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Dual-channel spatial–temporal difference graph neural network for PM\(_{2.5}\) forecasting

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Abstract

Accurate PM\(_{2.5}\) forecasting is significant for improving quality of life and human health. However, it is very challenging to capture the high spatiotemporal correlations and the complex diffusion processes of PM\(_{2.5}\). Most existing PM\(_{2.5}\) prediction methods only focus on spatiotemporal dependencies. In addition, the PM\(_{2.5}\) diffusion process with domain knowledge in deep learning is rarely considered. Therefore, how to simultaneously capture comprehensive spatiotemporal dependencies and model the complicated diffusion process of PM\(_{2.5}\) is still a challenge. To address this problem, we propose a dual-channel spatial–temporal difference graph neural network (DC-STDGN) to forecast future PM\(_{2.5}\) concentrations. DC-STDGN first constructs a dual-channel structure to obtain distance-based local neighboring information and the global hidden spatial correlation of the data. Then, a temporal convolution layer is designed to handle the long-term dependency. Finally, the spatial difference with domain knowledge is introduced to model the complex diffusion process and capture more comprehensive spatiotemporal correlations. The extensive experiments with three real-world datasets demonstrate the improved prediction performance of DC-STDGN over state-of-the-art baselines. DC-STDGN outperforms the second-best model by up to 16.9% improvement in mean absolute error, 8.9% improvement in root mean square error and 18.2% improvement in mean absolute scaled error.

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Notes

  1. https://github.com/shawnwang-tech/PM2.5-GNN.

  2. http://biendata.com/competition/kdd_2018/data/.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61976247).

Funding

This work is supported by the National Natural Science Foundation of China (No. 61976247).

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Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, People’s Republic of China

    Xiaocao Ouyang, Yan Yang, Yiling Zhang, Wei Zhou & Dongyu Guo

  2. Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Chengdu, 611756, People’s Republic of China

    Xiaocao Ouyang, Yan Yang, Yiling Zhang & Wei Zhou

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  1. Xiaocao Ouyang
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  2. Yan Yang
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  3. Yiling Zhang
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  4. Wei Zhou
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Contributions

XO was involved in the conceptualization, methodology, software, writing—original draft, validation, formal analysis, investigation, visualization and writing—review and editing. YY contributed to writing—review and editing, supervision, investigation, formal analysis and validation. YZ helped in writing—review and editing and resources WZ assisted in writing—review and editing. DG contributed to writing—review and editing.

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Correspondence to Yan Yang.

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Ouyang, X., Yang, Y., Zhang, Y. et al. Dual-channel spatial–temporal difference graph neural network for PM\(_{2.5}\) forecasting. Neural Comput & Applic 35, 7475–7494 (2023). https://doi.org/10.1007/s00521-022-08036-0

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