Machine learning surrogates for flash flood hazard mapping: CSI-optimized feature selection and spatial transferability

Flood Hazard Mapping is constrained by the computational cost of high-resolution hydrodynamic simulation. This study evaluates three Machine Learning (ML) models -Random Forest (RF), eXtreme Gradient Boosting (XGB), and a Feed-Forward Neural Network -as surrogates, addressing two gaps: retrieval of