Flood susceptibility assessment using multi-tier feature selection and ensemble boosting machine learning models

Flood susceptibility modeling (FSM) plays a key role in advancing proactive disaster risk reduction and spatial planning. This research developed FSM for the Buzău River catchment in Romania—a region historically vulnerable to recurrent flood events—using four state-of-the-art ensemble boosting algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. Initially, a comprehensive set of 13 flood conditioning factors was assessed, which was subsequently narrowed down to 9 essential factors through multi-tier feature selection strategies. Analysis of performance via receiver operating characteristic (ROC) andprecision–recall curves showed only marginal differences between the models; however, CatBoost excelled with an area under the ROC curve (AUC) of 0.972 and an average precision (AP) of 0.971, with XGBoost following closely behind. The SHAP (SHapley Additive exPlanations) analysis of the CatBoost model indicated that the Slope, Distance from Rivers, Topographic Wetness Index (TWI), and Land Use/Land Cover (LULC) are the key contributing factors. The novelty of this research is found in its comparative analysis of AdaBoost alongside three gradient boosting algorithms—CatBoost, LightGBM, and XGBoost—while utilizing explainable artificial intelligence (XAI) and a multi-tier feature selection strategy to create FSM that are precise and comprehensible. These strategies deliver robust tools for managing flood risks and reinforce the viability of data-driven modeling in the various catchments of Europe.