Flood Mapping and Modeling: Progress, Challenges and Future Directions

Vahid Isazadeh; Mahdieh Shirmohammadi; Saied Pirasteh; Mehdi Akhavan

doi:10.63335/j.hp.2026.0039

Abstract

Floods are among the most widespread and destructive natural hazards, posing persistent threats to human life, infrastructure, and socioeconomic systems worldwide. Over recent decades, flood mapping and modeling have evolved significantly due to advances in remote sensing, Geographic Information Systems (GIS), and data-intensive analytical methods. This review synthesizes recent progress and remaining challenges in flood susceptibility, inundation, hazard, and risk assessment, with particular emphasis on the growing role of machine learning, deep learning, and hybrid modeling frameworks for spatial prediction and decision support. Based on a comprehensive review of more than 130 peerreviewed studies, the paper examines methodological developments spanning conventional hydrological and hydraulic models, data-driven approaches, and integrated hybrid frameworks. These methods increasingly leverage multi-source geospatial data, including satellite imagery, digital elevation models, rainfall products, and socio-environmental indicators. Emerging research trends reveal a shift toward intelligent data fusion, ensemble modelling, hybrid architectures, and Generative Pre-trained Transformer (GPT) architectures that combine physical process understanding with learning-based algorithms to enhance predictive accuracy, robustness, and scalability across diverse climatic and urban settings. The review also highlights thematic advances in urban flood analysis, flash flood susceptibility mapping, real-time forecasting, and model performance evaluation under data uncertainty. Despite substantial progress, key challenges persist related to model generalization, interpretability, data quality, and operational implementation. By critically assessing current methodologies and research gaps, this study outlines future directions for next-generation Geospatial Artificial Intelligence (GeoAI) in flood mapping and modeling.

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