Landslides have serious implications for human security, infrastructure, and economic sustainability, especially in mountainous areas with geological vulnerability. This research conducts an overall evaluation of landslide susceptibility in the Akre watershed, Northern Iraq using Multi-Criteria Decision-Making (MCDM) methods in combination with ArcGIS Pro. Based on the Analytical Hierarchy Process (AHP), TOPSIS, Simple Additive Weighting (SAW), and Weighted Overlay (WO) methods, a stringent analysis was performed on nine conditioning factors of landslide events including slope, geology, rainfall, and land use. Selection of methods was based on their complementary decision-making frameworks, enabling model sensitivity and robustness comparisons. SAW emphasizes additive scoring, TOPSIS ranks alternatives based on proximity to an ideal solution, and WO offers a GIS integrated overlay approach widely used in spatial hazard mapping. The susceptibility distribution varied significantly among models. SAW classified 62% of the area as non-susceptible and only 12% as high/very high susceptibility; TOPSIS classified 41% as very low susceptibility, and 15% as high/very high susceptibility; WO presented a more balanced distribution, with 37% moderate, 30% sufficient, and 17% high/very high susceptibility. Despite methodological differences, all models reveal clear spatial trends of landslide susceptibility, with the northern and northeastern regions identified as areas of high risk owing to pronounced slopes, active structural lineaments, and intensive precipitation episodes. The Galy Zanta-Dinarta corridor, known locally as “Road of Death,” exhibits the highest susceptibility, further amplified by the flash floods of the Dinarta valley. Model validation using Receiver Operating Characteristic (ROC) and quality sum (Qs) revealed that SAW achieved the highest predictability (AUC = 0.96; Qs = 23.78), over TOPSIS (AUC = 0.87; Qs = 21.80) and WO (AUC = 0.94; Qs = 5.87). Agreement between the two methods reinforces validity and reliability of susceptibility models. The study stresses the importance of integrating geospatial and decision-support tools in environmental risk analysis.
Faisal et al. (Sat,) studied this question.