Disaster Risk Assessment Based on Variable Fuzzy Sets and Improved Information Diffusion Method

The predictive analysis of natural disasters and their consequences is challenging because of uncertainties and incomplete data. The present article studies the use of variable fuzzy sets (VFS) and the improved information diffusion method (IIDM) to construct a composite method. The proposed method aims to integrate multiple factors and quantification of uncertainties within a consistent system for catastrophic risk assessment. VFS have been selected because of several advantages. These sets can effectively eliminate the border effect on assessment result and monitor estimation standard error. The IIDM theory can obtain as much useful information as possible from the sample and improves system recognition accuracy. Both these methods contribute to a sound risk probability prediction of natural disasters. Furthermore, the application has been verified through flood and drought risk analyses in China, where the risks of different flood and drought grades have been obtained. The results indicate that the proposed method achieves a better detection stability of disaster risk with higher precision compared with other methods. The method is effective, practical, and applicable in disaster rescue and relief.