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This study aims to evaluate natural disaster risks and provide decision support for insurance companies by integrating the EWM-TOPSIS (Entropy Weighted Method combined with Technique for Order Preference by Similarity to Ideal Solution) and ARIMA (AutoRegressive Integrated Moving Average) models. Initially, through literature review and online data collection, we identified multiple factors associated with various natural disasters, including PPM (Parts Per Million), degrees Celsius, temperature, rainfall, altitude, frequency of natural disasters, and actual water barometric pressure. The entropy weight method was utilized to calculate the weights of these factors, which were then combined with the TOPSIS comprehensive evaluation method to determine the extent of disasters in different regions. Additionally, a random forest model was constructed to predict the underwriting risk for insurance companies, considering factors such as population density, income levels, and the strength of agricultural, industrial, and service sectors. Furthermore, the ARIMA model was employed for time series analysis of risk factors to forecast future risk trends, thereby identifying the optimal timing for insurance companies to assume risk. Finally, from the perspective of real estate developers, the study evaluated the potential benefits of constructing houses in different regions by combining the entropy weight method with the TOPSIS model. The findings indicate that Washington State has a high disaster severity index when considering the potential benefits of future real estate development, suggesting that real estate development in this area could yield higher returns. This study provides a scientific basis for risk management in the insurance industry and site selection decisions for real estate developers.
Zhou et al. (Tue,) studied this question.