Enhancing groundwater quality assessment with explainable machine learning
Uma Rani V1, Velumani A2, Selvi S3, Vidhya R4 and Ramya D5
Department of Computer Science and Engineering,Saveetha Engineering College, Thandalam, Chennai, Tamilnadu,India1
Department of Agricultural Engineering,Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai,Tamil Nadu,India2
Department of Artificial Intelligence and Data Science,Kangeyam Institute of Technology, Nathakadaiyur, Tiruppur,Tamil Nadu,India3
Department of Computer Science and Engineering,Velalar College of Engineering and Technology, Thindal, Erode,Tamil Nadu,India4
Department of Information Technology,Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Tamil Nadu,India5
Department of Agricultural Engineering,Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai,Tamil Nadu,India2
Department of Artificial Intelligence and Data Science,Kangeyam Institute of Technology, Nathakadaiyur, Tiruppur,Tamil Nadu,India3
Department of Computer Science and Engineering,Velalar College of Engineering and Technology, Thindal, Erode,Tamil Nadu,India4
Department of Information Technology,Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Tamil Nadu,India5
Corresponding Author : Uma Rani V
Recieved : 04-October-2024; Revised : 01-September-2025; Accepted : 08-September-2025
Abstract
Assessing groundwater quality is important in India due to significant variations influenced by geographical, climatic, and anthropogenic factors. The ground water quality index (GWQI) serves as an effective tool for evaluating the suitability of groundwater for drinking purposes. This study assesses groundwater quality across 51 locations in Coimbatore district, Tamil Nadu, India, using key parameters such as iron, fluoride, total dissolved solids, pH, turbidity, conductivity, and hardness. GWQI prediction was carried out using six different machine learning (ML) algorithms, with performance optimization achieved through random search cross-validation and stratified k-fold cross-validation. To enhance interpretability, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) were employed, providing insights into the contribution of individual features to GWQI predictions. These techniques enable instance-specific explanations, thereby improving model transparency and trustworthiness. The experimental results show that the proposed GWQI model attains a high R² score of 0.999 and a minimal RMSE of 0.003, outperforming all other ML models with superior prediction accuracy. This ML-driven GWQI assessment enhances reliability and precision, ensuring a more efficient evaluation of groundwater suitability for human consumption.
Keywords
Groundwater quality index, Machine learning, Shapley additive explanations, Local interpretable model-agnostic explanations, Water quality assessment.
Cite this article
V UR, A V, S S, R V, D R. Enhancing groundwater quality assessment with explainable machine learning. International Journal of Advanced Technology and Engineering Exploration. 2025;12(130):1379-1395. DOI : 10.19101/IJATEE.2024.111101813
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