Application of LDA and Multivariate Logistic Regression in Flood Forecasting and its AUC Evaluation

This study combined linear discriminant analysis (LDA) and multivariate logistic regression models to systematically analyze key indicators in flood prediction, aiming to identify factors that are closely related to flood occurrence and factors with weak correlation. By calculating and drawing weight distribution maps through the Python library, factors such as terrain drainage and ineffective disaster prevention that are highly correlated with flood occurrence, as well as factors with weak correlation such as wetland loss and infrastructure deterioration, were successfully identified. Model evaluation includes drawing ROC curves, calculating AUC values, and constructing confusion matrices to verify the performance of the model in flood prediction. The results show that the model performs well in key indicators such as prediction accuracy and recall rate, and the AUC value is close to 1, which further proves the robustness and effectiveness of the model. The research results not only provide a new perspective for flood prediction, but also provide data support for flood risk management and the formulation of early warning strategies.