Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

DOI10.1214/22-AOAS1642OpenAlexW3161820035WikidataQ117555898 ScholiaQ117555898MaRDI QIDQ2686054

François Pimont, Thomas Opitz, Jean-Luc Dupuy, Jonathan Koh

Publication date: 24 February 2023

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.08004

zbMATH Keywords

extreme-value theoryforest firesCox processBayesian hierarchical modelshared random effects

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (5)

Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA ⋮ Gradient boosting with extreme-value theory for wildfire prediction ⋮ A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes ⋮ A marginal modelling approach for predicting wildfire extremes across the contiguous United States ⋮ Data-driven chimney fire risk prediction using machine learning and point process tools

Uses Software

• INLA
• R-INLA
• ismev

Cites Work

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