Borewell Memory as Groundwater Intelligence: A Preprint Framework for Probabilistic Borewell Siting Using Historical Village Borewell Outcomes, GIS, Remote Sensing, and Hydrogeophysical Validation

Failed borewells impose large financial and emotional costs on groundwater-dependent farming communities, especially in hard-rock regions where productive water-bearing fractures can vary over short distances. Existing groundwater potential mapping research has advanced rapidly through GIS, remote sensing, multi-criteria decision analysis, geostatistics, machine learning, and hydrogeophysical surveys. However, much of this work remains either regional in scale, based mainly on successful well/spring occurrences, or disconnected from the lived village-level archive of successful, seasonal, dried, and failed borewells. This preprint proposes a community-scale framework called Borewell Memory as Groundwater Intelligence. The core hypothesis is that historical borewell outcomes from a village or neighboring village cluster, when accurately geo-referenced and integrated with geospatial conditioning factors and site-specific electrical resistivity validation, can improve probabilistic siting of new borewells and reduce blind-drilling risk. The proposed framework does not claim deterministic groundwater detection. Instead, it frames borewell siting as a probabilistic decision-support problem. The suggested dataset includes 400-600 borewell records with location, drilling year, total depth, first water strike, yield, seasonal reliability, failure history, lithology notes, water quality observations, and confidence scores. These records are linked to GIS layers derived from DEMs, satellite imagery, geology, geomorphology, drainage, lineaments, land use, soil, rainfall, and recharge structures. Models such as transparent logistic regression, Random Forest, XGBoost, spatial interpolation, and calibrated ensembles can produce groundwater-success probability, expected depth range, yield-risk class, and uncertainty maps. Shortlisted candidate zones should then be checked through hydrogeological field survey and electrical resistivity/VES/ERT before drilling. The novelty of the framework is not the use of GIS, remote sensing, machine learning, or resistivity individually. Those methods are already well established. The contribution is the proposed integration of successful and failed village borewell histories as a local training and validation archive, combined with uncertainty-aware modeling and field geophysical confirmation. The paper identifies overlapping research and commercial tools, clarifies the remaining gap, and proposes a pilot validation pathway for Indian village settings.