  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.
