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Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea.
Ly, Quang Viet; Nguyen, Xuan Cuong; Lê, Ngoc C; Truong, Tien-Dung; Hoang, Thu-Huong T; Park, Tae Jun; Maqbool, Tahir; Pyo, JongCheol; Cho, Kyung Hwa; Lee, Kwang-Sik; Hur, Jin.
Afiliação
  • Ly QV; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China.
  • Nguyen XC; Laboratory of Energy and Environmental Science, Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang 550000, Vietnam.
  • Lê NC; School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
  • Truong TD; School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
  • Hoang TT; School of Environmental Science and Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam. Electronic address: huong.hoangthithu@hust.edu.vn.
  • Park TJ; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
  • Maqbool T; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China.
  • Pyo J; Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, South Korea.
  • Cho KH; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, South Korea.
  • Lee KS; Korea Basic Science Institute, Yeongudanji-ro 162, Cheongwon-gu, Cheongju, Chungcheongbuk-do 28119, South Korea.
  • Hur J; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea. Electronic address: jinhur@sejong.ac.kr.
Sci Total Environ ; 797: 149040, 2021 Nov 25.
Article em En | MEDLINE | ID: mdl-34311376
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Monitoramento Ambiental / Rios Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: En Revista: Sci Total Environ Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Monitoramento Ambiental / Rios Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: En Revista: Sci Total Environ Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China