Multi-criteria Decision-Making Techniques for Groundwater Potentiality Mapping in Arid Regions: a Case Study of Wadi Yiba, Kingdom of Saudi Arabia

Freshwater demand is further stressed due to the impacts of climate change and the rapid expansion of the population, particularly in dry and extremely dry regions where groundwater serves as a crucial water source. This study employed various multi-criteria decision making (MCDM) techniques to identify areas with potential for groundwater in Wadi Yiba, Kingdom of Saudi Arabia (KSA). A total of 14 parameters, including Geology, land use and land cover (LULC), Drainage Density (DD), Lineament density (LD), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rainfall, and others, were selected to create thematic layers for the delineation of groundwater potential zones (GWPZ). Using three MCDM techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) methods, maps were generated to indicate the GWPZ in the study area. These maps were categorized into five categories: very low, low, medium, high, and very high. Results indicate that the GWPZ assessed by TOPSIS in Wadi Yiba categorizes 3% of the area as having very low groundwater potential, 9% as low, 29% as medium, 36% as high, and 23% as very high. In contrast, VIKOR classified the study region as 7%, 20%, 18%, 28%, and 26% for the five classes; while the EDAS study reveals a more balanced distribution, with 5.81%, 24.80%, 33.74%, 22.32% , and 13.33% for the five classes. We evaluated the accuracy of the final maps using the receiver operating characteristic-area under the curve (ROC-AUC) evaluation method. TOPSIS exhibited the highest accuracy (0.703), closely followed by VIKOR (0.701) and EDAS (0.557). TOPSIS and VIKOR were rated "excellent," while EDAS as "good". The findings are crucial for sustainable groundwater management in Wadi Yiba and underscore the importance of selecting appropriate methods in environmental modeling.