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PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model

Author

Listed:
  • Wenchao Ban

    (School of Ocean Engineering Equipment, Zhejiang Ocean University, Zhoushan 316000, China)

  • Liangduo Shen

    (School of Ocean Engineering Equipment, Zhejiang Ocean University, Zhoushan 316000, China)

Abstract

The current serious air pollution problem has become a closely investigated topic in people’s daily lives. If we want to provide a reasonable basis for haze prevention, then the prediction of PM2.5 concentrations becomes a crucial task. However, it is difficult to complete the task of PM2.5 concentration prediction using a single model; therefore, to address this problem, this paper proposes a fully adaptive noise ensemble empirical modal decomposition (CEEMDAN) algorithm combined with deep learning hybrid models. Firstly, the CEEMDAN algorithm was used to decompose the PM2.5 timeseries data into different modal components. Then long short-term memory (LSTM), a backpropagation (BP) neural network, a differential integrated moving average autoregressive model (ARIMA), and a support vector machine (SVM) were applied to each modal component. Lastly, the best prediction results of each component were superimposed and summed to obtain the final prediction results. The PM2.5 data of Hangzhou in recent years were substituted into the model for testing, which was compared with eight models, namely, LSTM, ARIMA, BP, SVM, CEEMDAN–ARIMA, CEEMDAN–LSTM, CEEMDAN–SVM, and CEEMDAN–BP. The results show that for the coupled CEEMDAN–LSTM–BP–ARIMA model, the prediction ability was better than all the other models, and the timeseries decomposition data of PM2.5 had their own characteristics. The data with different characteristics were predicted separately using appropriate models and the final combined model results obtained were the most satisfactory.

Suggested Citation

  • Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16128-:d:992023
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    References listed on IDEAS

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