Optimizing Artificial Groundwater Recharge Site Selection Using Integrated OWA-PCA Approach

Artificial groundwater recharge (AGR) is an effective method to replenish depleted aquifers in arid and semi-arid regions facing over-extraction, droughts, and rising demand. By channeling surface water underground (e.g., via infiltration basins or wells), AGR boosts storage, improves quality through soil filtration, and supports sustainable management in water-stressed areas. This research develops a novel framework integrating Ordered Weighted Averaging (OWA) and Principal Component Analysis (PCA) for optimal AGR site selection in water-scarce regions, using the Yazd-Ardakan watershed as a case study. From 17 geospatial layers, eight critical factors were identified (correlation >0.5): land use, slope gradient, proximity to aquifers, geological formations, pedological characteristics, elevation bands, temperature variations, and microclimate zones. PCA showed the first principal component (PC1) explaining 35.9% of variance, with dominant influence from pedological properties (weight=0.32), geological features (0.29), slope gradients (0.27), and elevation ranges (0.25). OWA and PCA outputs had a strong 64% positive correlation, validating these factors as key determinants for recharge suitability. The integrated approach reduces data processing by 30-40% compared to conventional methods, provides statistical validation of weights via PCA, and adapts well to arid/semi-arid environments. It offers water managers a cost-effective tool for sustainable AGR implementation and water security in climate-vulnerable regions.