标題: A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

作者: Wang, WL (Wang, Weilin); Mao, WJ (Mao, Wenjing); Tong, XL (Tong, Xueli); Xu, G (Xu, Gang)

來源出版物: REMOTE SENSING 卷: 13 期: 7 文獻号: 1284 DOI: 10.3390/rs13071284 出版年: APR 2021

摘要: Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 mu m) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R-2) = 0.74; root mean square error (RMSE) = 18.96 mu g/m(3)) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

入藏号: WOS:000638790400001

語言: English

文獻類型: Article

作者關鍵詞: air pollutant; PM2; 5; ConvLSTM; spatiotemporal correlation; long-term prediction

KeyWords Plus: PM2.5 CONCENTRATIONS; LEARNING-MODEL; REGRESSION; PM10; QUALITY; OPTIMIZATION; EXPOSURE; STATION; AREA

地址: [Wang, Weilin; Mao, Wenjing; Tong, Xueli] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Xu, Gang] Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

通訊作者地址: Xu, G (通訊作者)，Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

電子郵件地址: wangweilin@whu.edu.cn; wenjingmao@whu.edu.cn; xuelitong@whu.edu.cn; xugang@whu.edu.cn

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