Abstract

Thunderstorm disasters are one of the major meteorological disasters in China, causing significant human casualties and economic losses each year. Traditional loss compensation insurance is confronted with difficulties such as inspection and assessing, causing low claim processing efficiency, while index insurance can effectively overcome these deficiencies by triggering payment through objective indices. This paper is based on satellite remote sensing monitoring data, using a combination of principal component analysis, random forests, and fuzzy mathematical theory to construct a lightning risk index and design a complete index insurance product. Experimental validation based on historical satellite monitoring data has shown that the risk indices constructed in this paper can effectively capture the temporal and spatial variability of lightning activity. Random forest models have a relatively low fitting error of training labels, and the SHAP values reveal a characteristic weight of importance consistent with physical perception. The insurance product has a reasonable distribution of amount and compensation, and premium pricing balances actuarial fairness with market acceptability. The present methodology provides a transportable design path to monitor and transfer the lightning risk using multi-source remote sensing data, with some outreach value in the field of lightning and other natural disasters.

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