دورية أكاديمية
The potential of machine learning for weather index insurance
| العنوان: | The potential of machine learning for weather index insurance |
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| المؤلفون: | L. Cesarini, R. Figueiredo, B. Monteleone, M. L. V. Martina |
| المصدر: | Natural Hazards and Earth System Sciences, Vol 21, Pp 2379-2405 (2021) |
| بيانات النشر: | Copernicus Publications, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Environmental technology. Sanitary engineering LCC:Geography. Anthropology. Recreation LCC:Environmental sciences LCC:Geology |
| مصطلحات موضوعية: | Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350, Geology, QE1-996.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1561-8633 1684-9981 |
| Relation: | https://nhess.copernicus.org/articles/21/2379/2021/nhess-21-2379-2021.pdf; https://doaj.org/toc/1561-8633; https://doaj.org/toc/1684-9981 |
| DOI: | 10.5194/nhess-21-2379-2021 |
| URL الوصول: | https://doaj.org/article/68b53975bec749f09bfe52fb0f031fea |
| رقم الأكسشن: | edsdoj.68b53975bec749f09bfe52fb0f031fea |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 15618633 16849981 |
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| DOI: | 10.5194/nhess-21-2379-2021 |