana-2912	241	11	added	add	VERB
cana-2912	241	12	,	,	PUNCT
cana-2912	241	13	due	due	ADP
cana-2912	241	14	to	to	ADP
cana-2912	241	15	abrupt	abrupt	ADJ
cana-2912	241	16	weather	weather	NOUN
cana-2912	241	17	changes	change	NOUN
cana-2912	241	18	in	in	ADP
cana-2912	241	19	certain	certain	ADJ
cana-2912	241	20	years	year	NOUN
cana-2912	241	21	that	that	PRON
cana-2912	241	22	impacted	impact	VERB
cana-2912	241	23	the	the	DET
cana-2912	241	24	model	model	NOUN
cana-2912	241	25	's	's	PART
cana-2912	241	26	training	training	NOUN
cana-2912	241	27	.	.	PUNCT
cana-2912	242	1	a	a	DET
cana-2912	242	2	comparison	comparison	NOUN
cana-2912	242	3	of	of	ADP
cana-2912	242	4	different	different	ADJ
cana-2912	242	5	models	model	NOUN
cana-2912	242	6	was	be	AUX
cana-2912	242	7	conducted	conduct	VERB
cana-2912	242	8	.	.	PUNCT
cana-2912	243	1	fig	fig	NOUN
cana-2912	243	2	8	8	NUM
cana-2912	243	3	:	:	PUNCT
cana-2912	243	4	confusion	confusion	NOUN
cana-2912	243	5	matrix	matrix	NOUN
cana-2912	243	6	using	use	VERB
cana-2912	243	7	lstm	lstm	NOUN
cana-2912	243	8	fig	fig	NOUN
cana-2912	243	9	9	9	NUM
cana-2912	243	10	:	:	PUNCT
cana-2912	243	11	prediction	prediction	NOUN
cana-2912	243	12	plot	plot	NOUN
cana-2912	243	13	image	image	NOUN
cana-2912	243	14	using	use	VERB
cana-2912	243	15	xgboost	xgboost	ADV
cana-2912	243	16	fig	fig	NOUN
cana-2912	243	17	10	10	NUM
cana-2912	243	18	:	:	PUNCT
cana-2912	243	19	prediction	prediction	NOUN
cana-2912	243	20	plot	plot	NOUN
cana-2912	243	21	image	image	NOUN
cana-2912	243	22	using	use	VERB
cana-2912	243	23	random	random	ADJ
cana-2912	243	24	forest	forest	NOUN
cana-2912	243	25	communications	communication	NOUN
cana-2912	243	26	on	on	ADP
cana-2912	243	27	applied	apply	VERB
cana-2912	243	28	nonlinear	nonlinear	ADJ
cana-2912	243	29	analysis	analysis	NOUN
cana-2912	243	30	issn	issn	NOUN
cana-2912	243	31	:	:	PUNCT
cana-2912	243	32	1074	1074	NUM
cana-2912	243	33	-	-	PUNCT
cana-2912	243	34	133x	133x	NUM
cana-2912	243	35	vol	vol	NOUN
cana-2912	243	36	32	32	NUM
cana-2912	243	37	no	no	NOUN
cana-2912	243	38	.	.	PUNCT
cana-2912	244	1	5s	5s	NUM
cana-2912	244	2	(	(	PUNCT
cana-2912	244	3	2025	2025	NUM
cana-2912	244	4	)	)	PUNCT
cana-2912	244	5	14	14	NUM
cana-2912	244	6	fig	fig	NOUN
cana-2912	244	7	11	11	NUM
cana-2912	244	8	:	:	PUNCT
cana-2912	244	9	prediction	prediction	NOUN
cana-2912	244	10	plot	plot	NOUN
cana-2912	244	11	image	image	NOUN
cana-2912	244	12	using	use	VERB
cana-2912	244	13	polynomial	polynomial	ADJ
cana-2912	244	14	regression	regression	NOUN
cana-2912	244	15	fig	fig	NOUN
cana-2912	244	16	12	12	NUM
cana-2912	244	17	:	:	PUNCT
cana-2912	244	18	prediction	prediction	NOUN
cana-2912	244	19	plot	plot	NOUN
cana-2912	244	20	image	image	NOUN
cana-2912	244	21	using	use	VERB
cana-2912	244	22	svm	svm	ADJ
cana-2912	244	23	table	table	NOUN
cana-2912	244	24	3	3	NUM
cana-2912	244	25	:	:	PUNCT
cana-2912	244	26	evaluation	evaluation	NOUN
cana-2912	244	27	rubrics	rubric	NOUN
cana-2912	244	28	of	of	ADP
cana-2912	244	29	various	various	ADJ
cana-2912	244	30	ml	ml	NOUN
cana-2912	244	31	algorithms	algorithm	NOUN
cana-2912	244	32	for	for	ADP
cana-2912	244	33	rainfall	rainfall	NOUN
cana-2912	244	34	prediction	prediction	NOUN
cana-2912	244	35	algorithm	algorithm	NOUN
cana-2912	244	36	prediction	prediction	NOUN
cana-2912	244	37	plot	plot	NOUN
cana-2912	244	38	image	image	NOUN
cana-2912	244	39	accuracy	accuracy	NOUN
cana-2912	244	40	value	value	NOUN
cana-2912	244	41	lstm	lstm	NOUN
cana-2912	244	42	80.11	80.11	NUM
cana-2912	244	43	%	%	NOUN
cana-2912	244	44	xgboost	xgboost	ADV
cana-2912	244	45	65.65	65.65	NUM
cana-2912	244	46	%	%	NOUN
cana-2912	244	47	random	random	ADJ
cana-2912	244	48	forest	forest	NOUN
cana-2912	244	49	68.14	68.14	NUM
cana-2912	244	50	%	%	NOUN
cana-2912	244	51	polynomial	polynomial	ADJ
cana-2912	244	52	regression	regression	NOUN
cana-2912	244	53	69.89	69.89	NUM
cana-2912	244	54	%	%	NOUN
cana-2912	244	55	svm	svm	NOUN
cana-2912	244	56	70.96	70.96	NUM
cana-2912	244	57	%	%	NOUN
cana-2912	244	58	communications	communication	NOUN
cana-2912	244	59	on	on	ADP
cana-2912	244	60	applied	apply	VERB
cana-2912	244	61	nonlinear	nonlinear	ADJ
cana-2912	244	62	analysis	analysis	NOUN
cana-2912	244	63	issn	issn	NOUN
cana-2912	244	64	:	:	PUNCT
cana-2912	244	65	1074	1074	NUM
cana-2912	244	66	-	-	PUNCT
cana-2912	244	67	133x	133x	NUM
cana-2912	244	68	vol	vol	NOUN
cana-2912	244	69	32	32	NUM
cana-2912	244	70	no	no	NOUN
cana-2912	244	71	.	.	PUNCT
cana-2912	245	1	5s	5s	NUM
cana-2912	245	2	(	(	PUNCT
cana-2912	245	3	2025	2025	NUM
cana-2912	245	4	)	)	PUNCT
cana-2912	245	5	15	15	NUM
cana-2912	245	6	fig	fig	NOUN
cana-2912	245	7	.	.	NOUN
cana-2912	246	1	8	8	NUM
cana-2912	246	2	to	to	ADP
cana-2912	246	3	fig	fig	NOUN
cana-2912	246	4	.	.	PUNCT
cana-2912	247	1	12	12	NUM
cana-2912	247	2	illustrate	illustrate	ADJ
cana-2912	247	3	evaluation	evaluation	NOUN
cana-2912	247	4	plots	plot	NOUN
cana-2912	247	5	of	of	ADP
cana-2912	247	6	several	several	ADJ
cana-2912	247	7	models	model	NOUN
cana-2912	247	8	,	,	PUNCT
cana-2912	247	9	and	and	CCONJ
cana-2912	247	10	the	the	DET
cana-2912	247	11	findings	finding	NOUN
cana-2912	247	12	for	for	ADP
cana-2912	247	13	each	each	DET
cana-2912	247	14	model	model	NOUN
cana-2912	247	15	are	be	AUX
cana-2912	247	16	observed	observe	VERB
cana-2912	247	17	accordingly	accordingly	ADV
cana-2912	247	18	as	as	SCONJ
cana-2912	247	19	mentioned	mention	VERB
cana-2912	247	20	in	in	ADP
cana-2912	247	21	table	table	NOUN
cana-2912	247	22	3	3	NUM
cana-2912	247	23	.	.	PUNCT
cana-2912	248	1	the	the	DET
cana-2912	248	2	best	good	ADJ
cana-2912	248	3	results	result	NOUN
cana-2912	248	4	are	be	AUX
cana-2912	248	5	achieved	achieve	VERB
cana-2912	248	6	with	with	ADP
cana-2912	248	7	lstm	lstm	NOUN
cana-2912	248	8	offering	offer	VERB
cana-2912	248	9	the	the	DET
cana-2912	248	10	most	most	ADV
cana-2912	248	11	precise	precise	ADJ
cana-2912	248	12	and	and	CCONJ
cana-2912	248	13	reliable	reliable	ADJ
cana-2912	248	14	weather	weather	NOUN
cana-2912	248	15	forecasts	forecast	NOUN
cana-2912	248	16	,	,	PUNCT
cana-2912	248	17	balancing	balance	VERB
cana-2912	248	18	model	model	NOUN
cana-2912	248	19	flexibility	flexibility	NOUN
cana-2912	248	20	with	with	ADP
cana-2912	248	21	resilience	resilience	NOUN
cana-2912	248	22	.	.	PUNCT
cana-2912	249	1	5	5	X
cana-2912	249	2	.	.	X
cana-2912	249	3	conclusion	conclusion	NOUN
cana-2912	249	4	in	in	ADP
cana-2912	249	5	conclusion	conclusion	NOUN
cana-2912	249	6	,	,	PUNCT
cana-2912	249	7	the	the	DET
cana-2912	249	8	use	use	NOUN
cana-2912	249	9	of	of	ADP
cana-2912	249	10	machine	machine	NOUN
cana-2912	249	11	learning	learn	VERB
cana-2912	249	12	algorithms	algorithm	NOUN
cana-2912	249	13	for	for	ADP
cana-2912	249	14	weather	weather	NOUN
cana-2912	249	15	prediction	prediction	NOUN
cana-2912	249	16	represents	represent	VERB
cana-2912	249	17	an	an	DET
cana-2912	249	18	emerging	emerge	VERB
cana-2912	249	19	field	field	NOUN
cana-2912	249	20	with	with	ADP
cana-2912	249	21	the	the	DET
cana-2912	249	22	potential	potential	NOUN
cana-2912	249	23	to	to	PART
cana-2912	249	24	significantly	significantly	ADV
cana-2912	249	25	transform	transform	VERB
cana-2912	249	26	how	how	SCONJ
cana-2912	249	27	weather	weather	NOUN
cana-2912	249	28	patterns	pattern	NOUN
cana-2912	249	29	and	and	CCONJ
cana-2912	249	30	conditions	condition	NOUN
cana-2912	249	31	are	be	AUX
cana-2912	249	32	forecasted	forecast	VERB
cana-2912	249	33	.	.	PUNCT
cana-2912	250	1	various	various	ADJ
cana-2912	250	2	prediction	prediction	NOUN
cana-2912	250	3	models	model	NOUN
cana-2912	250	4	show	show	VERB
cana-2912	250	5	promise	promise	NOUN
cana-2912	250	6	in	in	ADP
cana-2912	250	7	this	this	DET
cana-2912	250	8	complex	complex	ADJ
cana-2912	250	9	domain	domain	NOUN
cana-2912	250	10	.	.	PUNCT
cana-2912	251	1	nonlinear	nonlinear	ADJ
cana-2912	251	2	ensemble	ensemble	ADJ
cana-2912	251	3	models	model	NOUN
cana-2912	251	4	demonstrate	demonstrate	VERB
cana-2912	251	5	exceptional	exceptional	ADJ
cana-2912	251	6	ability	ability	NOUN
cana-2912	251	7	to	to	PART
cana-2912	251	8	capture	capture	VERB
cana-2912	251	9	the	the	DET
cana-2912	251	10	complexities	complexity	NOUN
cana-2912	251	11	of	of	ADP
cana-2912	251	12	weather	weather	NOUN
cana-2912	251	13	patterns	pattern	NOUN
cana-2912	251	14	from	from	ADP
cana-2912	251	15	historical	historical	ADJ
cana-2912	251	16	data	datum	NOUN
cana-2912	251	17	.	.	PUNCT
cana-2912	252	1	however	however	ADV
cana-2912	252	2	,	,	PUNCT
cana-2912	252	3	challenges	challenge	NOUN
cana-2912	252	4	remain	remain	VERB
cana-2912	252	5	,	,	PUNCT
cana-2912	252	6	particularly	particularly	ADV
cana-2912	252	7	in	in	ADP
cana-2912	252	8	acquiring	acquire	VERB
cana-2912	252	9	sufficient	sufficient	ADJ
cana-2912	252	10	high	high	ADJ
cana-2912	252	11	-	-	PUNCT
cana-2912	252	12	quality	quality	NOUN
cana-2912	252	13	training	training	NOUN
cana-2912	252	14	data	datum	NOUN
cana-2912	252	15	and	and	CCONJ
cana-2912	252	16	adapting	adapt	VERB
cana-2912	252	17	to	to	ADP
cana-2912	252	18	abrupt	abrupt	ADJ
cana-2912	252	19	changes	change	NOUN
cana-2912	252	20	in	in	ADP
cana-2912	252	21	weather	weather	NOUN
cana-2912	252	22	systems	system	NOUN
cana-2912	252	23	.	.	PUNCT
cana-2912	253	1	while	while	SCONJ
cana-2912	253	2	linear	linear	ADJ
cana-2912	253	3	regression	regression	NOUN
cana-2912	253	4	models	model	NOUN
cana-2912	253	5	lack	lack	VERB
cana-2912	253	6	the	the	DET
cana-2912	253	7	flexibility	flexibility	NOUN
cana-2912	253	8	and	and	CCONJ
cana-2912	253	9	accuracy	accuracy	NOUN
cana-2912	253	10	needed	need	VERB
cana-2912	253	11	for	for	ADP
cana-2912	253	12	complex	complex	ADJ
cana-2912	253	13	predictions	prediction	NOUN
cana-2912	253	14	,	,	PUNCT
cana-2912	253	15	advanced	advanced	ADJ
cana-2912	253	16	machine	machine	NOUN
cana-2912	253	17	learning	learn	VERB
cana-2912	253	18	algorithms	algorithm	NOUN
cana-2912	253	19	have	have	VERB
cana-2912	253	20	the	the	DET
cana-2912	253	21	potential	potential	NOUN
cana-2912	253	22	to	to	PART
cana-2912	253	23	revolutionize	revolutionize	VERB
cana-2912	253	24	weather	weather	NOUN
cana-2912	253	25	forecasting	forecasting	NOUN
cana-2912	253	26	.	.	PUNCT
cana-2912	254	1	this	this	PRON
cana-2912	254	2	is	be	AUX
cana-2912	254	3	possible	possible	ADJ
cana-2912	254	4	through	through	ADP
cana-2912	254	5	meticulous	meticulous	ADJ
cana-2912	254	6	data	datum	NOUN
cana-2912	254	7	preprocessing	preprocessing	NOUN
cana-2912	254	8	and	and	CCONJ
cana-2912	254	9	regularization	regularization	NOUN
cana-2912	254	10	,	,	PUNCT
cana-2912	254	11	which	which	PRON
cana-2912	254	12	help	help	VERB
cana-2912	254	13	mitigate	mitigate	VERB
cana-2912	254	14	the	the	DET
cana-2912	254	15	risk	risk	NOUN
cana-2912	254	16	of	of	ADP
cana-2912	254	17	overfitting	overfitte	VERB
cana-2912	254	18	.	.	PUNCT
cana-2912	255	1	these	these	DET
cana-2912	255	2	algorithms	algorithm	NOUN
cana-2912	255	3	can	can	AUX
cana-2912	255	4	learn	learn	VERB
cana-2912	255	5	from	from	ADP
cana-2912	255	6	vast	vast	ADJ
cana-2912	255	7	amounts	amount	NOUN
cana-2912	255	8	of	of	ADP
cana-2912	255	9	past	past	ADJ
cana-2912	255	10	data	datum	NOUN
cana-2912	255	11	,	,	PUNCT
cana-2912	255	12	enabling	enable	VERB
cana-2912	255	13	them	they	PRON
cana-2912	255	14	to	to	PART
cana-2912	255	15	identify	identify	VERB
cana-2912	255	16	patterns	pattern	NOUN
cana-2912	255	17	and	and	CCONJ
cana-2912	255	18	generate	generate	VERB
cana-2912	255	19	precise	precise	ADJ
cana-2912	255	20	forecasts	forecast	NOUN
cana-2912	255	21	.	.	PUNCT
cana-2912	256	1	these	these	DET
cana-2912	256	2	capabilities	capability	NOUN
cana-2912	256	3	offer	offer	VERB
cana-2912	256	4	reliable	reliable	ADJ
cana-2912	256	5	predictions	prediction	NOUN
cana-2912	256	6	that	that	PRON
cana-2912	256	7	can	can	AUX
cana-2912	256	8	guide	guide	VERB
cana-2912	256	9	decision	decision	NOUN
cana-2912	256	10	-	-	PUNCT
cana-2912	256	11	making	making	NOUN
cana-2912	256	12	for	for	ADP
cana-2912	256	13	individuals	individual	NOUN
cana-2912	256	14	and	and	CCONJ
cana-2912	256	15	businesses	business	NOUN
cana-2912	256	16	across	across	ADP
cana-2912	256	17	various	various	ADJ
cana-2912	256	18	sectors	sector	NOUN
cana-2912	256	19	.	.	PUNCT
cana-2912	257	1	the	the	DET
cana-2912	257	2	adoption	adoption	NOUN
cana-2912	257	3	of	of	ADP
cana-2912	257	4	machine	machine	NOUN
cana-2912	257	5	learning	learn	VERB
cana-2912	257	6	in	in	ADP
cana-2912	257	7	weather	weather	NOUN
cana-2912	257	8	forecasting	forecasting	NOUN
cana-2912	257	9	could	could	AUX
cana-2912	257	10	significantly	significantly	ADV
cana-2912	257	11	impact	impact	VERB
cana-2912	257	12	industries	industry	NOUN
cana-2912	257	13	such	such	ADJ
cana-2912	257	14	as	as	ADP
cana-2912	257	15	transportation	transportation	NOUN
cana-2912	257	16	,	,	PUNCT
cana-2912	257	17	energy	energy	NOUN
cana-2912	257	18	production	production	NOUN
cana-2912	257	19	,	,	PUNCT
cana-2912	257	20	agriculture	agriculture	NOUN
cana-2912	257	21	,	,	PUNCT
cana-2912	257	22	and	and	CCONJ
cana-2912	257	23	disaster	disaster	NOUN
cana-2912	257	24	management	management	NOUN
cana-2912	257	25	.	.	PUNCT
cana-2912	258	1	furthermore	furthermore	ADV
cana-2912	258	2	,	,	PUNCT
cana-2912	258	3	these	these	DET
cana-2912	258	4	algorithms	algorithm	NOUN
cana-2912	258	5	can	can	AUX
cana-2912	258	6	be	be	AUX
cana-2912	258	7	continuously	continuously	ADV
cana-2912	258	8	updated	update	VERB
cana-2912	258	9	with	with	ADP
cana-2912	258	10	new	new	ADJ
cana-2912	258	11	meteorological	meteorological	ADJ
cana-2912	258	12	data	datum	NOUN
cana-2912	258	13	in	in	ADP
cana-2912	258	14	real	real	ADJ
cana-2912	258	15	-	-	PUNCT
cana-2912	258	16	time	time	NOUN
cana-2912	258	17	,	,	PUNCT
cana-2912	258	18	enhancing	enhance	VERB
cana-2912	258	19	their	their	PRON
cana-2912	258	20	usefulness	usefulness	NOUN
cana-2912	258	21	in	in	ADP
cana-2912	258	22	providing	provide	VERB
cana-2912	258	23	up	up	ADP
cana-2912	258	24	-	-	PUNCT
cana-2912	258	25	to	to	ADP
cana-2912	258	26	-	-	PUNCT
cana-2912	258	27	date	date	NOUN
cana-2912	258	28	forecasts	forecast	NOUN
cana-2912	258	29	.	.	PUNCT
cana-2912	259	1	despite	despite	SCONJ
cana-2912	259	2	the	the	DET
cana-2912	259	3	promising	promising	ADJ
cana-2912	259	4	benefits	benefit	NOUN
cana-2912	259	5	,	,	PUNCT
cana-2912	259	6	several	several	ADJ
cana-2912	259	7	obstacles	obstacle	NOUN
cana-2912	259	8	need	need	VERB
cana-2912	259	9	to	to	PART
cana-2912	259	10	be	be	AUX
cana-2912	259	11	addressed	address	VERB
cana-2912	259	12	.	.	PUNCT
cana-2912	260	1	the	the	DET
cana-2912	260	2	volume	volume	NOUN
cana-2912	260	3	of	of	ADP
cana-2912	260	4	data	datum	NOUN
cana-2912	260	5	required	require	VERB
cana-2912	260	6	for	for	ADP
cana-2912	260	7	algorithm	algorithm	NOUN
cana-2912	260	8	training	training	NOUN
cana-2912	260	9	remains	remain	VERB
cana-2912	260	10	a	a	DET
cana-2912	260	11	significant	significant	ADJ
cana-2912	260	12	challenge	challenge	NOUN
cana-2912	260	13	.	.	PUNCT
cana-2912	261	1	ensuring	ensure	VERB
cana-2912	261	2	that	that	SCONJ
cana-2912	261	3	the	the	DET
cana-2912	261	4	algorithms	algorithm	NOUN
cana-2912	261	5	are	be	AUX
cana-2912	261	6	trained	train	VERB
cana-2912	261	7	on	on	ADP
cana-2912	261	8	accurate	accurate	ADJ
cana-2912	261	9	,	,	PUNCT
cana-2912	261	10	reliable	reliable	ADJ
cana-2912	261	11	data	datum	NOUN
cana-2912	261	12	requires	require	VERB
cana-2912	261	13	extensive	extensive	ADJ
cana-2912	261	14	preprocessing	preprocessing	NOUN
cana-2912	261	15	due	due	ADP
cana-2912	261	16	to	to	ADP
cana-2912	261	17	the	the	DET
cana-2912	261	18	fragmented	fragmented	ADJ
cana-2912	261	19	and	and	CCONJ
cana-2912	261	20	inconsistent	inconsistent	ADJ
cana-2912	261	21	nature	nature	NOUN
cana-2912	261	22	of	of	ADP
cana-2912	261	23	weather	weather	NOUN
cana-2912	261	24	data	datum	NOUN
cana-2912	261	25	.	.	PUNCT
cana-2912	262	1	additionally	additionally	ADV
cana-2912	262	2	,	,	PUNCT
cana-2912	262	3	the	the	DET
cana-2912	262	4	inherent	inherent	ADJ
cana-2912	262	5	complexity	complexity	NOUN
cana-2912	262	6	of	of	ADP
cana-2912	262	7	weather	weather	NOUN
cana-2912	262	8	systems	system	NOUN
cana-2912	262	9	,	,	PUNCT
cana-2912	262	10	which	which	PRON
cana-2912	262	11	can	can	AUX
cana-2912	262	12	change	change	VERB
cana-2912	262	13	unexpectedly	unexpectedly	ADV
cana-2912	262	14	,	,	PUNCT
cana-2912	262	15	poses	pose	VERB
cana-2912	262	16	a	a	DET
cana-2912	262	17	challenge	challenge	NOUN
cana-2912	262	18	to	to	ADP
cana-2912	262	19	the	the	DET
cana-2912	262	20	model	model	NOUN
cana-2912	262	21	’s	’s	PART
cana-2912	262	22	adaptability	adaptability	NOUN
cana-2912	262	23	.	.	PUNCT
cana-2912	263	1	nonetheless	nonetheless	ADV
cana-2912	263	2	,	,	PUNCT
cana-2912	263	3	the	the	DET
cana-2912	263	4	application	application	NOUN
cana-2912	263	5	of	of	ADP
cana-2912	263	6	machine	machine	NOUN
cana-2912	263	7	learning	learning	NOUN
cana-2912	263	8	in	in	ADP
cana-2912	263	9	meteorological	meteorological	ADJ
cana-2912	263	10	forecasting	forecasting	NOUN
cana-2912	263	11	holds	hold	VERB
cana-2912	263	12	the	the	DET
cana-2912	263	13	potential	potential	NOUN
cana-2912	263	14	to	to	PART
cana-2912	263	15	substantially	substantially	ADV
cana-2912	263	16	increase	increase	VERB
cana-2912	263	17	prediction	prediction	NOUN
cana-2912	263	18	accuracy	accuracy	NOUN
cana-2912	263	19	.	.	PUNCT
cana-2912	264	1	with	with	ADP
cana-2912	264	2	continued	continue	VERB
cana-2912	264	3	innovation	innovation	NOUN
cana-2912	264	4	and	and	CCONJ
cana-2912	264	5	investment	investment	NOUN
cana-2912	264	6	,	,	PUNCT
cana-2912	264	7	machine	machine	NOUN
cana-2912	264	8	learning	learn	VERB
cana-2912	264	9	algorithms	algorithm	NOUN
cana-2912	264	10	could	could	AUX
cana-2912	264	11	fundamentally	fundamentally	ADV
cana-2912	264	12	change	change	VERB
cana-2912	264	13	how	how	SCONJ
cana-2912	264	14	weather	weather	NOUN
cana-2912	264	15	is	be	AUX
cana-2912	264	16	predicted	predict	VERB
cana-2912	264	17	,	,	PUNCT
cana-2912	264	18	providing	provide	VERB
cana-2912	264	19	valuable	valuable	ADJ
cana-2912	264	20	insights	insight	NOUN
cana-2912	264	21	for	for	ADP
cana-2912	264	22	businesses	business	NOUN
cana-2912	264	23	,	,	PUNCT
cana-2912	264	24	governments	government	NOUN
cana-2912	264	25	,	,	PUNCT
cana-2912	264	26	and	and	CCONJ
cana-2912	264	27	individuals	individual	NOUN
cana-2912	264	28	.	.	PUNCT
cana-2912	265	1	addressing	address	VERB
cana-2912	265	2	the	the	DET
cana-2912	265	3	challenges	challenge	NOUN
cana-2912	265	4	of	of	ADP
cana-2912	265	5	this	this	DET
cana-2912	265	6	field	field	NOUN
cana-2912	265	7	will	will	AUX
cana-2912	265	8	require	require	VERB
cana-2912	265	9	a	a	DET
cana-2912	265	10	multidisciplinary	multidisciplinary	ADJ
cana-2912	265	11	approach	approach	NOUN
cana-2912	265	12	that	that	SCONJ
cana-2912	265	13	integrates	integrate	NOUN
cana-2912	265	14	expertise	expertise	NOUN
cana-2912	265	15	in	in	ADP
cana-2912	265	16	machine	machine	NOUN
cana-2912	265	17	learning	learning	NOUN
cana-2912	265	18	,	,	PUNCT
cana-2912	265	19	meteorology	meteorology	NOUN
cana-2912	265	20	,	,	PUNCT
cana-2912	265	21	and	and	CCONJ
cana-2912	265	22	data	datum	NOUN
cana-2912	265	23	science	science	NOUN
cana-2912	265	24	.	.	PUNCT
cana-2912	266	1	references	reference	NOUN
cana-2912	266	2	[	[	X
cana-2912	266	3	1	1	NUM
cana-2912	266	4	]	]	PUNCT
cana-2912	266	5	schultz	schultz	PROPN
cana-2912	266	6	m.	m.	PROPN
cana-2912	266	7	g.	g.	PROPN
cana-2912	266	8	,	,	PUNCT
cana-2912	266	9	betancourt	betancourt	PROPN
cana-2912	266	10	c.	c.	PROPN
cana-2912	266	11	,	,	PUNCT
cana-2912	266	12	gong	gong	PROPN
cana-2912	266	13	b.	b.	PROPN
cana-2912	266	14	,	,	PUNCT
cana-2912	266	15	kleinert	kleinert	PROPN
cana-2912	266	16	f.	f.	PROPN
cana-2912	266	17	,	,	PUNCT
cana-2912	266	18	langguth	langguth	NOUN
cana-2912	266	19	m.	m.	NOUN
cana-2912	266	20	,	,	PUNCT
cana-2912	266	21	leufen	leufen	PROPN
cana-2912	266	22	l.	l.	PROPN
cana-2912	266	23	h.	h.	PROPN
cana-2912	266	24	,	,	PUNCT
cana-2912	266	25	mozaffari	mozaffari	ADJ
cana-2912	266	26	a.	a.	NOUN
cana-2912	266	27	and	and	CCONJ
cana-2912	266	28	stadtler	stadtler	NOUN
cana-2912	266	29	s.	s.	PROPN
cana-2912	266	30	2021	2021	NUM
cana-2912	266	31	,	,	PUNCT
cana-2912	266	32	“	"	PUNCT
cana-2912	266	33	can	can	AUX
cana-2912	266	34	deep	deep	ADV
cana-2912	266	35	learning	learning	NOUN
cana-2912	266	36	beat	beat	VERB
cana-2912	266	37	numerical	numerical	ADJ
cana-2912	266	38	weather	weather	PROPN
cana-2912	266	39	prediction	prediction	NOUN
cana-2912	266	40	?	?	PUNCT
cana-2912	266	41	”	"	PUNCT
cana-2912	266	42	,	,	PUNCT
cana-2912	266	43	philosophical	philosophical	ADJ
cana-2912	266	44	transactions	transaction	NOUN
cana-2912	266	45	a	a	DET
cana-2912	266	46	,	,	PUNCT
cana-2912	266	47	royal	royal	ADJ
cana-2912	266	48	society	society	NOUN
cana-2912	266	49	publishing	publishing	NOUN
cana-2912	266	50	,	,	PUNCT
cana-2912	266	51	a.37920200097	a.37920200097	PROPN
cana-2912	266	52	,	,	PUNCT
cana-2912	266	53	http://doi.org/10.1098/rsta.2020.0097	http://doi.org/10.1098/rsta.2020.0097	PROPN
cana-2912	267	1	[	[	X
cana-2912	267	2	2	2	NUM
cana-2912	267	3	]	]	PUNCT
cana-2912	267	4	jitcha	jitcha	NOUN
cana-2912	267	5	shivang	shivang	NOUN
cana-2912	267	6	,	,	PUNCT
cana-2912	267	7	s	s	NOUN
cana-2912	267	8	sridhar	sridhar	NOUN
cana-2912	267	9	,	,	PUNCT
cana-2912	267	10	“	"	PUNCT
cana-2912	267	11	weather	weather	NOUN
cana-2912	267	12	prediction	prediction	NOUN
cana-2912	267	13	for	for	ADP
cana-2912	267	14	indian	indian	ADJ
cana-2912	267	15	location	location	NOUN
cana-2912	267	16	using	use	VERB
cana-2912	267	17	machine	machine	NOUN
cana-2912	267	18	learning	learning	NOUN
cana-2912	267	19	”	"	PUNCT
cana-2912	267	20	,	,	PUNCT
cana-2912	267	21	international	international	ADJ
cana-2912	267	22	journal	journal	NOUN
cana-2912	267	23	of	of	ADP
cana-2912	267	24	pure	pure	ADJ
cana-2912	267	25	and	and	CCONJ
cana-2912	267	26	applied	applied	ADJ
cana-2912	267	27	mathematics	mathematic	NOUN
cana-2912	267	28	,	,	PUNCT
cana-2912	267	29	volume	volume	NOUN
cana-2912	268	1	118	118	NUM
cana-2912	268	2	no	no	NOUN
cana-2912	268	3	.	.	NOUN
cana-2912	269	1	22	22	NUM
cana-2912	269	2	2018	2018	NUM
cana-2912	269	3	,	,	PUNCT
cana-2912	269	4	1945	1945	NUM
cana-2912	269	5	-	-	SYM
cana-2912	269	6	1949	1949	NUM
cana-2912	269	7	.	.	PUNCT
cana-2912	270	1	[	[	X
cana-2912	270	2	3	3	X
cana-2912	270	3	]	]	PUNCT
cana-2912	270	4	l.	l.	PROPN
cana-2912	270	5	zhang	zhang	PROPN
cana-2912	270	6	and	and	CCONJ
cana-2912	270	7	j.	j.	PROPN
cana-2912	270	8	xia	xia	PROPN
cana-2912	270	9	,	,	PUNCT
cana-2912	270	10	“	"	PUNCT
cana-2912	270	11	flood	flood	NOUN
cana-2912	270	12	detection	detection	NOUN
cana-2912	270	13	using	use	VERB
cana-2912	270	14	multiple	multiple	ADJ
cana-2912	270	15	chinese	chinese	ADJ
cana-2912	270	16	satellite	satellite	NOUN
cana-2912	270	17	datasets	dataset	NOUN
cana-2912	270	18	during	during	ADP
cana-2912	270	19	2020	2020	NUM
cana-2912	270	20	china	china	PROPN
cana-2912	270	21	summer	summer	NOUN
cana-2912	270	22	floods	flood	NOUN
cana-2912	270	23	,	,	PUNCT
cana-2912	270	24	”	"	PUNCT
cana-2912	270	25	remote	remote	ADJ
cana-2912	270	26	sensing	sensing	NOUN
cana-2912	270	27	,	,	PUNCT
cana-2912	270	28	vol	vol	NOUN
cana-2912	270	29	.	.	PROPN
cana-2912	270	30	14	14	NUM
cana-2912	270	31	,	,	PUNCT
cana-2912	270	32	no	no	INTJ
cana-2912	270	33	.	.	NOUN
cana-2912	270	34	1	1	NUM
cana-2912	270	35	,	,	PUNCT
cana-2912	270	36	jan	jan	PROPN
cana-2912	270	37	.	.	PROPN
cana-2912	270	38	2022	2022	NUM
cana-2912	270	39	,	,	PUNCT
cana-2912	270	40	doi	doi	NOUN
cana-2912	270	41	:	:	PUNCT
cana-2912	270	42	10.3390	10.3390	NUM
cana-2912	270	43	/	/	SYM
cana-2912	270	44	rs14010051	rs14010051	PROPN
cana-2912	270	45	.	.	PUNCT
cana-2912	271	1	communications	communication	NOUN
cana-2912	271	2	on	on	ADP
cana-2912	271	3	applied	apply	VERB
cana-2912	271	4	nonlinear	nonlinear	ADJ
cana-2912	271	5	analysis	analysis	NOUN
cana-2912	271	6	issn	issn	NOUN
cana-2912	271	7	:	:	PUNCT
cana-2912	271	8	1074	1074	NUM
cana-2912	271	9	-	-	PUNCT
cana-2912	271	10	133x	133x	NUM
cana-2912	271	11	vol	vol	NOUN
cana-2912	271	12	32	32	NUM
cana-2912	271	13	no	no	NOUN
cana-2912	271	14	.	.	PUNCT
cana-2912	272	1	5s	5s	NUM
cana-2912	272	2	(	(	PUNCT
cana-2912	272	3	2025	2025	NUM
cana-2912	272	4	)	)	PUNCT
cana-2912	272	5	16	16	NUM
cana-2912	273	1	[	[	X
cana-2912	273	2	4	4	X
cana-2912	273	3	]	]	PUNCT
cana-2912	273	4	s.	s.	PROPN
cana-2912	273	5	pathak	pathak	PROPN
cana-2912	273	6	,	,	PUNCT
cana-2912	273	7	m.	m.	PROPN
cana-2912	273	8	liu	liu	PROPN
cana-2912	273	9	,	,	PUNCT
cana-2912	273	10	d.	d.	PROPN
cana-2912	273	11	jato	jato	PROPN
cana-2912	273	12	-	-	PUNCT
cana-2912	273	13	espino	espino	PROPN
cana-2912	273	14	,	,	PUNCT
cana-2912	273	15	and	and	CCONJ
cana-2912	273	16	c.	c.	PROPN
cana-2912	273	17	zevenbergen	zevenbergen	PROPN
cana-2912	273	18	,	,	PUNCT
cana-2912	273	19	“	"	PUNCT
cana-2912	273	20	social	social	ADJ
cana-2912	273	21	,	,	PUNCT
cana-2912	273	22	economic	economic	ADJ
cana-2912	273	23	and	and	CCONJ
cana-2912	273	24	environmental	environmental	ADJ
cana-2912	273	25	assessment	assessment	NOUN
cana-2912	273	26	of	of	ADP
cana-2912	273	27	urban	urban	ADJ
cana-2912	273	28	sub	sub	ADJ
cana-2912	273	29	-	-	ADJ
cana-2912	273	30	catchment	catchment	ADJ
cana-2912	273	31	flood	flood	NOUN
cana-2912	273	32	risks	risk	NOUN
cana-2912	273	33	using	use	VERB
cana-2912	273	34	a	a	DET
cana-2912	273	35	multi	multi	ADJ
cana-2912	273	36	-	-	ADJ
cana-2912	273	37	criteria	criterion	NOUN
cana-2912	273	38	approach	approach	NOUN
cana-2912	273	39	:	:	PUNCT
cana-2912	273	40	a	a	DET
cana-2912	273	41	case	case	NOUN
cana-2912	273	42	study	study	NOUN
cana-2912	273	43	in	in	ADP
cana-2912	273	44	mumbai	mumbai	PROPN
cana-2912	273	45	city	city	PROPN
cana-2912	273	46	,	,	PUNCT
cana-2912	273	47	india	india	PROPN
cana-2912	273	48	,	,	PUNCT
cana-2912	273	49	”	"	PUNCT
cana-2912	273	50	journal	journal	NOUN
cana-2912	273	51	of	of	ADP
cana-2912	273	52	hydrology	hydrology	NOUN
cana-2912	273	53	,	,	PUNCT
cana-2912	273	54	vol	vol	NOUN
cana-2912	273	55	.	.	PROPN
cana-2912	273	56	591	591	NUM
cana-2912	273	57	,	,	PUNCT
cana-2912	273	58	dec	dec	PROPN
cana-2912	273	59	.	.	PROPN
cana-2912	273	60	2020	2020	NUM
cana-2912	273	61	,	,	PUNCT
cana-2912	273	62	doi	doi	NOUN
cana-2912	273	63	:	:	PUNCT
cana-2912	273	64	10.1016	10.1016	NUM
cana-2912	273	65	/	/	SYM
cana-2912	273	66	j.jhydrol.2020.125216	j.jhydrol.2020.125216	ADJ
cana-2912	273	67	.	.	PUNCT
cana-2912	274	1	[	[	X
cana-2912	274	2	5	5	X
cana-2912	274	3	]	]	PUNCT
cana-2912	274	4	d.	d.	PROPN
cana-2912	274	5	l.	l.	PROPN
cana-2912	274	6	chang	chang	PROPN
cana-2912	274	7	,	,	PUNCT
cana-2912	274	8	s.	s.	PROPN
cana-2912	274	9	h.	h.	PROPN
cana-2912	274	10	yang	yang	PROPN
cana-2912	274	11	,	,	PUNCT
cana-2912	274	12	s.	s.	PROPN
cana-2912	274	13	l.	l.	PROPN
cana-2912	274	14	hsieh	hsieh	PROPN
cana-2912	274	15	,	,	PUNCT
cana-2912	274	16	h.	h.	PROPN
cana-2912	274	17	j.	j.	PROPN
cana-2912	274	18	wang	wang	PROPN
cana-2912	274	19	,	,	PUNCT
cana-2912	274	20	and	and	CCONJ
cana-2912	274	21	k.	k.	PROPN
cana-2912	274	22	c.	c.	PROPN
cana-2912	274	23	yeh	yeh	PROPN
cana-2912	274	24	,	,	PUNCT
cana-2912	274	25	“	"	PUNCT
cana-2912	274	26	artificial	artificial	ADJ
cana-2912	274	27	intelligence	intelligence	NOUN
cana-2912	274	28	methodologies	methodology	NOUN
cana-2912	274	29	applied	apply	VERB
cana-2912	274	30	to	to	PART
cana-2912	274	31	prompt	prompt	VERB
cana-2912	274	32	pluvial	pluvial	ADJ
cana-2912	274	33	flood	flood	NOUN
cana-2912	274	34	estimation	estimation	NOUN
cana-2912	274	35	and	and	CCONJ
cana-2912	274	36	prediction	prediction	NOUN
cana-2912	274	37	,	,	PUNCT
cana-2912	274	38	”	"	PUNCT
cana-2912	274	39	water	water	NOUN
cana-2912	274	40	(	(	PUNCT
cana-2912	274	41	switzerland	switzerland	PROPN
cana-2912	274	42	)	)	PUNCT
cana-2912	274	43	,	,	PUNCT
cana-2912	274	44	vol	vol	NOUN
cana-2912	274	45	.	.	PROPN
cana-2912	274	46	12	12	NUM
cana-2912	274	47	,	,	PUNCT
cana-2912	274	48	no	no	INTJ
cana-2912	274	49	.	.	NOUN
cana-2912	274	50	12	12	NUM
cana-2912	274	51	,	,	PUNCT
cana-2912	274	52	dec	dec	PROPN
cana-2912	274	53	.	.	PROPN
cana-2912	274	54	2020	2020	NUM
cana-2912	274	55	,	,	PUNCT
cana-2912	274	56	doi	doi	NOUN
cana-2912	274	57	:	:	PUNCT
cana-2912	274	58	10.3390	10.3390	NUM
cana-2912	274	59	/	/	SYM
cana-2912	274	60	w12123552	w12123552	NOUN
cana-2912	274	61	.	.	PUNCT
cana-2912	275	1	[	[	X
cana-2912	275	2	6	6	NUM
cana-2912	275	3	]	]	PUNCT
cana-2912	275	4	r.	r.	PROPN
cana-2912	275	5	bentivoglio	bentivoglio	PROPN
cana-2912	275	6	,	,	PUNCT
cana-2912	275	7	e.	e.	PROPN
cana-2912	275	8	isufi	isufi	PROPN
cana-2912	275	9	,	,	PUNCT
cana-2912	275	10	s.	s.	PROPN
cana-2912	275	11	nicolaas	nicolaas	PROPN
cana-2912	275	12	jonkman	jonkman	PROPN
cana-2912	275	13	,	,	PUNCT
cana-2912	275	14	and	and	CCONJ
cana-2912	275	15	r.	r.	PROPN
cana-2912	275	16	taormina	taormina	PROPN
cana-2912	275	17	,	,	PUNCT
cana-2912	275	18	“	"	PUNCT
cana-2912	275	19	deep	deep	ADJ
cana-2912	275	20	learning	learning	NOUN
cana-2912	275	21	methods	method	NOUN
cana-2912	275	22	for	for	ADP
cana-2912	275	23	flood	flood	NOUN
cana-2912	275	24	mapping	mapping	NOUN
cana-2912	275	25	:	:	PUNCT
cana-2912	275	26	a	a	DET
cana-2912	275	27	review	review	NOUN
cana-2912	275	28	of	of	ADP
cana-2912	275	29	existing	exist	VERB
cana-2912	275	30	applications	application	NOUN
cana-2912	275	31	and	and	CCONJ
cana-2912	275	32	future	future	ADJ
cana-2912	275	33	research	research	NOUN
cana-2912	275	34	directions	direction	NOUN
cana-2912	275	35	,	,	PUNCT
cana-2912	275	36	”	"	PUNCT
cana-2912	275	37	2021	2021	NUM
cana-2912	275	38	,	,	PUNCT
cana-2912	275	39	doi	doi	NOUN
cana-2912	275	40	:	:	PUNCT
cana-2912	275	41	10.5194	10.5194	NUM
cana-2912	275	42	/	/	SYM
cana-2912	275	43	hess-2021	hess-2021	NOUN
cana-2912	275	44	-	-	PUNCT
cana-2912	275	45	614	614	NUM
cana-2912	275	46	.	.	PUNCT
cana-2912	276	1	[	[	X
cana-2912	276	2	7	7	NUM
cana-2912	276	3	]	]	X
cana-2912	276	4	d	d	PROPN
cana-2912	276	5	sanjay	sanjay	PROPN
cana-2912	276	6	,	,	PUNCT
cana-2912	276	7	sawaitful	sawaitful	ADJ
cana-2912	276	8	,	,	PUNCT
cana-2912	276	9	kp	kp	PROPN
cana-2912	276	10	prof	prof	PROPN
cana-2912	276	11	.	.	PUNCT
cana-2912	276	12	wagh	wagh	PROPN
cana-2912	276	13	,	,	PUNCT
cana-2912	276	14	dr	dr	PROPN
cana-2912	276	15	.	.	PROPN
cana-2912	276	16	chatur	chatur	PROPN
cana-2912	276	17	,	,	PUNCT
cana-2912	276	18	“	"	PUNCT
cana-2912	276	19	classification	classification	NOUN
cana-2912	276	20	and	and	CCONJ
cana-2912	276	21	prediction	prediction	NOUN
cana-2912	276	22	of	of	ADP
cana-2912	276	23	future	future	ADJ
cana-2912	276	24	weather	weather	NOUN
cana-2912	276	25	by	by	ADP
cana-2912	276	26	using	use	VERB
cana-2912	276	27	backpropagation	backpropagation	NOUN
cana-2912	276	28	algorithm	algorithm	NOUN
cana-2912	276	29	–	–	PUNCT
cana-2912	276	30	an	an	DET
cana-2912	276	31	approach	approach	NOUN
cana-2912	276	32	”	"	PUNCT
cana-2912	276	33	.	.	PUNCT
cana-2912	277	1	[	[	X
cana-2912	277	2	8	8	NUM
cana-2912	277	3	]	]	X
cana-2912	277	4	soheila	soheila	PROPN
cana-2912	277	5	dehghani	dehghani	PROPN
cana-2912	277	6	,	,	PUNCT
cana-2912	277	7	ali	ali	PROPN
cana-2912	277	8	jafari	jafari	PROPN
cana-2912	277	9	,	,	PUNCT
cana-2912	277	10	and	and	CCONJ
cana-2912	277	11	hamidreza	hamidreza	PROPN
cana-2912	277	12	zareipour	zareipour	NOUN
cana-2912	277	13	,	,	PUNCT
cana-2912	277	14	“	"	PUNCT
cana-2912	277	15	a	a	DET
cana-2912	277	16	comparison	comparison	NOUN
cana-2912	277	17	of	of	ADP
cana-2912	277	18	linear	linear	ADJ
cana-2912	277	19	regression	regression	NOUN
cana-2912	277	20	and	and	CCONJ
cana-2912	277	21	artificial	artificial	ADJ
cana-2912	277	22	neural	neural	ADJ
cana-2912	277	23	network	network	NOUN
cana-2912	277	24	models	model	NOUN
cana-2912	277	25	for	for	ADP
cana-2912	277	26	weather	weather	NOUN
cana-2912	277	27	prediction	prediction	NOUN
cana-2912	277	28	”	"	PUNCT
cana-2912	278	1	[	[	X
cana-2912	278	2	9	9	NUM
cana-2912	278	3	]	]	X
cana-2912	278	4	wei	wei	PROPN
cana-2912	278	5	ma	ma	PROPN
cana-2912	278	6	,	,	PUNCT
cana-2912	278	7	bingliang	bingliang	PROPN
cana-2912	278	8	liu	liu	PROPN
cana-2912	278	9	,	,	PUNCT
cana-2912	278	10	and	and	CCONJ
cana-2912	278	11	yiran	yiran	PROPN
cana-2912	278	12	zhao	zhao	PROPN
cana-2912	278	13	wei	wei	PROPN
cana-2912	278	14	ma	ma	PROPN
cana-2912	278	15	,	,	PUNCT
cana-2912	278	16	bingliang	bingliang	PROPN
cana-2912	278	17	liu	liu	PROPN
cana-2912	278	18	,	,	PUNCT
cana-2912	278	19	and	and	CCONJ
cana-2912	278	20	yiran	yiran	PROPN
cana-2912	278	21	zhao	zhao	PROPN
cana-2912	278	22	,	,	PUNCT
cana-2912	278	23	"	"	PUNCT
cana-2912	278	24	an	an	DET
cana-2912	278	25	ensemble	ensemble	ADJ
cana-2912	278	26	method	method	NOUN
cana-2912	278	27	for	for	ADP
cana-2912	278	28	accurate	accurate	ADJ
cana-2912	278	29	weather	weather	NOUN
cana-2912	278	30	prediction	prediction	NOUN
cana-2912	278	31	using	use	VERB
cana-2912	278	32	regression	regression	NOUN
cana-2912	278	33	models	model	NOUN
cana-2912	278	34	.	.	PUNCT
cana-2912	278	35	"	"	PUNCT
cana-2912	279	1	[	[	X
cana-2912	279	2	10	10	NUM
cana-2912	279	3	]	]	PUNCT
cana-2912	279	4	hongmei	hongmei	PROPN
cana-2912	279	5	zhang	zhang	PROPN
cana-2912	279	6	,	,	PUNCT
cana-2912	279	7	rongli	rongli	PROPN
cana-2912	279	8	wang	wang	PROPN
cana-2912	279	9	,	,	PUNCT
cana-2912	279	10	and	and	CCONJ
cana-2912	279	11	tao	tao	PROPN
cana-2912	279	12	liu	liu	PROPN
cana-2912	279	13	,	,	PUNCT
cana-2912	279	14	"	"	PUNCT
cana-2912	279	15	weather	weather	NOUN
cana-2912	279	16	forecasting	forecasting	NOUN
cana-2912	279	17	using	use	VERB
cana-2912	279	18	support	support	NOUN
cana-2912	279	19	vector	vector	NOUN
cana-2912	279	20	regression	regression	NOUN
cana-2912	279	21	models	model	NOUN
cana-2912	279	22	"	"	PUNCT
cana-2912	279	23	.	.	PUNCT
cana-2912	280	1	[	[	X
cana-2912	280	2	11	11	NUM
cana-2912	280	3	]	]	SYM
cana-2912	280	4	shanshan	shanshan	PROPN
cana-2912	280	5	liu	liu	PROPN
cana-2912	280	6	,	,	PUNCT
cana-2912	280	7	hao	hao	PROPN
cana-2912	280	8	liu	liu	PROPN
cana-2912	280	9	,	,	PUNCT
cana-2912	280	10	and	and	CCONJ
cana-2912	280	11	zongmin	zongmin	PROPN
cana-2912	280	12	li	li	PROPN
cana-2912	280	13	,	,	PUNCT
cana-2912	280	14	“	"	PUNCT
cana-2912	280	15	integrating	integrate	VERB
cana-2912	280	16	regression	regression	NOUN
cana-2912	280	17	models	model	NOUN
cana-2912	280	18	with	with	ADP
cana-2912	280	19	clustering	clustering	ADJ
cana-2912	280	20	techniques	technique	NOUN
cana-2912	280	21	for	for	ADP
cana-2912	280	22	improved	improved	ADJ
cana-2912	280	23	weather	weather	NOUN
cana-2912	280	24	prediction	prediction	NOUN
cana-2912	280	25	”	"	PUNCT
cana-2912	280	26	.	.	PUNCT
cana-2912	281	1	[	[	X
cana-2912	281	2	12	12	NUM
cana-2912	281	3	]	]	X
cana-2912	281	4	khatri	khatri	PROPN
cana-2912	281	5	,	,	PUNCT
cana-2912	281	6	s.	s.	PROPN
cana-2912	281	7	,	,	PUNCT
cana-2912	281	8	kokane	kokane	NOUN
cana-2912	281	9	,	,	PUNCT
cana-2912	281	10	p.	p.	PROPN
cana-2912	281	11	,	,	PUNCT
cana-2912	281	12	kumar	kumar	PROPN
cana-2912	281	13	,	,	PUNCT
cana-2912	281	14	v.	v.	ADP
cana-2912	281	15	et	et	PROPN
cana-2912	281	16	al	al	PROPN
cana-2912	281	17	.	.	PROPN
cana-2912	281	18	prediction	prediction	NOUN
cana-2912	281	19	of	of	ADP
cana-2912	281	20	waterlogged	waterlogge	VERB
cana-2912	281	21	zones	zone	NOUN
cana-2912	281	22	under	under	ADP
cana-2912	281	23	heavy	heavy	ADJ
cana-2912	281	24	rainfall	rainfall	NOUN
cana-2912	281	25	conditions	condition	NOUN
cana-2912	281	26	using	use	VERB
cana-2912	281	27	machine	machine	NOUN
cana-2912	281	28	learning	learning	NOUN
cana-2912	281	29	and	and	CCONJ
cana-2912	281	30	gis	gis	NOUN
cana-2912	281	31	tools	tool	NOUN
cana-2912	281	32	:	:	PUNCT
cana-2912	281	33	a	a	DET
cana-2912	281	34	case	case	NOUN
cana-2912	281	35	study	study	NOUN
cana-2912	281	36	of	of	ADP
cana-2912	281	37	mumbai	mumbai	PROPN
cana-2912	281	38	.	.	PUNCT
cana-2912	282	1	geojournal	geojournal	ADJ
cana-2912	282	2	(	(	PUNCT
cana-2912	282	3	2022	2022	NUM
cana-2912	282	4	)	)	PUNCT
cana-2912	282	5	.	.	PUNCT
cana-2912	283	1	https://doi.org/10.1007/s10708-022-10731-3	https://doi.org/10.1007/s10708-022-10731-3	PROPN
cana-2912	283	2	.	.	PUNCT
cana-2912	284	1	[	[	X
cana-2912	284	2	13	13	NUM
cana-2912	284	3	]	]	PUNCT
cana-2912	284	4	i.	i.	NOUN
cana-2912	284	5	elkhrachy	elkhrachy	NOUN
cana-2912	284	6	,	,	PUNCT
cana-2912	284	7	“	"	PUNCT
cana-2912	284	8	flash	flash	VERB
cana-2912	284	9	flood	flood	NOUN
cana-2912	284	10	water	water	NOUN
cana-2912	284	11	depth	depth	NOUN
cana-2912	284	12	estimation	estimation	NOUN
cana-2912	284	13	using	use	VERB
cana-2912	284	14	sar	sar	NOUN
cana-2912	284	15	images	image	NOUN
cana-2912	284	16	,	,	PUNCT
cana-2912	284	17	digital	digital	ADJ
cana-2912	284	18	elevation	elevation	NOUN
cana-2912	284	19	models	model	NOUN
cana-2912	284	20	,	,	PUNCT
cana-2912	284	21	and	and	CCONJ
cana-2912	284	22	machine	machine	NOUN
cana-2912	284	23	learning	learning	NOUN
cana-2912	284	24	algorithms	algorithm	NOUN
cana-2912	284	25	,	,	PUNCT
cana-2912	284	26	”	"	PUNCT
cana-2912	284	27	remote	remote	ADJ
cana-2912	284	28	sensing	sensing	NOUN
cana-2912	284	29	,	,	PUNCT
cana-2912	284	30	vol	vol	NOUN
cana-2912	284	31	.	.	PROPN
cana-2912	284	32	14	14	NUM
cana-2912	284	33	,	,	PUNCT
cana-2912	284	34	no	no	INTJ
cana-2912	284	35	.	.	NOUN
cana-2912	284	36	3	3	NUM
cana-2912	284	37	,	,	PUNCT
cana-2912	284	38	feb	feb	PROPN
cana-2912	284	39	.	.	PROPN
cana-2912	284	40	2022	2022	NUM
cana-2912	284	41	,	,	PUNCT
cana-2912	284	42	doi	doi	NOUN
cana-2912	284	43	:	:	PUNCT
cana-2912	284	44	10.3390	10.3390	NUM
cana-2912	284	45	/	/	SYM
cana-2912	284	46	rs14030440	rs14030440	PROPN
cana-2912	284	47	.	.	PUNCT
cana-2912	285	1	[	[	X
cana-2912	285	2	14	14	NUM
cana-2912	285	3	]	]	PUNCT
cana-2912	285	4	m.	m.	PROPN
cana-2912	285	5	f.	f.	PROPN
cana-2912	285	6	abdullah	abdullah	PROPN
cana-2912	285	7	,	,	PUNCT
cana-2912	285	8	s.	s.	PROPN
cana-2912	285	9	siraj	siraj	PROPN
cana-2912	285	10	,	,	PUNCT
cana-2912	285	11	and	and	CCONJ
cana-2912	285	12	r.	r.	PROPN
cana-2912	285	13	e.	e.	PROPN
cana-2912	285	14	hodgett	hodgett	PROPN
cana-2912	285	15	,	,	PUNCT
cana-2912	285	16	“	"	PUNCT
cana-2912	285	17	an	an	DET
cana-2912	285	18	overview	overview	NOUN
cana-2912	285	19	of	of	ADP
cana-2912	285	20	multi	multi	ADJ
cana-2912	285	21	-	-	ADJ
cana-2912	285	22	criteria	criterion	NOUN
cana-2912	285	23	decision	decision	NOUN
cana-2912	285	24	analysis	analysis	NOUN
cana-2912	285	25	(	(	PUNCT
cana-2912	285	26	mcda	mcda	NOUN
cana-2912	285	27	)	)	PUNCT
cana-2912	285	28	application	application	NOUN
cana-2912	285	29	in	in	ADP
cana-2912	285	30	managing	manage	VERB
cana-2912	285	31	water	water	NOUN
cana-2912	285	32	-	-	PUNCT
cana-2912	285	33	related	relate	VERB
cana-2912	285	34	disaster	disaster	NOUN
cana-2912	285	35	events	event	NOUN
cana-2912	285	36	:	:	PUNCT
cana-2912	285	37	analyzing	analyze	VERB
cana-2912	285	38	20	20	NUM
cana-2912	285	39	years	year	NOUN
cana-2912	285	40	of	of	ADP
cana-2912	285	41	literature	literature	NOUN
cana-2912	285	42	for	for	ADP
cana-2912	285	43	flood	flood	NOUN
cana-2912	285	44	and	and	CCONJ
cana-2912	285	45	drought	drought	NOUN
cana-2912	285	46	events	event	NOUN
cana-2912	285	47	,	,	PUNCT
cana-2912	285	48	”	"	PUNCT
cana-2912	285	49	water	water	NOUN
cana-2912	285	50	(	(	PUNCT
cana-2912	285	51	switzerland	switzerland	PROPN
cana-2912	285	52	)	)	PUNCT
cana-2912	285	53	,	,	PUNCT
cana-2912	285	54	vol	vol	NOUN
cana-2912	285	55	.	.	PROPN
cana-2912	285	56	13	13	NUM
cana-2912	285	57	,	,	PUNCT
cana-2912	285	58	no	no	INTJ
cana-2912	285	59	.	.	NOUN
cana-2912	285	60	10	10	NUM
cana-2912	285	61	.	.	PUNCT
cana-2912	285	62	mdpi	mdpi	PROPN
cana-2912	285	63	ag	ag	PROPN
cana-2912	285	64	,	,	PUNCT
cana-2912	285	65	may	may	AUX
cana-2912	285	66	02	02	NUM
cana-2912	285	67	,	,	PUNCT
cana-2912	285	68	2021	2021	NUM
cana-2912	285	69	.	.	PUNCT
cana-2912	286	1	doi	doi	NOUN
cana-2912	286	2	:	:	PUNCT
cana-2912	286	3	10.3390	10.3390	NUM
cana-2912	286	4	/	/	SYM
cana-2912	286	5	w13101358	w13101358	NOUN
cana-2912	286	6	.	.	PUNCT
cana-2912	287	1	[	[	X
cana-2912	287	2	15	15	NUM
cana-2912	287	3	]	]	X
cana-2912	287	4	j.	j.	PROPN
cana-2912	287	5	chen	chen	PROPN
cana-2912	287	6	,	,	PUNCT
cana-2912	287	7	g.	g.	PROPN
cana-2912	287	8	huang	huang	PROPN
cana-2912	287	9	,	,	PUNCT
cana-2912	287	10	and	and	CCONJ
cana-2912	287	11	w.	w.	PROPN
cana-2912	287	12	chen	chen	PROPN
cana-2912	287	13	,	,	PUNCT
cana-2912	287	14	“	"	PUNCT
cana-2912	287	15	towards	towards	ADP
cana-2912	287	16	better	well	ADJ
cana-2912	287	17	flood	flood	NOUN
cana-2912	287	18	risk	risk	NOUN
cana-2912	287	19	management	management	NOUN
cana-2912	287	20	:	:	PUNCT
cana-2912	287	21	assessing	assess	VERB
cana-2912	287	22	flood	flood	NOUN
cana-2912	287	23	risk	risk	NOUN
cana-2912	287	24	and	and	CCONJ
cana-2912	287	25	investigating	investigate	VERB
cana-2912	287	26	the	the	DET
cana-2912	287	27	potential	potential	ADJ
cana-2912	287	28	mechanism	mechanism	NOUN
cana-2912	287	29	based	base	VERB
cana-2912	287	30	on	on	ADP
cana-2912	287	31	machine	machine	NOUN
cana-2912	287	32	learning	learning	NOUN
cana-2912	287	33	models	model	NOUN
cana-2912	287	34	,	,	PUNCT
cana-2912	287	35	”	"	PUNCT
cana-2912	287	36	journal	journal	NOUN
cana-2912	287	37	of	of	ADP
cana-2912	287	38	environmental	environmental	ADJ
cana-2912	287	39	management	management	NOUN
cana-2912	287	40	,	,	PUNCT
cana-2912	287	41	vol	vol	NOUN
cana-2912	287	42	.	.	PROPN
cana-2912	287	43	293	293	NUM
cana-2912	287	44	,	,	PUNCT
cana-2912	287	45	sep	sep	PROPN
cana-2912	287	46	.	.	PROPN
cana-2912	287	47	2021	2021	NUM
cana-2912	287	48	,	,	PUNCT
cana-2912	287	49	doi	doi	NOUN
cana-2912	287	50	:	:	PUNCT
cana-2912	287	51	10.1016	10.1016	NUM
cana-2912	287	52	/	/	SYM
cana-2912	287	53	j.jenvman.2021.112810	j.jenvman.2021.112810	NOUN
cana-2912	287	54	.	.	PUNCT
cana-2912	288	1	[	[	X
cana-2912	288	2	16	16	NUM
cana-2912	288	3	]	]	PUNCT
cana-2912	288	4	b.	b.	PROPN
cana-2912	288	5	kalantar	kalantar	PROPN
cana-2912	288	6	et	et	PROPN
cana-2912	288	7	al	al	PROPN
cana-2912	288	8	.	.	PROPN
cana-2912	288	9	,	,	PUNCT
cana-2912	288	10	“	"	PUNCT
cana-2912	288	11	deep	deep	ADJ
cana-2912	288	12	neural	neural	ADJ
cana-2912	288	13	network	network	NOUN
cana-2912	288	14	utilizing	utilize	VERB
cana-2912	288	15	remote	remote	ADJ
cana-2912	288	16	sensing	sense	VERB
cana-2912	288	17	datasets	dataset	NOUN
cana-2912	288	18	for	for	ADP
cana-2912	288	19	flood	flood	NOUN
cana-2912	288	20	hazard	hazard	NOUN
cana-2912	288	21	susceptibility	susceptibility	NOUN
cana-2912	288	22	mapping	mapping	NOUN
cana-2912	288	23	in	in	ADP
cana-2912	288	24	brisbane	brisbane	PROPN
cana-2912	288	25	,	,	PUNCT
cana-2912	288	26	australia	australia	PROPN
cana-2912	288	27	,	,	PUNCT
cana-2912	288	28	”	"	PUNCT
cana-2912	288	29	remote	remote	ADJ
cana-2912	288	30	sensing	sensing	NOUN
cana-2912	288	31	,	,	PUNCT
cana-2912	288	32	vol	vol	NOUN
cana-2912	288	33	.	.	PROPN
cana-2912	288	34	13	13	NUM
cana-2912	288	35	,	,	PUNCT
cana-2912	288	36	no	no	INTJ
cana-2912	288	37	.	.	NOUN
cana-2912	288	38	13	13	NUM
cana-2912	288	39	,	,	PUNCT
cana-2912	288	40	jul	jul	PROPN
cana-2912	288	41	.	.	PROPN
cana-2912	288	42	2021	2021	NUM
cana-2912	288	43	,	,	PUNCT
cana-2912	288	44	doi	doi	NOUN
cana-2912	288	45	:	:	PUNCT
cana-2912	288	46	10.3390	10.3390	NUM
cana-2912	288	47	/	/	SYM
cana-2912	288	48	rs13132638	rs13132638	NOUN
cana-2912	288	49	.	.	PUNCT
cana-2912	289	1	[	[	X
cana-2912	289	2	17	17	NUM
cana-2912	289	3	]	]	PUNCT
cana-2912	289	4	“	"	PUNCT
cana-2912	289	5	early	early	ADJ
cana-2912	289	6	flood	flood	NOUN
cana-2912	289	7	detection	detection	NOUN
cana-2912	289	8	and	and	CCONJ
cana-2912	289	9	alarming	alarming	ADJ
cana-2912	289	10	system	system	NOUN
cana-2912	289	11	using	use	VERB
cana-2912	289	12	machine	machine	NOUN
cana-2912	289	13	learning	learning	NOUN
cana-2912	289	14	techniques	technique	NOUN
cana-2912	289	15	,	,	PUNCT
cana-2912	289	16	”	"	PUNCT
cana-2912	289	17	2020	2020	NUM
cana-2912	289	18	.	.	PUNCT
cana-2912	290	1	[	[	X
cana-2912	290	2	18	18	NUM
cana-2912	290	3	]	]	X
cana-2912	290	4	v.	v.	CCONJ
cana-2912	290	5	katiyar	katiyar	PROPN
cana-2912	290	6	,	,	PUNCT
cana-2912	290	7	n.	n.	PROPN
cana-2912	290	8	tamkuan	tamkuan	PROPN
cana-2912	290	9	,	,	PUNCT
cana-2912	290	10	and	and	CCONJ
cana-2912	290	11	m.	m.	PROPN
cana-2912	290	12	nagai	nagai	PROPN
cana-2912	290	13	,	,	PUNCT
cana-2912	290	14	“	"	PUNCT
cana-2912	290	15	flood	flood	NOUN
cana-2912	290	16	area	area	NOUN
cana-2912	290	17	detection	detection	NOUN
cana-2912	290	18	using	use	VERB
cana-2912	290	19	sar	sar	NOUN
cana-2912	290	20	images	image	NOUN
cana-2912	290	21	with	with	ADP
cana-2912	290	22	deep	deep	ADJ
cana-2912	290	23	neural	neural	ADJ
cana-2912	290	24	network	network	NOUN
cana-2912	290	25	during	during	ADP
cana-2912	290	26	,	,	PUNCT
cana-2912	290	27	2020	2020	NUM
cana-2912	290	28	kyushu	kyushu	PROPN
cana-2912	290	29	flood	flood	PROPN
cana-2912	290	30	japan	japan	PROPN
cana-2912	290	31	,	,	PUNCT
cana-2912	290	32	”	"	PUNCT
cana-2912	290	33	2020	2020	NUM
cana-2912	290	34	.	.	PUNCT