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Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea.
Ly, Quang Viet; Tong, Ngoc Anh; Lee, Bo-Mi; Nguyen, Minh Hieu; Trung, Huynh Thanh; Le Nguyen, Phi; Hoang, Thu-Huong T; Hwang, Yuhoon; Hur, Jin.
Affiliation
  • Ly QV; Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea.
  • Tong NA; School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam.
  • Lee BM; Water Quality Assessment Research Division, National Institute of Environmental Research, Incheon 22689, South Korea.
  • Nguyen MH; School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam; School of Information and Communication Technology, Griffith University, Gold Coast, Australia.
  • Trung HT; Ecole Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland.
  • Le Nguyen P; School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam.
  • Hoang TT; School of Chemistry and Life Science, Hanoi University of Science and Technology, Hanoi 10000, Vietnam.
  • Hwang Y; Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea.
  • Hur J; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea. Electronic address: jinhur@sejong.ac.kr.
Sci Total Environ ; 901: 166467, 2023 Nov 25.
Article in En | MEDLINE | ID: mdl-37611716
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: Sci Total Environ Year: 2023 Type: Article Affiliation country: South Korea
Fulltext
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: Sci Total Environ Year: 2023 Type: Article Affiliation country: South Korea