A Machine Learning–Based Water Scarcity Prediction and Management System for Uttar Pradesh, India

Water scarcity poses a critical challenge to sustainable development in India, particularly in Uttar Pradesh where groundwater depletion, rainfall variability, and demand-supply imbalance threaten long-term water security.This paper presents a machine learning-based Water Scarcity Prediction and Management System that integrates multi-parameter hydrological and socio-economic data to forecast district-level scarcity and provide actionable decision support.Using a Random Forest classifier trained on a multi-year dataset (2010-2024) comprising rainfall, groundwater depth, reservoir levels, population, water demand, and supply, the system achieves high predictive performance.A Stream lit dashboard, analytics layer, and geospatial visualization (Folium) translate model outputs into policy-relevant insights.Results demonstrate the effectiveness of engineered features-water deficit and per-capita water-in identifying high-risk districts and seasonal patterns.The proposed framework offers a scalable, low-cost approach for early warning, monitoring, and planning of water resources in data-constrained regions.