The potential of machine learning for weather index insurance

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Luigi Cesarini (Author) Other; Rui Figueiredo (Author) FEUP; Beatrice Monteleone (Author) Other; Mario L. V. Martina (Author) Other

Journal

Title: Natural Hazards and Earth System Sciences

Vol. 21

Pages: 2379-2405

ISSN: 1561-8633

Publisher: Copernicus

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00V-9NE

DOI: 10.5194/nhess-21-2379-2021

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27