Assessment of typhoon disaster loss based on the factor analysis-random forest model

Abstract

Typhoon disasters in China’s coastal areas pose significant challenges for disaster prevention and mitigation, urban planning and national economic construction. This study aims to address the problem of incomparable disaster assessment indicators and low prediction accuracy of machine learning for small sample data. It establishes an index system based on the practical disaster investigation classification standards, which ensures data sources and uniformity. It also proposes a combination algorithm of factor analysis-random forest regression for direct economic loss prediction, which improves the typhoon disaster losses prediction. The results show that the optimized model has higher accuracy than single decision tree model, random forest model and factor analysis-decision tree model. The factor analysis method verifies the importance of influencing factors, which indicates that China faces great risks of coastal floods caused by super typhoons. The combination regression model can predict disaster losses reasonably, providing effective technical support for typhoon disaster assessment and management.