Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce supplies unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites artificial groundwater recharge (AGR) could be a strategic priority countries groundwater. Recent advances machine learning (ML) techniques provide valuable tools producing an AGR site suitability map (AGRSSM). This research developed ML algorithm identify most appropriate location Iranshahr, one of major districts East Iran characterized by severe drought excessive consumption. The area’s undue reliance on resources has resulted aquifer depletion socioeconomic problems. Nine digitized georeferenced data layers have been considered preparing AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from main rivers, quality, transmissivity soil. AGRSSM was trained validated using 1000 randomly selected points across study area with accuracy 97%. By comparing proposed those other methods, it discovered that intelligence method accurately determine sites. In summary, this uses novel approach optimal algorithms. Our findings practical implications policymakers resource managers looking address problem Iranshahr regions facing similar challenges. Future explore applicability our examine potential economic benefits