Natural mountain springs (dharas) provide essential drinking water and irrigation for remote tribal communities across hilly terrains. However, environmental degradation and changing rainfall patterns have led to alarming spring depletion. Identifying subterranean recharge zones (springsheds) and selecting suitable groundwater recharge intervention sites currently demands rare hydrogeological field surveys that cannot scale across vast tribal geographies.
Develop an AI/ML-enabled geospatial decision support system (DSS) that integrates satellite topography, geological structures, drainage, and climatic data to delineate probable spring recharge zones and prioritize suitable water recharge interventions.
Mountain hydrogeology in complex fractured-rock terrain is non-linear and obscured; accurate recharge mapping must handle sparse field observations while actively flagging high-risk landslide zones where water recharge could trigger slope collapse.
A GIS-based AI decision support system that ingests Digital Elevation Models (DEM), geological fault lineaments, land-use, and precipitation data to generate recharge potential maps, recommend site-specific interventions (contour trenches, check dams, recharge shafts), exclude geohazard risks, and provide a mobile app for field verification.