论文标题
通过超级学习预测法国干旱事件的成本
Forecasting the cost of drought events in France by Super Learning
论文作者
论文摘要
干旱事件是法国法律框架中第二种最昂贵的自然灾害类型，称为自然灾害补偿计划。 近年来，干旱事件在其地理规模和强度上是显着的。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。 该方法取决于超级学习（Van der Laan等，2007； Benkeser等，2018），这是一种通用聚合策略，是通过依靠算法库来学习通过临时风险函数确定数据定律的特征。该算法要么竞争（离散的超级学习）或协作（连续超级学习），分别确定了最佳性能算法或算法的组合。 我们的超级学习者考虑了干旱事件的空间和时间性质在数据中引起的复杂依赖性结构。
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.
