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

被引:12
作者
Abba, Sani I. [1 ]
Yassin, Mohamed A. [1 ]
Mubarak, Auwalu Saleh [2 ,3 ]
Shah, Syed Muzzamil Hussain [1 ]
Usman, Jamilu [1 ]
Oudah, Atheer Y. [4 ,5 ]
Naganna, Sujay Raghavendra [6 ]
Aljundi, Isam H. [1 ,7 ]
机构
[1] King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Membranes & Water Secur, Dhahran 31261, Saudi Arabia
[2] Near East Univ, Operat Res Ctr Healthcare, Mersin 10, TR-99138 Nicosia, Turkiye
[3] Aliko Dangote Univ Sci & Technol, Elect Engn Dept, Wudil 713101, Kano, Nigeria
[4] Univ Thi Qar, Coll Educ Pure Sci, Dept Comp Sci, Nasiriyah 64001, Iraq
[5] Al Ayen Univ, Sci Res Ctr, Informat & Commun Technol Res Grp, Nasiriyah 64001, Iraq
[6] Manipal Acad Higher Educ, Manipal Inst Technol Bengaluru, Dept Civil Engn, Manipal 576104, India
[7] King Fahd Univ Petr & Minerals, Dept Chem Engn, Dhahran 31261, Saudi Arabia
关键词
water resources; artificial intelligence; SHapley Additive exPlanations (SHAP); machine learning; pollution index; groundwater; Saudi Arabia;
D O I
10.3390/su152115655
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 + HCO3 + K + Ca, and C3 = SO4 + pH + NO3 + 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.
引用
收藏
页数:21
相关论文
共 43 条
[1]   Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi Arabia: The influencing factors, toxicity, and human health risks [J].
Abba S.I. ;
Egbueri J.C. ;
Benaafi M. ;
Usman J. ;
Usman A.G. ;
Aljundi I.H. .
Chemosphere, 2023, 336
[2]   Application of the Entropy Weighted Water Quality Index (EWQI) and the Pollution Index of Groundwater (PIG) to Assess Groundwater Quality for Drinking Purposes: A Case Study in a Rural Area of Telangana State, India [J].
Adimalla, Narsimha .
ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2021, 80 (01) :31-40
[3]   Groundwater chemistry integrating the pollution index of groundwater and evaluation of potential human health risk: A case study from hard rock terrain of south India [J].
Adimalla, Narsimha ;
Qian, Hui ;
Nandan, M. J. .
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2020, 206
[4]  
Al Tokhais A.S., 2008, P 3 INT C WAT RES AR
[5]   Groundwater Quality: The Application of Artificial Intelligence [J].
Al-Adhaileh, Mosleh Hmoud ;
Aldhyani, Theyazn H. H. ;
Alsaade, Fawaz Waselallah ;
Al-Yaari, Mohammed ;
Albaggar, Ali Khalaf Ahmed .
JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH, 2022, 2022
[6]  
Al-Khafaji Z., 2022, Knowledge-based. Eng. Sci., V3, P1
[7]   Hydrogeochemical characterization and groundwater quality assessment in Al-Hasa, Saudi Arabia [J].
Al-Omran, Abdulrasoul M. ;
Mousa, Mohammed A. ;
AlHarbi, Maged M. ;
Nadeem, Mahmoud E. A. .
ARABIAN JOURNAL OF GEOSCIENCES, 2018, 11 (04)
[8]   Hydrochemical characterization of groundwater under agricultural land in arid environment: a case study of Al-Kharj, Saudi Arabia [J].
Al-Omran, Abdulrasoul M. ;
Aly, Anwar A. ;
Al-Wabel, Mohammad I. ;
Sallam, Abdulazeam S. ;
Al-Shayaa, Mohammad S. .
ARABIAN JOURNAL OF GEOSCIENCES, 2016, 9 (01) :1-17
[9]  
[Anonymous], 2011, United Nations Office to Support the International Decade for Action Water for Life 2005-2015
[10]   Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane [J].
Baig, Nadeem ;
Abba, S. I. ;
Usman, Jamilu ;
Benaafi, Mohammed ;
Aljundi, Isam H. .
ENVIRONMENTAL SCIENCE-ADVANCES, 2023, 2 (10) :1446-1459