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

Forecasting the cost of drought events in France by Super Learning