A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method

Abstract

A flood is a common and highly destructive natural disaster. Recently, machine learning methods have been widely used in flood susceptibility analysis. This paper proposes a NHAND (New Height Above the Nearest Drainage) model as a framework to evaluate the effectiveness of both individual learners and ensemble models in addressing intricate flood-related challenges. The evaluation process encompasses critical dimensions such as prediction accuracy, model training duration, and stability. Research findings reveal that, compared to Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Lasso, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), Stacked Generalization (Stacking) outperforms in terms of predictive accuracy and stability. Meanwhile, XGBoost exhibits notable efficiency in terms of training duration. Additionally, the Shapley Additive Explanations (SHAP) method is employed to explain the predictions made by the XGBoost.