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A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction

Author

Listed:
  • Pengfei Li

    (School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China)

  • Tong Zhang

    (School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China)

  • Yantao Jin

    (School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China)

Abstract

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.

Suggested Citation

  • Pengfei Li & Tong Zhang & Yantao Jin, 2023. "A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7624-:d:1140523
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    Cited by:

    1. Song, Honglin & Li, Yutao & Fu, Chenyi & Xue, Feng & Zhao, Qiyue & Zheng, Xingyu & Jiang, Kunkun & Liu, Tianbiao, 2024. "Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Ye, Lin & Jin, Yifei & Wang, Kaifeng & Chen, Wei & Wang, Fei & Dai, Binhua, 2023. "A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment," Applied Energy, Elsevier, vol. 351(C).

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