Advancing Flood Disaster Risk Mapping Through Multi‐Sensor Fusion and Machine Learning

ABSTRACT Flood disaster risk assessment in large river basins remains a critical challenge due to sensor limitations, cloud contamination, and insufficient integration of hazard, exposure, and vulnerability indicators. This study presents an AI‐driven multi‐sensor flood risk mapping framework applied to the extreme 2020 flood event, integrating Synthetic Aperture Radar (SAR), optical remote sensing indices, and machine learning‐based land cover information. A hybrid flood detection algorithm was developed using Sentinel‐1 SAR VV backscatter (−17 dB threshold), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI) through logical OR fusion, ensuring robust flood delineation under persistent cloud cover. Agricultural damage was assessed using NDVI and EVI indices, while land cover dynamics and exposure were analyzed through Google's Dynamic World near‐real‐time classification products. Results indicate extensive flood inundation, with widespread expansion of surface water and significant disruption of cropland areas during the 2020 monsoon season. Vegetation condition analysis revealed marked declines in NDVI across flooded agricultural zones, indicating severe crop stress and productivity loss. Land cover transitions showed substantial temporary conversion of cropland and built‐up areas into flooded water classes, with total land cover area declining from 185,942.3 to 166,870.0 km 2 , highlighting high exposure in low‐lying agricultural corridors. The integrated flood risk model, combining hazard intensity, land cover exposure, and vegetation vulnerability, successfully identified high‐risk agricultural and peri‐urban hotspots across central and eastern area. The proposed framework demonstrates a scalable, automated approach for flood risk and impact assessment, offering valuable insights for disaster preparedness, agricultural resilience, and flood management strategies in flood‐prone regions.