Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

中文翻译：

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机器学习在天气指数保险方面的潜力

天气指数保险是自然灾害引发的灾害风险转移的创新工具。本文提出了一种使用机器学习算法识别极端洪水和干旱事件的方法，旨在降低与此类保险机制相关的基础风险。本研究选择的模型类型是神经网络和支持向量机，它们被广泛用于分类问题，它们是基于不同模型参数的组合探索数千种可能的配置而构建的。这些模型是在多米尼加共和国的背景下开发和测试的，基于 2000 年至 2019 年期间多个来源的数据。使用降雨和土壤水分数据，与逻辑回归模型相比，机器学习算法提供了强大的改进，用作两种危害的基线。此外，增加模型训练过程中提供的信息量被证明有利于性能，提高分类精度，并证实了这些算法利用大数据的能力及其在指数保险产品中的应用潜力。