Predictive Modeling of Groundwater Resources Using Machine Learning and Spatial Analysis

Groundwater serves as a primary source of freshwater foragriculture, domestic use, and industrial needs across India. However,rapid urbanization, over-extraction, and inconsistent rainfall patterns haveled to severe groundwater depletion in various regions. Existingmonitoring systems are either too sparse or delayed, limiting theireffectiveness for timely decision-making. To address these challenges, thisresearch proposes a scalable, AI-driven framework that integrates machinelearning and geospatial techniques for accurate groundwater levelprediction. The system utilizes multi-source datasets including historical well-levelrecords, rainfall statistics, land use patterns, population density, and digitalelevation models. An ensemble machine learning approach combiningRandom Forest, XGBoost, and Support Vector Machines (SVM) isemployed for temporal prediction, while spatial interpolation is handledusing geospatial techniques such as Inverse Distance Weighting (IDW).Missing data is addressed using imputation techniques and transferlearning is employed for improving prediction accuracy in data-scarceregions.The model is deployed as an interactive web platform built with Reactand Flask, allowing users to input geographic locations and retrievepredictive insights, alerts, and downloadable reports. The platform targetsboth citizens and government authorities, providing personalized forecasts,resource planning tools, and data visualizations. Results from theimplementation indicate improved prediction accuracy and practicalapplicability for sustainable water resource management.