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Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Author

Listed:
  • Yuan Huang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Junhao Yu

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Xiaohong Dai

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Zheng Huang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Yuanyuan Li

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

Abstract

Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.

Suggested Citation

  • Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4889-:d:796937
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    References listed on IDEAS

    as
    1. Cabaneros, Sheen Mclean & Calautit, John Kaiser & Hughes, Ben, 2020. "Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique," Ecological Modelling, Elsevier, vol. 424(C).
    2. Haibo Liu & Yujie Dong & Fuzhong Wang, 2020. "Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    3. Jianxian Cai & Xun Dai & Li Hong & Zhitao Gao & Zhongchao Qiu, 2020. "An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
    4. Meng Dun & Zhicun Xu & Yan Chen & Lifeng Wu, 2020. "Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, May.
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