Integrating remote sensing and machine learning for flood hazard zonation in Gomati district of Tripura, Northeast India

Abstract

Floods represent a major hazard in India’s sub-Himalayan regions, with recurrent events in Tripura causing significant socio-economic and infrastructural losses. The August 2024 flood in Gomati district highlighted the urgent need for accurate flood susceptibility mapping to guide risk mitigation and disaster preparedness. This study integrates remote sensing (RS), geographic information system (GIS) and machine learning (ML) approaches to assess flood susceptibility. Sentinel-1 synthetic aperture radar (SAR) data were used to map inundated areas, overcoming limitations of cloud cover in optical imagery. A comprehensive geospatial database was compiled, incorporating topographic, hydrological, climatic, land-use, soil and geological factors. Multicollinearity testing and feature selection ensured robust and independent predictors for model training. The random forest (RF) algorithm was applied to generate flood susceptibility maps, categorizing the district into very low, low, moderate, high and very high flood-prone zones. Model validation using the receiver operating characteristic (ROC) curve yielded a high area under the curve (AUC = 0.929), indicating strong predictive capability. Results identified the highest-risk areas along the Gomati and San Ganga river plains, driven by low elevation, gentle slopes, high drainage density and unconsolidated sediments. The study offers actionable insights for policymakers, enabling targeted flood mitigation, improved land-use planning, early warning systems and community-based disaster management strategies. The integrated methodology demonstrates the potential of combining SAR data and ML techniques for reliable flood susceptibility assessment, providing a replicable framework for other flood-prone regions.