GIS based hybrid multi criteria decision making on the fuzzy environment to delineate flood susceptibility region in kr. keureuto catchment area, North Aceh

• A Fuzzy AHP-TOPSIS hybrid model developed for flood susceptibility mapping • Integrates expert judgment with GIS-based multi-criteria decision analysis • Assesses flood risk using 11 weighted geomorphological and hydrological factors • Uses fuzzy logic to manage uncertainty in complex decisions • Ranks flood-prone areas via proximity to ideal solutions • Achieves strong model accuracy with AUC of 0.921 • Reliable for identifying high-risk flood zones in Keureuto basin Flood susceptibility mapping is a vital tool for risk assessment and disaster mitigation. This study presents a Geographic Information System (GIS)–based approach that integrates two fuzzy Multi-Criteria Decision-Making (MCDM) methods, fuzzy Analytical Hierarchy Process (FAHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS), to delineate flood-prone zones in the Kr. Keureuto catchment, north Aceh. The key innovation is applying a generalized-mean ranking method to define context-sensitive ideal and negative‐ideal solutions, replacing the usual fixed fuzzy positive and negative ideal solutions (FPIS/FNIS), to produce more robust weights and rankings under data uncertainty. Eleven hydrological, topographic, and land surface factors were analyzed, including rainfall, flow accumulation, elevation, slope, drainage density, proximity to rivers, topographic wetness index, land use/land cover, vegetation index, curvature, and soil type. Rainfall (weight: 0.223), flow accumulation (0.140), proximity to rivers (0.115), TWI (0.112), and drainage density (0.094) were the most influential factors. The resulting flood-susceptibility map shows that about 38 % of the basin, mainly downstream, is at high to very high risk, while the remaining 62 % is at moderate to low risk. Validation with observed flood records produced an area-under-the-curve- receiver operating characteristic (AUC-ROC) value of 0.921, demonstrating excellent predictive performance. By clearly linking spatial analysis with a more flexible decision framework, this research provides a practical decision-support tool for planners, disaster-management agencies, and local authorities to identify priority zones for structural and non-structural mitigation, guide land-use planning, and strengthen early-warning and evacuation strategies.