Research on Flood Risk Early Warning Evaluation Based on TOPSIS Entropy Weight Method and BP Neural Network

Flood disasters, as a common global natural hazard, pose a severe threat to human life and property security. To effectively prevent and respond to flood issues, conducting risk assessments based on data analysis is particularly crucial. The application of mathematical models enables quantitative analysis of flood risks in different regions, thereby identifying high-risk areas and the potential scale of losses. This methodology not only provides a scientific basis for formulating targeted emergency response and post-disaster recovery plans, but also encompasses multi-faceted strategies such as infrastructure optimization, ecological restoration, and the enhancement of community disaster preparedness awareness. Furthermore, in the face of increased flood frequency and intensity due to climate change, strengthening international cooperation and information sharing has become an important means of addressing flood disasters. In recent years, the field of flood disaster data analysis and prediction has witnessed new development opportunities. Studies have shown that the occurrence of floods is influenced by multiple factors. By using Pearson correlation coefficients for data preprocessing, key indicators can be accurately identified, thereby facilitating the formulation of effective flood control measures.