Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence

Sani I. Abba, Mohamed A. Yassin, Auwalu Saleh Mubarak, Syed Muzzamil Hussain Shah, Jamilu Usman, Atheer Y. Oudah, Sujay Raghavendra Naganna () and Isam H. Aljundi
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Sani I. Abba: Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Mohamed A. Yassin: Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Auwalu Saleh Mubarak: Operational Research Centre in Healthcare, Near East University, TRNC, Mersin 10, Nicosia 99138, Turkey
Syed Muzzamil Hussain Shah: Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Jamilu Usman: Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Atheer Y. Oudah: Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah 64001, Iraq
Sujay Raghavendra Naganna: Department of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India
Isam H. Aljundi: Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Sustainability, 2023, vol. 15, issue 21, 1-21

Abstract: The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental and public health challenges across the world. Water quality is an essential component in sustaining environmental health. This integrated study aimed to assess indexical and spatial water quality, potential contamination sources, and health risks associated with groundwater resources in Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. The physiochemical results indicated overall groundwater pollution. This study addresses the critical issue of drinking water resource suitability assessment by introducing an innovative approach based on the pollution index of groundwater (PIG). Focusing on the eastern region of Saudi Arabia, where water resource management is of paramount importance, we employed advanced machine learning (ML) models to forecast groundwater suitability using several combinations (C1 = EC + Na + Mg + Cl, C2 = TDS + TA + HCO 3 + K + Ca, and C3 = SO 4 + pH + NO 3 + F + Turb). Six ML models, including random forest (RF), decision trees (DT), XgBoost, CatBoost, linear regression, and support vector machines (SVM), were utilized to predict groundwater quality. These models, based on several performance criteria (MAPE, MAE, MSE, and DC), offer valuable insights into the complex relationships governing groundwater pollution with an accuracy of more than 90%. To enhance the transparency and interpretability of the ML models, we incorporated the local interpretable model-agnostic explanation method, SHapley Additive exPlanations (SHAP). SHAP allows us to interpret the prediction-making process of otherwise opaque black-box models. We believe that the integration of ML models and SHAP-based explainability offers a promising avenue for sustainable water resource management in Saudi Arabia and can serve as a model for addressing similar challenges worldwide. By bridging the gap between complex data-driven predictions and actionable insights, this study contributes to the advancement of environmental stewardship and water security in the region.

Keywords: water resources; artificial intelligence; SHapley Additive exPlanations (SHAP); machine learning; pollution index; groundwater; Saudi Arabia (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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