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Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

Author

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  • Jie Zhao

    (School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
    Key Lab of Northwest Water Resource, Environment and Ecology, MOE, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
    Shaanxi Key Lab of Environmental Engineering, Xi’an 710055, China)

  • Linjiang Yuan

    (School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
    Key Lab of Northwest Water Resource, Environment and Ecology, MOE, Xi’an University of Architecture and Technology, No. 13 Yanta Road, Xi’an 710055, China
    Shaanxi Key Lab of Environmental Engineering, Xi’an 710055, China)

  • Kun Sun

    (SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, 5230 Odense, Denmark)

  • Han Huang

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Panbo Guan

    (Department of Energy Conservation and Green Development, The 714 Research Institute of CSSC, Beijing 100101, China)

  • Ce Jia

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

Abstract

Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM 2.5 . However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM 2.5 /0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM 2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m 3 for Beijing, 1.33, 3.38, and 4.60 μg/m 3 for Guangzhou, 1.37, 4.19, and 6.35 μg/m 3 for Xi’an, and 2.20, 7.75, and 10.07 μg/m 3 for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM 2.5 concentrations from 1 to 12 h post.

Suggested Citation

  • Jie Zhao & Linjiang Yuan & Kun Sun & Han Huang & Panbo Guan & Ce Jia, 2022. "Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9430-:d:877899
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    References listed on IDEAS

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    1. Jianzhou Wang & Tong Niu & Rui Wang, 2017. "Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model," IJERPH, MDPI, vol. 14(3), pages 1-33, March.
    2. Lili Du & Yan Wang & Zhicheng Wu & Chenxiao Hou & Huiting Mao & Tao Li & Xiaoling Nie, 2019. "PM 2.5 -Bound Toxic Elements in an Urban City in East China: Concentrations, Sources, and Health Risks," IJERPH, MDPI, vol. 16(1), pages 1-13, January.
    3. Sang Won Choi & Brian H. S. Kim, 2021. "Applying PCA to Deep Learning Forecasting Models for Predicting PM 2.5," Sustainability, MDPI, vol. 13(7), pages 1-30, March.
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    Cited by:

    1. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Yuxuan Ma, 2022. "An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.

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