Flood Risk Research Based on BP Neural Network and K-means Clustering Algorithm

In this paper, based on multivariate analysis and neural network model, flood probability prediction and risk assessment are carried out. Firstly, the relationship between 20 indicators and the probability of flood occurrence is analyzed by Pearson correlation coefficient, and visualized. Secondly, the K-means clustering algorithm is used to cluster the flood probability, identify different risk categories and establish the early warning evaluation model. Further, the BP neural network model is used to predict the flood probability, and the prediction performance of the model is optimized by adjusting the network structure and the number of input features. The results show that the combination of multivariate analysis and neural network model can effectively improve the prediction accuracy of flood probability, and provide scientific basis for flood risk assessment and formulation of prevention and response strategies.