中国科学院机构知识库网格系统: Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

文献类型：期刊论文


作者	Yan, Rui 3,4; Liao, Jiaqiang 3,5; Yang, Jie 2,3; Sun, Wei 1,3,6; Nong, Mingyue 3; Li, Feipeng 3
刊名	EXPERT SYSTEMS WITH APPLICATIONS
出版日期	2021-05-01
卷号	169 页码:15
关键词	LSTM CNN Forecasting AQI Spatiotemporal clustering
ISSN号	0957-4174
DOI	10.1016/j.eswa.2020.114513
通讯作者	Sun, Wei(sunwei29@mail.sysu.edu.cn)
英文摘要	Effective air quality forecasting models are helpful for timely prevention and control of air pollution. However, the spatiotemporal distribution characteristics of air quality have not been fully considered in previous model development. This study attempts to establish a multi-time, multi-site forecasting model of Beijing's air quality by using deep learning network models based on spatiotemporal clustering and to compare them with a backpropagation neural network (BPNN). For the overall forecasting, the performances in next-hour forecasting were ranked in ascending order of the BPNN, the convolutional neural network (CNN), the long short-term memory (LSTM) model, and the CNN-LSTM, with the LSTM as the optimal model in the multiple-hour forecasting. The performance of the seasonal forecasting was not significantly improved compared to the overall forecasting. For the spatial clustering-based forecasting, cluster 2 forecasting generally outperforms cluster 1 and the overall forecasting. Overall, either the seasonal or the spatial clustering-based forecasting is more suitable for the improvement of the forecasting in a certain season or cluster. In terms of model type, both the CNN-LSTM and the LSTM generally have better performance than the CNN and the BPNN.
WOS关键词	YANGTZE-RIVER DELTA ; SHORT-TERM-MEMORY ; NEURAL-NETWORKS ; TIME-SERIES ; POLLUTION ; PM2.5 ; PREDICTION ; MODELS ; CHINA ; POLLUTANTS
资助项目	Top-Notch Young Talents of Pearl River Talents Plan[2019QN01G106] ; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)[99147-42080011] ; Hundred Talents Program of Sun Yat-Sen University[3700018841201] ; National Undergraduate Training Programs for Innovation and Entrepreneurship[201901211] ; National Program on Key Research Projects of China[2017YFC1502706]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
WOS记录号	WOS:000663708000034
出版者	PERGAMON-ELSEVIER SCIENCE LTD
资助机构	Top-Notch Young Talents of Pearl River Talents Plan ; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) ; Hundred Talents Program of Sun Yat-Sen University ; National Undergraduate Training Programs for Innovation and Entrepreneurship ; National Program on Key Research Projects of China
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164088]
专题	中国科学院地理科学与资源研究所
通讯作者	Sun, Wei
作者单位	1.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 6.Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada
推荐引用方式 GB/T 7714	Yan, Rui,Liao, Jiaqiang,Yang, Jie,et al. Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,169:15.
APA	Yan, Rui,Liao, Jiaqiang,Yang, Jie,Sun, Wei,Nong, Mingyue,&Li, Feipeng.(2021).Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering.EXPERT SYSTEMS WITH APPLICATIONS,169,15.
MLA	Yan, Rui,et al."Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering".EXPERT SYSTEMS WITH APPLICATIONS 169(2021):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

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