Analysis of Ground Water Level and Ground Water Pollution Using Machine Learning Algorithms

Abstract

Groundwater plays a crucial role in sustaining human life, agriculture, and ecosystems. However, due to increasing demand, pollution, and climatic variations, monitoring and analyzing groundwater levels and quality has become imperative. In this paper, we explore the use of machine learning algorithms to analyze groundwater levels and groundwater pollution, aiming to develop predictive models for sustainable groundwater management. The study investigates the application of supervised and unsupervised learning techniques to predict groundwater levels, identify pollution sources, and assess contamination risks. By integrating environmental data, including rainfall patterns, land use, and pollution levels, the paper demonstrates how machine learning can provide more accurate, scalable, and real-time analysis for groundwater management.