Abstract. 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 number 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：

---

大数据和机器学习在天气指数保险中的潜力

摘要。天气指数保险是针对自然灾害引发的灾难转移风险的创新工具。本文提出了一种使用机器学习算法来识别极端洪水和干旱事件的方法，旨在降低与这种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，它们是根据不同模型参数的组合探索成千上万种可能的配置而建立的。这些模型是在多米尼加共和国境内根据2000年至2019年期间多个来源的数据开发和测试的。使用降雨和土壤湿度数据，与用作两种危害基线的逻辑回归模型相比，机器学习算法提供了强大的改进。此外，增加模型训练期间提供的信息数量被证明对性能有好处，可以提高分类性能，并确认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。