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A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction.

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第一作者： Bo,Zhang

第一单位： College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, PR China. Electronic address: zhangbo@shnu.edu.cn.

作者： Bo,Zhang ^[1] ; Guojian,Zou ^[2] ; Dongming,Qin ^[3] ; Yunjie,Lu ^[4] ; Yupeng,Jin ^[5] ; Hui,Wang ^[6]

作者单位： College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, PR China. Electronic address: zhangbo@shnu.edu.cn. ^[1] The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, PR China. Electronic address: 2010768@tongji.edu.cn. ^[2] College of Electronic and Information Engineering, Tongji University, Shanghai, 201804 and now is with the 3Clear, Beijing 100029, PR China. Electronic address: qindm@3clear.com. ^[3] College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, PR China. Electronic address: 1000459475@smail.shnu.edu.cn. ^[4] College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, PR China. ^[5] School of Computing, University of Ulster, UK. Electronic address: h.wang@ulster.ac.uk. ^[6]

关键词 Air pollutant prediction Deep learning Encoder-Decoder model Long short term memory Numerical analysis Recurrent neural networks

DOI 10.1016/j.scitotenv.2020.144507

PMID 33418334

发布时间 2021-02-10

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