A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation

Ding, Chen; Wang, Guizhi; Zhang, Xinyue; Liu, Qi; Liu, Xiaodong

Abstract

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction.

Citation

Ding, C., Wang, G., Zhang, X., Liu, Q., & Liu, X. (2021). A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation. Environmental and Ecological Statistics, 128(3), 503-522. https://doi.org/10.1007/s10651-021-00501-8

• Journal Article Type: Article
• Acceptance Date: Apr 17, 2021
• Online Publication Date: Apr 27, 2021
• Publication Date: 2021
• Deposit Date: May 27, 2021
• Publicly Available Date: Apr 28, 2022
• Journal: Environmental and Ecological Statistics
• Print ISSN: 1352-8505
• Electronic ISSN: 1573-3009
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 128
• Issue: 3
• Pages: 503-522
• DOI: https://doi.org/10.1007/s10651-021-00501-8
• Keywords: Convolutional neural networks, Deep learning, Long short-term memory networks, PM2.5 prediction, Spatiotemporal correlation,
• Public URL: http://researchrepository.napier.ac.uk/Output/2775818