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GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution

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Abstract

Many crucial tasks in weather prediction require large-scale and long-term spatio-temporal predictions. However, these tasks usually face three challenges: high feature redundancy, dependence of long-term prediction, and complexity in spatial relations of geographical location. To overcome these challenges, the Graph Evolution-based Spatio-Temporal Dense Graph Neural (GE-STDGN) network is proposed in spatio-temporal series prediction1. In this paper, a graph structure learning and optimization method based on an Evolutionary Multi-objective Optimization (EMO) algorithm, called Graph Evolution (GE), is adopted to improve the model’s ability in analyzing complex node correlations. Then, to avoid the over-fitting caused by Graph Neural Network (GNN) and reduce the constraint of graph structure, a GNN based on dense connection, which is named DenseGNN, is applied to message passing. Finally, by combining the multi-head attention mechanism GRU model with DenseGNN, GE-STDGN can handle complex spatio-temporal series prediction tasks. Experimental results on a public real-world weather dataset demonstrate that our model steadily outperforms many state-of-the-art models.

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Notes

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

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Acknowledgements

This paper is supported by National Key R&D Program of China (2018YFB1004300).

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Authors and Affiliations

  1. School of computer science and engineering, Southeast University, Nanjing, 211189, China

    Qingjian Ni & Yuhui Wang

  2. Department of Computer Science, Boston University, Boston, USA

    Yifei Fang

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  1. Qingjian Ni
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  2. Yuhui Wang
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  3. Yifei Fang
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Correspondence to Qingjian Ni.

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Ni, Q., Wang, Y. & Fang, Y. GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution. Appl Intell 52, 7638–7652 (2022). https://doi.org/10.1007/s10489-021-02824-2

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