Machine learning framework for predicting water quality classification

Groundwater serves as the source for nearly half of the world's drinking water, yet understanding of global groundwater resources remains incomplete, and management of aquifers falls short, particularly concerning groundwater quality. This research offers insights into the groundwater quality in 242 stations of Maharashtra and Union Territory of Dadra and Nagar Haveli and nine parameters (pH, total dissolved solids (TDS), total hydrogen (TH), calcium (Ca2+), magnesium (Mg2+), chloride (Cl-), sulfate (SO42-), nitrate (NO3-), fluoride (F-)) were considered for computing the Water Quality Index (WQI) and hence Water Quality Classification (WQC) based on theWQI. This research introduces the utilization of Machine Learning (ML) models, specifically, Random Forest, AdaBoost, Gradient Boosting, XGBoost, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) model for predicting WQC and models are tested. Grid search method as a hyperparameter tuning of parameters is utilized to achieve the best possible performance of ML models. The performance metrics that are used for evaluating and reporting the performance of classification models are Accuracy, Precision, Recall, and F1 Score. SVM achieved the highest performance in predicting WQC. With accurate predictions of WQC, these findings have the potential to enhance NEP concerning water resources by facilitating ongoing improvements in water quality.