Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis
Water resources and their quality are paramount for urban development and maintaining ecological health, particularly in arid regions confronting water scarcity. This study assessed groundwater quality in water-stressed region in southern Iran using the newly developed Root Mean Square Water Quality...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014914 |
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| author | Amin Mohammadpour Ehsan Gharehchahi Mohammad Golaki Majid Amiri Gharaghani Fahime Ahmadian Soroush Abolfathi Mohammad Reza Samaei Md Galal Uddin Agnieszka I. Olbert Amin Mousavi Khaneghah |
| author_facet | Amin Mohammadpour Ehsan Gharehchahi Mohammad Golaki Majid Amiri Gharaghani Fahime Ahmadian Soroush Abolfathi Mohammad Reza Samaei Md Galal Uddin Agnieszka I. Olbert Amin Mousavi Khaneghah |
| author_sort | Amin Mohammadpour |
| collection | DOAJ |
| description | Water resources and their quality are paramount for urban development and maintaining ecological health, particularly in arid regions confronting water scarcity. This study assessed groundwater quality in water-stressed region in southern Iran using the newly developed Root Mean Square Water Quality Index (RMS-WQI) model in conjunction with a health risk assessment (HRA) to evaluate potential risks to human health. Analysis of groundwater samples revealed that approximately 99.41 % of sites met the permissible limits for pH, fluoride (F−), and nitrate (NO3−). Total dissolved solids (TDS) exceeded the recommended guidelines at nearly 63.90 % of locations. The RMS-WQI classified groundwater quality as ranging from ''marginal'' to ''good'', with scores between 43.20 and 85.33 (averaging 62.91±9.33). The Extremely Randomized Trees (ExT) algorithm demonstrated strong predictive capability for RMS-WQI, with sensitivity analysis identifying electrical conductivity (EC) and chloride (Cl−) as the most influential parameters. The HRA results indicated notable health risks from F⁻ and NO₃⁻ exposure, particularly among children, where the hazard index (HI) exceeded the safety threshold at 57.4 % of sites. Ingestion rate (IR) was the dominant contributor to HI across all age groups. NaCl is found to be a major constituent of the regional groundwater. These findings highlight the efficacy of integrating RMS-WQI with machine learning tools for a robust assessment of groundwater quality and associated health risks in arid environments. |
| format | Article |
| id | doaj-art-82e9f44384c24b4abae4019aecc06d6f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-82e9f44384c24b4abae4019aecc06d6f2025-08-20T03:26:30ZengElsevierResults in Engineering2590-12302025-09-012710542110.1016/j.rineng.2025.105421Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysisAmin Mohammadpour0Ehsan Gharehchahi1Mohammad Golaki2Majid Amiri Gharaghani3Fahime Ahmadian4Soroush Abolfathi5Mohammad Reza Samaei6Md Galal Uddin7Agnieszka I. Olbert8Amin Mousavi Khaneghah9Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, IranDepartment of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IranSchool of Engineering, University of Warwick, CV4 7AL, Coventry, UKDepartment of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IranSchool of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, National University of Ireland Galway, Ireland; Corresponding authors.School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, National University of Ireland Galway, IrelandFaculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, Saint Petersburg 191002, Russia; Corresponding authors.Water resources and their quality are paramount for urban development and maintaining ecological health, particularly in arid regions confronting water scarcity. This study assessed groundwater quality in water-stressed region in southern Iran using the newly developed Root Mean Square Water Quality Index (RMS-WQI) model in conjunction with a health risk assessment (HRA) to evaluate potential risks to human health. Analysis of groundwater samples revealed that approximately 99.41 % of sites met the permissible limits for pH, fluoride (F−), and nitrate (NO3−). Total dissolved solids (TDS) exceeded the recommended guidelines at nearly 63.90 % of locations. The RMS-WQI classified groundwater quality as ranging from ''marginal'' to ''good'', with scores between 43.20 and 85.33 (averaging 62.91±9.33). The Extremely Randomized Trees (ExT) algorithm demonstrated strong predictive capability for RMS-WQI, with sensitivity analysis identifying electrical conductivity (EC) and chloride (Cl−) as the most influential parameters. The HRA results indicated notable health risks from F⁻ and NO₃⁻ exposure, particularly among children, where the hazard index (HI) exceeded the safety threshold at 57.4 % of sites. Ingestion rate (IR) was the dominant contributor to HI across all age groups. NaCl is found to be a major constituent of the regional groundwater. These findings highlight the efficacy of integrating RMS-WQI with machine learning tools for a robust assessment of groundwater quality and associated health risks in arid environments.http://www.sciencedirect.com/science/article/pii/S2590123025014914Drinking waterWater quality indexMachine learningHealth risk assessmentSensitivity analysisUncertainty quantification |
| spellingShingle | Amin Mohammadpour Ehsan Gharehchahi Mohammad Golaki Majid Amiri Gharaghani Fahime Ahmadian Soroush Abolfathi Mohammad Reza Samaei Md Galal Uddin Agnieszka I. Olbert Amin Mousavi Khaneghah Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis Results in Engineering Drinking water Water quality index Machine learning Health risk assessment Sensitivity analysis Uncertainty quantification |
| title | Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis |
| title_full | Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis |
| title_fullStr | Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis |
| title_full_unstemmed | Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis |
| title_short | Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis |
| title_sort | advanced water quality assessment using machine learning source identification and probabilistic health risk analysis |
| topic | Drinking water Water quality index Machine learning Health risk assessment Sensitivity analysis Uncertainty quantification |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025014914 |
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