Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan

作者：Usman Basharat, Wenjing Zhang, Cuihong Han, Shoukat Husain Khan, Arshad Mehmood Abbasi, Sehrish Mahroof, Shuxin Li · 发表于：Ecotoxicology and Environmental Safety · 年份：2025 · DOI：10.1016/j.ecoenv.2025.118610 · 被引用次数：7 · 研究领域：Hydrological Forecasting Using AI、Groundwater and Watershed Analysis、Data Stream Mining Techniques

Groundwater quality monitoring is crucial for protecting the environment and human health. Machine learning (ML) offers substantial potential for enhancing groundwater quality prediction, classification, and identification of pollution indicators. This study evaluates various base ML algorithms and stacking ensemble classifiers (meta-classifiers) using data from 90 groundwater samples collected in District Bagh, Azad Kashmir, Pakistan. The aim was to establish a reliable method for predicting groundwater quality classification. Six supervised machine learning classifiers were utilized, namely Logistic Regression (LR), K-Nearest Neighbours (KNN), Decision Trees (DT), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB). These classifiers, along with their corresponding meta-classifiers (Meta-LR, Meta-KNN, Meta-DT, Meta-SVM, Meta-RF, and Meta-XGB), were developed and compared to evaluate their effectiveness in classifying and predicting groundwater quality. Evaluation metrics such as precision, recall, F1-score, accuracy, R 2 , RMSE and ROC curves were used to assess classifiers' performance. Among all the classifiers, SVM and its meta-classifier (Meta-SVM) emerged as the most effective, achieving the highest accuracy score of 0.85–0.89, F1-score (0.88–0.89), R 2 (0.88–1), RMSE (6.72), and Area Under the Curve (AUC) of 0.795. Meta-classifiers achieved better performance than base models for LR (0.85–0.92), SVM (0.88–1.00), and XGB (0.52–0.89). ...