State estimation of urban air pollution with statistical, physical, and super-learning graph models - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

State estimation of urban air pollution with statistical, physical, and super-learning graph models

Résumé

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.
Fichier principal
Vignette du fichier
DMS24.pdf (4.53 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY - Paternité

Dates et versions

hal-04455853 , version 1 (13-02-2024)

Licence

Paternité

Identifiants

Citer

Matthieu Dolbeault, Olga Mula, Agustín Somacal. State estimation of urban air pollution with statistical, physical, and super-learning graph models. 2024. ⟨hal-04455853⟩
16 Consultations
6 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More