Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms
Abstract Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluat...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-024-02330-z |
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| author | Sobhan Maleky Maryam Faraji Majid Hashemi Akbar Esfandyari |
| author_facet | Sobhan Maleky Maryam Faraji Majid Hashemi Akbar Esfandyari |
| author_sort | Sobhan Maleky |
| collection | DOAJ |
| description | Abstract Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluated. The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. The non-carcinogenic risk assessment revealed that children had the highest hazard quotient for oral and dermal intake, with values ranging from 0.47 to 13.53 for oral intake and 0.001 to 0.05 for dermal intake. The excess lifetime cancer risk of arsenic for children, adult females, and males was found to be from 2.5 × 10–4 to 7.2 × 10–3, 1.2 × 10–4 to 3.6 × 10–3, and 4.3 × 10–5 to 1.2 × 10–3, respectively. This study suggests that any effort to reduce the arsenic levels in the Jiroft population should take into account the health hazards associated with exposure to arsenic through drinking water. |
| format | Article |
| id | doaj-art-2ab6b65b26084dd7a356b082a3a7b257 |
| institution | Kabale University |
| issn | 2190-5487 2190-5495 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
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| series | Applied Water Science |
| spelling | doaj-art-2ab6b65b26084dd7a356b082a3a7b2572025-01-26T12:47:01ZengSpringerOpenApplied Water Science2190-54872190-54952024-12-0115112210.1007/s13201-024-02330-zInvestigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithmsSobhan Maleky0Maryam Faraji1Majid Hashemi2Akbar Esfandyari3Department of Environmental Health Engineering, School of Health, Jiroft University of Medical SciencesEnvironmental Health Engineering Research Center, Kerman University of Medical SciencesEnvironmental Health Engineering Research Center, Kerman University of Medical SciencesDepartment of Environmental Health Engineering, School of Health, Jiroft University of Medical SciencesAbstract Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluated. The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. The non-carcinogenic risk assessment revealed that children had the highest hazard quotient for oral and dermal intake, with values ranging from 0.47 to 13.53 for oral intake and 0.001 to 0.05 for dermal intake. The excess lifetime cancer risk of arsenic for children, adult females, and males was found to be from 2.5 × 10–4 to 7.2 × 10–3, 1.2 × 10–4 to 3.6 × 10–3, and 4.3 × 10–5 to 1.2 × 10–3, respectively. This study suggests that any effort to reduce the arsenic levels in the Jiroft population should take into account the health hazards associated with exposure to arsenic through drinking water.https://doi.org/10.1007/s13201-024-02330-zGroundwater quality indexMachine learning algorithmsHealth risk assessmentJiroft city |
| spellingShingle | Sobhan Maleky Maryam Faraji Majid Hashemi Akbar Esfandyari Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms Applied Water Science Groundwater quality index Machine learning algorithms Health risk assessment Jiroft city |
| title | Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms |
| title_full | Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms |
| title_fullStr | Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms |
| title_full_unstemmed | Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms |
| title_short | Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms |
| title_sort | investigation of groundwater quality indices and health risk assessment of water resources of jiroft city iran by machine learning algorithms |
| topic | Groundwater quality index Machine learning algorithms Health risk assessment Jiroft city |
| url | https://doi.org/10.1007/s13201-024-02330-z |
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