Application of machine learning methods for cost prediction of drought in France

Antoine Heranval (1) , Olivier Lopez (1) , Maud Thomas (1)

1 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation

This paper deals with the prediction of the total amount of a drought episode under the French "Catastrophe Naturelle" regime. Due to the specificity of this regime, a quick prediction of the final amount of an incident is particularly strategic. The approach that we use is based on a database constituted by the French Federation of Insurers in order to cover approximately 70% of the French market. Linking it with meteorological data and socioeconomic data allows to increase our vision of the exposure. Although the database is large, with a wide vision of the French metropolitan territory, data is imbalanced since a large majority of cities are not stroke by catastrophic events. Machine learning methods are used to compute a prediction.

Mots clés: Natural Catastrophe; Generalized Linear Models; Lasso and Elastic-Net penalties; Extreme Gradient Boosting; Random Forests Mathematics

Domaines: Statistiques [stat]

Antoine Heranval, Olivier Lopez, Maud Thomas. Application of machine learning methods for cost prediction of drought in France. 2021. ⟨hal-03310875v1⟩