Anticipating the cost of drought events in France by super learning
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Drought events are the second most expensive type of natural disaster within the legal framework of the French natural disasters compensation scheme. In recent years, droughts have been remarkable in their geographical scale and intensity. We develop a new methodology to anticipate the cost of a drought event in France. The methodology hinges on super learning and takes into account the complex dependence structure induced in the data by the spatial and temporal nature of drought events.
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