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An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting

Author

Listed:
  • Yadong Pei

    (Key Laboratory of Public Big Data Security Technology, Chongqing College of Mobile Communication, Chongqing 401420, China
    Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing 401420, China)

  • Chiou-Jye Huang

    (College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China)

  • Yamin Shen

    (School of Information Science and Technology, Donghua University, Shanghai 201620, China)

  • Yuxuan Ma

    (School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China)

Abstract

Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal graph neural network (MtemGNN), a novel PM2.5 prediction model named PMNet is proposed. Some studies consider using VMD to stabilize time series but ignore the problem that VMD parameters are difficult to select, so AVMD is proposed to solve the appealing problem. Effective correlation extraction between multivariate time series affects model prediction accuracy, so MtemGNN is used to extract complex non-Euclidean distance relationships between multivariate time series automatically. The outputs of AVMD and MtemGNN are integrated and fed to the gate recurrent unit (GRU) to learn the long-term and short-term dependence of time series. Compared to several baseline models—long short-term memory (LSTM), GRU, and StemGNN—PMNet has the best prediction performance. Ablation experiments show that the Mean Absolute Error (MAE) is reduced by 90.141%, 73.674%, and 40.556%, respectively, after adding AVMD, GRU, and MtemGNN to the next 12-h prediction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13191-:d:942038
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    References listed on IDEAS

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