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

Abstract

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.