Machine learning-driven flood susceptibility mapping in the coastal frontier of Feni District, Bangladesh

Floods are a frequent and destructive hazard in southern Bangladesh, particularly in low-lying regions like the Feni district. This study developed flood susceptibility maps for the coastal frontier of Feni using three machine learning models—Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbours (KNN). Fourteen topographic and environmental factors, including elevation, slope, MNWDI, MSAVI, rainfall, land use, and proximity to streams and roads, were used as inputs. A total of 199 flood and 132 non-flood points were identified through field surveys and satellite imagery. Multicollinearity checks confirmed variable independence. SVM achieved the highest train AUC (0.958) and test AUC (0.951), followed by LR and KNN. Susceptibility maps varied across models, with SVM predicting the greatest extent of high-risk zones. Feature importance analysis highlighted MSAVI, MNDWI, rainfall, road proximity, and Geology type as key predictors. The findings support flood planning, infrastructure development, and early warning strategies in Feni District.