Authors: G.Parvathidevi, L.Vishnu,K, Hanshithasai, J.Amarnath
Abstract: In the water resource management sector, ground water level prediction is a crucial issue to ensure sustainable water availability and prevent over- extraction. In this paper, machine learning techniques are used to predict groundwater levels by analyzing environmental and geological factors such as historical water levels, soil characteristics, topography, and climate conditions. Various predictive models, including GA-ANN, ICA-ANN, ELM, and ORELM, are applied to the dataset to improve accuracy in groundwater forecasting. The performance of these models is evaluated using metrics such as accuracy, precision, and F1-score, with the ORELM model achieving the highest accuracy of 92%. These AI-driven insights help in identifying optimal well locations, ensuring efficient water resource management and long-term sustainability.
