A SOFTWARE APPLICATION GROUND WATER LEVEL PREDICTOR

Abstract

Groundwater is a vital natural resource that supports agricultural, domestic, and industrial activities. However, increasing water demand, climate variability, and excessive groundwater extraction have made accurate groundwater level prediction essential for sustainable water resource management. This paper presents a software application designed to predict groundwater levels using historical groundwater data and relevant environmental parameters. The proposed system integrates data preprocessing, predictive modeling, and visualization techniques to provide users with accurate and easyto-understand groundwater level forecasts. Machine learning algorithms are employed to analyze historical trends and identify patterns that influence groundwater fluctuations. KEYEORDS: Groundwater Level Prediction, Groundwater Monitoring, Water Resource Management, Software Application, Machine Learning, Predictive Analytics, Time Series Forecasting, Environmental Data Analysis, Groundwater Conservation, Decision Support System, Data Visualization, Sustainable Water Management.