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Virtual sample generation empowers machine learning-based effluent prediction in constructed wetlands.
Dong, Qiyu; Bai, Shunwen; Wang, Zhen; Zhao, Xinyue; Yang, Shanshan; Ren, Nanqi.
Affiliation
  • Dong Q; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, 150090, Harbin, China.
  • Bai S; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, 150090, Harbin, China. Electronic address: baishunwen@hit.edu.cn.
  • Wang Z; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, 150090, Harbin, China.
  • Zhao X; College of Resource and Environment, Northeast Agricultural University, Harbin, 150030, China.
  • Yang S; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, 150090, Harbin, China.
  • Ren N; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, 150090, Harbin, China.
J Environ Manage ; 346: 118961, 2023 Nov 15.
Article in En | MEDLINE | ID: mdl-37708683
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Environ Manage Year: 2023 Document type: Article Affiliation country: Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Environ Manage Year: 2023 Document type: Article Affiliation country: Country of publication: