Design of Weather Index Insurance from a Risk Reduction Perspective: Based on Sequential Residual Learning Algorithm

While traditional agricultural insurance provides disaster compensation to maintain producers" incentives, its inherent payout delays significantly constrain its effectiveness in enhancing farmers" disaster prevention, mitigation, and relief capabilities. From a risk reduction perspective, this study aims to enable weather index insurance to promptly respond to deteriorating weather conditions at each crop growth stage, providing rapid payouts. Inspiring from the concept of residual learning in gradient boosting algorithms, a loss prediction is conducted at each crop growth stage. Payouts are triggered whenever predicted losses exceed a predefined threshold at any stage. Algorithm based on sequential residual learning effectively predicts yield losses. This stage-by-stage risk assessment approach ensures rapid payouts, enabling timely responses to weather disasters during early and mid-stage crop growth.