{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:25:00Z","timestamp":1773807900356,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Wildfires generating damage to assets are extremely rare in France. The peril is not covered by the French natural catastrophes insurance scheme (law of 13 July 1982). In the context of the changing climate, Caisse Centrale de R\u00e9assurance\u2014the French state-owned reinsurance company involved in the Nat Cat insurance scheme\u2014decided to develop its knowledge on the national exposure of France to wildfire risks. Current and future forest fires events have to be anticipated in case one of the events threatens buildings. The present work introduces the development of a catastrophe loss risk model (Cat model) for forest fires for the French metropolitan area. Cat models are the tools used by the (re)insurance sector to assess their portfolios\u2019 exposure to natural disasters. The open-source national Prometh\u00e9e database focusing on the South of France for the period 1973\u20132019 was used as training data for the development of the hazard unit using machine learning-based methods. As a result, we observed an extension of the exposure to wildfire in northern areas, namely Landes, Pays-de-la-Loire, and Bretagne, under the RCP 4.5 scenario. The work highlighted the need to understand the multi-peril exposure of the French country and the related economic damage. This is the first study of this kind performed by a reinsurance company in collaboration with a scholarly institute, in this case EURIA Brest.<\/jats:p>","DOI":"10.3390\/app12031635","type":"journal-article","created":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T11:35:17Z","timestamp":1643974517000},"page":"1635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Modelling Fire Risk Exposure for France Using Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Baptiste","family":"Gualdi","sequence":"first","affiliation":[{"name":"EURIA EURo Institut d\u2019Actuariat Brest, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Binet-St\u00e9phan","sequence":"additional","affiliation":[{"name":"EURIA EURo Institut d\u2019Actuariat Brest, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Bahabi","sequence":"additional","affiliation":[{"name":"EURIA EURo Institut d\u2019Actuariat Brest, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1389-5611","authenticated-orcid":false,"given":"Roxane","family":"Marchal","sequence":"additional","affiliation":[{"name":"Caisse Centrale de R\u00e9assurance, Department R&D Cat and Agriculture Modelling, 75008 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Moncoulon","sequence":"additional","affiliation":[{"name":"Caisse Centrale de R\u00e9assurance, Department R&D Cat and Agriculture Modelling, 75008 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sfetsos, A., Giroud, F., Clemencau, A., Varela, V., Freissinet, C., Lecroart, J., Vlachogiannis, D., Politi, N., Karozis, S., and Gkotsis, I. 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