Articles | Volume 21, issue 8
Research article
11 Aug 2021
Research article |  | 11 Aug 2021

The potential of machine learning for weather index insurance

Luigi Cesarini, Rui Figueiredo, Beatrice Monteleone, and Mario L. V. Martina

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Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv [preprint], arXiv:1603.04467, 2016. a
ADRC and UNDRR: GLIDE number, available at: (last access: 21 June 2020), Asian Disaster Reduction Centre (ADRC), Kobe, Japan, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m
African Union: African Risk Capacity: Transforming disaster risk management & financing in Africa, available at: (last access: 21 June 2020), 2021. a
Aksoy, S. and Haralick, R. M.: Feature normalization and likelihood-based similarity measures for image retrieval, Pattern Recogn. Lett., 22, 563–582,, 2001. a
Alipour, A., Ahmadalipour, A., Abbaszadeh, P., and Moradkhani, H.: Leveraging machine learning for predicting flash flood damage in the Southeast US, Environ. Res. Lett., 15, 024011,, 2020. a
Short summary
Weather index insurance is an innovative program used to manage the risk associated with natural disasters, providing instantaneous financial support to the insured party. This paper proposes a methodology that exploits the power of machine learning to identify extreme events for which a payout from the insurance could be delivered. The improvements achieved using these algorithms are an encouraging step forward in the promotion and implementation of this insurance instrument.
Final-revised paper