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A new ensemble spatio-temporal PM2.5 prediction method based on graph attention recursive networks and reinforcement learning

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  • Tan, Jing
  • Liu, Hui
  • Li, Yanfei
  • Yin, Shi
  • Yu, Chengqing

Abstract

Inhalable particulate matter with a diameter of less than 2.5 μm spatio-temporal prediction technology is an important tool for environmental governance in urban traffic congestion areas. A new Ensemble Graph Attention Reinforcement Learning Recursive Network is proposed to create a multi-data-driven spatio-temporal prediction method with excellent application value. The modeling process includes three basic steps. In step I, the graph attention network is used to effectively aggregate the spatio-temporal correlation characteristics of the original air pollutant data. In step II, the features extracted from the graph attention network are transferred to the long short-term memory network and the temporal convolutional network and the prediction models are constructed respectively. In step III, the reinforcement learning algorithm effectively analyzes the adaptability of the two different models to the data sets and realizes ensemble based on continuous optimization of weights. By comparing the experimental results of the listed cases, the following points can be summarized: (a) the graph attention network can effectively aggregate the spatio-temporal correlation characteristics of the original data and optimize the performance of the predictor. (b) The reinforcement learning algorithm effectively realizes the integration of several neural networks and improves the comprehensive adaptability and generalization capabilities of the model. (c) The proposed model in this paper has great application potential and value in spatial and temporal prediction and has achieved better performance than the other 25 benchmark models.

Suggested Citation

  • Tan, Jing & Liu, Hui & Li, Yanfei & Yin, Shi & Yu, Chengqing, 2022. "A new ensemble spatio-temporal PM2.5 prediction method based on graph attention recursive networks and reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006154
    DOI: 10.1016/j.chaos.2022.112405
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    References listed on IDEAS

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    Cited by:

    1. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).

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