太阳成8722(中国)有限公司-GREEN NO.1

标题: A hybrid integrated deep learning model for predicting various air pollutants

作者: Mao, WJ (Mao, Wenjing); Jiao, LM (Jiao, Limin); Wang, WL (Wang, Weilin); Wang, JL (Wang, Jianlong); Tong, XL (Tong, Xueli); Zhao, SL (Zhao, Suli)

来源出版物: GISCIENCE & REMOTE SENSING DOI: 10.1080/15481603.2021.1988429 提前访问日期: OCT 2021

摘要: Air pollution is a significant urban issue, with practical applications for pollution control, urban environmental management planning, and urban construction. However, owing to the complexity and differences in spatiotemporal changes for various types of pollution, it is challenging to establish a framework that can capture the spatiotemporal correlations of different types of air pollution and obtain high prediction accuracy. In this paper, we proposed a deep learning framework suitable for predicting various air pollutants: a graph convolutional temporal sliding long short-term memory (GT-LSTM) model. The hybrid integrated model combines graph convolutional networks and long short-term networks based on a strategy with temporal sliding. Herein, the graph convolution networks gather neighbor information for spatial dependency modeling based on the spatial adjacency matrices of different pollutants and the graph convolution operator with parameter sharing. LSTM networks with a temporal sliding strategy are used to learn dynamic air pollution changes for temporal dependency modeling. The framework was applied to predict the average concentrations of PM2.5, PM10, O-3, CO, SO2, and NO2 in the Bejing-Tianjin-Hebei (BTH) region for the next 24 hours. Experiments demonstrated that the proposed GT-LSTM model could extract high-level spatiotemporal features and achieve higher accuracy and stability than state-of-the-art baselines. Advancement in this methodology can assist in providing decision support capabilities to mitigate air quality issues.

地址: [Mao, Wenjing; Jiao, Limin; Wang, Weilin; Tong, Xueli; Zhao, Suli] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

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

[Wang, Jianlong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China.

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

Jiao, LM (通讯作者)，Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Peoples R China.

电子邮件地址: lmjiao@whu.edu.cn

影响因子：6.238