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For: Guo W, Gao Z, Guo H, Cao W. Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models. Sci Total Environ 2023;897:165511. [PMID: 37442467 DOI: 10.1016/j.scitotenv.2023.165511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/01/2023] [Accepted: 07/11/2023] [Indexed: 07/15/2023]
Number Cited by Other Article(s)
1
Li X, Liang G, He B, Ning Y, Yang Y, Wang L, Wang G. Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis. ENVIRONMENTAL RESEARCH 2025;267:120683. [PMID: 39710236 DOI: 10.1016/j.envres.2024.120683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 12/17/2024] [Accepted: 12/19/2024] [Indexed: 12/24/2024]
2
Muduli A, Chattopadhyay PB. Assessing hydrogeochemical facies and Groundwater Quality Index in rapidly urbanizing coastal region: a GIS-based approach with machine learning for enhanced management. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-35662-z. [PMID: 39729220 DOI: 10.1007/s11356-024-35662-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 11/24/2024] [Indexed: 12/28/2024]
3
Yue K, Yang Y, Qian K, Li Y, Pan H, Li J, Xie X. Spatial distribution and hydrogeochemical processes of high iodine groundwater in the Hetao Basin, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024;953:176116. [PMID: 39245383 DOI: 10.1016/j.scitotenv.2024.176116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/23/2024] [Accepted: 09/05/2024] [Indexed: 09/10/2024]
4
Proshad R, Rahim MA, Rahman M, Asif MR, Dey HC, Khurram D, Al MA, Islam M, Idris AM. Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024;951:175746. [PMID: 39182771 DOI: 10.1016/j.scitotenv.2024.175746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/24/2024] [Accepted: 08/22/2024] [Indexed: 08/27/2024]
5
Mutailipu M, Yang Y, Zuo K, Xue Q, Wang Q, Xue F, Wang G. Estimation of CO2-Brine Interfacial Tension Based on an Advanced Intelligent Algorithm Model: Application for Carbon Saline Aquifer Sequestration. ACS OMEGA 2024;9:37265-37277. [PMID: 39246457 PMCID: PMC11375710 DOI: 10.1021/acsomega.4c04888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 08/03/2024] [Accepted: 08/07/2024] [Indexed: 09/10/2024]
6
Yin S, Yang L, Yu J, Ban R, Wen Q, Wei B, Guo Z. Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024;368:122237. [PMID: 39163674 DOI: 10.1016/j.jenvman.2024.122237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/13/2024] [Accepted: 08/16/2024] [Indexed: 08/22/2024]
7
Boudibi S, Fadlaoui H, Hiouani F, Bouzidi N, Aissaoui A, Khomri ZE. Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-34440-1. [PMID: 39042194 DOI: 10.1007/s11356-024-34440-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024]
8
Fu Y, Cao W, Nan T, Ren Y, Li Z. Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024;916:170247. [PMID: 38272097 DOI: 10.1016/j.scitotenv.2024.170247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/30/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024]
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