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

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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.

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Acknowledgements

This work was supported by the Major Program of the National Social Science Fund of China (Grant No. 17ZDA092).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chen Ding & Guizhi Wang

  2. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Xinyue Zhang

  3. Shandong Beiming Medical Technology Ltd, Jinan, 250000, China

    Qi Liu

  4. School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK

    Xiaodong Liu

Authors
  1. Chen Ding
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  2. Guizhi Wang
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  3. Xinyue Zhang
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  4. Qi Liu
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  5. Xiaodong Liu
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Corresponding author

Correspondence to Guizhi Wang.

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The authors declare no conflicts of interest.

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Handling editor: Luiz Duczmal, PhD.

Appendix

Appendix

See Fig. 13.

Fig. 13
figure 13

The probability distribution of different continuous variables after logarithmic transformation

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Ding, C., Wang, G., Zhang, X. et al. A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation. Environ Ecol Stat 28, 503–522 (2021). https://doi.org/10.1007/s10651-021-00501-8

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  • DOI: https://doi.org/10.1007/s10651-021-00501-8

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