Groundwater Level Prediction Using Multiple Machine Learning Techniques

Computer Engineering

Predicting groundwater levels is important for managing water resources sustainably. Accurate predictions can aid in the efficient allocation of water resources, preventing over-extraction, and minimizing environmental impacts. In the present study, we employ ML (Machine Learning) algorithms to predict groundwater levels based on historical data. We utilize a comprehensive dataset comprising groundwater level measurements, meteorological data, and other relevant parameters. The dataset is preprocessed to handle missing values and ensure its suitability for ML. Four ML algorithms, including SVM (Support Vector Machine), Logistic Regression, KNN (K-Nearest Neighbors), and Gradient Boosting, are employed for groundwater level prediction. These algorithms are trained on historical data and evaluated using appropriate metrics. Our experiments reveal the effectiveness of ML in predicting groundwater levels. We present comparative results for the four algorithms, including accuracy scores and predictive capabilities. Additionally, we visualize the model performance through confusion matrices.The comparative analysis highlights the strengths and weaknesses of each algorithm in groundwater level prediction. We discuss how these findings impact water resource management and the viability of embedding predictive models into decision support systems.

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