Multi-Year Flood Inundation Modelling in Kolhapur, India Using SAR Observations and Machine Learning

Flood forecasting in monsoon-dominated river basins is challenged by sparse hydrological observations, rapid urban expansion, and strong seasonal variability.This study presents a multi-year machine-learning framework (2017-2025) that integrates Sentinel-1 SAR flood extents, CHIRPS and IMD rainfall, ERA5-Land hydrometeorological variables, and SRTM-derived physiographic attributes to predict flood inundation in Kolhapur, India.Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models were trained using progressively expanding temporal windows to evaluate the effect of multi-year hydroclimatic diversity on predictive skill.RF consistently achieved the highest accuracy and spatial coherence, with strong generalization to the independently withheld 2025 flood event.The multi-year approach captures monsoon variability-including extreme floods, prolonged wet spells, and deficit rainfall years-allowing the models to learn interannual rainfall-runoff-inundation relationships.Antecedent hydrological indicators such as cumulative rainfall, runoff, and soil moisture were identified as key predictors enabling short-range flood forecasting before inundation occurs.Beyond the local case study, the workflow demonstrates strong transferability to other monsoon-affected and data-scarce basins because it relies on globally available satellite datasets, terrain-based predictors, and non-parametric ML algorithms.The methodology is operationally scalable and adaptable for flood early-warning systems in rapidly urbanizing river basins worldwide.