<![CDATA[Urban flooding increasingly affects rapidly urbanizing tropical cities, where terrain, rainfall, and anthropogenic surface modification interact to shape spatial flood patterns. This study develops a GIS–machine learning framework to model urban flood susceptibility in Bandar Lampung, Indonesia, using a multi-year flood inventory (2015–2024). A balanced dataset (n = 308; 1:1 flood to pseudo-absence ratio) was constructed using buffered pseudo-absence sampling with spatial separation constraints to reduce bias. Nine environmental and infrastructure-related predictors were evaluated using Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). Model performance was assessed through five-fold stratified cross-validation, generalization gap analysis (Train AUC − CV AUC), learning curves, and a 20% hold-out test set. GB achieved the highest cross-validation performance (CV AUC = 0.8953), followed by RF (0.8782), SVM (0.8007), and LR (0.6925). However, ensemble models exhibited larger generalization gaps (RF = 0.1218; GB = 0.1047) compared to LR (0.0333), indicating stronger overfitting tendencies. Learning curves confirmed that LR maintained the most stable convergence between training and validation scores. On the independent test set (n = 61), GB achieved the highest predictive accuracy (ROC AUC = 0.9462), whereas LR showed lower discriminative performance (AUC = 0.7065) but greater validation stability. Flood susceptibility was concentrated in low-elevation areas, near major roads, and adjacent to river networks. By integrating learning curve diagnostics with cross-validation and hold-out testing, this study provides a rigorous framework for model selection in data-limited urban environments.]]>
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