Flooding is a frequent and intense natural hazard in Bangladesh, particularly in river-dominated regions, exacerbated by the seasonal monsoon rains. Gaibandha, situated in the northern part of the country, is highly vulnerable to flooding, resulting in significant damage and loss of life and property every year. This study aims to identify flood-conditioning factors, local-level susceptibility, comparing multi-criteria decision-making (MCDM) models for determining the flood-prone areas and generating the flood susceptible map for Gaibandha district. This study investigates the key hydro-geomorphological factors that affect flood susceptibility in the data-scarce Gaibandha district, including elevation, slope, aspect, topographic wetness index, rainfall, curvature, drainage density, normalized difference vegetation index, distance from rivers, distance from road, land use/land cover, and topographic roughness index. All the factors are statistically significant, derived from multicollinearity and Pearson’s correlation method. The study applied three MCDM techniques: analytical hierarchy process (AHP), analytic network process (ANP), and fuzzy AHP (FAHP), to map areas most susceptible to flooding. The findings revealed that 44–54% of the district is moderately susceptible to floods, 12–16% fell within the high-susceptibility zone, and only a small portion (0.7–1.1%) was classified as very highly susceptible. The predictive performance of the flood susceptibility maps produced by each method was assessed using the receiver operating characteristic curve. In the traditional approach, ANP achieved the highest accuracy, with an area under the curve of 0.885, marginally exceeding the results of FAHP (0.883) and AHP (0.881). In contrast, FAHP (0.739) performed slightly better than AHP (0.727) and ANP (0.727) in DeLong’s test and bootstrapping, revealing the higher accuracy in both methods. Thus, ANP emerged as the most reliable approach for distinguishing flood-susceptible zones in the context of Gaibandha District and supported datasets, while FAHP depicts higher accuracy in DeLong’s test and bootstrapping for the study area. This study provides an innovative and reliable approach for mapping flood susceptibility in Gaibandha and similar regions, offering valuable insights for flood susceptibility management. The results can guide policymakers in formulating effective mitigation strategies and enhancing community resilience to flooding.
Piya et al. (Wed,) studied this question.