Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis
Study region: The study area is located in the urban area of Chongqing City, the largest metropolis in southwestern China. Study focus: Various hydrochemical processes and water quality prediction are unknown, hampering the sustainable development of metropolis. In this study, geochemical model, ent...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825000515 |
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| author | Zhan Xie Weiting Liu Si Chen Rongwen Yao Chang Yang Xingjun Zhang Junyi Li Yangshuang Wang Yunhui Zhang |
| author_facet | Zhan Xie Weiting Liu Si Chen Rongwen Yao Chang Yang Xingjun Zhang Junyi Li Yangshuang Wang Yunhui Zhang |
| author_sort | Zhan Xie |
| collection | DOAJ |
| description | Study region: The study area is located in the urban area of Chongqing City, the largest metropolis in southwestern China. Study focus: Various hydrochemical processes and water quality prediction are unknown, hampering the sustainable development of metropolis. In this study, geochemical model, entropy-weighted water quality index (EWQI), and machine learning (ML) methods were applied to explore the hydrochemical processes and predict the groundwater quality for drinking purposes. New hydrological insights for the region: The self-organizing map classifies the groundwater samples into 2 clusters. Cluster 1, predominantly located along ridge areas, exhibited HCO3–Ca as the primary hydrochemical facie. Carbonate dissolution, cation exchange processes, and agricultural activities dominated the groundwater chemistry of Cluster 1. HCO3–Ca and HCO3–Na types were the dominant hydrochemical types of Cluster 2 in valley areas. Silicate weathering, cation exchange processes, and domestic sewage were the driving factors controlling the hydrochemistry of Cluster 2. EWQI results showed that 59.48 %, 31.90 % and 8.62 % of samples were excellent, good and medium for drinking, respectively. Four supervised machine learning methods were conducted to predict drinking water quality. Linear regression demonstrated the best correlation of 0.9999. The findings offer invaluable insights into groundwater suitability and evolution processes in a typical population density area and ensure a secure and sustainable domestic water supply worldwide. |
| format | Article |
| id | doaj-art-d7c191a706ec457e862863d4aaa28641 |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-d7c191a706ec457e862863d4aaa286412025-08-20T03:01:35ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-04-015810222710.1016/j.ejrh.2025.102227Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolisZhan Xie0Weiting Liu1Si Chen2Rongwen Yao3Chang Yang4Xingjun Zhang5Junyi Li6Yangshuang Wang7Yunhui Zhang8Yibin Research Institute, Southwest Jiaotong University, Yibin 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China; Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, ChinaYibin Research Institute, Southwest Jiaotong University, Yibin 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China; Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, ChinaObservation and Research Station of Ecological Restoration for Chongqing Typical Mining Areas, Ministry of Natural Resources, Chongqing Institute of Geology and Mineral Resources, ChinaYibin Research Institute, Southwest Jiaotong University, Yibin 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China; Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, ChinaObservation and Research Station of Ecological Restoration for Chongqing Typical Mining Areas, Ministry of Natural Resources, Chongqing Institute of Geology and Mineral Resources, ChinaObservation and Research Station of Ecological Restoration for Chongqing Typical Mining Areas, Ministry of Natural Resources, Chongqing Institute of Geology and Mineral Resources, ChinaObservation and Research Station of Ecological Restoration for Chongqing Typical Mining Areas, Ministry of Natural Resources, Chongqing Institute of Geology and Mineral Resources, ChinaYibin Research Institute, Southwest Jiaotong University, Yibin 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China; Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, ChinaYibin Research Institute, Southwest Jiaotong University, Yibin 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China; Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, China; Corresponding author at: Yibin Research Institute, Southwest Jiaotong University, Yibin 644000, China.Study region: The study area is located in the urban area of Chongqing City, the largest metropolis in southwestern China. Study focus: Various hydrochemical processes and water quality prediction are unknown, hampering the sustainable development of metropolis. In this study, geochemical model, entropy-weighted water quality index (EWQI), and machine learning (ML) methods were applied to explore the hydrochemical processes and predict the groundwater quality for drinking purposes. New hydrological insights for the region: The self-organizing map classifies the groundwater samples into 2 clusters. Cluster 1, predominantly located along ridge areas, exhibited HCO3–Ca as the primary hydrochemical facie. Carbonate dissolution, cation exchange processes, and agricultural activities dominated the groundwater chemistry of Cluster 1. HCO3–Ca and HCO3–Na types were the dominant hydrochemical types of Cluster 2 in valley areas. Silicate weathering, cation exchange processes, and domestic sewage were the driving factors controlling the hydrochemistry of Cluster 2. EWQI results showed that 59.48 %, 31.90 % and 8.62 % of samples were excellent, good and medium for drinking, respectively. Four supervised machine learning methods were conducted to predict drinking water quality. Linear regression demonstrated the best correlation of 0.9999. The findings offer invaluable insights into groundwater suitability and evolution processes in a typical population density area and ensure a secure and sustainable domestic water supply worldwide.http://www.sciencedirect.com/science/article/pii/S2214581825000515Groundwater environmentMachine learning approachesHydrochemical processesWater quality indexWater quality prediction |
| spellingShingle | Zhan Xie Weiting Liu Si Chen Rongwen Yao Chang Yang Xingjun Zhang Junyi Li Yangshuang Wang Yunhui Zhang Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis Journal of Hydrology: Regional Studies Groundwater environment Machine learning approaches Hydrochemical processes Water quality index Water quality prediction |
| title | Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| title_full | Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| title_fullStr | Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| title_full_unstemmed | Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| title_short | Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| title_sort | machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis |
| topic | Groundwater environment Machine learning approaches Hydrochemical processes Water quality index Water quality prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825000515 |
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