AI-Driven Groundwater Level Enhancement System using Advanced Prediction Algorithms - S, Ranganathan; K, Ranjith Kumar; M, Vignesh (Journal of Soft Computing Paradigm, 2024)
This research focuses on predicting water sources in various areas by analyzing historical data on groundwater levels, rainfall, and borewells. The study explores the relationships between groundwater levels and environmental factors, emphasizing the influence of rainfall on aquifer recharge. Borewell data, including depth and water quality, is incorporated to identify potential water sources. The research involves data cleaning, exploratory analysis, and machine learning to predict groundwater levels based on diverse features such as rainfall patterns and geographical characteristics. Spatial analysis using GIS tools visualizes the distribution of groundwater levels and rainfall. The model’s performance is evaluated, considering metrics and local hydrogeological conditions, with an emphasis on integrating borewell data. Continuous monitoring and updates ensure the model’s ongoing relevance. This integrated approach aims to provide insights for sustainable water resource management, assisting decision-makers in planning water sources in diverse areas.