Quantifying Uncertainty in Himalayan Flood Susceptibility Mapping: A Comparative Analysis of AHP and Fuzzy AHP in Rudraprayag District, Uttarakhand

Flood susceptibility mapping in the steep and geomorphically complex Himalayan terrain remains inherently challenging due to sparse observational data, strong process nonlinearity, and uncertainty embedded in judgment-based decision frameworks. Conventional Multi-Criteria Decision Making (MCDM) approaches, most notably the crisp Analytic Hierarchy Process (AHP), rely on deterministic pairwise judgments that inadequately represent the vagueness, subjectivity, and cognitive bias associated with hazard assessment in high-relief mountain environments.

This study addresses these limitations by systematically comparing classical AHP with a Fuzzy AHP (FAHP) framework for flood susceptibility mapping in the data-scarce Rudraprayag District of Uttarakhand, India (1984 km²), a region frequently impacted by extreme hydro-meteorological events. Thirteen geo-environmental conditioning factors were integrated within a GIS environment at 30 m spatial resolution, encompassing topographic attributes (elevation, slope, curvature, aspect), hydrological indices (HAND, TWI, drainage density, distance to river), and environmental controls (rainfall, geology, LULC, NDVI, distance to roads). To robustly handle the full 13×13 comparison matrix and avoid zero-weight artifacts commonly associated with fuzzy extent analysis, FAHP was implemented using Buckley’s geometric mean method with triangular fuzzy numbers, explicitly capturing uncertainty bounds in pairwise comparison judgments.

Results demonstrate that FAHP yields a smoother and more balanced weight distribution compared to crisp AHP. While slope remains the dominant control, its rigid dominance is reduced, allowing geomorphically subtle yet physically meaningful factors such as curvature and aspect to exert greater influence. Validation against an independent flood inventory derived from Google Earth Engine, evaluated using ROC–AUC analysis, confirms the superior predictive performance of FAHP (AUC = 0.837) relative to classical AHP (AUC = 0.806).

Overall, the findings highlight that incorporating fuzzy uncertainty into MCDM frameworks significantly enhances the robustness and defensibility of flood susceptibility assessments. FAHP thus provides a more uncertainty-aware and process-sensitive hazard baseline, particularly suited for data-scarce Himalayan regions where judgment-based weighting remains unavoidable in disaster risk reduction and spatial planning.