Chinese Journal of Agrometeorology ›› 2026, Vol. 47 ›› Issue (3): 443-455.doi: 10.3969/j.issn.1000-6362.2026.03.011

Previous Articles     Next Articles

Claim Threshold of Precipitation Insurance in Hunan Province Based on Machine Learning

ZHOU Wei, LIAO Chun-hua, WANG Yao, GUAN Jian-wen, LI Hao   

  1. 1. Hunan Provincial Meteorological Service Center/Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Changsha 410118, China; 2. China Meteorological Administration Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau Key Laboratory, Xi’an 710000; 3. People´s Insurance Corporation of China Hunan Branch, Changsha 410119
  • Received:2025-06-19 Online:2026-03-20 Published:2026-03-17

Abstract:

In order to clarify the relationship between precipitation events and insurance claims and enhance meteorological insurance services, this study utilized 703 insurance claim records and hourly precipitation data from Hunan province (20212024). A random forest model was used to construct thresholds for precipitation insurance claims in countylevel urban areas in Hunan province, followed by a significance test. The results showed that: 1the random forest model outperformed XGBoost and LightGBM in precision, Critical success index (CSI) and accuracy, demonstrating superior simulation of insurance claim thresholds. It effectively evaluated the thresholds across different locations and time windows. 2Cumulative precipitation 120h, 96h and 72h prior to claims, along with the maximum hourly precipitation whthin 120h, showed high feature importance scores, identifying them as key claiminfluencing factors. 3Significant spatial heterogeneity existed in precipitation claim thresholds. The conclusion that 'higher precipitation intensity let to a higher likelihood of claims' did not hold spatially. Lowthreshold regions (cumulative 120h precipitation <50mm) accounted for 40% of claimed areas, indicating high sensitivity to precipitation events and vulnerability to disasters. Highthreshold regions (cumulative 96h precipitation >65mm) constituted 16%, suggesting claims occur only under intense precipitation. 4The Chisquare tests confirmed statistically significant relationships between the precipitation insurance claim thresholds of 6 prefecturelevel urban areas and 19 countylevel cities (districts) level urban areas constructed based on random forests, effective discrimination of claim triggers across precipitation intensities. The research results reveal an inherent correlation between the spatiotemporal distribution of precipitation and insurance claims, which can provide a scientific basis for insurance meteorological warnings and disaster prevention and reduction in Hunan province. 

Key words: Machine learning, Precipitation, Insurance, Threshold, Hunan province