Drought events are the second most expensive type of natural disaster within the French legal framework known as the natural disasters compensation scheme. In recent years, drought events have been remarkable in their geographical scale and intensity. We develop and apply a new methodology to forecast the cost of a drought event in France. The methodology hinges on Super Learning (van der Laan et al., 2007; Benkeser et al., 2018), a general aggregation strategy to learn a feature of the law of the data identified through an ad hoc risk function by relying on a library of algorithms. The algorithms either compete (discrete Super Learning) or collaborate (continuous Super Learning), with a cross-validation scheme determining the best performing algorithm or combination of algorithms, respectively. Our Super Learner takes into account the complex dependence structure induced in the data by the spatial and temporal nature of drought events.

翻译：干旱事件是法国自然灾害补偿机制法律框架内第二昂贵的自然灾害类型。近年来，干旱事件在地理范围和强度方面尤为显著。我们开发并应用了一种新方法来预测法国干旱事件的成本。该方法基于超级学习法（van der Laan等，2007；Benkeser等，2018），这是一种通用聚合策略，通过依赖算法库并依据特定风险函数识别出的数据分布特征进行学习。这些算法要么相互竞争（离散超级学习法），要么相互协作（连续超级学习法），并通过交叉验证方案分别确定表现最佳的算法或算法组合。我们的超级学习器充分考虑了干旱事件在时空特性下数据中产生的复杂依赖结构。