Research on Flood Disaster Risk Assessment Based on Random Forest Algorithm

In flood disaster risk assessment, disaster-pregnant environments, disaster-bearing bodies, and disaster-causing factors are the core categories of risk assessment indicators. A flood disaster risk assessment model based on the random forest algorithm can be constructed by manually identifying the samples and organizing the training data set using the Bagging method. SVM can be selected as a control model for verification to evaluate the model performance further. According to the evaluation of the importance of various influencing factors, it can be found that precipitation, flood duration, and soil moisture content in the local environment are the core factors for evaluating flood disaster risk. In order to cope with the small amount of data for high disaster risk levels, this paper uses the cutoff mechanism in $\mathbf{R}$ language to correct the random forest results in the voting stage and obtains good results. The research in this paper provides a new idea based on artificial intelligence for flood disaster risk assessment.