id	sid	tid	token	lemma	pos
fcis-20387	1	1	frontiers	frontier	NOUN
fcis-20387	1	2	in	in	ADP
fcis-20387	1	3	computing	computing	NOUN
fcis-20387	1	4	and	and	CCONJ
fcis-20387	1	5	intelligent	intelligent	ADJ
fcis-20387	1	6	systems	system	NOUN
fcis-20387	1	7	issn	issn	VERB
fcis-20387	1	8	:	:	PUNCT
fcis-20387	1	9	2832	2832	NUM
fcis-20387	1	10	-	-	SYM
fcis-20387	1	11	6024	6024	NUM
fcis-20387	1	12	|	|	NOUN
fcis-20387	1	13	vol	vol	NOUN
fcis-20387	1	14	.	.	PROPN
fcis-20387	2	1	8	8	NUM
fcis-20387	2	2	,	,	PUNCT
fcis-20387	2	3	no	no	INTJ
fcis-20387	2	4	.	.	NOUN
fcis-20387	2	5	1	1	NUM
fcis-20387	2	6	,	,	PUNCT
fcis-20387	2	7	2024	2024	NUM
fcis-20387	2	8	43	43	NUM
fcis-20387	2	9	research	research	NOUN
fcis-20387	2	10	on	on	ADP
fcis-20387	2	11	air	air	NOUN
fcis-20387	2	12	quality	quality	NOUN
fcis-20387	2	13	prediction	prediction	NOUN
fcis-20387	2	14	based	base	VERB
fcis-20387	2	15	on	on	ADP
fcis-20387	2	16	neural	neural	ADJ
fcis-20387	2	17	networks	network	NOUN
fcis-20387	2	18	ruihao	ruihao	NOUN
fcis-20387	2	19	wan	wan	PROPN
fcis-20387	2	20	university	university	PROPN
fcis-20387	2	21	of	of	ADP
fcis-20387	2	22	southern	southern	PROPN
fcis-20387	2	23	california	california	PROPN
fcis-20387	2	24	,	,	PUNCT
fcis-20387	2	25	los	los	PROPN
fcis-20387	2	26	angeles	angeles	PROPN
fcis-20387	2	27	,	,	PUNCT
fcis-20387	2	28	california	california	PROPN
fcis-20387	2	29	,	,	PUNCT
fcis-20387	2	30	usa	usa	PROPN
fcis-20387	2	31	abstract	abstract	PROPN
fcis-20387	2	32	:	:	PUNCT
fcis-20387	2	33	in	in	ADP
fcis-20387	2	34	view	view	NOUN
fcis-20387	2	35	of	of	ADP
fcis-20387	2	36	the	the	DET
fcis-20387	2	37	increasingly	increasingly	ADV
fcis-20387	2	38	serious	serious	ADJ
fcis-20387	2	39	air	air	NOUN
fcis-20387	2	40	pollution	pollution	NOUN
fcis-20387	2	41	problem	problem	NOUN
fcis-20387	2	42	,	,	PUNCT
fcis-20387	2	43	to	to	PART
fcis-20387	2	44	alleviate	alleviate	VERB
fcis-20387	2	45	the	the	DET
fcis-20387	2	46	harmful	harmful	ADJ
fcis-20387	2	47	effects	effect	NOUN
fcis-20387	2	48	of	of	ADP
fcis-20387	2	49	air	air	NOUN
fcis-20387	2	50	pollution	pollution	NOUN
fcis-20387	2	51	on	on	ADP
fcis-20387	2	52	human	human	ADJ
fcis-20387	2	53	body	body	NOUN
fcis-20387	2	54	and	and	CCONJ
fcis-20387	2	55	society	society	NOUN
fcis-20387	2	56	,	,	PUNCT
fcis-20387	2	57	this	this	DET
fcis-20387	2	58	paper	paper	NOUN
fcis-20387	2	59	studies	study	VERB
fcis-20387	2	60	the	the	DET
fcis-20387	2	61	prediction	prediction	NOUN
fcis-20387	2	62	of	of	ADP
fcis-20387	2	63	air	air	NOUN
fcis-20387	2	64	quality	quality	NOUN
fcis-20387	2	65	.	.	PUNCT
fcis-20387	3	1	due	due	ADP
fcis-20387	3	2	to	to	ADP
fcis-20387	3	3	the	the	DET
fcis-20387	3	4	nonlinear	nonlinear	ADJ
fcis-20387	3	5	,	,	PUNCT
fcis-20387	3	6	regional	regional	ADJ
fcis-20387	3	7	and	and	CCONJ
fcis-20387	3	8	dispersive	dispersive	ADJ
fcis-20387	3	9	characteristics	characteristic	NOUN
fcis-20387	3	10	of	of	ADP
fcis-20387	3	11	pollutant	pollutant	ADJ
fcis-20387	3	12	data	datum	NOUN
fcis-20387	3	13	,	,	PUNCT
fcis-20387	3	14	the	the	DET
fcis-20387	3	15	effective	effective	ADJ
fcis-20387	3	16	utilization	utilization	NOUN
fcis-20387	3	17	rate	rate	NOUN
fcis-20387	3	18	of	of	ADP
fcis-20387	3	19	data	datum	NOUN
fcis-20387	3	20	is	be	AUX
fcis-20387	3	21	low	low	ADJ
fcis-20387	3	22	and	and	CCONJ
fcis-20387	3	23	the	the	DET
fcis-20387	3	24	prediction	prediction	NOUN
fcis-20387	3	25	process	process	NOUN
fcis-20387	3	26	is	be	AUX
fcis-20387	3	27	extremely	extremely	ADV
fcis-20387	3	28	complicated	complicated	ADJ
fcis-20387	3	29	.	.	PUNCT
fcis-20387	4	1	how	how	SCONJ
fcis-20387	4	2	to	to	PART
fcis-20387	4	3	effectively	effectively	ADV
fcis-20387	4	4	build	build	VERB
fcis-20387	4	5	a	a	DET
fcis-20387	4	6	prediction	prediction	NOUN
fcis-20387	4	7	model	model	NOUN
fcis-20387	4	8	and	and	CCONJ
fcis-20387	4	9	improve	improve	VERB
fcis-20387	4	10	the	the	DET
fcis-20387	4	11	prediction	prediction	NOUN
fcis-20387	4	12	accuracy	accuracy	NOUN
fcis-20387	4	13	of	of	ADP
fcis-20387	4	14	air	air	NOUN
fcis-20387	4	15	quality	quality	NOUN
fcis-20387	4	16	is	be	AUX
fcis-20387	4	17	a	a	DET
fcis-20387	4	18	hot	hot	ADJ
fcis-20387	4	19	issue	issue	NOUN
fcis-20387	4	20	in	in	ADP
fcis-20387	4	21	current	current	ADJ
fcis-20387	4	22	research	research	NOUN
fcis-20387	4	23	.	.	PUNCT
fcis-20387	5	1	this	this	DET
fcis-20387	5	2	paper	paper	NOUN
fcis-20387	5	3	mainly	mainly	ADV
fcis-20387	5	4	introduces	introduce	VERB
fcis-20387	5	5	the	the	DET
fcis-20387	5	6	current	current	ADJ
fcis-20387	5	7	research	research	NOUN
fcis-20387	5	8	status	status	NOUN
fcis-20387	5	9	of	of	ADP
fcis-20387	5	10	air	air	NOUN
fcis-20387	5	11	quality	quality	NOUN
fcis-20387	5	12	prediction	prediction	NOUN
fcis-20387	5	13	.	.	PUNCT
fcis-20387	6	1	keywords	keyword	NOUN
fcis-20387	6	2	:	:	PUNCT
fcis-20387	6	3	air	air	NOUN
fcis-20387	6	4	quality	quality	NOUN
fcis-20387	6	5	prediction	prediction	NOUN
fcis-20387	6	6	;	;	PUNCT
fcis-20387	6	7	graph	graph	VERB
fcis-20387	6	8	convolutional	convolutional	ADJ
fcis-20387	6	9	neural	neural	ADJ
fcis-20387	6	10	network	network	NOUN
fcis-20387	6	11	;	;	PUNCT
fcis-20387	6	12	intelligent	intelligent	ADJ
fcis-20387	6	13	optimization	optimization	NOUN
fcis-20387	6	14	algorithm	algorithm	NOUN
fcis-20387	6	15	.	.	PUNCT
fcis-20387	7	1	1	1	X
fcis-20387	7	2	.	.	X
fcis-20387	7	3	introduction	introduction	NOUN
fcis-20387	7	4	with	with	ADP
fcis-20387	7	5	the	the	DET
fcis-20387	7	6	continuous	continuous	ADJ
fcis-20387	7	7	advancement	advancement	NOUN
fcis-20387	7	8	of	of	ADP
fcis-20387	7	9	urbanization	urbanization	NOUN
fcis-20387	7	10	,	,	PUNCT
fcis-20387	7	11	automobile	automobile	NOUN
fcis-20387	7	12	exhaust	exhaust	NOUN
fcis-20387	7	13	and	and	CCONJ
fcis-20387	7	14	industrial	industrial	ADJ
fcis-20387	7	15	emissions	emission	NOUN
fcis-20387	7	16	are	be	AUX
fcis-20387	7	17	increasing	increase	VERB
fcis-20387	7	18	,	,	PUNCT
fcis-20387	7	19	and	and	CCONJ
fcis-20387	7	20	the	the	DET
fcis-20387	7	21	air	air	NOUN
fcis-20387	7	22	pollution	pollution	NOUN
fcis-20387	7	23	problem	problem	NOUN
fcis-20387	7	24	is	be	AUX
fcis-20387	7	25	becoming	become	VERB
fcis-20387	7	26	increasingly	increasingly	ADV
fcis-20387	7	27	serious	serious	ADJ
fcis-20387	7	28	,	,	PUNCT
fcis-20387	7	29	which	which	PRON
fcis-20387	7	30	has	have	VERB
fcis-20387	7	31	a	a	DET
fcis-20387	7	32	very	very	ADV
fcis-20387	7	33	serious	serious	ADJ
fcis-20387	7	34	impact	impact	NOUN
fcis-20387	7	35	on	on	ADP
fcis-20387	7	36	the	the	DET
fcis-20387	7	37	sustainable	sustainable	ADJ
fcis-20387	7	38	development	development	NOUN
fcis-20387	7	39	of	of	ADP
fcis-20387	7	40	the	the	DET
fcis-20387	7	41	country	country	NOUN
fcis-20387	7	42	and	and	CCONJ
fcis-20387	7	43	public	public	ADJ
fcis-20387	7	44	health	health	NOUN
fcis-20387	7	45	.	.	PUNCT
fcis-20387	8	1	from	from	ADP
fcis-20387	8	2	the	the	DET
fcis-20387	8	3	individual	individual	ADJ
fcis-20387	8	4	level	level	NOUN
fcis-20387	8	5	,	,	PUNCT
fcis-20387	8	6	air	air	NOUN
fcis-20387	8	7	pollution	pollution	NOUN
fcis-20387	8	8	has	have	VERB
fcis-20387	8	9	a	a	DET
fcis-20387	8	10	very	very	ADV
fcis-20387	8	11	serious	serious	ADJ
fcis-20387	8	12	harm	harm	NOUN
fcis-20387	8	13	to	to	ADP
fcis-20387	8	14	the	the	DET
fcis-20387	8	15	health	health	NOUN
fcis-20387	8	16	of	of	ADP
fcis-20387	8	17	the	the	DET
fcis-20387	8	18	public	public	NOUN
fcis-20387	8	19	.	.	PUNCT
fcis-20387	9	1	long	long	ADJ
fcis-20387	9	2	-	-	PUNCT
fcis-20387	9	3	term	term	NOUN
fcis-20387	9	4	exposure	exposure	NOUN
fcis-20387	9	5	to	to	ADP
fcis-20387	9	6	polluted	polluted	ADJ
fcis-20387	9	7	atmospheric	atmospheric	ADJ
fcis-20387	9	8	environment	environment	NOUN
fcis-20387	9	9	will	will	AUX
fcis-20387	9	10	lead	lead	VERB
fcis-20387	9	11	to	to	ADP
fcis-20387	9	12	dizziness	dizziness	NOUN
fcis-20387	9	13	,	,	PUNCT
fcis-20387	9	14	skin	skin	NOUN
fcis-20387	9	15	lesions	lesion	NOUN
fcis-20387	9	16	and	and	CCONJ
fcis-20387	9	17	damage	damage	NOUN
fcis-20387	9	18	to	to	ADP
fcis-20387	9	19	normal	normal	ADJ
fcis-20387	9	20	cardiopulmonary	cardiopulmonary	ADJ
fcis-20387	9	21	function	function	NOUN
fcis-20387	9	22	,	,	PUNCT
fcis-20387	9	23	etc	etc	X
fcis-20387	9	24	.	.	X
fcis-20387	9	25	,	,	PUNCT
fcis-20387	9	26	and	and	CCONJ
fcis-20387	9	27	more	more	ADV
fcis-20387	9	28	seriously	seriously	ADV
fcis-20387	9	29	,	,	PUNCT
fcis-20387	9	30	it	it	PRON
fcis-20387	9	31	will	will	AUX
fcis-20387	9	32	induce	induce	VERB
fcis-20387	9	33	cancer	cancer	NOUN
fcis-20387	9	34	.	.	PUNCT
fcis-20387	10	1	children	child	NOUN
fcis-20387	10	2	,	,	PUNCT
fcis-20387	10	3	the	the	DET
fcis-20387	10	4	elderly	elderly	ADJ
fcis-20387	10	5	and	and	CCONJ
fcis-20387	10	6	people	people	NOUN
fcis-20387	10	7	with	with	ADP
fcis-20387	10	8	respiratory	respiratory	ADJ
fcis-20387	10	9	and	and	CCONJ
fcis-20387	10	10	cardiovascular	cardiovascular	ADJ
fcis-20387	10	11	problems	problem	NOUN
fcis-20387	10	12	are	be	AUX
fcis-20387	10	13	particularly	particularly	ADV
fcis-20387	10	14	affected	affect	VERB
fcis-20387	10	15	.	.	PUNCT
fcis-20387	11	1	the	the	DET
fcis-20387	11	2	study	study	NOUN
fcis-20387	11	3	found	find	VERB
fcis-20387	11	4	that	that	SCONJ
fcis-20387	11	5	nearly	nearly	ADV
fcis-20387	11	6	2	2	NUM
fcis-20387	11	7	million	million	NUM
fcis-20387	11	8	childhood	childhood	NOUN
fcis-20387	11	9	asthma	asthma	NOUN
fcis-20387	11	10	cases	case	NOUN
fcis-20387	11	11	were	be	AUX
fcis-20387	11	12	linked	link	VERB
fcis-20387	11	13	to	to	ADP
fcis-20387	11	14	nitrogen	nitrogen	NOUN
fcis-20387	11	15	dioxide	dioxide	NOUN
fcis-20387	11	16	pollution	pollution	NOUN
fcis-20387	11	17	,	,	PUNCT
fcis-20387	11	18	two	two	NUM
fcis-20387	11	19	-	-	PUNCT
fcis-20387	11	20	thirds	third	NOUN
fcis-20387	11	21	of	of	ADP
fcis-20387	11	22	which	which	PRON
fcis-20387	11	23	occurred	occur	VERB
fcis-20387	11	24	in	in	ADP
fcis-20387	11	25	cities	city	NOUN
fcis-20387	11	26	[	[	X
fcis-20387	11	27	1	1	NUM
fcis-20387	11	28	]	]	PUNCT
fcis-20387	11	29	,	,	PUNCT
fcis-20387	11	30	and	and	CCONJ
fcis-20387	11	31	that	that	SCONJ
fcis-20387	11	32	in	in	ADP
fcis-20387	11	33	2019	2019	NUM
fcis-20387	11	34	86	86	NUM
fcis-20387	11	35	%	%	NOUN
fcis-20387	11	36	of	of	ADP
fcis-20387	11	37	the	the	DET
fcis-20387	11	38	global	global	ADJ
fcis-20387	11	39	urban	urban	ADJ
fcis-20387	11	40	population	population	NOUN
fcis-20387	11	41	had	have	VERB
fcis-20387	11	42	excessive	excessive	ADJ
fcis-20387	11	43	pm2.5	pm2.5	ADJ
fcis-20387	11	44	exposure	exposure	NOUN
fcis-20387	11	45	levels	level	NOUN
fcis-20387	11	46	,	,	PUNCT
fcis-20387	11	47	which	which	PRON
fcis-20387	11	48	resulted	result	VERB
fcis-20387	11	49	in	in	ADP
fcis-20387	11	50	1.8	1.8	NUM
fcis-20387	11	51	million	million	NUM
fcis-20387	11	52	deaths	death	NOUN
fcis-20387	11	53	[	[	X
fcis-20387	11	54	2	2	NUM
fcis-20387	11	55	]	]	PUNCT
fcis-20387	11	56	.	.	PUNCT
fcis-20387	12	1	the	the	DET
fcis-20387	12	2	world	world	PROPN
fcis-20387	12	3	health	health	PROPN
fcis-20387	12	4	organization	organization	NOUN
fcis-20387	12	5	(	(	PUNCT
fcis-20387	12	6	who	who	PRON
fcis-20387	12	7	)	)	PUNCT
fcis-20387	12	8	released	release	VERB
fcis-20387	12	9	a	a	DET
fcis-20387	12	10	report	report	NOUN
fcis-20387	12	11	on	on	ADP
fcis-20387	12	12	october	october	PROPN
fcis-20387	12	13	11	11	NUM
fcis-20387	12	14	,	,	PUNCT
fcis-20387	12	15	2021	2021	NUM
fcis-20387	12	16	,	,	PUNCT
fcis-20387	12	17	stating	state	VERB
fcis-20387	12	18	that	that	SCONJ
fcis-20387	12	19	air	air	NOUN
fcis-20387	12	20	pollution	pollution	NOUN
fcis-20387	12	21	has	have	AUX
fcis-20387	12	22	become	become	VERB
fcis-20387	12	23	one	one	NUM
fcis-20387	12	24	of	of	ADP
fcis-20387	12	25	the	the	DET
fcis-20387	12	26	"	"	PUNCT
fcis-20387	12	27	biggest	big	ADJ
fcis-20387	12	28	environmental	environmental	ADJ
fcis-20387	12	29	problems	problem	NOUN
fcis-20387	12	30	threatening	threaten	VERB
fcis-20387	12	31	human	human	ADJ
fcis-20387	12	32	health	health	NOUN
fcis-20387	12	33	"	"	PUNCT
fcis-20387	12	34	,	,	PUNCT
fcis-20387	12	35	with	with	ADP
fcis-20387	12	36	13	13	NUM
fcis-20387	12	37	people	people	NOUN
fcis-20387	12	38	dying	die	VERB
fcis-20387	12	39	every	every	DET
fcis-20387	12	40	minute	minute	NOUN
fcis-20387	12	41	worldwide	worldwide	ADV
fcis-20387	12	42	due	due	ADP
fcis-20387	12	43	to	to	ADP
fcis-20387	12	44	air	air	NOUN
fcis-20387	12	45	pollution	pollution	NOUN
fcis-20387	12	46	[	[	X
fcis-20387	12	47	3	3	NUM
fcis-20387	12	48	]	]	PUNCT
fcis-20387	12	49	.	.	PUNCT
fcis-20387	13	1	at	at	ADP
fcis-20387	13	2	the	the	DET
fcis-20387	13	3	national	national	ADJ
fcis-20387	13	4	level	level	NOUN
fcis-20387	13	5	,	,	PUNCT
fcis-20387	13	6	air	air	NOUN
fcis-20387	13	7	pollution	pollution	NOUN
fcis-20387	13	8	will	will	AUX
fcis-20387	13	9	affect	affect	VERB
fcis-20387	13	10	industrial	industrial	ADJ
fcis-20387	13	11	and	and	CCONJ
fcis-20387	13	12	agricultural	agricultural	ADJ
fcis-20387	13	13	production	production	NOUN
fcis-20387	13	14	,	,	PUNCT
fcis-20387	13	15	causing	cause	VERB
fcis-20387	13	16	huge	huge	ADJ
fcis-20387	13	17	human	human	NOUN
fcis-20387	13	18	,	,	PUNCT
fcis-20387	13	19	material	material	NOUN
fcis-20387	13	20	and	and	CCONJ
fcis-20387	13	21	economic	economic	ADJ
fcis-20387	13	22	losses	loss	NOUN
fcis-20387	13	23	,	,	PUNCT
fcis-20387	13	24	and	and	CCONJ
fcis-20387	13	25	have	have	VERB
fcis-20387	13	26	a	a	DET
fcis-20387	13	27	long	long	ADJ
fcis-20387	13	28	-	-	PUNCT
fcis-20387	13	29	term	term	NOUN
fcis-20387	13	30	impact	impact	NOUN
fcis-20387	13	31	on	on	ADP
fcis-20387	13	32	the	the	DET
fcis-20387	13	33	country	country	NOUN
fcis-20387	13	34	's	's	PART
fcis-20387	13	35	economic	economic	ADJ
fcis-20387	13	36	development	development	NOUN
fcis-20387	14	1	[	[	X
fcis-20387	14	2	4	4	NUM
fcis-20387	14	3	-	-	SYM
fcis-20387	14	4	5	5	NUM
fcis-20387	14	5	]	]	PUNCT
fcis-20387	14	6	,	,	PUNCT
fcis-20387	14	7	as	as	ADV
fcis-20387	14	8	well	well	ADV
fcis-20387	14	9	as	as	ADP
fcis-20387	14	10	many	many	ADJ
fcis-20387	14	11	other	other	ADJ
fcis-20387	14	12	social	social	ADJ
fcis-20387	14	13	problems	problem	NOUN
fcis-20387	14	14	caused	cause	VERB
fcis-20387	14	15	by	by	ADP
fcis-20387	14	16	economic	economic	ADJ
fcis-20387	14	17	impact	impact	NOUN
fcis-20387	14	18	.	.	PUNCT
fcis-20387	15	1	common	common	ADJ
fcis-20387	15	2	sources	source	NOUN
fcis-20387	15	3	of	of	ADP
fcis-20387	15	4	air	air	NOUN
fcis-20387	15	5	pollution	pollution	NOUN
fcis-20387	15	6	include	include	VERB
fcis-20387	15	7	natural	natural	ADJ
fcis-20387	15	8	pollution	pollution	NOUN
fcis-20387	15	9	sources	source	NOUN
fcis-20387	15	10	and	and	CCONJ
fcis-20387	15	11	man	man	NOUN
fcis-20387	15	12	-	-	PUNCT
fcis-20387	15	13	made	make	VERB
fcis-20387	15	14	pollution	pollution	NOUN
fcis-20387	15	15	sources	source	NOUN
fcis-20387	15	16	,	,	PUNCT
fcis-20387	15	17	natural	natural	ADJ
fcis-20387	15	18	pollution	pollution	NOUN
fcis-20387	15	19	sources	source	NOUN
fcis-20387	15	20	are	be	AUX
fcis-20387	15	21	unavoidable	unavoidable	ADJ
fcis-20387	15	22	,	,	PUNCT
fcis-20387	15	23	such	such	ADJ
fcis-20387	15	24	as	as	ADP
fcis-20387	15	25	volcanic	volcanic	ADJ
fcis-20387	15	26	eruption	eruption	NOUN
fcis-20387	15	27	,	,	PUNCT
fcis-20387	15	28	natural	natural	ADJ
fcis-20387	15	29	disasters	disaster	NOUN
fcis-20387	15	30	,	,	PUNCT
fcis-20387	15	31	volcanic	volcanic	ADJ
fcis-20387	15	32	eruption	eruption	NOUN
fcis-20387	15	33	will	will	AUX
fcis-20387	15	34	produce	produce	VERB
fcis-20387	15	35	a	a	DET
fcis-20387	15	36	lot	lot	NOUN
fcis-20387	15	37	of	of	ADP
fcis-20387	15	38	volcanic	volcanic	ADJ
fcis-20387	15	39	ash	ash	NOUN
fcis-20387	15	40	and	and	CCONJ
fcis-20387	15	41	harmful	harmful	ADJ
fcis-20387	15	42	gases	gas	NOUN
fcis-20387	15	43	;	;	PUNCT
fcis-20387	15	44	natural	natural	ADJ
fcis-20387	15	45	disasters	disaster	NOUN
fcis-20387	15	46	include	include	VERB
fcis-20387	15	47	wind	wind	NOUN
fcis-20387	15	48	and	and	CCONJ
fcis-20387	15	49	dust	dust	NOUN
fcis-20387	15	50	storms	storm	NOUN
fcis-20387	15	51	.	.	PUNCT
fcis-20387	16	1	man	man	NOUN
fcis-20387	16	2	-	-	PUNCT
fcis-20387	16	3	made	make	VERB
fcis-20387	16	4	pollution	pollution	NOUN
fcis-20387	16	5	sources	source	NOUN
fcis-20387	16	6	are	be	AUX
fcis-20387	16	7	mainly	mainly	ADV
fcis-20387	16	8	produced	produce	VERB
fcis-20387	16	9	along	along	ADP
fcis-20387	16	10	with	with	ADP
fcis-20387	16	11	economic	economic	ADJ
fcis-20387	16	12	development	development	NOUN
fcis-20387	16	13	,	,	PUNCT
fcis-20387	16	14	including	include	VERB
fcis-20387	16	15	industrial	industrial	ADJ
fcis-20387	16	16	and	and	CCONJ
fcis-20387	16	17	agricultural	agricultural	ADJ
fcis-20387	16	18	gas	gas	NOUN
fcis-20387	16	19	emissions	emission	NOUN
fcis-20387	16	20	and	and	CCONJ
fcis-20387	16	21	transportation	transportation	NOUN
fcis-20387	16	22	gas	gas	NOUN
fcis-20387	16	23	emissions	emission	NOUN
fcis-20387	16	24	.	.	PUNCT
fcis-20387	17	1	in	in	ADP
fcis-20387	17	2	the	the	DET
fcis-20387	17	3	past	past	NOUN
fcis-20387	17	4	,	,	PUNCT
fcis-20387	17	5	the	the	DET
fcis-20387	17	6	state	state	NOUN
fcis-20387	17	7	ignored	ignore	VERB
fcis-20387	17	8	the	the	DET
fcis-20387	17	9	damage	damage	NOUN
fcis-20387	17	10	to	to	ADP
fcis-20387	17	11	the	the	DET
fcis-20387	17	12	environment	environment	NOUN
fcis-20387	17	13	for	for	ADP
fcis-20387	17	14	the	the	DET
fcis-20387	17	15	sake	sake	NOUN
fcis-20387	17	16	of	of	ADP
fcis-20387	17	17	economic	economic	ADJ
fcis-20387	17	18	construction	construction	NOUN
fcis-20387	17	19	,	,	PUNCT
fcis-20387	17	20	and	and	CCONJ
fcis-20387	17	21	now	now	ADV
fcis-20387	17	22	the	the	DET
fcis-20387	17	23	state	state	NOUN
fcis-20387	17	24	has	have	AUX
fcis-20387	17	25	put	put	VERB
fcis-20387	17	26	forward	forward	ADV
fcis-20387	17	27	a	a	DET
fcis-20387	17	28	series	series	NOUN
fcis-20387	17	29	of	of	ADP
fcis-20387	17	30	policies	policy	NOUN
fcis-20387	17	31	to	to	PART
fcis-20387	17	32	strive	strive	VERB
fcis-20387	17	33	for	for	ADP
fcis-20387	17	34	harmony	harmony	NOUN
fcis-20387	17	35	between	between	ADP
fcis-20387	17	36	man	man	NOUN
fcis-20387	17	37	and	and	CCONJ
fcis-20387	17	38	nature	nature	NOUN
fcis-20387	17	39	,	,	PUNCT
fcis-20387	17	40	so	so	SCONJ
fcis-20387	17	41	as	as	SCONJ
fcis-20387	17	42	to	to	PART
fcis-20387	17	43	achieve	achieve	VERB
fcis-20387	17	44	the	the	DET
fcis-20387	17	45	purpose	purpose	NOUN
fcis-20387	17	46	of	of	ADP
fcis-20387	17	47	sustainable	sustainable	ADJ
fcis-20387	17	48	development	development	NOUN
fcis-20387	17	49	and	and	CCONJ
fcis-20387	17	50	alleviate	alleviate	VERB
fcis-20387	17	51	the	the	DET
fcis-20387	17	52	adverse	adverse	ADJ
fcis-20387	17	53	effects	effect	NOUN
fcis-20387	17	54	of	of	ADP
fcis-20387	17	55	air	air	NOUN
fcis-20387	17	56	pollution	pollution	NOUN
fcis-20387	17	57	on	on	ADP
fcis-20387	17	58	human	human	ADJ
fcis-20387	17	59	beings	being	NOUN
fcis-20387	17	60	.	.	PUNCT
fcis-20387	18	1	the	the	DET
fcis-20387	18	2	state	state	NOUN
fcis-20387	18	3	has	have	AUX
fcis-20387	18	4	put	put	VERB
fcis-20387	18	5	forward	forward	ADV
fcis-20387	18	6	many	many	ADJ
fcis-20387	18	7	policies	policy	NOUN
fcis-20387	18	8	,	,	PUNCT
fcis-20387	18	9	from	from	ADP
fcis-20387	18	10	the	the	DET
fcis-20387	18	11	"	"	PUNCT
fcis-20387	18	12	twelfth	twelfth	ADJ
fcis-20387	18	13	five	five	NUM
fcis-20387	18	14	-	-	PUNCT
fcis-20387	18	15	year	year	NOUN
fcis-20387	18	16	plan	plan	NOUN
fcis-20387	18	17	for	for	ADP
fcis-20387	18	18	national	national	ADJ
fcis-20387	18	19	environmental	environmental	ADJ
fcis-20387	18	20	protection	protection	PROPN
fcis-20387	18	21	"	"	PUNCT
fcis-20387	18	22	proposed	propose	VERB
fcis-20387	18	23	by	by	ADP
fcis-20387	18	24	the	the	DET
fcis-20387	18	25	state	state	PROPN
fcis-20387	18	26	council	council	PROPN
fcis-20387	18	27	in	in	ADP
fcis-20387	18	28	2011	2011	NUM
fcis-20387	18	29	to	to	ADP
fcis-20387	18	30	the	the	DET
fcis-20387	18	31	"	"	PUNCT
fcis-20387	18	32	guiding	guide	VERB
fcis-20387	18	33	opinions	opinion	NOUN
fcis-20387	18	34	on	on	ADP
fcis-20387	18	35	building	build	VERB
fcis-20387	18	36	a	a	DET
fcis-20387	18	37	modern	modern	ADJ
fcis-20387	18	38	environmental	environmental	ADJ
fcis-20387	18	39	governance	governance	NOUN
fcis-20387	18	40	system	system	NOUN
fcis-20387	18	41	"	"	PUNCT
fcis-20387	18	42	proposed	propose	VERB
fcis-20387	18	43	by	by	ADP
fcis-20387	18	44	the	the	DET
fcis-20387	18	45	general	general	ADJ
fcis-20387	18	46	office	office	NOUN
fcis-20387	18	47	of	of	ADP
fcis-20387	18	48	the	the	DET
fcis-20387	18	49	cpc	cpc	PROPN
fcis-20387	18	50	central	central	PROPN
fcis-20387	18	51	committee	committee	PROPN
fcis-20387	18	52	and	and	CCONJ
fcis-20387	18	53	the	the	DET
fcis-20387	18	54	general	general	ADJ
fcis-20387	18	55	office	office	NOUN
fcis-20387	18	56	of	of	ADP
fcis-20387	18	57	the	the	DET
fcis-20387	18	58	state	state	NOUN
fcis-20387	18	59	council	council	PROPN
fcis-20387	18	60	in	in	ADP
fcis-20387	18	61	2020	2020	NUM
fcis-20387	18	62	,	,	PUNCT
fcis-20387	18	63	all	all	PRON
fcis-20387	18	64	of	of	ADP
fcis-20387	18	65	which	which	PRON
fcis-20387	18	66	show	show	VERB
fcis-20387	18	67	the	the	DET
fcis-20387	18	68	importance	importance	NOUN
fcis-20387	18	69	of	of	ADP
fcis-20387	18	70	the	the	DET
fcis-20387	18	71	state	state	NOUN
fcis-20387	18	72	to	to	PART
fcis-20387	18	73	air	air	NOUN
fcis-20387	18	74	pollution	pollution	NOUN
fcis-20387	18	75	control	control	NOUN
fcis-20387	18	76	.	.	PUNCT
fcis-20387	19	1	at	at	ADP
fcis-20387	19	2	present	present	ADJ
fcis-20387	19	3	,	,	PUNCT
fcis-20387	19	4	most	most	ADJ
fcis-20387	19	5	parts	part	NOUN
fcis-20387	19	6	of	of	ADP
fcis-20387	19	7	the	the	DET
fcis-20387	19	8	country	country	NOUN
fcis-20387	19	9	are	be	AUX
fcis-20387	19	10	facing	face	VERB
fcis-20387	19	11	severe	severe	ADJ
fcis-20387	19	12	air	air	NOUN
fcis-20387	19	13	pollution	pollution	NOUN
fcis-20387	19	14	problems	problem	NOUN
fcis-20387	19	15	,	,	PUNCT
fcis-20387	19	16	how	how	SCONJ
fcis-20387	19	17	to	to	PART
fcis-20387	19	18	improve	improve	VERB
fcis-20387	19	19	and	and	CCONJ
fcis-20387	19	20	control	control	VERB
fcis-20387	19	21	air	air	NOUN
fcis-20387	19	22	pollution	pollution	NOUN
fcis-20387	19	23	is	be	AUX
fcis-20387	19	24	still	still	ADV
fcis-20387	19	25	the	the	DET
fcis-20387	19	26	focus	focus	NOUN
fcis-20387	19	27	of	of	ADP
fcis-20387	19	28	the	the	DET
fcis-20387	19	29	national	national	PROPN
fcis-20387	19	30	environmental	environmental	PROPN
fcis-20387	19	31	protection	protection	PROPN
fcis-20387	19	32	department	department	PROPN
fcis-20387	19	33	.	.	PUNCT
fcis-20387	20	1	2	2	X
fcis-20387	20	2	.	.	X
fcis-20387	20	3	current	current	ADJ
fcis-20387	20	4	status	status	NOUN
fcis-20387	20	5	of	of	ADP
fcis-20387	20	6	research	research	NOUN
fcis-20387	20	7	on	on	ADP
fcis-20387	20	8	air	air	NOUN
fcis-20387	20	9	quality	quality	NOUN
fcis-20387	20	10	prediction	prediction	NOUN
fcis-20387	20	11	with	with	ADP
fcis-20387	20	12	the	the	DET
fcis-20387	20	13	rapid	rapid	ADJ
fcis-20387	20	14	development	development	NOUN
fcis-20387	20	15	of	of	ADP
fcis-20387	20	16	neural	neural	ADJ
fcis-20387	20	17	networks	network	NOUN
fcis-20387	20	18	,	,	PUNCT
fcis-20387	20	19	more	more	ADJ
fcis-20387	20	20	and	and	CCONJ
fcis-20387	20	21	more	more	ADJ
fcis-20387	20	22	researchers	researcher	NOUN
fcis-20387	20	23	use	use	VERB
fcis-20387	20	24	neural	neural	ADJ
fcis-20387	20	25	network	network	NOUN
fcis-20387	20	26	-	-	PUNCT
fcis-20387	20	27	based	base	VERB
fcis-20387	20	28	models	model	NOUN
fcis-20387	20	29	in	in	ADP
fcis-20387	20	30	the	the	DET
fcis-20387	20	31	field	field	NOUN
fcis-20387	20	32	of	of	ADP
fcis-20387	20	33	air	air	NOUN
fcis-20387	20	34	quality	quality	NOUN
fcis-20387	20	35	prediction	prediction	NOUN
fcis-20387	20	36	.	.	PUNCT
fcis-20387	21	1	however	however	ADV
fcis-20387	21	2	,	,	PUNCT
fcis-20387	21	3	the	the	DET
fcis-20387	21	4	hyperparameters	hyperparameter	NOUN
fcis-20387	21	5	in	in	ADP
fcis-20387	21	6	the	the	DET
fcis-20387	21	7	prediction	prediction	NOUN
fcis-20387	21	8	model	model	NOUN
fcis-20387	21	9	,	,	PUNCT
fcis-20387	21	10	such	such	ADJ
fcis-20387	21	11	as	as	ADP
fcis-20387	21	12	the	the	DET
fcis-20387	21	13	number	number	NOUN
fcis-20387	21	14	of	of	ADP
fcis-20387	21	15	network	network	NOUN
fcis-20387	21	16	layers	layer	NOUN
fcis-20387	21	17	,	,	PUNCT
fcis-20387	21	18	the	the	DET
fcis-20387	21	19	number	number	NOUN
fcis-20387	21	20	of	of	ADP
fcis-20387	21	21	neurons	neuron	NOUN
fcis-20387	21	22	in	in	ADP
fcis-20387	21	23	each	each	DET
fcis-20387	21	24	layer	layer	NOUN
fcis-20387	21	25	,	,	PUNCT
fcis-20387	21	26	the	the	DET
fcis-20387	21	27	learning	learning	NOUN
fcis-20387	21	28	rate	rate	NOUN
fcis-20387	21	29	and	and	CCONJ
fcis-20387	21	30	other	other	ADJ
fcis-20387	21	31	parameters	parameter	NOUN
fcis-20387	21	32	that	that	PRON
fcis-20387	21	33	affect	affect	VERB
fcis-20387	21	34	the	the	DET
fcis-20387	21	35	performance	performance	NOUN
fcis-20387	21	36	of	of	ADP
fcis-20387	21	37	the	the	DET
fcis-20387	21	38	model	model	NOUN
fcis-20387	21	39	,	,	PUNCT
fcis-20387	21	40	are	be	AUX
fcis-20387	21	41	often	often	ADV
fcis-20387	21	42	determined	determine	VERB
fcis-20387	21	43	subjectively	subjectively	ADV
fcis-20387	21	44	,	,	PUNCT
fcis-20387	21	45	making	make	VERB
fcis-20387	21	46	the	the	DET
fcis-20387	21	47	prediction	prediction	NOUN
fcis-20387	21	48	accuracy	accuracy	NOUN
fcis-20387	21	49	of	of	ADP
fcis-20387	21	50	the	the	DET
fcis-20387	21	51	model	model	NOUN
fcis-20387	21	52	less	less	ADJ
fcis-20387	21	53	than	than	ADP
fcis-20387	21	54	the	the	DET
fcis-20387	21	55	optimal	optimal	ADJ
fcis-20387	21	56	state	state	NOUN
fcis-20387	21	57	.	.	PUNCT
fcis-20387	22	1	as	as	ADP
fcis-20387	22	2	one	one	NUM
fcis-20387	22	3	of	of	ADP
fcis-20387	22	4	the	the	DET
fcis-20387	22	5	most	most	ADV
fcis-20387	22	6	traditional	traditional	ADJ
fcis-20387	22	7	neural	neural	ADJ
fcis-20387	22	8	networks	network	NOUN
fcis-20387	22	9	,	,	PUNCT
fcis-20387	22	10	back	back	NOUN
fcis-20387	22	11	-	-	PUNCT
fcis-20387	22	12	propagation	propagation	NOUN
fcis-20387	22	13	(	(	PUNCT
fcis-20387	22	14	bp	bp	PROPN
fcis-20387	22	15	)	)	PUNCT
fcis-20387	22	16	neural	neural	ADJ
fcis-20387	22	17	networks	network	NOUN
fcis-20387	22	18	have	have	VERB
fcis-20387	22	19	disadvantages	disadvantage	NOUN
fcis-20387	22	20	such	such	ADJ
fcis-20387	22	21	as	as	ADP
fcis-20387	22	22	easy	easy	ADJ
fcis-20387	22	23	to	to	PART
fcis-20387	22	24	stop	stop	VERB
fcis-20387	22	25	training	training	NOUN
fcis-20387	22	26	at	at	ADP
fcis-20387	22	27	the	the	DET
fcis-20387	22	28	local	local	ADJ
fcis-20387	22	29	optimum	optimum	ADJ
fcis-20387	22	30	point	point	NOUN
fcis-20387	22	31	and	and	CCONJ
fcis-20387	22	32	easy	easy	ADJ
fcis-20387	22	33	to	to	PART
fcis-20387	22	34	affect	affect	VERB
fcis-20387	22	35	the	the	DET
fcis-20387	22	36	model	model	NOUN
fcis-20387	22	37	performance	performance	NOUN
fcis-20387	22	38	by	by	ADP
fcis-20387	22	39	parameter	parameter	NOUN
fcis-20387	22	40	values	value	NOUN
fcis-20387	22	41	[	[	X
fcis-20387	22	42	6	6	NUM
fcis-20387	22	43	]	]	PUNCT
fcis-20387	22	44	,	,	PUNCT
fcis-20387	22	45	which	which	PRON
fcis-20387	22	46	makes	make	VERB
fcis-20387	22	47	many	many	ADJ
fcis-20387	22	48	scholars	scholar	NOUN
fcis-20387	22	49	use	use	VERB
fcis-20387	22	50	various	various	ADJ
fcis-20387	22	51	methods	method	NOUN
fcis-20387	22	52	to	to	PART
fcis-20387	22	53	improve	improve	VERB
fcis-20387	22	54	them	they	PRON
fcis-20387	22	55	.	.	PUNCT
fcis-20387	23	1	in	in	ADP
fcis-20387	23	2	these	these	DET
fcis-20387	23	3	studies	study	NOUN
fcis-20387	23	4	,	,	PUNCT
fcis-20387	23	5	a	a	DET
fcis-20387	23	6	lot	lot	NOUN
fcis-20387	23	7	of	of	ADP
fcis-20387	23	8	achievements	achievement	NOUN
fcis-20387	23	9	have	have	AUX
fcis-20387	23	10	been	be	AUX
fcis-20387	23	11	made	make	VERB
fcis-20387	23	12	,	,	PUNCT
fcis-20387	23	13	such	such	ADJ
fcis-20387	23	14	as	as	ADP
fcis-20387	23	15	using	use	VERB
fcis-20387	23	16	bayesian	bayesian	NOUN
fcis-20387	23	17	normalization	normalization	NOUN
fcis-20387	23	18	to	to	PART
fcis-20387	23	19	improve	improve	VERB
fcis-20387	23	20	the	the	DET
fcis-20387	23	21	generalization	generalization	NOUN
fcis-20387	23	22	ability	ability	NOUN
fcis-20387	23	23	of	of	ADP
fcis-20387	23	24	the	the	DET
fcis-20387	23	25	network	network	NOUN
fcis-20387	23	26	and	and	CCONJ
fcis-20387	23	27	improving	improve	VERB
fcis-20387	23	28	the	the	DET
fcis-20387	23	29	gradient	gradient	ADJ
fcis-20387	23	30	descent	descent	NOUN
fcis-20387	23	31	method	method	NOUN
fcis-20387	23	32	.	.	PUNCT
fcis-20387	24	1	in	in	ADP
fcis-20387	24	2	addition	addition	NOUN
fcis-20387	24	3	,	,	PUNCT
fcis-20387	24	4	some	some	DET
fcis-20387	24	5	scholars	scholar	NOUN
fcis-20387	24	6	also	also	ADV
fcis-20387	24	7	use	use	VERB
fcis-20387	24	8	heuristic	heuristic	ADJ
fcis-20387	24	9	algorithms	algorithm	NOUN
fcis-20387	24	10	to	to	PART
fcis-20387	24	11	improve	improve	VERB
fcis-20387	24	12	the	the	DET
fcis-20387	24	13	prediction	prediction	NOUN
fcis-20387	24	14	model	model	NOUN
fcis-20387	24	15	of	of	ADP
fcis-20387	24	16	bp	bp	PROPN
fcis-20387	24	17	neural	neural	ADJ
fcis-20387	24	18	network	network	NOUN
fcis-20387	24	19	.	.	PUNCT
fcis-20387	25	1	zhou	zhou	PROPN
fcis-20387	25	2	et	et	PROPN
fcis-20387	25	3	al	al	PROPN
fcis-20387	25	4	.	.	PUNCT
fcis-20387	26	1	[	[	X
fcis-20387	26	2	7	7	X
fcis-20387	26	3	]	]	PUNCT
fcis-20387	26	4	optimized	optimize	VERB
fcis-20387	26	5	bp	bp	PROPN
fcis-20387	26	6	neural	neural	ADJ
fcis-20387	26	7	network	network	NOUN
fcis-20387	26	8	by	by	ADP
fcis-20387	26	9	combining	combine	VERB
fcis-20387	26	10	genetic	genetic	ADJ
fcis-20387	26	11	algorithm	algorithm	NOUN
fcis-20387	26	12	(	(	PUNCT
fcis-20387	26	13	ga	ga	NOUN
fcis-20387	26	14	)	)	PUNCT
fcis-20387	26	15	and	and	CCONJ
fcis-20387	26	16	simulated	simulate	VERB
fcis-20387	26	17	annealing	annealing	NOUN
fcis-20387	26	18	(	(	PUNCT
fcis-20387	26	19	sa	sa	NOUN
fcis-20387	26	20	)	)	PUNCT
fcis-20387	26	21	algorithms	algorithm	NOUN
fcis-20387	26	22	,	,	PUNCT
fcis-20387	26	23	and	and	CCONJ
fcis-20387	26	24	the	the	DET
fcis-20387	26	25	results	result	NOUN
fcis-20387	26	26	show	show	VERB
fcis-20387	26	27	that	that	SCONJ
fcis-20387	26	28	the	the	DET
fcis-20387	26	29	ga	ga	PROPN
fcis-20387	26	30	-	-	PUNCT
fcis-20387	26	31	sa	sa	PROPN
fcis-20387	26	32	based	base	VERB
fcis-20387	26	33	bp	bp	PROPN
fcis-20387	26	34	neural	neural	ADJ
fcis-20387	26	35	network	network	NOUN
fcis-20387	26	36	has	have	VERB
fcis-20387	26	37	strong	strong	ADJ
fcis-20387	26	38	generalization	generalization	NOUN
fcis-20387	26	39	ability	ability	NOUN
fcis-20387	26	40	and	and	CCONJ
fcis-20387	26	41	global	global	ADJ
fcis-20387	26	42	search	search	NOUN
fcis-20387	26	43	ability	ability	NOUN
fcis-20387	26	44	,	,	PUNCT
fcis-20387	26	45	and	and	CCONJ
fcis-20387	26	46	high	high	ADJ
fcis-20387	26	47	accuracy	accuracy	NOUN
fcis-20387	26	48	rate	rate	NOUN
fcis-20387	26	49	.	.	PUNCT
fcis-20387	27	1	huang	huang	PROPN
fcis-20387	28	1	[	[	X
fcis-20387	28	2	8	8	NUM
fcis-20387	28	3	]	]	PUNCT
fcis-20387	28	4	put	put	VERB
fcis-20387	28	5	forward	forward	ADV
fcis-20387	28	6	a	a	DET
fcis-20387	28	7	kind	kind	NOUN
fcis-20387	28	8	of	of	ADV
fcis-20387	28	9	based	base	VERB
fcis-20387	28	10	on	on	ADP
fcis-20387	28	11	improved	improve	VERB
fcis-20387	28	12	particle	particle	NOUN
fcis-20387	28	13	swarm	swarm	NOUN
fcis-20387	28	14	optimization	optimization	NOUN
fcis-20387	28	15	algorithm	algorithm	NOUN
fcis-20387	28	16	(	(	PUNCT
fcis-20387	28	17	particle	particle	NOUN
fcis-20387	28	18	swarm	swarm	NOUN
fcis-20387	28	19	optimization	optimization	NOUN
fcis-20387	28	20	,	,	PUNCT
fcis-20387	28	21	pso	pso	NOUN
fcis-20387	28	22	)	)	PUNCT
fcis-20387	28	23	method	method	NOUN
fcis-20387	28	24	of	of	ADP
fcis-20387	28	25	bp	bp	PROPN
fcis-20387	28	26	neural	neural	ADJ
fcis-20387	28	27	network	network	NOUN
fcis-20387	28	28	to	to	PART
fcis-20387	28	29	predict	predict	VERB
fcis-20387	28	30	the	the	DET
fcis-20387	28	31	aqi	aqi	PROPN
fcis-20387	28	32	,	,	PUNCT
fcis-20387	28	33	make	make	VERB
fcis-20387	28	34	the	the	DET
fcis-20387	28	35	prediction	prediction	NOUN
fcis-20387	28	36	results	result	VERB
fcis-20387	28	37	more	more	ADV
fcis-20387	28	38	accurate	accurate	ADJ
fcis-20387	28	39	.	.	PUNCT
fcis-20387	29	1	other	other	ADJ
fcis-20387	29	2	prediction	prediction	NOUN
fcis-20387	29	3	models	model	NOUN
fcis-20387	29	4	have	have	AUX
fcis-20387	29	5	also	also	ADV
fcis-20387	29	6	been	be	AUX
fcis-20387	29	7	improved	improve	VERB
fcis-20387	29	8	.	.	PUNCT
fcis-20387	30	1	fan	fan	NOUN
fcis-20387	30	2	wenting	wente	VERB
fcis-20387	31	1	[	[	X
fcis-20387	31	2	9	9	NUM
fcis-20387	31	3	]	]	PUNCT
fcis-20387	31	4	et	et	NOUN
fcis-20387	31	5	al	al	PROPN
fcis-20387	31	6	proposed	propose	VERB
fcis-20387	31	7	the	the	DET
fcis-20387	31	8	optimized	optimize	VERB
fcis-20387	31	9	support	support	NOUN
fcis-20387	31	10	vector	vector	NOUN
fcis-20387	31	11	machine	machine	NOUN
fcis-20387	31	12	(	(	PUNCT
fcis-20387	31	13	svm	svm	ADJ
fcis-20387	31	14	)	)	PUNCT
fcis-20387	31	15	prediction	prediction	NOUN
fcis-20387	31	16	model	model	NOUN
fcis-20387	31	17	based	base	VERB
fcis-20387	31	18	on	on	ADP
fcis-20387	31	19	the	the	DET
fcis-20387	31	20	improved	improve	VERB
fcis-20387	31	21	firefly	firefly	NOUN
fcis-20387	31	22	algorithm	algorithm	NOUN
fcis-20387	31	23	(	(	PUNCT
fcis-20387	31	24	fa	fa	NOUN
fcis-20387	31	25	)	)	PUNCT
fcis-20387	31	26	.	.	PUNCT
fcis-20387	32	1	gao	gao	PROPN
fcis-20387	32	2	shuai	shuai	PROPN
fcis-20387	32	3	et	et	PROPN
fcis-20387	32	4	al	al	PROPN
fcis-20387	32	5	.	.	PUNCT
fcis-20387	33	1	[	[	X
fcis-20387	33	2	10	10	NUM
fcis-20387	33	3	]	]	PUNCT
fcis-20387	33	4	proposed	propose	VERB
fcis-20387	33	5	a	a	DET
fcis-20387	33	6	method	method	NOUN
fcis-20387	33	7	that	that	SCONJ
fcis-20387	33	8	combined	combine	VERB
fcis-20387	33	9	svm	svm	NOUN
fcis-20387	33	10	with	with	ADP
fcis-20387	33	11	the	the	DET
fcis-20387	33	12	traditional	traditional	ADJ
fcis-20387	33	13	moth	moth	ADJ
fcis-20387	33	14	-	-	PUNCT
fcis-20387	33	15	flame	flame	NOUN
fcis-20387	33	16	optimization	optimization	NOUN
fcis-20387	33	17	(	(	PUNCT
fcis-20387	33	18	mfo	mfo	NOUN
fcis-20387	33	19	)	)	PUNCT
fcis-20387	33	20	algorithm	algorithm	NOUN
fcis-20387	33	21	.	.	PUNCT
fcis-20387	34	1	in	in	ADP
fcis-20387	34	2	addition	addition	NOUN
fcis-20387	34	3	,	,	PUNCT
fcis-20387	34	4	some	some	DET
fcis-20387	34	5	scholars	scholar	NOUN
fcis-20387	34	6	have	have	AUX
fcis-20387	34	7	proposed	propose	VERB
fcis-20387	34	8	optimization	optimization	NOUN
fcis-20387	34	9	algorithms	algorithm	NOUN
fcis-20387	34	10	to	to	PART
fcis-20387	34	11	optimize	optimize	VERB
fcis-20387	34	12	lstm	lstm	NOUN
fcis-20387	34	13	.	.	PUNCT
fcis-20387	35	1	44	44	NUM
fcis-20387	35	2	zhang	zhang	PROPN
fcis-20387	35	3	et	et	PROPN
fcis-20387	35	4	al	al	PROPN
fcis-20387	35	5	.	.	PUNCT
fcis-20387	36	1	[	[	X
fcis-20387	36	2	11	11	NUM
fcis-20387	36	3	]	]	PUNCT
fcis-20387	36	4	optimized	optimize	VERB
fcis-20387	36	5	lstm	lstm	PROPN
fcis-20387	36	6	neural	neural	ADJ
fcis-20387	36	7	network	network	NOUN
fcis-20387	36	8	parameters	parameter	NOUN
fcis-20387	36	9	,	,	PUNCT
fcis-20387	36	10	and	and	CCONJ
fcis-20387	36	11	then	then	ADV
fcis-20387	36	12	applied	apply	VERB
fcis-20387	36	13	them	they	PRON
fcis-20387	36	14	to	to	PART
fcis-20387	36	15	air	air	NOUN
fcis-20387	36	16	quality	quality	NOUN
fcis-20387	36	17	prediction	prediction	NOUN
fcis-20387	36	18	of	of	ADP
fcis-20387	36	19	different	different	ADJ
fcis-20387	36	20	cities	city	NOUN
fcis-20387	36	21	,	,	PUNCT
fcis-20387	36	22	and	and	CCONJ
fcis-20387	36	23	achieved	achieve	VERB
fcis-20387	36	24	certain	certain	ADJ
fcis-20387	36	25	results	result	NOUN
fcis-20387	36	26	.	.	PUNCT
fcis-20387	37	1	al	al	PROPN
fcis-20387	37	2	-	-	PUNCT
fcis-20387	37	3	janabi	janabi	NOUN
fcis-20387	37	4	et	et	NOUN
fcis-20387	37	5	al	al	PROPN
fcis-20387	37	6	.	.	PUNCT
fcis-20387	38	1	[	[	X
fcis-20387	38	2	12	12	NUM
fcis-20387	38	3	]	]	PUNCT
fcis-20387	38	4	used	use	VERB
fcis-20387	38	5	pso	pso	NOUN
fcis-20387	38	6	to	to	PART
fcis-20387	38	7	optimize	optimize	VERB
fcis-20387	38	8	lstm	lstm	PROPN
fcis-20387	38	9	’s	’s	PART
fcis-20387	38	10	weight	weight	NOUN
fcis-20387	38	11	,	,	PUNCT
fcis-20387	38	12	deviation	deviation	NOUN
fcis-20387	38	13	,	,	PUNCT
fcis-20387	38	14	number	number	NOUN
fcis-20387	38	15	of	of	ADP
fcis-20387	38	16	hidden	hidden	ADJ
fcis-20387	38	17	layers	layer	NOUN
fcis-20387	38	18	,	,	PUNCT
fcis-20387	38	19	number	number	NOUN
fcis-20387	38	20	of	of	ADP
fcis-20387	38	21	nodes	node	NOUN
fcis-20387	38	22	in	in	ADP
fcis-20387	38	23	each	each	DET
fcis-20387	38	24	hidden	hide	VERB
fcis-20387	38	25	layer	layer	NOUN
fcis-20387	38	26	and	and	CCONJ
fcis-20387	38	27	activation	activation	NOUN
fcis-20387	38	28	function	function	NOUN
fcis-20387	38	29	parameters	parameter	NOUN
fcis-20387	38	30	to	to	PART
fcis-20387	38	31	predict	predict	VERB
fcis-20387	38	32	the	the	DET
fcis-20387	38	33	air	air	NOUN
fcis-20387	38	34	pollutant	pollutant	ADJ
fcis-20387	38	35	concentration	concentration	NOUN
fcis-20387	38	36	for	for	ADP
fcis-20387	38	37	the	the	DET
fcis-20387	38	38	next	next	ADJ
fcis-20387	38	39	two	two	NUM
fcis-20387	38	40	days	day	NOUN
fcis-20387	38	41	.	.	PUNCT
fcis-20387	39	1	from	from	ADP
fcis-20387	39	2	the	the	DET
fcis-20387	39	3	above	above	ADJ
fcis-20387	39	4	research	research	NOUN
fcis-20387	39	5	,	,	PUNCT
fcis-20387	39	6	it	it	PRON
fcis-20387	39	7	can	can	AUX
fcis-20387	39	8	be	be	AUX
fcis-20387	39	9	found	find	VERB
fcis-20387	39	10	that	that	SCONJ
fcis-20387	39	11	the	the	DET
fcis-20387	39	12	improved	improve	VERB
fcis-20387	39	13	neural	neural	ADJ
fcis-20387	39	14	network	network	NOUN
fcis-20387	39	15	based	base	VERB
fcis-20387	39	16	on	on	ADP
fcis-20387	39	17	intelligent	intelligent	ADJ
fcis-20387	39	18	optimization	optimization	NOUN
fcis-20387	39	19	algorithm	algorithm	NOUN
fcis-20387	39	20	has	have	AUX
fcis-20387	39	21	been	be	AUX
fcis-20387	39	22	widely	widely	ADV
fcis-20387	39	23	used	use	VERB
fcis-20387	39	24	and	and	CCONJ
fcis-20387	39	25	can	can	AUX
fcis-20387	39	26	get	get	VERB
fcis-20387	39	27	very	very	ADV
fcis-20387	39	28	good	good	ADJ
fcis-20387	39	29	results	result	NOUN
fcis-20387	39	30	.	.	PUNCT
fcis-20387	40	1	therefore	therefore	ADV
fcis-20387	40	2	,	,	PUNCT
fcis-20387	40	3	it	it	PRON
fcis-20387	40	4	is	be	AUX
fcis-20387	40	5	particularly	particularly	ADV
fcis-20387	40	6	important	important	ADJ
fcis-20387	40	7	to	to	PART
fcis-20387	40	8	select	select	VERB
fcis-20387	40	9	a	a	DET
fcis-20387	40	10	suitable	suitable	ADJ
fcis-20387	40	11	optimization	optimization	NOUN
fcis-20387	40	12	algorithm	algorithm	NOUN
fcis-20387	40	13	for	for	ADP
fcis-20387	40	14	the	the	DET
fcis-20387	40	15	parameter	parameter	NOUN
fcis-20387	40	16	optimization	optimization	NOUN
fcis-20387	40	17	of	of	ADP
fcis-20387	40	18	neural	neural	ADJ
fcis-20387	40	19	network	network	NOUN
fcis-20387	40	20	.	.	PUNCT
fcis-20387	41	1	sawyer	sawyer	PROPN
fcis-20387	41	2	found	find	VERB
fcis-20387	41	3	must	must	AUX
fcis-20387	41	4	search	search	VERB
fcis-20387	41	5	algorithm	algorithm	NOUN
fcis-20387	41	6	(	(	PUNCT
fcis-20387	41	7	beetle	beetle	NOUN
fcis-20387	41	8	antennae	antennae	NOUN
fcis-20387	41	9	search	search	NOUN
fcis-20387	41	10	algorithm	algorithm	NOUN
fcis-20387	41	11	,	,	PUNCT
fcis-20387	41	12	bas	bas	PROPN
fcis-20387	41	13	)	)	PUNCT
fcis-20387	41	14	in	in	ADP
fcis-20387	41	15	time	time	NOUN
fcis-20387	41	16	complexity	complexity	NOUN
fcis-20387	41	17	and	and	CCONJ
fcis-20387	41	18	space	space	NOUN
fcis-20387	41	19	complexity	complexity	NOUN
fcis-20387	41	20	is	be	AUX
fcis-20387	41	21	lower	low	ADJ
fcis-20387	41	22	than	than	ADP
fcis-20387	41	23	most	most	ADJ
fcis-20387	41	24	of	of	ADP
fcis-20387	41	25	the	the	DET
fcis-20387	41	26	swarm	swarm	NOUN
fcis-20387	41	27	intelligence	intelligence	NOUN
fcis-20387	41	28	algorithm	algorithm	NOUN
fcis-20387	41	29	and	and	CCONJ
fcis-20387	41	30	higher	high	ADJ
fcis-20387	41	31	efficiency	efficiency	NOUN
fcis-20387	41	32	.	.	PUNCT
fcis-20387	42	1	manifold	manifold	ADJ
fcis-20387	42	2	method	method	NOUN
fcis-20387	42	3	et	et	PROPN
fcis-20387	42	4	al	al	PROPN
fcis-20387	42	5	.	.	PUNCT
fcis-20387	43	1	[	[	X
fcis-20387	43	2	13	13	NUM
fcis-20387	43	3	]	]	PUNCT
fcis-20387	43	4	compared	compare	VERB
fcis-20387	43	5	bas	bas	PROPN
fcis-20387	43	6	algorithm	algorithm	NOUN
fcis-20387	43	7	with	with	ADP
fcis-20387	43	8	many	many	ADJ
fcis-20387	43	9	new	new	ADJ
fcis-20387	43	10	and	and	CCONJ
fcis-20387	43	11	mainstream	mainstream	ADJ
fcis-20387	43	12	intelligent	intelligent	ADJ
fcis-20387	43	13	optimization	optimization	NOUN
fcis-20387	43	14	algorithms	algorithm	NOUN
fcis-20387	43	15	and	and	CCONJ
fcis-20387	43	16	found	find	VERB
fcis-20387	43	17	that	that	SCONJ
fcis-20387	43	18	bas	bas	PROPN
fcis-20387	43	19	algorithm	algorithm	NOUN
fcis-20387	43	20	has	have	VERB
fcis-20387	43	21	strong	strong	ADJ
fcis-20387	43	22	competitiveness	competitiveness	NOUN
fcis-20387	43	23	.	.	PUNCT
fcis-20387	44	1	compared	compare	VERB
fcis-20387	44	2	with	with	ADP
fcis-20387	44	3	pso	pso	NOUN
fcis-20387	44	4	algorithm	algorithm	NOUN
fcis-20387	44	5	,	,	PUNCT
fcis-20387	44	6	bas	bas	PROPN
fcis-20387	44	7	algorithm	algorithm	NOUN
fcis-20387	44	8	has	have	VERB
fcis-20387	44	9	stronger	strong	ADJ
fcis-20387	44	10	ability	ability	NOUN
fcis-20387	44	11	to	to	PART
fcis-20387	44	12	jump	jump	VERB
fcis-20387	44	13	out	out	ADP
fcis-20387	44	14	of	of	ADP
fcis-20387	44	15	local	local	ADJ
fcis-20387	44	16	extreme	extreme	ADJ
fcis-20387	44	17	values	value	NOUN
fcis-20387	44	18	and	and	CCONJ
fcis-20387	44	19	faster	fast	ADJ
fcis-20387	44	20	convergence	convergence	NOUN
fcis-20387	44	21	speed	speed	NOUN
fcis-20387	44	22	.	.	PUNCT
fcis-20387	45	1	compared	compare	VERB
fcis-20387	45	2	with	with	ADP
fcis-20387	45	3	ga	ga	PROPN
fcis-20387	45	4	algorithm	algorithm	NOUN
fcis-20387	45	5	,	,	PUNCT
fcis-20387	45	6	bas	bas	PROPN
fcis-20387	45	7	algorithm	algorithm	PROPN
fcis-20387	45	8	has	have	VERB
fcis-20387	45	9	the	the	DET
fcis-20387	45	10	advantages	advantage	NOUN
fcis-20387	45	11	of	of	ADP
fcis-20387	45	12	simple	simple	ADJ
fcis-20387	45	13	operation	operation	NOUN
fcis-20387	45	14	,	,	PUNCT
fcis-20387	45	15	less	less	ADJ
fcis-20387	45	16	computation	computation	NOUN
fcis-20387	45	17	and	and	CCONJ
fcis-20387	45	18	faster	fast	ADV
fcis-20387	45	19	running	running	NOUN
fcis-20387	45	20	speed	speed	NOUN
fcis-20387	45	21	.	.	PUNCT
fcis-20387	46	1	compared	compare	VERB
fcis-20387	46	2	with	with	ADP
fcis-20387	46	3	fa	fa	PROPN
fcis-20387	46	4	algorithm	algorithm	NOUN
fcis-20387	46	5	,	,	PUNCT
fcis-20387	46	6	bas	bas	PROPN
fcis-20387	46	7	algorithm	algorithm	NOUN
fcis-20387	46	8	is	be	AUX
fcis-20387	46	9	less	less	ADV
fcis-20387	46	10	affected	affect	VERB
fcis-20387	46	11	by	by	ADP
fcis-20387	46	12	parameter	parameter	NOUN
fcis-20387	46	13	sensitivity	sensitivity	NOUN
fcis-20387	46	14	.	.	PUNCT
fcis-20387	47	1	compared	compare	VERB
fcis-20387	47	2	with	with	ADP
fcis-20387	47	3	bat	bat	NOUN
fcis-20387	47	4	algorithm	algorithm	NOUN
fcis-20387	47	5	(	(	PUNCT
fcis-20387	47	6	ba	ba	NOUN
fcis-20387	47	7	)	)	PUNCT
fcis-20387	47	8	and	and	CCONJ
fcis-20387	47	9	artificial	artificial	ADJ
fcis-20387	47	10	bee	bee	NOUN
fcis-20387	47	11	colony	colony	NOUN
fcis-20387	47	12	(	(	PUNCT
fcis-20387	47	13	abc	abc	PROPN
fcis-20387	47	14	)	)	PUNCT
fcis-20387	47	15	,	,	PUNCT
fcis-20387	47	16	bas	bas	PROPN
fcis-20387	47	17	algorithm	algorithm	PROPN
fcis-20387	47	18	has	have	VERB
fcis-20387	47	19	higher	high	ADJ
fcis-20387	47	20	computational	computational	ADJ
fcis-20387	47	21	efficiency	efficiency	NOUN
fcis-20387	47	22	and	and	CCONJ
fcis-20387	47	23	lower	low	ADJ
fcis-20387	47	24	computational	computational	ADJ
fcis-20387	47	25	complexity	complexity	NOUN
fcis-20387	47	26	.	.	PUNCT
fcis-20387	48	1	however	however	ADV
fcis-20387	48	2	,	,	PUNCT
fcis-20387	48	3	bas	bas	PROPN
fcis-20387	48	4	algorithm	algorithm	NOUN
fcis-20387	48	5	is	be	AUX
fcis-20387	48	6	also	also	ADV
fcis-20387	48	7	prone	prone	ADJ
fcis-20387	48	8	to	to	PART
fcis-20387	48	9	fall	fall	VERB
fcis-20387	48	10	into	into	ADP
fcis-20387	48	11	local	local	ADJ
fcis-20387	48	12	extreme	extreme	ADJ
fcis-20387	48	13	values	value	NOUN
fcis-20387	48	14	and	and	CCONJ
fcis-20387	48	15	the	the	DET
fcis-20387	48	16	optimization	optimization	NOUN
fcis-20387	48	17	results	result	NOUN
fcis-20387	48	18	are	be	AUX
fcis-20387	48	19	unstable	unstable	ADJ
fcis-20387	48	20	.	.	PUNCT
fcis-20387	49	1	therefore	therefore	ADV
fcis-20387	49	2	,	,	PUNCT
fcis-20387	49	3	this	this	DET
fcis-20387	49	4	paper	paper	NOUN
fcis-20387	49	5	improves	improve	VERB
fcis-20387	49	6	bas	bas	PROPN
fcis-20387	49	7	algorithm	algorithm	NOUN
fcis-20387	49	8	to	to	PART
fcis-20387	49	9	solve	solve	VERB
fcis-20387	49	10	the	the	DET
fcis-20387	49	11	above	above	ADJ
fcis-20387	49	12	problems	problem	NOUN
fcis-20387	49	13	and	and	CCONJ
fcis-20387	49	14	applies	apply	VERB
fcis-20387	49	15	it	it	PRON
fcis-20387	49	16	to	to	PART
fcis-20387	49	17	optimize	optimize	VERB
fcis-20387	49	18	the	the	DET
fcis-20387	49	19	air	air	NOUN
fcis-20387	49	20	quality	quality	NOUN
fcis-20387	49	21	prediction	prediction	NOUN
fcis-20387	49	22	model	model	NOUN
fcis-20387	49	23	.	.	PUNCT
fcis-20387	50	1	air	air	PROPN
fcis-20387	50	2	quality	quality	PROPN
fcis-20387	50	3	prediction	prediction	NOUN
fcis-20387	50	4	refers	refer	VERB
fcis-20387	50	5	to	to	ADP
fcis-20387	50	6	the	the	DET
fcis-20387	50	7	analysis	analysis	NOUN
fcis-20387	50	8	of	of	ADP
fcis-20387	50	9	air	air	NOUN
fcis-20387	50	10	quality	quality	NOUN
fcis-20387	50	11	time	time	NOUN
fcis-20387	50	12	series	series	PROPN
fcis-20387	50	13	data	datum	NOUN
fcis-20387	50	14	and	and	CCONJ
fcis-20387	50	15	other	other	ADJ
fcis-20387	50	16	factors	factor	NOUN
fcis-20387	50	17	affecting	affect	VERB
fcis-20387	50	18	air	air	NOUN
fcis-20387	50	19	quality	quality	NOUN
fcis-20387	50	20	,	,	PUNCT
fcis-20387	50	21	such	such	ADJ
fcis-20387	50	22	as	as	ADP
fcis-20387	50	23	meteorological	meteorological	ADJ
fcis-20387	50	24	data	datum	NOUN
fcis-20387	50	25	,	,	PUNCT
fcis-20387	50	26	factory	factory	NOUN
fcis-20387	50	27	waste	waste	NOUN
fcis-20387	50	28	emission	emission	NOUN
fcis-20387	50	29	data	datum	NOUN
fcis-20387	50	30	and	and	CCONJ
fcis-20387	50	31	vehicle	vehicle	NOUN
fcis-20387	50	32	flow	flow	NOUN
fcis-20387	50	33	data	datum	NOUN
fcis-20387	50	34	,	,	PUNCT
fcis-20387	50	35	and	and	CCONJ
fcis-20387	50	36	the	the	DET
fcis-20387	50	37	establishment	establishment	NOUN
fcis-20387	50	38	of	of	ADP
fcis-20387	50	39	a	a	DET
fcis-20387	50	40	prediction	prediction	NOUN
fcis-20387	50	41	model	model	NOUN
fcis-20387	50	42	to	to	PART
fcis-20387	50	43	predict	predict	VERB
fcis-20387	50	44	and	and	CCONJ
fcis-20387	50	45	estimate	estimate	VERB
fcis-20387	50	46	the	the	DET
fcis-20387	50	47	change	change	NOUN
fcis-20387	50	48	trend	trend	NOUN
fcis-20387	50	49	of	of	ADP
fcis-20387	50	50	air	air	NOUN
fcis-20387	50	51	quality	quality	NOUN
fcis-20387	50	52	and	and	CCONJ
fcis-20387	50	53	air	air	NOUN
fcis-20387	50	54	quality	quality	NOUN
fcis-20387	50	55	index	index	NOUN
fcis-20387	50	56	in	in	ADP
fcis-20387	50	57	the	the	DET
fcis-20387	50	58	future	future	ADJ
fcis-20387	50	59	period	period	NOUN
fcis-20387	50	60	.	.	PUNCT
fcis-20387	51	1	through	through	ADP
fcis-20387	51	2	the	the	DET
fcis-20387	51	3	study	study	NOUN
fcis-20387	51	4	of	of	ADP
fcis-20387	51	5	air	air	NOUN
fcis-20387	51	6	quality	quality	NOUN
fcis-20387	51	7	prediction	prediction	NOUN
fcis-20387	51	8	,	,	PUNCT
fcis-20387	51	9	the	the	DET
fcis-20387	51	10	public	public	NOUN
fcis-20387	51	11	can	can	AUX
fcis-20387	51	12	be	be	AUX
fcis-20387	51	13	provided	provide	VERB
fcis-20387	51	14	with	with	ADP
fcis-20387	51	15	the	the	DET
fcis-20387	51	16	latest	late	ADJ
fcis-20387	51	17	air	air	NOUN
fcis-20387	51	18	quality	quality	NOUN
fcis-20387	51	19	status	status	NOUN
fcis-20387	51	20	,	,	PUNCT
fcis-20387	51	21	so	so	SCONJ
fcis-20387	51	22	that	that	SCONJ
fcis-20387	51	23	they	they	PRON
fcis-20387	51	24	can	can	AUX
fcis-20387	51	25	make	make	VERB
fcis-20387	51	26	corresponding	corresponding	ADJ
fcis-20387	51	27	protection	protection	NOUN
fcis-20387	51	28	measures	measure	NOUN
fcis-20387	51	29	according	accord	VERB
fcis-20387	51	30	to	to	ADP
fcis-20387	51	31	their	their	PRON
fcis-20387	51	32	actual	actual	ADJ
fcis-20387	51	33	conditions	condition	NOUN
fcis-20387	51	34	,	,	PUNCT
fcis-20387	51	35	and	and	CCONJ
fcis-20387	51	36	it	it	PRON
fcis-20387	51	37	can	can	AUX
fcis-20387	51	38	also	also	ADV
fcis-20387	51	39	provide	provide	VERB
fcis-20387	51	40	intuitive	intuitive	ADJ
fcis-20387	51	41	reference	reference	NOUN
fcis-20387	51	42	basis	basis	NOUN
fcis-20387	51	43	for	for	SCONJ
fcis-20387	51	44	the	the	DET
fcis-20387	51	45	ecological	ecological	ADJ
fcis-20387	51	46	environment	environment	PROPN
fcis-20387	51	47	department	department	PROPN
fcis-20387	51	48	to	to	PART
fcis-20387	51	49	formulate	formulate	VERB
fcis-20387	51	50	various	various	ADJ
fcis-20387	51	51	environmental	environmental	ADJ
fcis-20387	51	52	improvement	improvement	NOUN
fcis-20387	51	53	plans	plan	NOUN
fcis-20387	51	54	to	to	PART
fcis-20387	51	55	guide	guide	VERB
fcis-20387	51	56	the	the	DET
fcis-20387	51	57	prevention	prevention	NOUN
fcis-20387	51	58	and	and	CCONJ
fcis-20387	51	59	control	control	NOUN
fcis-20387	51	60	of	of	ADP
fcis-20387	51	61	air	air	NOUN
fcis-20387	51	62	pollution	pollution	NOUN
fcis-20387	52	1	[	[	X
fcis-20387	52	2	14	14	NUM
fcis-20387	52	3	]	]	PUNCT
fcis-20387	52	4	.	.	PUNCT
fcis-20387	53	1	deep	deep	ADJ
fcis-20387	53	2	learning	learning	NOUN
fcis-20387	53	3	is	be	AUX
fcis-20387	53	4	developed	develop	VERB
fcis-20387	53	5	based	base	VERB
fcis-20387	53	6	on	on	ADP
fcis-20387	53	7	artificial	artificial	ADJ
fcis-20387	53	8	neural	neural	ADJ
fcis-20387	53	9	networks	network	NOUN
fcis-20387	53	10	and	and	CCONJ
fcis-20387	53	11	belongs	belong	VERB
fcis-20387	53	12	to	to	ADP
fcis-20387	53	13	a	a	DET
fcis-20387	53	14	subset	subset	NOUN
fcis-20387	53	15	of	of	ADP
fcis-20387	53	16	machine	machine	NOUN
fcis-20387	53	17	learning	learning	NOUN
fcis-20387	53	18	.	.	PUNCT
fcis-20387	54	1	the	the	DET
fcis-20387	54	2	biggest	big	ADJ
fcis-20387	54	3	advantage	advantage	NOUN
fcis-20387	54	4	of	of	ADP
fcis-20387	54	5	deep	deep	ADJ
fcis-20387	54	6	learning	learning	NOUN
fcis-20387	54	7	technology	technology	NOUN
fcis-20387	54	8	compared	compare	VERB
fcis-20387	54	9	to	to	ADP
fcis-20387	54	10	traditional	traditional	ADJ
fcis-20387	54	11	methods	method	NOUN
fcis-20387	54	12	is	be	AUX
fcis-20387	54	13	the	the	DET
fcis-20387	54	14	learning	learning	NOUN
fcis-20387	54	15	and	and	CCONJ
fcis-20387	54	16	feature	feature	VERB
fcis-20387	54	17	extraction	extraction	NOUN
fcis-20387	54	18	capabilities	capability	NOUN
fcis-20387	54	19	of	of	ADP
fcis-20387	54	20	deep	deep	ADJ
fcis-20387	54	21	learning	learning	NOUN
fcis-20387	54	22	models	model	NOUN
fcis-20387	54	23	themselves	themselves	PRON
fcis-20387	54	24	.	.	PUNCT
fcis-20387	55	1	based	base	VERB
fcis-20387	55	2	on	on	ADP
fcis-20387	55	3	deep	deep	ADJ
fcis-20387	55	4	learning	learning	NOUN
fcis-20387	55	5	methods	method	NOUN
fcis-20387	55	6	,	,	PUNCT
fcis-20387	55	7	manual	manual	ADJ
fcis-20387	55	8	work	work	NOUN
fcis-20387	55	9	in	in	ADP
fcis-20387	55	10	the	the	DET
fcis-20387	55	11	feature	feature	NOUN
fcis-20387	55	12	extraction	extraction	NOUN
fcis-20387	55	13	stage	stage	NOUN
fcis-20387	55	14	can	can	AUX
fcis-20387	55	15	be	be	AUX
fcis-20387	55	16	reduced	reduce	VERB
fcis-20387	55	17	to	to	ADP
fcis-20387	55	18	a	a	DET
fcis-20387	55	19	certain	certain	ADJ
fcis-20387	55	20	extent	extent	NOUN
fcis-20387	55	21	,	,	PUNCT
fcis-20387	55	22	and	and	CCONJ
fcis-20387	55	23	the	the	DET
fcis-20387	55	24	prediction	prediction	NOUN
fcis-20387	55	25	accuracy	accuracy	NOUN
fcis-20387	55	26	of	of	ADP
fcis-20387	55	27	the	the	DET
fcis-20387	55	28	model	model	NOUN
fcis-20387	55	29	has	have	AUX
fcis-20387	55	30	been	be	AUX
fcis-20387	55	31	improved	improve	VERB
fcis-20387	55	32	compared	compare	VERB
fcis-20387	55	33	to	to	ADP
fcis-20387	55	34	traditional	traditional	ADJ
fcis-20387	55	35	methods	method	NOUN
fcis-20387	55	36	.	.	PUNCT
fcis-20387	56	1	this	this	PRON
fcis-20387	56	2	is	be	AUX
fcis-20387	56	3	why	why	SCONJ
fcis-20387	56	4	this	this	DET
fcis-20387	56	5	article	article	NOUN
fcis-20387	56	6	proposes	propose	VERB
fcis-20387	56	7	to	to	PART
fcis-20387	56	8	study	study	VERB
fcis-20387	56	9	air	air	NOUN
fcis-20387	56	10	quality	quality	NOUN
fcis-20387	56	11	prediction	prediction	NOUN
fcis-20387	56	12	based	base	VERB
fcis-20387	56	13	on	on	ADP
fcis-20387	56	14	deep	deep	ADJ
fcis-20387	56	15	learning	learning	NOUN
fcis-20387	56	16	.	.	PUNCT
fcis-20387	57	1	deep	deep	ADJ
fcis-20387	57	2	neural	neural	ADJ
fcis-20387	57	3	networks	network	NOUN
fcis-20387	57	4	have	have	AUX
fcis-20387	57	5	demonstrated	demonstrate	VERB
fcis-20387	57	6	their	their	PRON
fcis-20387	57	7	analytical	analytical	ADJ
fcis-20387	57	8	capabilities	capability	NOUN
fcis-20387	57	9	in	in	ADP
fcis-20387	57	10	sequence	sequence	NOUN
fcis-20387	57	11	data	datum	NOUN
fcis-20387	57	12	,	,	PUNCT
fcis-20387	57	13	but	but	CCONJ
fcis-20387	57	14	there	there	PRON
fcis-20387	57	15	is	be	VERB
fcis-20387	57	16	still	still	ADV
fcis-20387	57	17	limited	limit	VERB
fcis-20387	57	18	research	research	NOUN
fcis-20387	57	19	on	on	ADP
fcis-20387	57	20	deep	deep	ADJ
fcis-20387	57	21	learning	learning	NOUN
fcis-20387	57	22	models	model	NOUN
fcis-20387	57	23	for	for	ADP
fcis-20387	57	24	spatiotemporal	spatiotemporal	ADJ
fcis-20387	57	25	feature	feature	NOUN
fcis-20387	57	26	extraction	extraction	NOUN
fcis-20387	57	27	.	.	PUNCT
fcis-20387	58	1	in	in	ADP
fcis-20387	58	2	order	order	NOUN
fcis-20387	58	3	to	to	PART
fcis-20387	58	4	design	design	VERB
fcis-20387	58	5	better	well	ADJ
fcis-20387	58	6	spatiotemporal	spatiotemporal	ADJ
fcis-20387	58	7	sequence	sequence	NOUN
fcis-20387	58	8	prediction	prediction	NOUN
fcis-20387	58	9	models	model	NOUN
fcis-20387	58	10	based	base	VERB
fcis-20387	58	11	on	on	ADP
fcis-20387	58	12	deep	deep	ADJ
fcis-20387	58	13	learning	learning	NOUN
fcis-20387	58	14	technology	technology	NOUN
fcis-20387	58	15	,	,	PUNCT
fcis-20387	58	16	several	several	ADJ
fcis-20387	58	17	mainstream	mainstream	ADJ
fcis-20387	58	18	deep	deep	ADJ
fcis-20387	58	19	learning	learning	NOUN
fcis-20387	58	20	models	model	NOUN
fcis-20387	58	21	for	for	ADP
fcis-20387	58	22	air	air	NOUN
fcis-20387	58	23	quality	quality	NOUN
fcis-20387	58	24	prediction	prediction	NOUN
fcis-20387	58	25	will	will	AUX
fcis-20387	58	26	be	be	AUX
fcis-20387	58	27	introduced	introduce	VERB
fcis-20387	58	28	below	below	ADV
fcis-20387	58	29	.	.	PUNCT
fcis-20387	59	1	due	due	ADP
fcis-20387	59	2	to	to	ADP
fcis-20387	59	3	the	the	DET
fcis-20387	59	4	nonlinear	nonlinear	ADJ
fcis-20387	59	5	,	,	PUNCT
fcis-20387	59	6	regional	regional	ADJ
fcis-20387	59	7	and	and	CCONJ
fcis-20387	59	8	dispersive	dispersive	ADJ
fcis-20387	59	9	characteristics	characteristic	NOUN
fcis-20387	59	10	of	of	ADP
fcis-20387	59	11	pollutant	pollutant	ADJ
fcis-20387	59	12	data	datum	NOUN
fcis-20387	59	13	,	,	PUNCT
fcis-20387	59	14	the	the	DET
fcis-20387	59	15	effective	effective	ADJ
fcis-20387	59	16	utilization	utilization	NOUN
fcis-20387	59	17	rate	rate	NOUN
fcis-20387	59	18	of	of	ADP
fcis-20387	59	19	data	datum	NOUN
fcis-20387	59	20	is	be	AUX
fcis-20387	59	21	low	low	ADJ
fcis-20387	59	22	and	and	CCONJ
fcis-20387	59	23	the	the	DET
fcis-20387	59	24	prediction	prediction	NOUN
fcis-20387	59	25	process	process	NOUN
fcis-20387	59	26	is	be	AUX
fcis-20387	59	27	extremely	extremely	ADV
fcis-20387	59	28	complicated	complicated	ADJ
fcis-20387	59	29	.	.	PUNCT
fcis-20387	60	1	how	how	SCONJ
fcis-20387	60	2	to	to	PART
fcis-20387	60	3	effectively	effectively	ADV
fcis-20387	60	4	build	build	VERB
fcis-20387	60	5	a	a	DET
fcis-20387	60	6	prediction	prediction	NOUN
fcis-20387	60	7	model	model	NOUN
fcis-20387	60	8	and	and	CCONJ
fcis-20387	60	9	improve	improve	VERB
fcis-20387	60	10	the	the	DET
fcis-20387	60	11	prediction	prediction	NOUN
fcis-20387	60	12	accuracy	accuracy	NOUN
fcis-20387	60	13	of	of	ADP
fcis-20387	60	14	air	air	NOUN
fcis-20387	60	15	quality	quality	NOUN
fcis-20387	60	16	is	be	AUX
fcis-20387	60	17	a	a	DET
fcis-20387	60	18	hot	hot	ADJ
fcis-20387	60	19	issue	issue	NOUN
fcis-20387	60	20	in	in	ADP
fcis-20387	60	21	current	current	ADJ
fcis-20387	60	22	research	research	NOUN
fcis-20387	60	23	.	.	PUNCT
fcis-20387	61	1	based	base	VERB
fcis-20387	61	2	on	on	ADP
fcis-20387	61	3	deep	deep	ADJ
fcis-20387	61	4	learning	learning	NOUN
fcis-20387	61	5	,	,	PUNCT
fcis-20387	61	6	this	this	DET
fcis-20387	61	7	paper	paper	NOUN
fcis-20387	61	8	builds	build	VERB
fcis-20387	61	9	a	a	DET
fcis-20387	61	10	spatialtemporal	spatialtemporal	ADJ
fcis-20387	61	11	feature	feature	NOUN
fcis-20387	61	12	extraction	extraction	NOUN
fcis-20387	61	13	air	air	NOUN
fcis-20387	61	14	quality	quality	NOUN
fcis-20387	61	15	prediction	prediction	NOUN
fcis-20387	61	16	model	model	NOUN
fcis-20387	61	17	,	,	PUNCT
fcis-20387	61	18	dynamically	dynamically	ADV
fcis-20387	61	19	analyzes	analyze	VERB
fcis-20387	61	20	the	the	DET
fcis-20387	61	21	spatial	spatial	ADJ
fcis-20387	61	22	relationship	relationship	NOUN
fcis-20387	61	23	between	between	ADP
fcis-20387	61	24	monitoring	monitor	VERB
fcis-20387	61	25	stations	station	NOUN
fcis-20387	61	26	,	,	PUNCT
fcis-20387	61	27	and	and	CCONJ
fcis-20387	61	28	learns	learn	VERB
fcis-20387	61	29	the	the	DET
fcis-20387	61	30	internal	internal	ADJ
fcis-20387	61	31	change	change	NOUN
fcis-20387	61	32	rule	rule	NOUN
fcis-20387	61	33	of	of	ADP
fcis-20387	61	34	historical	historical	ADJ
fcis-20387	61	35	air	air	NOUN
fcis-20387	61	36	quality	quality	NOUN
fcis-20387	61	37	data	datum	NOUN
fcis-20387	61	38	based	base	VERB
fcis-20387	61	39	on	on	ADP
fcis-20387	61	40	the	the	DET
fcis-20387	61	41	deep	deep	ADJ
fcis-20387	61	42	learning	learning	NOUN
fcis-20387	61	43	model	model	NOUN
fcis-20387	61	44	to	to	PART
fcis-20387	61	45	achieve	achieve	VERB
fcis-20387	61	46	deeper	deep	ADJ
fcis-20387	61	47	air	air	NOUN
fcis-20387	61	48	quality	quality	NOUN
fcis-20387	61	49	prediction	prediction	NOUN
fcis-20387	61	50	.	.	PUNCT
fcis-20387	62	1	as	as	ADP
fcis-20387	62	2	an	an	DET
fcis-20387	62	3	important	important	ADJ
fcis-20387	62	4	means	mean	NOUN
fcis-20387	62	5	of	of	ADP
fcis-20387	62	6	air	air	NOUN
fcis-20387	62	7	pollution	pollution	NOUN
fcis-20387	62	8	prevention	prevention	NOUN
fcis-20387	62	9	and	and	CCONJ
fcis-20387	62	10	control	control	NOUN
fcis-20387	62	11	,	,	PUNCT
fcis-20387	62	12	air	air	NOUN
fcis-20387	62	13	quality	quality	NOUN
fcis-20387	62	14	prediction	prediction	NOUN
fcis-20387	62	15	plays	play	VERB
fcis-20387	62	16	an	an	DET
fcis-20387	62	17	indispensable	indispensable	ADJ
fcis-20387	62	18	role	role	NOUN
fcis-20387	62	19	for	for	SCONJ
fcis-20387	62	20	the	the	DET
fcis-20387	62	21	country	country	NOUN
fcis-20387	62	22	to	to	PART
fcis-20387	62	23	take	take	VERB
fcis-20387	62	24	appropriate	appropriate	ADJ
fcis-20387	62	25	prevention	prevention	NOUN
fcis-20387	62	26	and	and	CCONJ
fcis-20387	62	27	control	control	NOUN
fcis-20387	62	28	measures	measure	NOUN
fcis-20387	62	29	.	.	PUNCT
fcis-20387	63	1	as	as	SCONJ
fcis-20387	63	2	is	be	AUX
fcis-20387	63	3	well	well	ADV
fcis-20387	63	4	known	know	VERB
fcis-20387	63	5	,	,	PUNCT
fcis-20387	63	6	substances	substance	NOUN
fcis-20387	63	7	in	in	ADP
fcis-20387	63	8	the	the	DET
fcis-20387	63	9	air	air	NOUN
fcis-20387	63	10	are	be	AUX
fcis-20387	63	11	constantly	constantly	ADV
fcis-20387	63	12	moving	move	VERB
fcis-20387	63	13	,	,	PUNCT
fcis-20387	63	14	so	so	CCONJ
fcis-20387	63	15	the	the	DET
fcis-20387	63	16	concentration	concentration	NOUN
fcis-20387	63	17	of	of	ADP
fcis-20387	63	18	pollutants	pollutant	NOUN
fcis-20387	63	19	monitored	monitor	VERB
fcis-20387	63	20	by	by	ADP
fcis-20387	63	21	air	air	NOUN
fcis-20387	63	22	quality	quality	NOUN
fcis-20387	63	23	monitoring	monitoring	NOUN
fcis-20387	63	24	stations	station	NOUN
fcis-20387	63	25	is	be	AUX
fcis-20387	63	26	also	also	ADV
fcis-20387	63	27	constantly	constantly	ADV
fcis-20387	63	28	changing	change	VERB
fcis-20387	63	29	and	and	CCONJ
fcis-20387	63	30	easily	easily	ADV
fcis-20387	63	31	influenced	influence	VERB
fcis-20387	63	32	by	by	ADP
fcis-20387	63	33	the	the	DET
fcis-20387	63	34	surrounding	surround	VERB
fcis-20387	63	35	environment	environment	NOUN
fcis-20387	63	36	,	,	PUNCT
fcis-20387	63	37	which	which	PRON
fcis-20387	63	38	leads	lead	VERB
fcis-20387	63	39	to	to	ADP
fcis-20387	63	40	a	a	DET
fcis-20387	63	41	certain	certain	ADJ
fcis-20387	63	42	spatial	spatial	ADJ
fcis-20387	63	43	correlation	correlation	NOUN
fcis-20387	63	44	between	between	ADP
fcis-20387	63	45	different	different	ADJ
fcis-20387	63	46	stations	station	NOUN
fcis-20387	63	47	.	.	PUNCT
fcis-20387	64	1	when	when	SCONJ
fcis-20387	64	2	the	the	DET
fcis-20387	64	3	trend	trend	NOUN
fcis-20387	64	4	of	of	ADP
fcis-20387	64	5	most	most	ADJ
fcis-20387	64	6	data	datum	NOUN
fcis-20387	64	7	changes	change	NOUN
fcis-20387	64	8	between	between	ADP
fcis-20387	64	9	different	different	ADJ
fcis-20387	64	10	sites	site	NOUN
fcis-20387	64	11	is	be	AUX
fcis-20387	64	12	the	the	DET
fcis-20387	64	13	same	same	ADJ
fcis-20387	64	14	or	or	CCONJ
fcis-20387	64	15	opposite	opposite	ADJ
fcis-20387	64	16	and	and	CCONJ
fcis-20387	64	17	the	the	DET
fcis-20387	64	18	fluctuation	fluctuation	NOUN
fcis-20387	64	19	amplitude	amplitude	NOUN
fcis-20387	64	20	is	be	AUX
fcis-20387	64	21	similar	similar	ADJ
fcis-20387	64	22	,	,	PUNCT
fcis-20387	64	23	it	it	PRON
fcis-20387	64	24	is	be	AUX
fcis-20387	64	25	considered	consider	VERB
fcis-20387	64	26	that	that	SCONJ
fcis-20387	64	27	there	there	PRON
fcis-20387	64	28	is	be	VERB
fcis-20387	64	29	spatial	spatial	ADJ
fcis-20387	64	30	correlation	correlation	NOUN
fcis-20387	64	31	between	between	ADP
fcis-20387	64	32	sites	site	NOUN
fcis-20387	64	33	.	.	PUNCT
fcis-20387	65	1	during	during	ADP
fcis-20387	65	2	the	the	DET
fcis-20387	65	3	model	model	NOUN
fcis-20387	65	4	training	training	NOUN
fcis-20387	65	5	process	process	NOUN
fcis-20387	65	6	,	,	PUNCT
fcis-20387	65	7	combining	combine	VERB
fcis-20387	65	8	site	site	NOUN
fcis-20387	65	9	data	datum	NOUN
fcis-20387	65	10	with	with	ADP
fcis-20387	65	11	spatial	spatial	ADJ
fcis-20387	65	12	correlation	correlation	NOUN
fcis-20387	65	13	for	for	ADP
fcis-20387	65	14	joint	joint	ADJ
fcis-20387	65	15	training	training	NOUN
fcis-20387	65	16	can	can	AUX
fcis-20387	65	17	learn	learn	VERB
fcis-20387	65	18	effective	effective	ADJ
fcis-20387	65	19	spatial	spatial	ADJ
fcis-20387	65	20	features	feature	NOUN
fcis-20387	65	21	that	that	PRON
fcis-20387	65	22	exist	exist	VERB
fcis-20387	65	23	within	within	ADP
fcis-20387	65	24	them	they	PRON
fcis-20387	65	25	,	,	PUNCT
fcis-20387	65	26	which	which	PRON
fcis-20387	65	27	is	be	AUX
fcis-20387	65	28	more	more	ADV
fcis-20387	65	29	conducive	conducive	ADJ
fcis-20387	65	30	to	to	ADP
fcis-20387	65	31	improving	improve	VERB
fcis-20387	65	32	prediction	prediction	NOUN
fcis-20387	65	33	accuracy	accuracy	NOUN
fcis-20387	65	34	.	.	PUNCT
fcis-20387	66	1	on	on	ADP
fcis-20387	66	2	the	the	DET
fcis-20387	66	3	contrary	contrary	NOUN
fcis-20387	66	4	,	,	PUNCT
fcis-20387	66	5	if	if	SCONJ
fcis-20387	66	6	site	site	NOUN
fcis-20387	66	7	data	datum	NOUN
fcis-20387	66	8	that	that	PRON
fcis-20387	66	9	is	be	AUX
fcis-20387	66	10	not	not	PART
fcis-20387	66	11	related	relate	VERB
fcis-20387	66	12	to	to	ADP
fcis-20387	66	13	the	the	DET
fcis-20387	66	14	target	target	NOUN
fcis-20387	66	15	site	site	NOUN
fcis-20387	66	16	is	be	AUX
fcis-20387	66	17	included	include	VERB
fcis-20387	66	18	,	,	PUNCT
fcis-20387	66	19	it	it	PRON
fcis-20387	66	20	will	will	AUX
fcis-20387	66	21	affect	affect	VERB
fcis-20387	66	22	the	the	DET
fcis-20387	66	23	model	model	NOUN
fcis-20387	66	24	's	's	PART
fcis-20387	66	25	learning	learning	NOUN
fcis-20387	66	26	ability	ability	NOUN
fcis-20387	66	27	and	and	CCONJ
fcis-20387	66	28	reduce	reduce	VERB
fcis-20387	66	29	learning	learn	VERB
fcis-20387	66	30	accuracy	accuracy	NOUN
fcis-20387	66	31	.	.	PUNCT
fcis-20387	67	1	choosing	choose	VERB
fcis-20387	67	2	relevant	relevant	ADJ
fcis-20387	67	3	sites	site	NOUN
fcis-20387	67	4	has	have	VERB
fcis-20387	67	5	a	a	DET
fcis-20387	67	6	significant	significant	ADJ
fcis-20387	67	7	impact	impact	NOUN
fcis-20387	67	8	on	on	ADP
fcis-20387	67	9	the	the	DET
fcis-20387	67	10	prediction	prediction	NOUN
fcis-20387	67	11	accuracy	accuracy	NOUN
fcis-20387	67	12	of	of	ADP
fcis-20387	67	13	the	the	DET
fcis-20387	67	14	model	model	NOUN
fcis-20387	67	15	.	.	PUNCT
fcis-20387	68	1	in	in	ADP
fcis-20387	68	2	previous	previous	ADJ
fcis-20387	68	3	studies	study	NOUN
fcis-20387	68	4	,	,	PUNCT
fcis-20387	68	5	although	although	SCONJ
fcis-20387	68	6	researchers	researcher	NOUN
fcis-20387	68	7	considered	consider	VERB
fcis-20387	68	8	the	the	DET
fcis-20387	68	9	impact	impact	NOUN
fcis-20387	68	10	of	of	ADP
fcis-20387	68	11	selecting	select	VERB
fcis-20387	68	12	relevant	relevant	ADJ
fcis-20387	68	13	sites	site	NOUN
fcis-20387	68	14	on	on	ADP
fcis-20387	68	15	later	later	ADJ
fcis-20387	68	16	prediction	prediction	NOUN
fcis-20387	68	17	,	,	PUNCT
fcis-20387	68	18	they	they	PRON
fcis-20387	68	19	only	only	ADV
fcis-20387	68	20	conducted	conduct	VERB
fcis-20387	68	21	static	static	ADJ
fcis-20387	68	22	correlation	correlation	NOUN
fcis-20387	68	23	analysis	analysis	NOUN
fcis-20387	68	24	on	on	ADP
fcis-20387	68	25	different	different	ADJ
fcis-20387	68	26	sites	site	NOUN
fcis-20387	68	27	using	use	VERB
fcis-20387	68	28	pearson	pearson	PROPN
fcis-20387	68	29	correlation	correlation	NOUN
fcis-20387	68	30	coefficients	coefficient	NOUN
fcis-20387	68	31	based	base	VERB
fcis-20387	68	32	on	on	ADP
fcis-20387	68	33	aqi	aqi	PROPN
fcis-20387	68	34	time	time	NOUN
fcis-20387	68	35	series	series	PROPN
fcis-20387	68	36	data	datum	NOUN
fcis-20387	68	37	between	between	ADP
fcis-20387	68	38	sites	site	NOUN
fcis-20387	68	39	,	,	PUNCT
fcis-20387	68	40	without	without	ADP
fcis-20387	68	41	considering	consider	VERB
fcis-20387	68	42	that	that	SCONJ
fcis-20387	68	43	the	the	DET
fcis-20387	68	44	correlation	correlation	NOUN
fcis-20387	68	45	between	between	ADP
fcis-20387	68	46	sites	site	NOUN
fcis-20387	68	47	in	in	ADP
fcis-20387	68	48	space	space	NOUN
fcis-20387	68	49	changes	change	NOUN
fcis-20387	68	50	dynamically	dynamically	ADV
fcis-20387	68	51	over	over	ADP
fcis-20387	68	52	time	time	NOUN
fcis-20387	68	53	.	.	PUNCT
fcis-20387	69	1	therefore	therefore	ADV
fcis-20387	69	2	,	,	PUNCT
fcis-20387	69	3	using	use	VERB
fcis-20387	69	4	static	static	ADJ
fcis-20387	69	5	correlation	correlation	NOUN
fcis-20387	69	6	analysis	analysis	NOUN
fcis-20387	69	7	methods	method	NOUN
fcis-20387	69	8	still	still	ADV
fcis-20387	69	9	results	result	VERB
fcis-20387	69	10	in	in	ADP
fcis-20387	69	11	some	some	DET
fcis-20387	69	12	noise	noise	NOUN
fcis-20387	69	13	in	in	ADP
fcis-20387	69	14	the	the	DET
fcis-20387	69	15	training	training	NOUN
fcis-20387	69	16	data	datum	NOUN
fcis-20387	69	17	,	,	PUNCT
fcis-20387	69	18	which	which	PRON
fcis-20387	69	19	affects	affect	VERB
fcis-20387	69	20	the	the	DET
fcis-20387	69	21	prediction	prediction	NOUN
fcis-20387	69	22	accuracy	accuracy	NOUN
fcis-20387	69	23	of	of	ADP
fcis-20387	69	24	the	the	DET
fcis-20387	69	25	model	model	NOUN
fcis-20387	69	26	.	.	PUNCT
fcis-20387	70	1	3	3	X
fcis-20387	70	2	.	.	X
fcis-20387	70	3	current	current	ADJ
fcis-20387	70	4	research	research	NOUN
fcis-20387	70	5	status	status	NOUN
fcis-20387	70	6	of	of	ADP
fcis-20387	70	7	air	air	NOUN
fcis-20387	70	8	quality	quality	NOUN
fcis-20387	70	9	prediction	prediction	NOUN
fcis-20387	70	10	based	base	VERB
fcis-20387	70	11	on	on	ADP
fcis-20387	70	12	optimized	optimize	VERB
fcis-20387	70	13	neural	neural	ADJ
fcis-20387	70	14	networks	network	NOUN
fcis-20387	70	15	with	with	ADP
fcis-20387	70	16	the	the	DET
fcis-20387	70	17	rapid	rapid	ADJ
fcis-20387	70	18	development	development	NOUN
fcis-20387	70	19	of	of	ADP
fcis-20387	70	20	neural	neural	ADJ
fcis-20387	70	21	networks	network	NOUN
fcis-20387	70	22	,	,	PUNCT
fcis-20387	70	23	more	more	ADJ
fcis-20387	70	24	and	and	CCONJ
fcis-20387	70	25	more	more	ADJ
fcis-20387	70	26	researchers	researcher	NOUN
fcis-20387	70	27	are	be	AUX
fcis-20387	70	28	using	use	VERB
fcis-20387	70	29	neural	neural	ADJ
fcis-20387	70	30	network	network	NOUN
fcis-20387	70	31	-	-	PUNCT
fcis-20387	70	32	based	base	VERB
fcis-20387	70	33	models	model	NOUN
fcis-20387	70	34	in	in	ADP
fcis-20387	70	35	the	the	DET
fcis-20387	70	36	field	field	NOUN
fcis-20387	70	37	of	of	ADP
fcis-20387	70	38	air	air	NOUN
fcis-20387	70	39	quality	quality	NOUN
fcis-20387	70	40	prediction	prediction	NOUN
fcis-20387	70	41	.	.	PUNCT
fcis-20387	71	1	however	however	ADV
fcis-20387	71	2	,	,	PUNCT
fcis-20387	71	3	hyperparameters	hyperparameter	NOUN
fcis-20387	71	4	in	in	ADP
fcis-20387	71	5	prediction	prediction	NOUN
fcis-20387	71	6	models	model	NOUN
fcis-20387	71	7	,	,	PUNCT
fcis-20387	71	8	such	such	ADJ
fcis-20387	71	9	as	as	ADP
fcis-20387	71	10	the	the	DET
fcis-20387	71	11	number	number	NOUN
fcis-20387	71	12	of	of	ADP
fcis-20387	71	13	network	network	NOUN
fcis-20387	71	14	layers	layer	NOUN
fcis-20387	71	15	,	,	PUNCT
fcis-20387	71	16	the	the	DET
fcis-20387	71	17	number	number	NOUN
fcis-20387	71	18	of	of	ADP
fcis-20387	71	19	neurons	neuron	NOUN
fcis-20387	71	20	in	in	ADP
fcis-20387	71	21	each	each	DET
fcis-20387	71	22	layer	layer	NOUN
fcis-20387	71	23	,	,	PUNCT
fcis-20387	71	24	learning	learn	VERB
fcis-20387	71	25	rate	rate	NOUN
fcis-20387	71	26	,	,	PUNCT
fcis-20387	71	27	and	and	CCONJ
fcis-20387	71	28	other	other	ADJ
fcis-20387	71	29	parameters	parameter	NOUN
fcis-20387	71	30	that	that	PRON
fcis-20387	71	31	affect	affect	VERB
fcis-20387	71	32	model	model	NOUN
fcis-20387	71	33	performance	performance	NOUN
fcis-20387	71	34	,	,	PUNCT
fcis-20387	71	35	are	be	AUX
fcis-20387	71	36	often	often	ADV
fcis-20387	71	37	subjectively	subjectively	ADV
fcis-20387	71	38	determined	determine	VERB
fcis-20387	71	39	by	by	ADP
fcis-20387	71	40	humans	human	NOUN
fcis-20387	71	41	,	,	PUNCT
fcis-20387	71	42	resulting	result	VERB
fcis-20387	71	43	in	in	ADP
fcis-20387	71	44	suboptimal	suboptimal	ADJ
fcis-20387	71	45	prediction	prediction	NOUN
fcis-20387	71	46	accuracy	accuracy	NOUN
fcis-20387	71	47	.	.	PUNCT
fcis-20387	72	1	backpropagation	backpropagation	NOUN
fcis-20387	72	2	neural	neural	ADJ
fcis-20387	72	3	network	network	NOUN
fcis-20387	72	4	is	be	AUX
fcis-20387	72	5	one	one	NUM
fcis-20387	72	6	of	of	ADP
fcis-20387	72	7	the	the	DET
fcis-20387	72	8	most	most	ADV
fcis-20387	72	9	traditional	traditional	ADJ
fcis-20387	72	10	neural	neural	ADJ
fcis-20387	72	11	networks	network	NOUN
fcis-20387	72	12	,	,	PUNCT
fcis-20387	72	13	which	which	PRON
fcis-20387	72	14	has	have	VERB
fcis-20387	72	15	drawbacks	drawback	NOUN
fcis-20387	72	16	such	such	ADJ
fcis-20387	72	17	as	as	ADP
fcis-20387	72	18	easily	easily	ADV
fcis-20387	72	19	stopping	stop	VERB
fcis-20387	72	20	training	training	NOUN
fcis-20387	72	21	at	at	ADP
fcis-20387	72	22	local	local	ADJ
fcis-20387	72	23	optima	optima	NOUN
fcis-20387	72	24	and	and	CCONJ
fcis-20387	72	25	being	be	AUX
fcis-20387	72	26	easily	easily	ADV
fcis-20387	72	27	affected	affect	VERB
fcis-20387	72	28	by	by	ADP
fcis-20387	72	29	parameter	parameter	NOUN
fcis-20387	72	30	values	value	NOUN
fcis-20387	72	31	,	,	PUNCT
fcis-20387	72	32	leading	lead	VERB
fcis-20387	72	33	many	many	ADJ
fcis-20387	72	34	scholars	scholar	NOUN
fcis-20387	72	35	to	to	PART
fcis-20387	72	36	use	use	VERB
fcis-20387	72	37	various	various	ADJ
fcis-20387	72	38	methods	method	NOUN
fcis-20387	72	39	to	to	PART
fcis-20387	72	40	improve	improve	VERB
fcis-20387	72	41	it	it	PRON
fcis-20387	72	42	.	.	PUNCT
fcis-20387	73	1	in	in	ADP
fcis-20387	73	2	these	these	DET
fcis-20387	73	3	studies	study	NOUN
fcis-20387	73	4	,	,	PUNCT
fcis-20387	73	5	many	many	ADJ
fcis-20387	73	6	achievements	achievement	NOUN
fcis-20387	73	7	have	have	AUX
fcis-20387	73	8	been	be	AUX
fcis-20387	73	9	made	make	VERB
fcis-20387	73	10	,	,	PUNCT
fcis-20387	73	11	such	such	ADJ
fcis-20387	73	12	as	as	ADP
fcis-20387	73	13	using	use	VERB
fcis-20387	73	14	bayesian	bayesian	NOUN
fcis-20387	73	15	normalization	normalization	NOUN
fcis-20387	73	16	to	to	PART
fcis-20387	73	17	improve	improve	VERB
fcis-20387	73	18	the	the	DET
fcis-20387	73	19	network	network	NOUN
fcis-20387	73	20	's	's	PART
fcis-20387	73	21	generalization	generalization	NOUN
fcis-20387	73	22	ability	ability	NOUN
fcis-20387	73	23	and	and	CCONJ
fcis-20387	73	24	improving	improve	VERB
fcis-20387	73	25	the	the	DET
fcis-20387	73	26	gradient	gradient	ADJ
fcis-20387	73	27	descent	descent	NOUN
fcis-20387	73	28	method	method	NOUN
fcis-20387	73	29	.	.	PUNCT
fcis-20387	74	1	in	in	ADP
fcis-20387	74	2	addition	addition	NOUN
fcis-20387	74	3	,	,	PUNCT
fcis-20387	74	4	some	some	DET
fcis-20387	74	5	scholars	scholar	NOUN
fcis-20387	74	6	have	have	AUX
fcis-20387	74	7	also	also	ADV
fcis-20387	74	8	used	use	VERB
fcis-20387	74	9	heuristic	heuristic	ADJ
fcis-20387	74	10	algorithms	algorithm	NOUN
fcis-20387	74	11	to	to	PART
fcis-20387	74	12	improve	improve	VERB
fcis-20387	74	13	the	the	DET
fcis-20387	74	14	prediction	prediction	NOUN
fcis-20387	74	15	model	model	NOUN
fcis-20387	74	16	of	of	ADP
fcis-20387	74	17	bp	bp	PROPN
fcis-20387	74	18	neural	neural	ADJ
fcis-20387	74	19	networks	network	NOUN
fcis-20387	74	20	.	.	PUNCT
fcis-20387	75	1	zhou	zhou	PROPN
fcis-20387	75	2	et	et	PROPN
fcis-20387	75	3	al	al	PROPN
fcis-20387	75	4	.	.	PROPN
fcis-20387	75	5	combined	combine	VERB
fcis-20387	75	6	genetic	genetic	ADJ
fcis-20387	75	7	algorithm	algorithm	NOUN
fcis-20387	75	8	and	and	CCONJ
fcis-20387	75	9	simulated	simulate	VERB
fcis-20387	75	10	annealing	anneal	VERB
fcis-20387	75	11	algorithm	algorithm	NOUN
fcis-20387	75	12	to	to	PART
fcis-20387	75	13	optimize	optimize	VERB
fcis-20387	75	14	the	the	DET
fcis-20387	75	15	bp	bp	PROPN
fcis-20387	75	16	neural	neural	ADJ
fcis-20387	75	17	network	network	NOUN
fcis-20387	75	18	,	,	PUNCT
fcis-20387	75	19	and	and	CCONJ
fcis-20387	75	20	the	the	DET
fcis-20387	75	21	results	result	NOUN
fcis-20387	75	22	showed	show	VERB
fcis-20387	75	23	that	that	SCONJ
fcis-20387	75	24	the	the	DET
fcis-20387	75	25	ga	ga	PROPN
fcis-20387	75	26	-	-	PUNCT
fcis-20387	75	27	sa	sa	PROPN
fcis-20387	75	28	based	base	VERB
fcis-20387	75	29	bp	bp	PROPN
fcis-20387	75	30	neural	neural	ADJ
fcis-20387	75	31	network	network	NOUN
fcis-20387	75	32	has	have	VERB
fcis-20387	75	33	strong	strong	ADJ
fcis-20387	75	34	generalization	generalization	NOUN
fcis-20387	75	35	ability	ability	NOUN
fcis-20387	75	36	and	and	CCONJ
fcis-20387	75	37	global	global	ADJ
fcis-20387	75	38	search	search	NOUN
fcis-20387	75	39	ability	ability	NOUN
fcis-20387	75	40	,	,	PUNCT
fcis-20387	75	41	with	with	ADP
fcis-20387	75	42	high	high	ADJ
fcis-20387	75	43	accuracy	accuracy	NOUN
fcis-20387	75	44	.	.	PUNCT
fcis-20387	76	1	huang	huang	PROPN
fcis-20387	76	2	et	et	PROPN
fcis-20387	76	3	al	al	PROPN
fcis-20387	76	4	.	.	PROPN
fcis-20387	76	5	proposed	propose	VERB
fcis-20387	76	6	a	a	DET
fcis-20387	76	7	bp	bp	PROPN
fcis-20387	76	8	neural	neural	ADJ
fcis-20387	76	9	network	network	NOUN
fcis-20387	76	10	method	method	NOUN
fcis-20387	76	11	based	base	VERB
fcis-20387	76	12	on	on	ADP
fcis-20387	76	13	improved	improve	VERB
fcis-20387	76	14	particle	particle	NOUN
fcis-20387	76	15	swarm	swarm	NOUN
fcis-20387	76	16	optimization	optimization	NOUN
fcis-20387	76	17	algorithm	algorithm	NOUN
fcis-20387	76	18	to	to	PART
fcis-20387	76	19	predict	predict	VERB
fcis-20387	76	20	aqi	aqi	PROPN
fcis-20387	76	21	,	,	PUNCT
fcis-20387	76	22	making	make	VERB
fcis-20387	76	23	the	the	DET
fcis-20387	76	24	prediction	prediction	NOUN
fcis-20387	76	25	results	result	VERB
fcis-20387	76	26	more	more	ADV
fcis-20387	76	27	accurate	accurate	ADJ
fcis-20387	76	28	.	.	PUNCT
fcis-20387	77	1	45	45	NUM
fcis-20387	77	2	other	other	ADJ
fcis-20387	77	3	prediction	prediction	NOUN
fcis-20387	77	4	models	model	NOUN
fcis-20387	77	5	have	have	AUX
fcis-20387	77	6	also	also	ADV
fcis-20387	77	7	been	be	AUX
fcis-20387	77	8	improved	improve	VERB
fcis-20387	77	9	,	,	PUNCT
fcis-20387	77	10	and	and	CCONJ
fcis-20387	77	11	fan	fan	NOUN
fcis-20387	77	12	wenting	wente	VERB
fcis-20387	77	13	et	et	PROPN
fcis-20387	77	14	al	al	PROPN
fcis-20387	77	15	.	.	PROPN
fcis-20387	77	16	proposed	propose	VERB
fcis-20387	77	17	optimizing	optimize	VERB
fcis-20387	77	18	support	support	NOUN
fcis-20387	77	19	vector	vector	NOUN
fcis-20387	77	20	machine	machine	NOUN
fcis-20387	77	21	prediction	prediction	NOUN
fcis-20387	77	22	models	model	NOUN
fcis-20387	77	23	based	base	VERB
fcis-20387	77	24	on	on	ADP
fcis-20387	77	25	an	an	DET
fcis-20387	77	26	improved	improved	ADJ
fcis-20387	77	27	firefly	firefly	NOUN
fcis-20387	77	28	algorithm	algorithm	NOUN
fcis-20387	77	29	.	.	PUNCT
fcis-20387	78	1	gao	gao	PROPN
fcis-20387	78	2	shuai	shuai	PROPN
fcis-20387	78	3	et	et	PROPN
fcis-20387	78	4	al	al	PROPN
fcis-20387	78	5	.	.	PROPN
fcis-20387	78	6	proposed	propose	VERB
fcis-20387	78	7	a	a	DET
fcis-20387	78	8	method	method	NOUN
fcis-20387	78	9	that	that	PRON
fcis-20387	78	10	combines	combine	VERB
fcis-20387	78	11	svm	svm	NOUN
fcis-20387	78	12	with	with	ADP
fcis-20387	78	13	traditional	traditional	ADJ
fcis-20387	78	14	moth	moth	NOUN
fcis-20387	78	15	to	to	ADP
fcis-20387	78	16	flame	flame	NOUN
fcis-20387	78	17	optimization	optimization	NOUN
fcis-20387	78	18	algorithms	algorithm	NOUN
fcis-20387	78	19	.	.	PUNCT
fcis-20387	79	1	in	in	ADP
fcis-20387	79	2	addition	addition	NOUN
fcis-20387	79	3	,	,	PUNCT
fcis-20387	79	4	some	some	DET
fcis-20387	79	5	scholars	scholar	NOUN
fcis-20387	79	6	have	have	AUX
fcis-20387	79	7	proposed	propose	VERB
fcis-20387	79	8	optimization	optimization	NOUN
fcis-20387	79	9	algorithms	algorithm	NOUN
fcis-20387	79	10	to	to	PART
fcis-20387	79	11	optimize	optimize	VERB
fcis-20387	79	12	lstm	lstm	NOUN
fcis-20387	79	13	.	.	PUNCT
fcis-20387	80	1	zhang	zhang	PROPN
fcis-20387	80	2	et	et	PROPN
fcis-20387	80	3	al	al	PROPN
fcis-20387	80	4	.	.	PROPN
fcis-20387	80	5	optimized	optimize	VERB
fcis-20387	80	6	lstm	lstm	PROPN
fcis-20387	80	7	neural	neural	ADJ
fcis-20387	80	8	network	network	NOUN
fcis-20387	80	9	parameters	parameter	NOUN
fcis-20387	80	10	and	and	CCONJ
fcis-20387	80	11	applied	apply	VERB
fcis-20387	80	12	them	they	PRON
fcis-20387	80	13	to	to	PART
fcis-20387	80	14	air	air	NOUN
fcis-20387	80	15	quality	quality	NOUN
fcis-20387	80	16	prediction	prediction	NOUN
fcis-20387	80	17	in	in	ADP
fcis-20387	80	18	different	different	ADJ
fcis-20387	80	19	cities	city	NOUN
fcis-20387	80	20	,	,	PUNCT
fcis-20387	80	21	achieving	achieve	VERB
fcis-20387	80	22	certain	certain	ADJ
fcis-20387	80	23	results	result	NOUN
fcis-20387	80	24	.	.	PUNCT
fcis-20387	81	1	al	al	PROPN
fcis-20387	81	2	janabi	janabi	NOUN
fcis-20387	81	3	et	et	PROPN
fcis-20387	81	4	al	al	PROPN
fcis-20387	81	5	.	.	PROPN
fcis-20387	81	6	used	use	VERB
fcis-20387	81	7	pso	pso	NOUN
fcis-20387	81	8	to	to	PART
fcis-20387	81	9	optimize	optimize	VERB
fcis-20387	81	10	the	the	DET
fcis-20387	81	11	weights	weight	NOUN
fcis-20387	81	12	,	,	PUNCT
fcis-20387	81	13	biases	bias	NOUN
fcis-20387	81	14	,	,	PUNCT
fcis-20387	81	15	number	number	NOUN
fcis-20387	81	16	of	of	ADP
fcis-20387	81	17	hidden	hidden	ADJ
fcis-20387	81	18	layers	layer	NOUN
fcis-20387	81	19	,	,	PUNCT
fcis-20387	81	20	number	number	NOUN
fcis-20387	81	21	of	of	ADP
fcis-20387	81	22	nodes	node	NOUN
fcis-20387	81	23	in	in	ADP
fcis-20387	81	24	each	each	DET
fcis-20387	81	25	hidden	hide	VERB
fcis-20387	81	26	layer	layer	NOUN
fcis-20387	81	27	,	,	PUNCT
fcis-20387	81	28	and	and	CCONJ
fcis-20387	81	29	activation	activation	NOUN
fcis-20387	81	30	function	function	NOUN
fcis-20387	81	31	parameters	parameter	NOUN
fcis-20387	81	32	of	of	ADP
fcis-20387	81	33	lstm	lstm	PROPN
fcis-20387	81	34	,	,	PUNCT
fcis-20387	81	35	achieving	achieve	VERB
fcis-20387	81	36	prediction	prediction	NOUN
fcis-20387	81	37	of	of	ADP
fcis-20387	81	38	air	air	NOUN
fcis-20387	81	39	pollutant	pollutant	ADJ
fcis-20387	81	40	concentrations	concentration	NOUN
fcis-20387	81	41	for	for	ADP
fcis-20387	81	42	the	the	DET
fcis-20387	81	43	next	next	ADJ
fcis-20387	81	44	two	two	NUM
fcis-20387	81	45	days	day	NOUN
fcis-20387	81	46	.	.	PUNCT
fcis-20387	82	1	from	from	ADP
fcis-20387	82	2	the	the	DET
fcis-20387	82	3	above	above	ADJ
fcis-20387	82	4	research	research	NOUN
fcis-20387	82	5	,	,	PUNCT
fcis-20387	82	6	it	it	PRON
fcis-20387	82	7	can	can	AUX
fcis-20387	82	8	be	be	AUX
fcis-20387	82	9	found	find	VERB
fcis-20387	82	10	that	that	SCONJ
fcis-20387	82	11	improving	improve	VERB
fcis-20387	82	12	neural	neural	ADJ
fcis-20387	82	13	networks	network	NOUN
fcis-20387	82	14	based	base	VERB
fcis-20387	82	15	on	on	ADP
fcis-20387	82	16	intelligent	intelligent	ADJ
fcis-20387	82	17	optimization	optimization	NOUN
fcis-20387	82	18	algorithms	algorithm	NOUN
fcis-20387	82	19	has	have	AUX
fcis-20387	82	20	been	be	AUX
fcis-20387	82	21	widely	widely	ADV
fcis-20387	82	22	applied	apply	VERB
fcis-20387	82	23	and	and	CCONJ
fcis-20387	82	24	can	can	AUX
fcis-20387	82	25	achieve	achieve	VERB
fcis-20387	82	26	very	very	ADV
fcis-20387	82	27	good	good	ADJ
fcis-20387	82	28	results	result	NOUN
fcis-20387	82	29	.	.	PUNCT
fcis-20387	83	1	therefore	therefore	ADV
fcis-20387	83	2	,	,	PUNCT
fcis-20387	83	3	it	it	PRON
fcis-20387	83	4	is	be	AUX
fcis-20387	83	5	particularly	particularly	ADV
fcis-20387	83	6	important	important	ADJ
fcis-20387	83	7	to	to	PART
fcis-20387	83	8	choose	choose	VERB
fcis-20387	83	9	suitable	suitable	ADJ
fcis-20387	83	10	optimization	optimization	NOUN
fcis-20387	83	11	algorithms	algorithm	NOUN
fcis-20387	83	12	for	for	ADP
fcis-20387	83	13	optimizing	optimize	VERB
fcis-20387	83	14	neural	neural	ADJ
fcis-20387	83	15	network	network	NOUN
fcis-20387	83	16	parameters	parameter	NOUN
fcis-20387	83	17	.	.	PUNCT
fcis-20387	84	1	through	through	ADP
fcis-20387	84	2	research	research	NOUN
fcis-20387	84	3	,	,	PUNCT
fcis-20387	84	4	it	it	PRON
fcis-20387	84	5	has	have	AUX
fcis-20387	84	6	been	be	AUX
fcis-20387	84	7	found	find	VERB
fcis-20387	84	8	that	that	SCONJ
fcis-20387	84	9	the	the	DET
fcis-20387	84	10	beetle	beetle	NOUN
fcis-20387	84	11	whisker	whisker	NOUN
fcis-20387	84	12	search	search	NOUN
fcis-20387	84	13	algorithm	algorithm	NOUN
fcis-20387	84	14	has	have	VERB
fcis-20387	84	15	lower	low	ADJ
fcis-20387	84	16	time	time	NOUN
fcis-20387	84	17	and	and	CCONJ
fcis-20387	84	18	spatial	spatial	ADJ
fcis-20387	84	19	complexity	complexity	NOUN
fcis-20387	84	20	compared	compare	VERB
fcis-20387	84	21	to	to	ADP
fcis-20387	84	22	most	most	ADJ
fcis-20387	84	23	swarm	swarm	NOUN
fcis-20387	84	24	intelligence	intelligence	NOUN
fcis-20387	84	25	algorithms	algorithm	NOUN
fcis-20387	84	26	and	and	CCONJ
fcis-20387	84	27	higher	high	ADJ
fcis-20387	84	28	operational	operational	ADJ
fcis-20387	84	29	efficiency	efficiency	NOUN
fcis-20387	84	30	.	.	PUNCT
fcis-20387	85	1	liao	liao	PROPN
fcis-20387	85	2	liefa	liefa	PROPN
fcis-20387	85	3	et	et	PROPN
fcis-20387	85	4	al	al	PROPN
fcis-20387	85	5	.	.	PROPN
fcis-20387	85	6	compared	compare	VERB
fcis-20387	85	7	the	the	DET
fcis-20387	85	8	bas	bas	PROPN
fcis-20387	85	9	algorithm	algorithm	NOUN
fcis-20387	85	10	with	with	ADP
fcis-20387	85	11	many	many	ADJ
fcis-20387	85	12	new	new	ADJ
fcis-20387	85	13	and	and	CCONJ
fcis-20387	85	14	mainstream	mainstream	ADJ
fcis-20387	85	15	intelligent	intelligent	ADJ
fcis-20387	85	16	optimization	optimization	NOUN
fcis-20387	85	17	algorithms	algorithm	NOUN
fcis-20387	85	18	and	and	CCONJ
fcis-20387	85	19	found	find	VERB
fcis-20387	85	20	that	that	SCONJ
fcis-20387	85	21	it	it	PRON
fcis-20387	85	22	has	have	VERB
fcis-20387	85	23	strong	strong	ADJ
fcis-20387	85	24	competitiveness	competitiveness	NOUN
fcis-20387	85	25	.	.	PUNCT
fcis-20387	86	1	compared	compare	VERB
fcis-20387	86	2	with	with	ADP
fcis-20387	86	3	the	the	DET
fcis-20387	86	4	pso	pso	NOUN
fcis-20387	86	5	algorithm	algorithm	NOUN
fcis-20387	86	6	,	,	PUNCT
fcis-20387	86	7	the	the	DET
fcis-20387	86	8	bas	bas	PROPN
fcis-20387	86	9	algorithm	algorithm	PROPN
fcis-20387	86	10	has	have	VERB
fcis-20387	86	11	a	a	DET
fcis-20387	86	12	stronger	strong	ADJ
fcis-20387	86	13	ability	ability	NOUN
fcis-20387	86	14	to	to	PART
fcis-20387	86	15	jump	jump	VERB
fcis-20387	86	16	out	out	ADP
fcis-20387	86	17	of	of	ADP
fcis-20387	86	18	local	local	ADJ
fcis-20387	86	19	extremes	extreme	NOUN
fcis-20387	86	20	and	and	CCONJ
fcis-20387	86	21	converges	converge	VERB
fcis-20387	86	22	faster	fast	ADV
fcis-20387	86	23	;	;	PUNCT
fcis-20387	86	24	compared	compare	VERB
fcis-20387	86	25	with	with	ADP
fcis-20387	86	26	ga	ga	PROPN
fcis-20387	86	27	algorithm	algorithm	NOUN
fcis-20387	86	28	,	,	PUNCT
fcis-20387	86	29	bas	bas	PROPN
fcis-20387	86	30	algorithm	algorithm	PROPN
fcis-20387	86	31	has	have	VERB
fcis-20387	86	32	simple	simple	ADJ
fcis-20387	86	33	operation	operation	NOUN
fcis-20387	86	34	,	,	PUNCT
fcis-20387	86	35	small	small	ADJ
fcis-20387	86	36	computational	computational	ADJ
fcis-20387	86	37	complexity	complexity	NOUN
fcis-20387	86	38	,	,	PUNCT
fcis-20387	86	39	and	and	CCONJ
fcis-20387	86	40	faster	fast	ADV
fcis-20387	86	41	running	run	VERB
fcis-20387	86	42	speed	speed	NOUN
fcis-20387	86	43	;	;	PUNCT
fcis-20387	86	44	compared	compare	VERB
fcis-20387	86	45	with	with	ADP
fcis-20387	86	46	the	the	DET
fcis-20387	86	47	fa	fa	PROPN
fcis-20387	86	48	algorithm	algorithm	NOUN
fcis-20387	86	49	,	,	PUNCT
fcis-20387	86	50	the	the	DET
fcis-20387	86	51	bas	bas	PROPN
fcis-20387	86	52	algorithm	algorithm	NOUN
fcis-20387	86	53	is	be	AUX
fcis-20387	86	54	less	less	ADV
fcis-20387	86	55	affected	affect	VERB
fcis-20387	86	56	by	by	ADP
fcis-20387	86	57	parameter	parameter	NOUN
fcis-20387	86	58	sensitivity	sensitivity	NOUN
fcis-20387	86	59	;	;	PUNCT
fcis-20387	86	60	compared	compare	VERB
fcis-20387	86	61	with	with	ADP
fcis-20387	86	62	bat	bat	NOUN
fcis-20387	86	63	algorithm	algorithm	NOUN
fcis-20387	86	64	(	(	PUNCT
fcis-20387	86	65	ba	ba	NOUN
fcis-20387	86	66	)	)	PUNCT
fcis-20387	86	67	and	and	CCONJ
fcis-20387	86	68	artificial	artificial	ADJ
fcis-20387	86	69	bee	bee	NOUN
fcis-20387	86	70	colony	colony	NOUN
fcis-20387	86	71	(	(	PUNCT
fcis-20387	86	72	abc	abc	PROPN
fcis-20387	86	73	)	)	PUNCT
fcis-20387	86	74	,	,	PUNCT
fcis-20387	86	75	bas	bas	PROPN
fcis-20387	86	76	algorithm	algorithm	PROPN
fcis-20387	86	77	has	have	VERB
fcis-20387	86	78	higher	high	ADJ
fcis-20387	86	79	computational	computational	ADJ
fcis-20387	86	80	efficiency	efficiency	NOUN
fcis-20387	86	81	and	and	CCONJ
fcis-20387	86	82	lower	low	ADJ
fcis-20387	86	83	computational	computational	ADJ
fcis-20387	86	84	complexity	complexity	NOUN
fcis-20387	86	85	.	.	PUNCT
fcis-20387	87	1	however	however	ADV
fcis-20387	87	2	,	,	PUNCT
fcis-20387	87	3	the	the	DET
fcis-20387	87	4	bas	bas	PROPN
fcis-20387	87	5	algorithm	algorithm	NOUN
fcis-20387	87	6	also	also	ADV
fcis-20387	87	7	has	have	VERB
fcis-20387	87	8	the	the	DET
fcis-20387	87	9	problem	problem	NOUN
fcis-20387	87	10	of	of	ADP
fcis-20387	87	11	easily	easily	ADV
fcis-20387	87	12	falling	fall	VERB
fcis-20387	87	13	into	into	ADP
fcis-20387	87	14	local	local	ADJ
fcis-20387	87	15	extremes	extreme	NOUN
fcis-20387	87	16	and	and	CCONJ
fcis-20387	87	17	unstable	unstable	ADJ
fcis-20387	87	18	optimization	optimization	NOUN
fcis-20387	87	19	results	result	NOUN
fcis-20387	87	20	.	.	PUNCT
fcis-20387	88	1	4	4	X
fcis-20387	88	2	.	.	X
fcis-20387	88	3	closing	closing	NOUN
fcis-20387	88	4	remarks	remark	NOUN
fcis-20387	88	5	as	as	SCONJ
fcis-20387	88	6	we	we	PRON
fcis-20387	88	7	all	all	PRON
fcis-20387	88	8	know	know	VERB
fcis-20387	88	9	,	,	PUNCT
fcis-20387	88	10	substances	substance	NOUN
fcis-20387	88	11	in	in	ADP
fcis-20387	88	12	the	the	DET
fcis-20387	88	13	air	air	NOUN
fcis-20387	88	14	are	be	AUX
fcis-20387	88	15	constantly	constantly	ADV
fcis-20387	88	16	moving	move	VERB
fcis-20387	88	17	,	,	PUNCT
fcis-20387	88	18	so	so	CCONJ
fcis-20387	88	19	the	the	DET
fcis-20387	88	20	concentration	concentration	NOUN
fcis-20387	88	21	of	of	ADP
fcis-20387	88	22	pollutants	pollutant	NOUN
fcis-20387	88	23	monitored	monitor	VERB
fcis-20387	88	24	by	by	ADP
fcis-20387	88	25	air	air	NOUN
fcis-20387	88	26	quality	quality	NOUN
fcis-20387	88	27	monitoring	monitoring	NOUN
fcis-20387	88	28	stations	station	NOUN
fcis-20387	88	29	is	be	AUX
fcis-20387	88	30	constantly	constantly	ADV
fcis-20387	88	31	changing	change	VERB
fcis-20387	88	32	and	and	CCONJ
fcis-20387	88	33	easily	easily	ADV
fcis-20387	88	34	affected	affect	VERB
fcis-20387	88	35	by	by	ADP
fcis-20387	88	36	the	the	DET
fcis-20387	88	37	surrounding	surround	VERB
fcis-20387	88	38	environment	environment	NOUN
fcis-20387	88	39	,	,	PUNCT
fcis-20387	88	40	which	which	PRON
fcis-20387	88	41	leads	lead	VERB
fcis-20387	88	42	to	to	ADP
fcis-20387	88	43	a	a	DET
fcis-20387	88	44	certain	certain	ADJ
fcis-20387	88	45	spatial	spatial	ADJ
fcis-20387	88	46	correlation	correlation	NOUN
fcis-20387	88	47	between	between	ADP
fcis-20387	88	48	different	different	ADJ
fcis-20387	88	49	stations	station	NOUN
fcis-20387	88	50	.	.	PUNCT
fcis-20387	89	1	when	when	SCONJ
fcis-20387	89	2	the	the	DET
fcis-20387	89	3	change	change	NOUN
fcis-20387	89	4	trend	trend	NOUN
fcis-20387	89	5	of	of	ADP
fcis-20387	89	6	most	most	ADJ
fcis-20387	89	7	data	datum	NOUN
fcis-20387	89	8	of	of	ADP
fcis-20387	89	9	different	different	ADJ
fcis-20387	89	10	sites	site	NOUN
fcis-20387	89	11	is	be	AUX
fcis-20387	89	12	the	the	DET
fcis-20387	89	13	same	same	ADJ
fcis-20387	89	14	or	or	CCONJ
fcis-20387	89	15	opposite	opposite	ADJ
fcis-20387	89	16	and	and	CCONJ
fcis-20387	89	17	the	the	DET
fcis-20387	89	18	fluctuation	fluctuation	NOUN
fcis-20387	89	19	amplitude	amplitude	NOUN
fcis-20387	89	20	is	be	AUX
fcis-20387	89	21	similar	similar	ADJ
fcis-20387	89	22	,	,	PUNCT
fcis-20387	89	23	it	it	PRON
fcis-20387	89	24	is	be	AUX
fcis-20387	89	25	considered	consider	VERB
fcis-20387	89	26	that	that	SCONJ
fcis-20387	89	27	there	there	PRON
fcis-20387	89	28	is	be	VERB
fcis-20387	89	29	spatial	spatial	ADJ
fcis-20387	89	30	correlation	correlation	NOUN
fcis-20387	89	31	between	between	ADP
fcis-20387	89	32	sites	site	NOUN
fcis-20387	89	33	.	.	PUNCT
fcis-20387	90	1	the	the	DET
fcis-20387	90	2	air	air	NOUN
fcis-20387	90	3	quality	quality	NOUN
fcis-20387	90	4	situation	situation	NOUN
fcis-20387	90	5	is	be	AUX
fcis-20387	90	6	influenced	influence	VERB
fcis-20387	90	7	by	by	ADP
fcis-20387	90	8	various	various	ADJ
fcis-20387	90	9	complex	complex	ADJ
fcis-20387	90	10	factors	factor	NOUN
fcis-20387	90	11	,	,	PUNCT
fcis-20387	90	12	such	such	ADJ
fcis-20387	90	13	as	as	ADP
fcis-20387	90	14	meteorological	meteorological	ADJ
fcis-20387	90	15	factors	factor	NOUN
fcis-20387	90	16	,	,	PUNCT
fcis-20387	90	17	geographical	geographical	ADJ
fcis-20387	90	18	location	location	NOUN
fcis-20387	90	19	,	,	PUNCT
fcis-20387	90	20	national	national	ADJ
fcis-20387	90	21	policies	policy	NOUN
fcis-20387	90	22	,	,	PUNCT
fcis-20387	90	23	etc	etc	X
fcis-20387	90	24	.	.	X
fcis-20387	90	25	after	after	ADP
fcis-20387	90	26	excluding	exclude	VERB
fcis-20387	90	27	other	other	ADJ
fcis-20387	90	28	factors	factor	NOUN
fcis-20387	90	29	,	,	PUNCT
fcis-20387	90	30	this	this	DET
fcis-20387	90	31	article	article	NOUN
fcis-20387	90	32	conducts	conduct	VERB
fcis-20387	90	33	research	research	NOUN
fcis-20387	90	34	on	on	ADP
fcis-20387	90	35	predicting	predict	VERB
fcis-20387	90	36	the	the	DET
fcis-20387	90	37	trend	trend	NOUN
fcis-20387	90	38	of	of	ADP
fcis-20387	90	39	air	air	NOUN
fcis-20387	90	40	quality	quality	NOUN
fcis-20387	90	41	changes	change	NOUN
fcis-20387	90	42	based	base	VERB
fcis-20387	90	43	on	on	ADP
fcis-20387	90	44	historical	historical	ADJ
fcis-20387	90	45	air	air	NOUN
fcis-20387	90	46	quality	quality	NOUN
fcis-20387	90	47	data	datum	NOUN
fcis-20387	90	48	and	and	CCONJ
fcis-20387	90	49	meteorological	meteorological	ADJ
fcis-20387	90	50	data	datum	NOUN
fcis-20387	90	51	.	.	PUNCT
fcis-20387	91	1	because	because	SCONJ
fcis-20387	91	2	substances	substance	NOUN
fcis-20387	91	3	in	in	ADP
fcis-20387	91	4	the	the	DET
fcis-20387	91	5	air	air	NOUN
fcis-20387	91	6	are	be	AUX
fcis-20387	91	7	constantly	constantly	ADV
fcis-20387	91	8	moving	move	VERB
fcis-20387	91	9	,	,	PUNCT
fcis-20387	91	10	the	the	DET
fcis-20387	91	11	concentration	concentration	NOUN
fcis-20387	91	12	of	of	ADP
fcis-20387	91	13	pollutants	pollutant	NOUN
fcis-20387	91	14	monitored	monitor	VERB
fcis-20387	91	15	by	by	ADP
fcis-20387	91	16	monitoring	monitor	VERB
fcis-20387	91	17	stations	station	NOUN
fcis-20387	91	18	is	be	AUX
fcis-20387	91	19	also	also	ADV
fcis-20387	91	20	constantly	constantly	ADV
fcis-20387	91	21	changing	change	VERB
fcis-20387	91	22	and	and	CCONJ
fcis-20387	91	23	easily	easily	ADV
fcis-20387	91	24	influenced	influence	VERB
fcis-20387	91	25	by	by	ADP
fcis-20387	91	26	the	the	DET
fcis-20387	91	27	surrounding	surround	VERB
fcis-20387	91	28	environment	environment	NOUN
fcis-20387	91	29	.	.	PUNCT
fcis-20387	92	1	however	however	ADV
fcis-20387	92	2	,	,	PUNCT
fcis-20387	92	3	the	the	DET
fcis-20387	92	4	specific	specific	ADJ
fcis-20387	92	5	impact	impact	NOUN
fcis-20387	92	6	is	be	AUX
fcis-20387	92	7	unknown	unknown	ADJ
fcis-20387	92	8	,	,	PUNCT
fcis-20387	92	9	but	but	CCONJ
fcis-20387	92	10	this	this	DET
fcis-20387	92	11	relationship	relationship	NOUN
fcis-20387	92	12	can	can	AUX
fcis-20387	92	13	be	be	AUX
fcis-20387	92	14	explored	explore	VERB
fcis-20387	92	15	through	through	ADP
fcis-20387	92	16	existing	exist	VERB
fcis-20387	92	17	historical	historical	ADJ
fcis-20387	92	18	data	datum	NOUN
fcis-20387	92	19	.	.	PUNCT
fcis-20387	93	1	how	how	SCONJ
fcis-20387	93	2	to	to	PART
fcis-20387	93	3	effectively	effectively	ADV
fcis-20387	93	4	construct	construct	VERB
fcis-20387	93	5	prediction	prediction	NOUN
fcis-20387	93	6	models	model	NOUN
fcis-20387	93	7	and	and	CCONJ
fcis-20387	93	8	improve	improve	VERB
fcis-20387	93	9	the	the	DET
fcis-20387	93	10	accuracy	accuracy	NOUN
fcis-20387	93	11	of	of	ADP
fcis-20387	93	12	air	air	NOUN
fcis-20387	93	13	quality	quality	NOUN
fcis-20387	93	14	prediction	prediction	NOUN
fcis-20387	93	15	is	be	AUX
fcis-20387	93	16	currently	currently	ADV
fcis-20387	93	17	a	a	DET
fcis-20387	93	18	hot	hot	ADJ
fcis-20387	93	19	research	research	NOUN
fcis-20387	93	20	topic	topic	NOUN
fcis-20387	93	21	.	.	PUNCT
fcis-20387	94	1	considering	consider	VERB
fcis-20387	94	2	the	the	DET
fcis-20387	94	3	spatial	spatial	ADJ
fcis-20387	94	4	correlation	correlation	NOUN
fcis-20387	94	5	between	between	ADP
fcis-20387	94	6	monitoring	monitor	VERB
fcis-20387	94	7	stations	station	NOUN
fcis-20387	94	8	,	,	PUNCT
fcis-20387	94	9	this	this	DET
fcis-20387	94	10	paper	paper	NOUN
fcis-20387	94	11	proposes	propose	VERB
fcis-20387	94	12	using	use	VERB
fcis-20387	94	13	unsupervised	unsupervised	ADJ
fcis-20387	94	14	learning	learning	NOUN
fcis-20387	94	15	clustering	cluster	VERB
fcis-20387	94	16	algorithms	algorithm	NOUN
fcis-20387	94	17	to	to	PART
fcis-20387	94	18	classify	classify	VERB
fcis-20387	94	19	stations	station	NOUN
fcis-20387	94	20	with	with	ADP
fcis-20387	94	21	similar	similar	ADJ
fcis-20387	94	22	attributes	attribute	NOUN
fcis-20387	94	23	.	.	PUNCT
fcis-20387	95	1	deep	deep	ADJ
fcis-20387	95	2	learning	learning	NOUN
fcis-20387	95	3	technology	technology	NOUN
fcis-20387	95	4	,	,	PUNCT
fcis-20387	95	5	as	as	ADP
fcis-20387	95	6	a	a	DET
fcis-20387	95	7	complex	complex	ADJ
fcis-20387	95	8	machine	machine	NOUN
fcis-20387	95	9	learning	learning	NOUN
fcis-20387	95	10	algorithm	algorithm	NOUN
fcis-20387	95	11	,	,	PUNCT
fcis-20387	95	12	autonomously	autonomously	ADV
fcis-20387	95	13	learns	learn	VERB
fcis-20387	95	14	the	the	DET
fcis-20387	95	15	intrinsic	intrinsic	ADJ
fcis-20387	95	16	features	feature	NOUN
fcis-20387	95	17	of	of	ADP
fcis-20387	95	18	data	datum	NOUN
fcis-20387	95	19	by	by	ADP
fcis-20387	95	20	establishing	establish	VERB
fcis-20387	95	21	multi	multi	ADJ
fcis-20387	95	22	-	-	ADJ
fcis-20387	95	23	layer	layer	ADJ
fcis-20387	95	24	neural	neural	ADJ
fcis-20387	95	25	networks	network	NOUN
fcis-20387	95	26	.	.	PUNCT
fcis-20387	96	1	based	base	VERB
fcis-20387	96	2	on	on	ADP
fcis-20387	96	3	this	this	PRON
fcis-20387	96	4	,	,	PUNCT
fcis-20387	96	5	this	this	DET
fcis-20387	96	6	article	article	NOUN
fcis-20387	96	7	proposes	propose	VERB
fcis-20387	96	8	an	an	DET
fcis-20387	96	9	air	air	NOUN
fcis-20387	96	10	quality	quality	NOUN
fcis-20387	96	11	prediction	prediction	NOUN
fcis-20387	96	12	model	model	NOUN
fcis-20387	96	13	based	base	VERB
fcis-20387	96	14	on	on	ADP
fcis-20387	96	15	clustering	cluster	VERB
fcis-20387	96	16	technology	technology	NOUN
fcis-20387	96	17	and	and	CCONJ
fcis-20387	96	18	hybrid	hybrid	ADJ
fcis-20387	96	19	deep	deep	ADJ
fcis-20387	96	20	neural	neural	ADJ
fcis-20387	96	21	network	network	NOUN
fcis-20387	96	22	.	.	PUNCT
fcis-20387	97	1	in	in	ADP
fcis-20387	97	2	order	order	NOUN
fcis-20387	97	3	to	to	PART
fcis-20387	97	4	obtain	obtain	VERB
fcis-20387	97	5	more	more	ADV
fcis-20387	97	6	accurate	accurate	ADJ
fcis-20387	97	7	prediction	prediction	NOUN
fcis-20387	97	8	results	result	NOUN
fcis-20387	97	9	,	,	PUNCT
fcis-20387	97	10	the	the	DET
fcis-20387	97	11	intelligent	intelligent	ADJ
fcis-20387	97	12	optimization	optimization	NOUN
fcis-20387	97	13	algorithm	algorithm	NOUN
fcis-20387	97	14	is	be	AUX
fcis-20387	97	15	improved	improve	VERB
fcis-20387	97	16	and	and	CCONJ
fcis-20387	97	17	the	the	DET
fcis-20387	97	18	parameters	parameter	NOUN
fcis-20387	97	19	of	of	ADP
fcis-20387	97	20	the	the	DET
fcis-20387	97	21	prediction	prediction	NOUN
fcis-20387	97	22	model	model	NOUN
fcis-20387	97	23	are	be	AUX
fcis-20387	97	24	further	far	ADV
fcis-20387	97	25	optimized	optimize	VERB
fcis-20387	97	26	.	.	PUNCT
fcis-20387	98	1	in	in	ADP
fcis-20387	98	2	recent	recent	ADJ
fcis-20387	98	3	years	year	NOUN
fcis-20387	98	4	,	,	PUNCT
fcis-20387	98	5	domestic	domestic	ADJ
fcis-20387	98	6	and	and	CCONJ
fcis-20387	98	7	foreign	foreign	ADJ
fcis-20387	98	8	scholars	scholar	NOUN
fcis-20387	98	9	have	have	AUX
fcis-20387	98	10	also	also	ADV
fcis-20387	98	11	proposed	propose	VERB
fcis-20387	98	12	other	other	ADJ
fcis-20387	98	13	methods	method	NOUN
fcis-20387	98	14	for	for	ADP
fcis-20387	98	15	air	air	NOUN
fcis-20387	98	16	quality	quality	NOUN
fcis-20387	98	17	prediction	prediction	NOUN
fcis-20387	98	18	.	.	PUNCT
fcis-20387	99	1	han	han	PROPN
fcis-20387	99	2	xiaoguang	xiaoguang	PROPN
fcis-20387	99	3	et	et	PROPN
fcis-20387	99	4	al	al	PROPN
fcis-20387	99	5	.	.	PROPN
fcis-20387	100	1	effectively	effectively	ADV
fcis-20387	100	2	combined	combined	ADJ
fcis-20387	100	3	grayscale	grayscale	NOUN
fcis-20387	100	4	correlation	correlation	NOUN
fcis-20387	100	5	methods	method	NOUN
fcis-20387	100	6	and	and	CCONJ
fcis-20387	100	7	radial	radial	ADJ
fcis-20387	100	8	basis	basis	NOUN
fcis-20387	100	9	function	function	NOUN
fcis-20387	100	10	neural	neural	ADJ
fcis-20387	100	11	networks	network	NOUN
fcis-20387	100	12	(	(	PUNCT
fcis-20387	100	13	rbf	rbf	PROPN
fcis-20387	100	14	)	)	PUNCT
fcis-20387	100	15	to	to	PART
fcis-20387	100	16	select	select	VERB
fcis-20387	100	17	indicator	indicator	NOUN
fcis-20387	100	18	factors	factor	NOUN
fcis-20387	100	19	using	use	VERB
fcis-20387	100	20	grey	grey	ADJ
fcis-20387	100	21	methods	method	NOUN
fcis-20387	100	22	.	.	PUNCT
fcis-20387	101	1	the	the	DET
fcis-20387	101	2	rbf	rbf	PROPN
fcis-20387	101	3	neural	neural	PROPN
fcis-20387	101	4	network	network	NOUN
fcis-20387	101	5	predicted	predict	VERB
fcis-20387	101	6	the	the	DET
fcis-20387	101	7	main	main	ADJ
fcis-20387	101	8	indices	index	NOUN
fcis-20387	101	9	and	and	CCONJ
fcis-20387	101	10	achieved	achieve	VERB
fcis-20387	101	11	accurate	accurate	ADJ
fcis-20387	101	12	prediction	prediction	NOUN
fcis-20387	101	13	of	of	ADP
fcis-20387	101	14	tianjin	tianjin	PROPN
fcis-20387	101	15	's	's	PART
fcis-20387	101	16	air	air	NOUN
fcis-20387	101	17	quality	quality	NOUN
fcis-20387	101	18	.	.	PUNCT
fcis-20387	102	1	gao	gao	PROPN
fcis-20387	102	2	shuai	shuai	PROPN
fcis-20387	102	3	et	et	PROPN
fcis-20387	102	4	al	al	PROPN
fcis-20387	102	5	.	.	PROPN
fcis-20387	102	6	further	further	PROPN
fcis-20387	102	7	validated	validate	VERB
fcis-20387	102	8	the	the	DET
fcis-20387	102	9	effectiveness	effectiveness	NOUN
fcis-20387	102	10	and	and	CCONJ
fcis-20387	102	11	accuracy	accuracy	NOUN
fcis-20387	102	12	of	of	ADP
fcis-20387	102	13	neural	neural	ADJ
fcis-20387	102	14	networks	network	NOUN
fcis-20387	102	15	in	in	ADP
fcis-20387	102	16	air	air	NOUN
fcis-20387	102	17	quality	quality	NOUN
fcis-20387	102	18	monitoring	monitoring	NOUN
fcis-20387	102	19	and	and	CCONJ
fcis-20387	102	20	prediction	prediction	NOUN
fcis-20387	102	21	by	by	ADP
fcis-20387	102	22	combining	combine	VERB
fcis-20387	102	23	bp	bp	PROPN
fcis-20387	102	24	neural	neural	ADJ
fcis-20387	102	25	networks	network	NOUN
fcis-20387	102	26	with	with	ADP
fcis-20387	102	27	mind	mind	NOUN
fcis-20387	102	28	evolutionary	evolutionary	ADJ
fcis-20387	102	29	algorithm	algorithm	NOUN
fcis-20387	102	30	(	(	PUNCT
fcis-20387	102	31	mea	mea	NOUN
fcis-20387	102	32	)	)	PUNCT
fcis-20387	102	33	.	.	PUNCT
fcis-20387	103	1	yuan	yuan	NOUN
fcis-20387	103	2	et	et	PROPN
fcis-20387	103	3	al	al	PROPN
fcis-20387	103	4	.	.	PROPN
fcis-20387	103	5	used	use	VERB
fcis-20387	103	6	collaborative	collaborative	ADJ
fcis-20387	103	7	filtering	filtering	NOUN
fcis-20387	103	8	to	to	PART
fcis-20387	103	9	distinguish	distinguish	VERB
fcis-20387	103	10	the	the	DET
fcis-20387	103	11	weights	weight	NOUN
fcis-20387	103	12	of	of	ADP
fcis-20387	103	13	different	different	ADJ
fcis-20387	103	14	air	air	NOUN
fcis-20387	103	15	indicators	indicator	NOUN
fcis-20387	103	16	and	and	CCONJ
fcis-20387	103	17	simulated	simulate	VERB
fcis-20387	103	18	the	the	DET
fcis-20387	103	19	situation	situation	NOUN
fcis-20387	103	20	of	of	ADP
fcis-20387	103	21	air	air	NOUN
fcis-20387	103	22	pollution	pollution	NOUN
fcis-20387	103	23	using	use	VERB
fcis-20387	103	24	bp	bp	PROPN
fcis-20387	103	25	neural	neural	ADJ
fcis-20387	103	26	network	network	NOUN
fcis-20387	103	27	,	,	PUNCT
fcis-20387	103	28	providing	provide	VERB
fcis-20387	103	29	favorable	favorable	ADJ
fcis-20387	103	30	conditions	condition	NOUN
fcis-20387	103	31	for	for	ADP
fcis-20387	103	32	air	air	NOUN
fcis-20387	103	33	pollution	pollution	NOUN
fcis-20387	103	34	prevention	prevention	NOUN
fcis-20387	103	35	and	and	CCONJ
fcis-20387	103	36	control	control	NOUN
fcis-20387	103	37	work	work	NOUN
fcis-20387	103	38	.	.	PUNCT
fcis-20387	104	1	hu	hu	PROPN
fcis-20387	104	2	et	et	PROPN
fcis-20387	104	3	al	al	PROPN
fcis-20387	104	4	.	.	PROPN
fcis-20387	104	5	proposed	propose	VERB
fcis-20387	104	6	an	an	DET
fcis-20387	104	7	elman	elman	PROPN
fcis-20387	104	8	neural	neural	ADJ
fcis-20387	104	9	network	network	NOUN
fcis-20387	104	10	prediction	prediction	NOUN
fcis-20387	104	11	method	method	NOUN
fcis-20387	104	12	based	base	VERB
fcis-20387	104	13	on	on	ADP
fcis-20387	104	14	chaos	chaos	NOUN
fcis-20387	104	15	theory	theory	NOUN
fcis-20387	104	16	,	,	PUNCT
fcis-20387	104	17	which	which	PRON
fcis-20387	104	18	effectively	effectively	ADV
fcis-20387	104	19	learns	learn	VERB
fcis-20387	104	20	nonlinear	nonlinear	ADJ
fcis-20387	104	21	relationships	relationship	NOUN
fcis-20387	104	22	in	in	ADP
fcis-20387	104	23	air	air	NOUN
fcis-20387	104	24	quality	quality	NOUN
fcis-20387	104	25	data	datum	NOUN
fcis-20387	104	26	.	.	PUNCT
fcis-20387	105	1	implementation	implementation	NOUN
fcis-20387	105	2	analysis	analysis	NOUN
fcis-20387	105	3	shows	show	VERB
fcis-20387	105	4	that	that	SCONJ
fcis-20387	105	5	the	the	DET
fcis-20387	105	6	elman	elman	PROPN
fcis-20387	105	7	chaotic	chaotic	ADJ
fcis-20387	105	8	prediction	prediction	NOUN
fcis-20387	105	9	model	model	NOUN
fcis-20387	105	10	has	have	VERB
fcis-20387	105	11	good	good	ADJ
fcis-20387	105	12	predictive	predictive	ADJ
fcis-20387	105	13	performance	performance	NOUN
fcis-20387	105	14	and	and	CCONJ
fcis-20387	105	15	application	application	NOUN
fcis-20387	105	16	value	value	NOUN
fcis-20387	105	17	.	.	PUNCT
fcis-20387	106	1	sayegh	sayegh	AUX
fcis-20387	106	2	et	et	PROPN
fcis-20387	106	3	al	al	PROPN
fcis-20387	106	4	.	.	PROPN
fcis-20387	106	5	conducted	conduct	VERB
fcis-20387	106	6	comparative	comparative	ADJ
fcis-20387	106	7	experiments	experiment	NOUN
fcis-20387	106	8	using	use	VERB
fcis-20387	106	9	multiple	multiple	ADJ
fcis-20387	106	10	linear	linear	ADJ
fcis-20387	106	11	regression	regression	NOUN
fcis-20387	106	12	models	model	NOUN
fcis-20387	106	13	(	(	PUNCT
fcis-20387	106	14	mlrm	mlrm	PROPN
fcis-20387	106	15	)	)	PUNCT
fcis-20387	106	16	,	,	PUNCT
fcis-20387	106	17	quantile	quantile	ADJ
fcis-20387	106	18	regression	regression	NOUN
fcis-20387	106	19	models	model	NOUN
fcis-20387	106	20	(	(	PUNCT
fcis-20387	106	21	qrm	qrm	PROPN
fcis-20387	106	22	)	)	PUNCT
fcis-20387	106	23	,	,	PUNCT
fcis-20387	106	24	generalized	generalize	VERB
fcis-20387	106	25	additive	additive	ADJ
fcis-20387	106	26	models	model	NOUN
fcis-20387	106	27	(	(	PUNCT
fcis-20387	106	28	gam	gam	NOUN
fcis-20387	106	29	)	)	PUNCT
fcis-20387	106	30	,	,	PUNCT
fcis-20387	106	31	and	and	CCONJ
fcis-20387	106	32	enhanced	enhance	VERB
fcis-20387	106	33	regression	regression	NOUN
fcis-20387	106	34	trees	tree	NOUN
fcis-20387	106	35	(	(	PUNCT
fcis-20387	106	36	brt	brt	PROPN
fcis-20387	106	37	)	)	PUNCT
fcis-20387	106	38	,	,	PUNCT
fcis-20387	106	39	using	use	VERB
fcis-20387	106	40	meteorological	meteorological	ADJ
fcis-20387	106	41	data	datum	NOUN
fcis-20387	106	42	and	and	CCONJ
fcis-20387	106	43	chemical	chemical	NOUN
fcis-20387	106	44	species	specie	NOUN
fcis-20387	106	45	from	from	ADP
fcis-20387	106	46	mecca	mecca	PROPN
fcis-20387	106	47	,	,	PUNCT
fcis-20387	106	48	saudi	saudi	PROPN
fcis-20387	106	49	arabia	arabia	PROPN
fcis-20387	106	50	as	as	ADP
fcis-20387	106	51	covariates	covariate	NOUN
fcis-20387	106	52	to	to	PART
fcis-20387	106	53	predict	predict	VERB
fcis-20387	106	54	pm10	pm10	PROPN
fcis-20387	106	55	concentrations	concentration	NOUN
fcis-20387	106	56	for	for	ADP
fcis-20387	106	57	the	the	DET
fcis-20387	106	58	next	next	ADJ
fcis-20387	106	59	hour	hour	NOUN
fcis-20387	106	60	.	.	PUNCT
fcis-20387	107	1	the	the	DET
fcis-20387	107	2	experiment	experiment	NOUN
fcis-20387	107	3	shows	show	VERB
fcis-20387	107	4	that	that	SCONJ
fcis-20387	107	5	qrm	qrm	PROPN
fcis-20387	107	6	has	have	VERB
fcis-20387	107	7	a	a	DET
fcis-20387	107	8	better	well	ADJ
fcis-20387	107	9	predictive	predictive	ADJ
fcis-20387	107	10	effect	effect	NOUN
fcis-20387	107	11	on	on	ADP
fcis-20387	107	12	pm10	pm10	PROPN
fcis-20387	107	13	hourly	hourly	ADJ
fcis-20387	107	14	concentration	concentration	NOUN
fcis-20387	107	15	.	.	PUNCT
fcis-20387	108	1	compared	compare	VERB
fcis-20387	108	2	with	with	ADP
fcis-20387	108	3	other	other	ADJ
fcis-20387	108	4	models	model	NOUN
fcis-20387	108	5	,	,	PUNCT
fcis-20387	108	6	the	the	DET
fcis-20387	108	7	superiority	superiority	NOUN
fcis-20387	108	8	of	of	ADP
fcis-20387	108	9	qrm	qrm	PROPN
fcis-20387	108	10	model	model	NOUN
fcis-20387	108	11	lies	lie	VERB
fcis-20387	108	12	in	in	ADP
fcis-20387	108	13	its	its	PRON
fcis-20387	108	14	ability	ability	NOUN
fcis-20387	108	15	to	to	PART
fcis-20387	108	16	simulate	simulate	VERB
fcis-20387	108	17	the	the	DET
fcis-20387	108	18	contribution	contribution	NOUN
fcis-20387	108	19	of	of	ADP
fcis-20387	108	20	covariates	covariate	NOUN
fcis-20387	108	21	at	at	ADP
fcis-20387	108	22	different	different	ADJ
fcis-20387	108	23	quantiles	quantile	NOUN
fcis-20387	108	24	of	of	ADP
fcis-20387	108	25	model	model	NOUN
fcis-20387	108	26	variables	variable	NOUN
fcis-20387	108	27	.	.	PUNCT
fcis-20387	109	1	references	reference	NOUN
fcis-20387	109	2	[	[	X
fcis-20387	109	3	1	1	X
fcis-20387	109	4	]	]	PUNCT
fcis-20387	109	5	anenber	anenber	PROPN
fcis-20387	109	6	s	s	PART
fcis-20387	109	7	c	c	PROPN
fcis-20387	109	8	,	,	PUNCT
fcis-20387	109	9	mohegh	mohegh	VERB
fcis-20387	109	10	a	a	PRON
fcis-20387	109	11	,	,	PUNCT
fcis-20387	109	12	goldberg	goldberg	PROPN
fcis-20387	109	13	d	d	PROPN
fcis-20387	109	14	l	l	PROPN
fcis-20387	109	15	,	,	PUNCT
fcis-20387	109	16	et	et	PROPN
fcis-20387	109	17	al	al	PROPN
fcis-20387	109	18	.	.	PUNCT
fcis-20387	110	1	long	long	ADJ
fcis-20387	110	2	-	-	PUNCT
fcis-20387	110	3	term	term	NOUN
fcis-20387	110	4	trends	trend	NOUN
fcis-20387	110	5	in	in	ADP
fcis-20387	110	6	urban	urban	ADJ
fcis-20387	110	7	no2	no2	NOUN
fcis-20387	110	8	concentrations	concentration	NOUN
fcis-20387	110	9	and	and	CCONJ
fcis-20387	110	10	associated	associate	VERB
fcis-20387	110	11	paediatric	paediatric	ADJ
fcis-20387	110	12	asthma	asthma	NOUN
fcis-20387	110	13	incidence	incidence	NOUN
fcis-20387	110	14	:	:	PUNCT
fcis-20387	110	15	estimates	estimate	NOUN
fcis-20387	110	16	from	from	ADP
fcis-20387	110	17	global	global	ADJ
fcis-20387	110	18	datasets[j	datasets[j	PROPN
fcis-20387	110	19	]	]	PUNCT
fcis-20387	110	20	.	.	PUNCT
fcis-20387	111	1	the	the	DET
fcis-20387	111	2	lancet	lancet	PROPN
fcis-20387	111	3	planetary	planetary	PROPN
fcis-20387	111	4	health	health	NOUN
fcis-20387	111	5	,	,	PUNCT
fcis-20387	111	6	2022	2022	NUM
fcis-20387	111	7	,	,	PUNCT
fcis-20387	111	8	6	6	NUM
fcis-20387	111	9	(	(	PUNCT
fcis-20387	111	10	1):e49	1):e49	NUM
fcis-20387	111	11	-	-	PUNCT
fcis-20387	111	12	e58	e58	NOUN
fcis-20387	111	13	.	.	PUNCT
fcis-20387	112	1	[	[	X
fcis-20387	112	2	2	2	NUM
fcis-20387	112	3	]	]	PUNCT
fcis-20387	112	4	southerland	southerland	NOUN
fcis-20387	112	5	v	v	ADP
fcis-20387	112	6	a	a	PRON
fcis-20387	112	7	,	,	PUNCT
fcis-20387	112	8	brauer	brauer	PROPN
fcis-20387	112	9	m	m	PROPN
fcis-20387	112	10	,	,	PUNCT
fcis-20387	112	11	mohegh	mohegh	VERB
fcis-20387	112	12	a	a	PRON
fcis-20387	112	13	,	,	PUNCT
fcis-20387	112	14	et	et	PROPN
fcis-20387	112	15	al	al	PROPN
fcis-20387	112	16	.	.	PROPN
fcis-20387	113	1	global	global	ADJ
fcis-20387	113	2	urban	urban	ADJ
fcis-20387	113	3	temporal	temporal	ADJ
fcis-20387	113	4	trends	trend	NOUN
fcis-20387	113	5	in	in	ADP
fcis-20387	113	6	fine	fine	ADJ
fcis-20387	113	7	particulate	particulate	NOUN
fcis-20387	113	8	matter	matter	NOUN
fcis-20387	113	9	(	(	PUNCT
fcis-20387	113	10	pm2ꞏ5	pm2ꞏ5	PROPN
fcis-20387	113	11	)	)	PUNCT
fcis-20387	113	12	and	and	CCONJ
fcis-20387	113	13	attributable	attributable	ADJ
fcis-20387	113	14	health	health	NOUN
fcis-20387	113	15	burdens	burden	NOUN
fcis-20387	113	16	:	:	PUNCT
fcis-20387	113	17	estimates	estimate	NOUN
fcis-20387	113	18	from	from	ADP
fcis-20387	113	19	global	global	ADJ
fcis-20387	113	20	datasets[j	datasets[j	PROPN
fcis-20387	113	21	]	]	PUNCT
fcis-20387	113	22	.	.	PUNCT
fcis-20387	114	1	the	the	DET
fcis-20387	114	2	lancet	lancet	PROPN
fcis-20387	114	3	planetary	planetary	PROPN
fcis-20387	114	4	health	health	NOUN
fcis-20387	114	5	,	,	PUNCT
fcis-20387	114	6	2022	2022	NUM
fcis-20387	114	7	,	,	PUNCT
fcis-20387	114	8	6	6	NUM
fcis-20387	114	9	(	(	PUNCT
fcis-20387	114	10	2):e139e146	2):e139e146	NUM
fcis-20387	114	11	.	.	PUNCT
fcis-20387	115	1	[	[	X
fcis-20387	115	2	3	3	X
fcis-20387	115	3	]	]	X
fcis-20387	115	4	luo	luo	PROPN
fcis-20387	115	5	shi	shi	PROPN
fcis-20387	115	6	.	.	PUNCT
fcis-20387	116	1	who	who	PRON
fcis-20387	116	2	:	:	PUNCT
fcis-20387	116	3	13	13	NUM
fcis-20387	116	4	people	people	NOUN
fcis-20387	116	5	die	die	VERB
fcis-20387	116	6	every	every	DET
fcis-20387	116	7	minute	minute	NOUN
fcis-20387	116	8	from	from	ADP
fcis-20387	116	9	air	air	NOUN
fcis-20387	116	10	pollution	pollution	NOUN
fcis-20387	116	11	[	[	X
fcis-20387	116	12	n	n	X
fcis-20387	116	13	]	]	PUNCT
fcis-20387	116	14	.	.	PUNCT
fcis-20387	117	1	global	global	ADJ
fcis-20387	117	2	times,2021	times,2021	PROPN
fcis-20387	117	3	-	-	PUNCT
fcis-20387	117	4	10	10	NUM
fcis-20387	117	5	-	-	PUNCT
fcis-20387	117	6	13(005	13(005	NOUN
fcis-20387	117	7	)	)	PUNCT
fcis-20387	117	8	.	.	PUNCT
fcis-20387	118	1	[	[	X
fcis-20387	118	2	4	4	NUM
fcis-20387	118	3	]	]	X
fcis-20387	118	4	dong	dong	PROPN
fcis-20387	118	5	k	k	PROPN
fcis-20387	118	6	,	,	PUNCT
fcis-20387	118	7	zeng	zeng	PROPN
fcis-20387	118	8	x.	x.	PROPN
fcis-20387	118	9	public	public	ADJ
fcis-20387	118	10	willingness	willingness	NOUN
fcis-20387	118	11	to	to	PART
fcis-20387	118	12	pay	pay	VERB
fcis-20387	118	13	for	for	ADP
fcis-20387	118	14	urban	urban	ADJ
fcis-20387	118	15	smog	smog	NOUN
fcis-20387	118	16	mitigation	mitigation	NOUN
fcis-20387	118	17	and	and	CCONJ
fcis-20387	118	18	its	its	PRON
fcis-20387	118	19	determinants	determinant	NOUN
fcis-20387	118	20	:	:	PUNCT
fcis-20387	118	21	a	a	DET
fcis-20387	118	22	case	case	NOUN
fcis-20387	118	23	study	study	NOUN
fcis-20387	118	24	of	of	ADP
fcis-20387	118	25	beijing	beijing	PROPN
fcis-20387	118	26	,	,	PUNCT
fcis-20387	118	27	china	china	PROPN
fcis-20387	119	1	[	[	X
fcis-20387	119	2	j	j	X
fcis-20387	119	3	]	]	X
fcis-20387	119	4	.	.	PUNCT
fcis-20387	119	5	atmospheric	atmospheric	PROPN
fcis-20387	119	6	environment	environment	NOUN
fcis-20387	119	7	,	,	PUNCT
fcis-20387	119	8	2017	2017	NUM
fcis-20387	119	9	,	,	PUNCT
fcis-20387	119	10	173:355	173:355	PROPN
fcis-20387	119	11	-	-	PUNCT
fcis-20387	119	12	363	363	NUM
fcis-20387	119	13	.	.	PUNCT
fcis-20387	120	1	[	[	X
fcis-20387	120	2	5	5	NUM
fcis-20387	120	3	]	]	PUNCT
fcis-20387	120	4	zhu	zhu	PROPN
fcis-20387	120	5	s	s	PROPN
fcis-20387	120	6	,	,	PUNCT
fcis-20387	120	7	yang	yang	PROPN
fcis-20387	120	8	l	l	PROPN
fcis-20387	120	9	,	,	PUNCT
fcis-20387	120	10	wang	wang	PROPN
fcis-20387	120	11	w	w	PROPN
fcis-20387	120	12	,	,	PUNCT
fcis-20387	120	13	et	et	PROPN
fcis-20387	120	14	al	al	PROPN
fcis-20387	120	15	.	.	PUNCT
fcis-20387	120	16	optimal	optimal	ADJ
fcis-20387	120	17	-	-	PUNCT
fcis-20387	120	18	combined	combine	VERB
fcis-20387	120	19	model	model	NOUN
fcis-20387	120	20	for	for	ADP
fcis-20387	120	21	air	air	NOUN
fcis-20387	120	22	quality	quality	NOUN
fcis-20387	120	23	index	index	NOUN
fcis-20387	120	24	forecasting	forecasting	NOUN
fcis-20387	120	25	:	:	PUNCT
fcis-20387	120	26	5	5	NUM
fcis-20387	120	27	cities	city	NOUN
fcis-20387	120	28	in	in	ADP
fcis-20387	120	29	north	north	PROPN
fcis-20387	120	30	china	china	PROPN
fcis-20387	121	1	[	[	X
fcis-20387	121	2	j	j	X
fcis-20387	121	3	]	]	X
fcis-20387	121	4	.	.	PUNCT
fcis-20387	121	5	environmental	environmental	ADJ
fcis-20387	121	6	pollution	pollution	NOUN
fcis-20387	121	7	,	,	PUNCT
fcis-20387	121	8	2018	2018	NUM
fcis-20387	121	9	,	,	PUNCT
fcis-20387	121	10	243:842	243:842	NOUN
fcis-20387	121	11	-	-	SYM
fcis-20387	121	12	850	850	NUM
fcis-20387	121	13	.	.	PUNCT
fcis-20387	122	1	[	[	X
fcis-20387	122	2	6	6	NUM
fcis-20387	122	3	]	]	PUNCT
fcis-20387	122	4	xu	xu	PROPN
fcis-20387	122	5	h.	h.	PROPN
fcis-20387	122	6	application	application	NOUN
fcis-20387	122	7	of	of	ADP
fcis-20387	122	8	bp	bp	PROPN
fcis-20387	122	9	neural	neural	ADJ
fcis-20387	122	10	network	network	NOUN
fcis-20387	122	11	optimization	optimization	NOUN
fcis-20387	122	12	based	base	VERB
fcis-20387	122	13	on	on	ADP
fcis-20387	122	14	improved	improved	ADJ
fcis-20387	122	15	drosophila	drosophila	NOUN
fcis-20387	122	16	algorithm	algorithm	NOUN
fcis-20387	122	17	in	in	ADP
fcis-20387	122	18	air	air	NOUN
fcis-20387	122	19	quality	quality	NOUN
fcis-20387	122	20	prediction	prediction	NOUN
fcis-20387	123	1	[	[	X
fcis-20387	123	2	d	d	X
fcis-20387	123	3	]	]	X
fcis-20387	123	4	.	.	PUNCT
fcis-20387	124	1	nanchang	nanchang	PROPN
fcis-20387	124	2	:	:	PUNCT
fcis-20387	124	3	nanchang	nanchang	PROPN
fcis-20387	124	4	university,2020	university,2020	PROPN
fcis-20387	124	5	.	.	PUNCT
fcis-20387	125	1	[	[	X
fcis-20387	125	2	7	7	X
fcis-20387	125	3	]	]	PUNCT
fcis-20387	125	4	z.	z.	PROPN
fcis-20387	125	5	kang	kang	PROPN
fcis-20387	125	6	,	,	PUNCT
fcis-20387	125	7	z.	z.	PROPN
fcis-20387	125	8	qu	qu	PROPN
fcis-20387	125	9	,	,	PUNCT
fcis-20387	125	10	application	application	NOUN
fcis-20387	125	11	of	of	ADP
fcis-20387	125	12	bp	bp	PROPN
fcis-20387	125	13	neural	neural	ADJ
fcis-20387	125	14	network	network	NOUN
fcis-20387	125	15	optimized	optimize	VERB
fcis-20387	125	16	by	by	ADP
fcis-20387	125	17	genetic	genetic	ADJ
fcis-20387	125	18	simulated	simulate	VERB
fcis-20387	125	19	annealing	anneal	VERB
fcis-20387	125	20	algorithm	algorithm	NOUN
fcis-20387	125	21	to	to	ADP
fcis-20387	125	22	prediction	prediction	NOUN
fcis-20387	125	23	of	of	ADP
fcis-20387	125	24	air	air	NOUN
fcis-20387	125	25	quality	quality	NOUN
fcis-20387	125	26	index	index	NOUN
fcis-20387	125	27	in	in	ADP
fcis-20387	125	28	lanzhou[c	lanzhou[c	PROPN
fcis-20387	125	29	]	]	PUNCT
fcis-20387	125	30	.	.	PUNCT
fcis-20387	126	1	2017	2017	NUM
fcis-20387	126	2	2nd	2nd	ADJ
fcis-20387	126	3	ieee	ieee	PROPN
fcis-20387	126	4	international	international	PROPN
fcis-20387	126	5	conference	conference	NOUN
fcis-20387	126	6	on	on	ADP
fcis-20387	126	7	computational	computational	ADJ
fcis-20387	126	8	intelligence	intelligence	NOUN
fcis-20387	126	9	and	and	CCONJ
fcis-20387	126	10	applications	application	NOUN
fcis-20387	126	11	(	(	PUNCT
fcis-20387	126	12	iccia	iccia	NOUN
fcis-20387	126	13	)	)	PUNCT
fcis-20387	126	14	,	,	PUNCT
fcis-20387	126	15	2017:155	2017:155	NOUN
fcis-20387	126	16	-	-	SYM
fcis-20387	126	17	160	160	NUM
fcis-20387	126	18	.	.	PUNCT
fcis-20387	126	19	46	46	NUM
fcis-20387	127	1	[	[	NOUN
fcis-20387	127	2	8	8	NUM
fcis-20387	127	3	]	]	X
fcis-20387	127	4	y.	y.	PROPN
fcis-20387	127	5	huang	huang	PROPN
fcis-20387	127	6	,	,	PUNCT
fcis-20387	127	7	y.	y.	PROPN
fcis-20387	127	8	xiang	xiang	PROPN
fcis-20387	127	9	,	,	PUNCT
fcis-20387	127	10	r.	r.	PROPN
fcis-20387	127	11	zhao	zhao	PROPN
fcis-20387	127	12	,	,	PUNCT
fcis-20387	127	13	et	et	PROPN
fcis-20387	127	14	al	al	PROPN
fcis-20387	127	15	.	.	PROPN
fcis-20387	127	16	,	,	PUNCT
fcis-20387	127	17	air	air	NOUN
fcis-20387	127	18	quality	quality	NOUN
fcis-20387	127	19	prediction	prediction	NOUN
fcis-20387	127	20	using	use	VERB
fcis-20387	127	21	improved	improved	ADJ
fcis-20387	127	22	pso	pso	NOUN
fcis-20387	127	23	-	-	PUNCT
fcis-20387	127	24	bp	bp	PROPN
fcis-20387	127	25	neural	neural	ADJ
fcis-20387	127	26	network[j	network[j	PROPN
fcis-20387	127	27	]	]	PUNCT
fcis-20387	127	28	.	.	PUNCT
fcis-20387	128	1	ieee	ieee	NOUN
fcis-20387	128	2	access	access	NOUN
fcis-20387	128	3	,	,	PUNCT
fcis-20387	128	4	2020	2020	NUM
fcis-20387	128	5	,	,	PUNCT
fcis-20387	128	6	8:99346	8:99346	NUM
fcis-20387	128	7	-	-	SYM
fcis-20387	128	8	99353	99353	NUM
fcis-20387	128	9	.	.	PUNCT
fcis-20387	129	1	[	[	X
fcis-20387	129	2	9	9	NUM
fcis-20387	129	3	]	]	SYM
fcis-20387	129	4	fan	fan	NOUN
fcis-20387	129	5	wenting	wenting	NOUN
fcis-20387	129	6	,	,	PUNCT
fcis-20387	129	7	wang	wang	PROPN
fcis-20387	129	8	xiao	xiao	PROPN
fcis-20387	129	9	.	.	PROPN
fcis-20387	129	10	pm	pm	VERB
fcis-20387	129	11	2.5	2.5	NUM
fcis-20387	129	12	prediction	prediction	NOUN
fcis-20387	129	13	based	base	VERB
fcis-20387	129	14	on	on	ADP
fcis-20387	129	15	improved	improved	ADJ
fcis-20387	129	16	firefly	firefly	NOUN
fcis-20387	129	17	optimization	optimization	NOUN
fcis-20387	129	18	support	support	NOUN
fcis-20387	129	19	vector	vector	NOUN
fcis-20387	129	20	machine	machine	NOUN
fcis-20387	130	1	[	[	X
fcis-20387	130	2	j	j	X
fcis-20387	130	3	]	]	X
fcis-20387	130	4	.	.	PUNCT
fcis-20387	131	1	journal	journal	PROPN
fcis-20387	131	2	of	of	ADP
fcis-20387	131	3	computer	computer	NOUN
fcis-20387	131	4	systems	system	NOUN
fcis-20387	131	5	and	and	CCONJ
fcis-20387	131	6	applications	application	NOUN
fcis-20387	131	7	,	,	PUNCT
fcis-20387	131	8	2019	2019	NUM
fcis-20387	131	9	,	,	PUNCT
fcis-20387	131	10	28(1	28(1	NOUN
fcis-20387	131	11	):	):	PUNCT
fcis-20387	131	12	134	134	NUM
fcis-20387	131	13	-	-	SYM
fcis-20387	131	14	139	139	NUM
fcis-20387	131	15	.	.	PUNCT
fcis-20387	132	1	[	[	X
fcis-20387	132	2	10	10	NUM
fcis-20387	132	3	]	]	X
fcis-20387	132	4	gao	gao	PROPN
fcis-20387	132	5	shuai	shuai	PROPN
fcis-20387	132	6	,	,	PUNCT
fcis-20387	132	7	hu	hu	PROPN
fcis-20387	132	8	hongping	hongping	PROPN
fcis-20387	132	9	,	,	PUNCT
fcis-20387	132	10	li	li	PROPN
fcis-20387	132	11	yang	yang	PROPN
fcis-20387	132	12	,	,	PUNCT
fcis-20387	132	13	et	et	PROPN
fcis-20387	132	14	al	al	PROPN
fcis-20387	132	15	.	.	PROPN
fcis-20387	132	16	air	air	PROPN
fcis-20387	132	17	quality	quality	PROPN
fcis-20387	132	18	index	index	NOUN
fcis-20387	132	19	prediction	prediction	NOUN
fcis-20387	132	20	based	base	VERB
fcis-20387	132	21	on	on	ADP
fcis-20387	132	22	mfo	mfo	NOUN
fcis-20387	132	23	-	-	ADJ
fcis-20387	132	24	svm	svm	PROPN
fcis-20387	132	25	[	[	X
fcis-20387	132	26	j	j	X
fcis-20387	132	27	]	]	X
fcis-20387	132	28	.	.	PUNCT
fcis-20387	133	1	journal	journal	PROPN
fcis-20387	133	2	of	of	ADP
fcis-20387	133	3	north	north	PROPN
fcis-20387	133	4	university	university	PROPN
fcis-20387	133	5	of	of	ADP
fcis-20387	133	6	china	china	PROPN
fcis-20387	133	7	:	:	PUNCT
fcis-20387	133	8	natural	natural	ADJ
fcis-20387	133	9	science	science	NOUN
fcis-20387	133	10	edition	edition	NOUN
fcis-20387	133	11	,	,	PUNCT
fcis-20387	133	12	2018	2018	NUM
fcis-20387	133	13	,	,	PUNCT
fcis-20387	133	14	39(4):7	39(4):7	NUM
fcis-20387	133	15	-	-	SYM
fcis-20387	133	16	13	13	NUM
fcis-20387	133	17	.	.	PUNCT
fcis-20387	134	1	(	(	PUNCT
fcis-20387	134	2	in	in	ADP
fcis-20387	134	3	chinese	chinese	PROPN
fcis-20387	134	4	)	)	PUNCT
fcis-20387	135	1	[	[	X
fcis-20387	135	2	11	11	NUM
fcis-20387	135	3	]	]	X
fcis-20387	135	4	zhang	zhang	PROPN
fcis-20387	135	5	s	s	PROPN
fcis-20387	135	6	,	,	PUNCT
fcis-20387	135	7	lin	lin	PROPN
fcis-20387	135	8	m	m	PROPN
fcis-20387	135	9	,	,	PUNCT
fcis-20387	135	10	zou	zou	PROPN
fcis-20387	135	11	x	x	PROPN
fcis-20387	135	12	,	,	PUNCT
fcis-20387	135	13	et	et	PROPN
fcis-20387	135	14	al	al	PROPN
fcis-20387	135	15	.	.	PUNCT
fcis-20387	136	1	lstm	lstm	PROPN
fcis-20387	136	2	-	-	PUNCT
fcis-20387	136	3	based	base	VERB
fcis-20387	136	4	air	air	NOUN
fcis-20387	136	5	quality	quality	NOUN
fcis-20387	136	6	predicted	predict	VERB
fcis-20387	136	7	model	model	NOUN
fcis-20387	136	8	for	for	ADP
fcis-20387	136	9	large	large	ADJ
fcis-20387	136	10	cities	city	NOUN
fcis-20387	136	11	in	in	ADP
fcis-20387	136	12	china[j	china[j	PROPN
fcis-20387	136	13	]	]	PUNCT
fcis-20387	136	14	.	.	PUNCT
fcis-20387	137	1	nature	nature	PROPN
fcis-20387	137	2	environment	environment	PROPN
fcis-20387	137	3	,	,	PUNCT
fcis-20387	137	4	2020	2020	NUM
fcis-20387	137	5	,	,	PUNCT
fcis-20387	137	6	19(1	19(1	NUM
fcis-20387	137	7	):	):	PUNCT
fcis-20387	137	8	229	229	NUM
fcis-20387	137	9	-	-	SYM
fcis-20387	137	10	236	236	NUM
fcis-20387	137	11	.	.	PUNCT
fcis-20387	138	1	[	[	X
fcis-20387	138	2	12	12	NUM
fcis-20387	138	3	]	]	X
fcis-20387	138	4	al	al	PROPN
fcis-20387	138	5	-	-	PUNCT
fcis-20387	138	6	janabi	janabi	NOUN
fcis-20387	138	7	,	,	PUNCT
fcis-20387	138	8	s.	s.	PROPN
fcis-20387	138	9	,	,	PUNCT
fcis-20387	138	10	mohammad	mohammad	PROPN
fcis-20387	138	11	,	,	PUNCT
fcis-20387	138	12	m.	m.	PROPN
fcis-20387	138	13	&	&	CCONJ
fcis-20387	138	14	al	al	PROPN
fcis-20387	138	15	-	-	PUNCT
fcis-20387	138	16	sultan	sultan	PROPN
fcis-20387	138	17	,	,	PUNCT
fcis-20387	138	18	a.	a.	NOUN
fcis-20387	138	19	a	a	DET
fcis-20387	138	20	new	new	ADJ
fcis-20387	138	21	method	method	NOUN
fcis-20387	138	22	for	for	ADP
fcis-20387	138	23	prediction	prediction	NOUN
fcis-20387	138	24	of	of	ADP
fcis-20387	138	25	air	air	NOUN
fcis-20387	138	26	pollution	pollution	NOUN
fcis-20387	138	27	based	base	VERB
fcis-20387	138	28	on	on	ADP
fcis-20387	138	29	intelligent	intelligent	ADJ
fcis-20387	138	30	computation	computation	NOUN
fcis-20387	138	31	[	[	X
fcis-20387	138	32	j	j	X
fcis-20387	138	33	]	]	X
fcis-20387	138	34	.	.	PUNCT
fcis-20387	139	1	soft	soft	ADJ
fcis-20387	139	2	computer	computer	NOUN
fcis-20387	139	3	,	,	PUNCT
fcis-20387	139	4	2020	2020	NUM
fcis-20387	139	5	,	,	PUNCT
fcis-20387	139	6	24:661	24:661	NUM
fcis-20387	139	7	-	-	SYM
fcis-20387	139	8	680	680	NUM
fcis-20387	139	9	.	.	PUNCT
fcis-20387	140	1	[	[	X
fcis-20387	140	2	13	13	NUM
fcis-20387	140	3	]	]	X
fcis-20387	140	4	liao	liao	PROPN
fcis-20387	140	5	liefa	liefa	PROPN
fcis-20387	140	6	,	,	PUNCT
fcis-20387	140	7	yang	yang	PROPN
fcis-20387	140	8	hong	hong	PROPN
fcis-20387	140	9	.	.	PUNCT
fcis-20387	141	1	a	a	DET
fcis-20387	141	2	survey	survey	NOUN
fcis-20387	141	3	on	on	ADP
fcis-20387	141	4	the	the	DET
fcis-20387	141	5	search	search	NOUN
fcis-20387	141	6	algorithm	algorithm	NOUN
fcis-20387	141	7	of	of	ADP
fcis-20387	141	8	longniu	longniu	NOUN
fcis-20387	141	9	whiskers	whisker	NOUN
fcis-20387	142	1	[	[	X
fcis-20387	142	2	j	j	X
fcis-20387	142	3	]	]	X
fcis-20387	142	4	.	.	PUNCT
fcis-20387	143	1	computer	computer	NOUN
fcis-20387	143	2	engineering	engineering	NOUN
fcis-20387	143	3	and	and	CCONJ
fcis-20387	143	4	applications	application	NOUN
fcis-20387	143	5	,	,	PUNCT
fcis-20387	143	6	2021	2021	NUM
fcis-20387	143	7	,	,	PUNCT
fcis-20387	143	8	57(12):54	57(12):54	NUM
fcis-20387	143	9	-	-	SYM
fcis-20387	143	10	64	64	NUM
fcis-20387	143	11	.	.	PUNCT
fcis-20387	144	1	[	[	X
fcis-20387	144	2	14	14	NUM
fcis-20387	144	3	]	]	X
fcis-20387	144	4	wang	wang	PROPN
fcis-20387	144	5	t	t	PROPN
fcis-20387	144	6	,	,	PUNCT
fcis-20387	144	7	jiang	jiang	PROPN
fcis-20387	144	8	f	f	PROPN
fcis-20387	144	9	,	,	PUNCT
fcis-20387	144	10	deng	deng	PROPN
fcis-20387	144	11	j	j	PROPN
fcis-20387	144	12	,	,	PUNCT
fcis-20387	144	13	et	et	PROPN
fcis-20387	144	14	al	al	PROPN
fcis-20387	144	15	.	.	PUNCT
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fcis-20387	145	2	air	air	PROPN
fcis-20387	145	3	quality	quality	NOUN
fcis-20387	145	4	and	and	CCONJ
fcis-20387	145	5	regional	regional	ADJ
fcis-20387	145	6	haze	haze	NOUN
fcis-20387	145	7	weather	weather	NOUN
fcis-20387	145	8	forecast	forecast	NOUN
fcis-20387	145	9	for	for	ADP
fcis-20387	145	10	yangtze	yangtze	PROPN
fcis-20387	145	11	river	river	PROPN
fcis-20387	145	12	delta	delta	PROPN
fcis-20387	145	13	region	region	NOUN
fcis-20387	146	1	[	[	X
fcis-20387	146	2	j	j	X
fcis-20387	146	3	]	]	X
fcis-20387	146	4	.	.	PUNCT
fcis-20387	147	1	atmospheric	atmospheric	PROPN
fcis-20387	147	2	environment	environment	NOUN
fcis-20387	147	3	,	,	PUNCT
fcis-20387	147	4	2012	2012	NUM
fcis-20387	147	5	,	,	PUNCT
fcis-20387	147	6	58:70	58:70	NUM
fcis-20387	147	7	-	-	SYM
fcis-20387	147	8	83	83	NUM
fcis-20387	147	9	.	.	PUNCT