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硕士生毛文婧,焦利民的论文在BUILDING AND ENVIRONMENT 刊出
发布时间:2022-05-31 15:27:16     发布者:易真     浏览次数:

标题: Long time series ozone prediction in China: A novel dynamic spatiotemporal deep learning approach

作者: Mao, WJ (Mao, Wenjing); Jiao, LM (Jiao, Limin); Wang, WL (Wang, Weilin)

来源出版物: BUILDING AND ENVIRONMENT : 218 文献号: 109087 DOI: 10.1016/j.buildenv.2022.109087 出版年: JUN 15 2022

摘要: Ozone pollution is a global environmental problem becoming increasingly prominent in China. It is of great significance to achieve long-term and high-precision ground-level ozone prediction on large scales to improve the efficiency of environmental governance. In this paper, we developed a dynamic graph convolutional and sequence to sequence embedded with the attention mechanism model (DG-ASeqseq) for predicting daily maximum 8-h average ozone (MDA8 O3) concentrations over China the next seven days. In the proposed approach, changeable spatial correlations are modelled by graph convolutional operations on dynamic graphs constructed based on multiple information of historical change, and temporal correlations in long time series are modelled through the sequence to sequence networks embedded with the attention mechanism. Results show the reliability and effectiveness of the proposed model, and it is superior to other benchmark models in simulating long-term spatiotemporal variations of O3 concentrations in large scale areas. Moreover, the proposed model has good prediction capability in severe O3 pollution events. Advancement in this methodology could provide guidance for the government's coordinated control of regional pollution to help improve air quality and jointly safeguard global climate security.

作者关键词: Air pollution prediction; Ozone pollution; Deep learning; Graph convolution; Attention mechanism

地址: [Mao, Wenjing; Jiao, Limin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Mao, Wenjing; Jiao, Limin] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

[Wang, Weilin] Hunan Agr Univ, Coll Resources & Environm, Changsha 410128, Peoples R China.

通讯作者地址: Jiao, LM (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: wenjingmao@whu.edu.cn; lmjiao@whu.edu.cn; wangweilin@whu.edu.cn

影响因子:6.456

 

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