State estimation of urban air pollution with statistical, physical, and super-learning graph models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917582289764352 |
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| author | Dolbeault, Matthieu Mula, Olga Somacal, Agustín |
| author_facet | Dolbeault, Matthieu Mula, Olga Somacal, Agustín |
| contents | We consider the problem of real-time reconstruction of urban air pollution maps. The task is challenging due to the heterogeneous sources of available data, the scarcity of direct measurements, the presence of noise, and the large surfaces that need to be considered. In this work, we introduce different reconstruction methods based on posing the problem on city graphs. Our strategies can be classified as fully data-driven, physics-driven, or hybrid, and we combine them with super-learning models. The performance of the methods is tested in the case of the inner city of Paris, France. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | State estimation of urban air pollution with statistical, physical, and super-learning graph models Dolbeault, Matthieu Mula, Olga Somacal, Agustín Machine Learning Numerical Analysis Physics and Society We consider the problem of real-time reconstruction of urban air pollution maps. The task is challenging due to the heterogeneous sources of available data, the scarcity of direct measurements, the presence of noise, and the large surfaces that need to be considered. In this work, we introduce different reconstruction methods based on posing the problem on city graphs. Our strategies can be classified as fully data-driven, physics-driven, or hybrid, and we combine them with super-learning models. The performance of the methods is tested in the case of the inner city of Paris, France. |
| title | State estimation of urban air pollution with statistical, physical, and super-learning graph models |
| topic | Machine Learning Numerical Analysis Physics and Society |
| url | https://arxiv.org/abs/2402.02812 |