Simulation-based high-resolution fire danger mapping using deep learning
Frédéric Allaire
A B , Jean-Baptiste Filippi C * , Vivien Mallet A B and Florence Vaysse D
A Institut national de recherche en informatique et en automatique (INRIA), 2 rue Simone Iff, 75012 Paris, France.
B Sorbonne Université, Laboratoire Jacques-Louis Lions, 75005 Paris, France.
C Centre national de la recherche scientifique (CNRS), Sciences pour l’Environnement – Unité Mixte de Recherche 6134, Università di Corsica, Campus Grossetti, 20250 Corte, France.
D Météo-France, Centre régional Sud-Est, 2 bd Château Double, 13090 Aix-en-Provence, France.
International Journal of Wildland Fire 31(4) 379-394 https://doi.org/10.1071/WF21143
Submitted: 27 March 2021 Accepted: 18 February 2022 Published: 6 April 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Wildfire occurrence and behaviour are difficult to predict locally for the next day. In the present work, we propose relying on fire spread simulations to provide a fire danger index representative of the potential for fire spread that includes not only weather but also surrounding vegetation and orography. This is achieved using an artificial neural network emulator called DeepFire, trained based on simulated fire sizes. To determine how relevant this index can be in the assessment of next-day fire danger, the application of DeepFire to fire danger mapping using actual weather forecasts is studied. DeepFire forecasts for 13 fairly large fires that occurred in Corsica are analysed and compared with corresponding forecasts using another fire danger index used in operational conditions, highlighting the differences in terms of precision and the expected results of such predictions. The weather forecasts from which the weather inputs of DeepFire are determined have high spatial resolution and high frequency, which also applies to the fire danger predictions. Additionally, input uncertainty is propagated through DeepFire, resulting in ensembles of emulated fire size. Several approaches are proposed to analyse the results and provide fire danger maps and ratings using this new simulation-based prediction system.
Keywords: Corsica, deep learning, fire danger, fire weather, high resolution, potential fire size, probability distributions, wildfire risk, wildfire simulation.
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