Applied Sciences  
Adticle Modelling Fire Risk Exposure for France Using Machine Learning  
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ceptibility). This leads to the development of detailed models to assess the fire propagation, ignition and dynamic, as exemplified by the well-known models of the literature (FlamMap, FSPro, FARSITe, FIRETACTIC, FEPS, HYPSLIT, PHOENIX, and Minimum Travel Time) [1, 4, 10, 11, 12, 13, 14]. The insurance sector also developed models at the asset level, modelling the roofs, walls, and windows which were the most susceptible to burning with destruction functions [3, 11, 15, 16, 17]. In comparison to the US or Australia, southern Europe records less burnt surface, such as the 7.4 million ha burnt between 2000 and 2018 [18]. For France, from 1982 to 2017, 12 events were recorded for a total of 350,000 ha burnt in the Mediterranean area (EM-DAT data 2021, [[https://public.emdat.be/](https://public.emdat.be/)]([https://public.emdat.be/](https://public.emdat.be/)), (accessed on 2 August 2021)). Then, comparing these elements to the Promethee database (Promethee data, [[https://www.promethee.com/](https://www.promethee.com/)]([https://www.promethee.com/](https://www.promethee.com/)), (accessed on 12 October 2020)), France records an accumulation of small events (approximately 2000 events per year) with small-to-medium surfaces, with strong spatial and temporal fluctuations (approximately 7.3 ha burnt per year). At the end of August 2021, the South of France recorded a large wildfire event of 8100 ha in the natural park Plaine des Maures. The event was extreme in terms of its propagation speed of up to 8 km/h, destroying a dozen houses in Val de Gilly Grinaud. It is the worst wildfire event occurring in France since 2-3 September 2003, when flames destroyed--in the same location--20,000 ha of forest, with three lives lost. It was demonstrated that the 1994 fire protection was successful [19]. On the contrary, for other countries, as explained by [20], there is a lack of international coordinated safety procedure for wildfires. Nevertheless, considering climate change, it is important to anticipate the future exposure. Indeed, France is the fourth European country in terms of forest cover, with 17 million ha of forest, which means an increase of exposure in the next few years. Climate change affects the frequency of wildfires due to anomalous maximum temperatures, lower humidity, higher maximum wind speed, and fewer rainy days [5, 21, 22, 23]. A study projected the fire danger due to climate change in Southern France [24]. In 2019, a lot of kermes oak trees died due to the heatwave, as the lethal temperature was reached with temperatures of more than 60 \({}^{\circ}\)C measured (French Ministry of Ecology, 2021, [[https://www.ecologie.gouv.fr/prevention-des-feux-foret](https://www.ecologie.gouv.fr/prevention-des-feux-foret)]([https://www.ecologie.gouv.fr/prevention-des-feux-foret](https://www.ecologie.gouv.fr/prevention-des-feux-foret)), (accessed on 7 September 2021)). There is a clear need to develop a model to identify the exposed areas, and to protect them from significant losses (to the ecosystem and to the economy). Then, taking into account those elements, CCR experts in natural disaster modelling covered by the Nat Cat scheme raised interest in forest fires. In collaboration with EURIA Brest, we developed a Cat model from scratch within seven months, from data collection and hazard modelling through machine learning to exposure and damage estimates. A Cat model is the tool of the (re)insurance sector to estimate the consequences of natural disasters on their portfolios. It is composed of three submodules: hazard, vulnerability and damage units (Figure 1). We aimed to test the ability of machine learning-based methods to model the fire hazard, namely burnt surface and occurrence [25]. The hazards themselves, such as fire smoke and earth imagery, were not the target of this study.