Sustainable Groundwater Quality Management in Agriindustrial Landscape: A Conceptual Framework that Integrates IoT AI Model, Green IT and Vrikshayurveda

We propose a comprehensive, multi-tiered framework that integrates four components: (1) sophisticated AI/ML models for contamination prediction, (2) IoT-enabled real-time sensing networks, (3) Vrikshayurveda organic soil and water conservation methodologies, and (4) Green IT/AI infrastructure to facilitate sustainable and effective groundwater management. Objective: The AI/ML layer utilizes ensemble and deep learning models to forecast pollutant levels (NO₃⁻, F⁻, heavy metals) with above 90% accuracy. The IoT layer utilizes solar-powered LoRaWAN/NB-IoT nodes for continuous monitoring of pH, TDS, turbidity, and temperature, providing edge-based anomaly detectors for sub-second notifications. Panchagavya, Jeevamrutha, and Kunapajala are three Vrikshayurvedic components that improve soil structure, boost microbial activity, and reduce heavy metal leaching. This leads to an increase of up to 30% in water retention capacity and a decrease in the percolation of NO₃⁻ and F⁻ into their respective aquifers. Biodiversity, emphasizing Vrikshayurveda's methodologies such as mulching, intercropping, and lunar planting cycles, contributes to aquifer recharge, pollution mitigation, and the enhancement of soil health. The Green IT and Green AI methodologies, including energy-efficient data centrism (PUE less than 1.2), can substantially reduce and/or slow down system energy consumption by 40-60%. These layers collectively form a cohesive decision support framework for sustainable groundwater management in Industrial and Agri-industrial sceneries.