Authors: Assistant Professor S. Venkateswara Rao, K. Bhavitha
Abstract: Groundwater serves as one of the most important freshwater resources for domestic consumption, agriculture, industrial activities, and ecological sustainability. Rapid urbanization, increasing population growth, irregular rainfall patterns, and climate change have significantly influenced groundwater availability, making variables, limiting their predictive capability in dynamic groundwater systems. Recent developments in Machine Learning (ML) provide powerful data-driven techniques capable of discovering hidden patterns from historical hydrogeological observations, enabling more reliable groundwater forecasting for sustainable resource management. The proposed approach utilizes historical groundwater observations together with geographical attributes such as latitude, longitude, and water depth to develop an accurate predictive model. The complete framework consists of data collection, preprocessing, feature engineering, regression model development, hyperparameter optimization, and performance evaluation. During preprocessing, missing values, duplicate records, and inconsistencies are removed while the dataset is normalized to improve model stability. The optimized regression geographical variables and groundwater levels, enabling accurate prediction of future groundwater conditions. The effectiveness of the proposed framework is evaluated using standard regression performance metrics, Experimental analysis demonstrates that the developed model successfully captures groundwater variation patterns while providing reliable prediction accuracy. The reported results indicate a Mean Squared Error (MSE) of approximately 0.025 and an R² value of about 0.85, demonstrating superior predictive capability compared with conventional groundwater estimation approaches. The proposed machine learning framework offers an efficient and scalable solution for groundwater monitoring, environmental planning, and sustainable water resource management, assisting policymakers and resource managers in making informed decisions for long-term groundwater conservation.
