Performance Comparison of Machine Learning Algorithms in Groundwater Potability Prediction

Abstract

Rising global water demand has resulted in the overuse of groundwater resources and a decline in groundwater quality. Physical and chemical characteristics significantly impacted by geological formations and human activities determine how groundwater quality varies. An accurate and reliable assessment of groundwater resource information is the key element for effective management and enhancement of groundwater quality. The utilization of modern Machine Learning (ML) techniques in groundwater quality assessment provides insights for policymakers in suggesting remedies and management approaches for groundwater quality issues. Machine Learning models outperform other simulation models, using input and output datasets without considering the intricate relationship of the model to be analyzed and decreasing computational efforts. Comparison of various ML techniques, including Ensemble, Nonlinear, and Linear models for the prediction of groundwater potability is the main objective of this study. The presence of potable groundwater suggests that the water is fit for human consumption. The proposed approach makes use of eight ML algorithms i.e. Gradient Boosting Classifier (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Regression (LR) and Stochastic Gradient Descent (SGD) algorithm. According to the results, the Ensemble ML models outperformed well followed by the Nonlinear models, and Linear classification ML models have comparatively less accuracy and reliability.