Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting
Rapidly accumulating, large-scale and long-term meteorological data provide unprecedented opportunities for data-driven meteorological models and fine-grained numerical weather prediction. Many existing approaches based on deep learning models, e.g., recurrent neutral networks and graph neural netwo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10848064/ |
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| Summary: | Rapidly accumulating, large-scale and long-term meteorological data provide unprecedented opportunities for data-driven meteorological models and fine-grained numerical weather prediction. Many existing approaches based on deep learning models, e.g., recurrent neutral networks and graph neural networks, have been proposed for weather forecasting. However, the subtle spatial correlations hidden in the vast amount of historical meteorological data have not been fully explored, such as dynamic spatial correlation. In this paper, we propose STGAMAM, which integrates Spatial-Temporal fusion Graph neural networks with a novel Adjacency Matrix and self-Attention Mechanisms to capture both long-term temporal periodicity and short-term spatial-temporal dependencies based on mixed adjacency via graph attention networks and then makes fine-grained prediction on concatenated features which combines diverse correlations. Our approach is validated by extensive experiment on two real-world datasets, which demonstrates the superiority of the proposed method over existing methods. |
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| ISSN: | 2169-3536 |