Flood Susceptibility Mapping Based on Supervised Machine Learning Algorithms with Integrated Uncertainty and Explainability Analysis: A Case Study in Quang Nam, Vietnam

Abstract

Flood is among the natural hazards that have occurred with increasing frequency and have changed in complex ways, with serious impacts on people's lives. A flood susceptibility map the basis for zoning areas at risk of flooding became a necessary tool not only for managers but also for residents to adapt to the broader context of climate change. This study aims to present the use of a supervised machine learning model to create a flood susceptibility map of Quang Nam province (now Da Nang city, Vietnam). Four algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were tested. Based on the evaluation metrics and uncertainty analysis, RF not only achieved the best predictive performance (AUC of 0.9418) but also demonstrated higher stability and reliability. An explainability analysis using Shapley additive explanation (SHAP) and Leave-One-Covariate-Out (LOCO) was conducted to assess the contribution of conditioning factors and in