Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908798209228800 |
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| author | Cui, Zhiqing Zhong, Siru Jin, Ming Pan, Shirui Wen, Qingsong Liang, Yuxuan |
| author_facet | Cui, Zhiqing Zhong, Siru Jin, Ming Pan, Shirui Wen, Qingsong Liang, Yuxuan |
| contents | Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose OmniAir, a semantic topology learning framework tailored for global station-level prediction. By encoding invariant physical environmental attributes into generalizable station identities and dynamically constructing adaptive sparse topologies, our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks. We further curate WorldAir, a massive dataset covering over 7,800 stations worldwide. Extensive experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models, while effectively bridging the monitoring gap in data-sparse regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21899 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting Cui, Zhiqing Zhong, Siru Jin, Ming Pan, Shirui Wen, Qingsong Liang, Yuxuan Machine Learning Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose OmniAir, a semantic topology learning framework tailored for global station-level prediction. By encoding invariant physical environmental attributes into generalizable station identities and dynamically constructing adaptive sparse topologies, our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks. We further curate WorldAir, a massive dataset covering over 7,800 stations worldwide. Extensive experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models, while effectively bridging the monitoring gap in data-sparse regions. |
| title | Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.21899 |