A Multi-Agent Artificial Intelligence Framework for Sustainable Groundwater Governance and Precision Borewell Management

Unregulated groundwater extraction and high borewell failure rates posesevere socio-economic risks in semi-arid regions such as the Indore–Malwaplateau. This paper presents RaKsh, an intelligent multi-layer decision support system that shifts groundwater management from reactive extractionto predictive, policy-aware governance. The system integrates an Internetof-Things (IoT) based field data layer, a Random Forest prediction enginefor borewell feasibility, and a knowledge layer driven by a multi-agent LargeLanguage Model (LLM) “Council” that interprets hydrogeological, ecologicaland legal evidence. The Borewell Feasibility Index (BFI) combines rechargepotential, historical success rate and local well density into an interpretableindicator. Experiments on 1,250 historical drilling records from the regionshow that the RaKsh Random Forest model achieves 92.1% accuracy andsubstantially outperforms linear and Support Vector Machine baselines. TheLLM Council applies a conservative veto logic to prevent ecologically orlegally risky drilling despite optimistic statistical predictions. RaKsh illustrates how socio-technical artificial intelligence solutions can evolve into scalable, mission-oriented innovations for sustainable groundwater governance.