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Optimization of water quality index models using machine learning approaches.
Ding, Fei; Zhang, Wenjie; Cao, Shaohua; Hao, Shilong; Chen, Liangyao; Xie, Xin; Li, Wenpan; Jiang, Mingcen.
Affiliation
  • Ding F; Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China. Electronic address: dingfei@bjut.edu.cn.
  • Zhang W; Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.
  • Cao S; State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China.
  • Hao S; Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.
  • Chen L; Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.
  • Xie X; State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China.
  • Li W; State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China.
  • Jiang M; State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China. Electronic address: jiangmc@cnemc.cn.
Water Res ; 243: 120337, 2023 Sep 01.
Article in En | MEDLINE | ID: mdl-37473509
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Pollutants, Chemical / Water Quality Type of study: Prognostic_studies Country/Region as subject: Asia Language: En Journal: Water Res Year: 2023 Document type: Article
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Pollutants, Chemical / Water Quality Type of study: Prognostic_studies Country/Region as subject: Asia Language: En Journal: Water Res Year: 2023 Document type: Article