Geospatial Technologies in Flood Risk Management and Modeling: Applications and Innovations

Abstract

Floods rank among the most destructive natural hazards globally, leading to widespread socio-economic losses, infrastructure damage, environmental degradation, and threats to human life. The increasing frequency and intensity of flood events, driven by climate change, urbanization, and land use changes, have underscored the urgent need for accurate flood risk assessment and effective management strategies. Recent advances in geospatial technologies, including remote sensing (RS), geographic information systems (GIS), and hydrological and hydraulic modeling, have significantly improved our ability to monitor, predict, and mitigate flood impacts. This paper provides a comprehensive overview of the application of geospatial technologies in flood risk management and modeling. It explores the use of satellite-based RS data for flood extent mapping, surface water monitoring, and post-disaster damage assessment, as well as digital elevation models (DEMs) for delineating flood-prone areas and modeling hydrodynamic processes. The integration of geospatial datasets with hydrological and hydraulic models enables more accurate flood forecasting and scenario analysis, supporting early warning systems and emergency response planning. Emerging technologies, such as artificial intelligence (AI), machine learning, and geospatial big data platforms, have further enhanced predictive capabilities and automated hazard assessment at regional and global scales. Key applications discussed include flood hazard mapping, floodplain delineation, vulnerability and risk assessment, urban flood management, and disaster mitigation planning. Despite these advancements, several challenges persist, including data availability and resolution constraints, model uncertainties, and the need for real-time integration of multi-source datasets.