PM2.5 forecasting under distribution shift: A graph learning approach
We present a new benchmark task for graph-based machine learning, aiming to predict future air quality (PM2.5 concentration) observed by a geographically distributed network of environmental sensors. While prior work has successfully applied Graph Neural Networks (GNNs) on a wide family of spatio-te...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
KeAi Communications Co. Ltd.
2024-01-01
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| coleção: | AI Open |
| Assuntos: | |
| Acesso em linha: | http://www.sciencedirect.com/science/article/pii/S2666651023000220 |
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