International Journal of Environment and Climate Change

Floods remain among the most recurrent and destructive natural hazards worldwide, with their frequency and magnitude projected to increase under global climate change. Robust and context-sensitive flood susceptibility mapping is therefore essential for advancing disaster risk reduction, adaptive land use planning, and climate resilience. Geographic Information Systems (GIS) provide a spatially explicit platform for integrating hydrological, geomorphological, and anthropogenic data. Building on this foundation, multi-criteria decision analysis techniques, particularly the Analytical Hierarchy Process (AHP), offer structured frameworks for incorporating expert judgment and socio-environmental complexity. This paper presents a comparative evaluation of two methodological paradigms: a GIS-based statistical model employing the Relative Frequency Ratio (RFR), and a GIS–AHP framework that combines spatial analysis with expert-driven weighting of hydrological, morphometric, land cover, and anthropogenic parameters. The GIS-RFR approach demonstrates high predictive accuracy through empirical correlations between flood inventories and conditioning factors, while the GIS–AHP model enhances inclusivity by integrating multiple dimensions of environmental and human drivers, validated against remote sensing data. The comparative analysis highlights the methodological trade-offs between the objectivity and reproducibility of statistical modeling versus the adaptability and contextual sensitivity of expert-based decision frameworks. The synthesis emphasizes that no single approach is universally superior, rather, methodological choice should be guided by data availability, regional hydrological conditions, and policy priorities. Future directions point toward hybrid frameworks that integrate statistical rigor, expert knowledge, and machine learning, thereby reducing uncertainty and enhancing the actionable value of susceptibility maps.