Abstract
Groundwater (GW) resources provide a large share of the world’s water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was selected and 13 factors were extracted from the DEM. A spring location dataset was obtained from the water sector organizations and, along with the non-spring locations, fed into machine learning algorithms for training and validation. For delineating reliable GW potential, algorithms including random forest (RF) and its …