Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models.

Jiang, Shan; Liang, Yuzhen; Shi, Songlin; Wu, Chunya; Shi, Zhenqing

Jiang, Shan; Liang, Yuzhen; Shi, Songlin; Wu, Chunya; Shi, Zhenqing.

Afiliação

Jiang S; School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006,
Liang Y; School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006,
Shi S; School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006,
Wu C; School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006,
Shi Z; School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006,

Sci Total Environ ; 904: 166623, 2023 Dec 15.

Article em En | MEDLINE | ID: mdl-37652371

Assuntos

Algoritmos; Aprendizado de Máquina; Modelos Lineares; Biodegradação Ambiental; Software; Compostos Orgânicos/química

Palavras-chave

Biodegradation; Knowledge transfer; Machine learning; Quantitative structure biodegradability relationship; SHapley additive exPlanations

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2023 Tipo de documento: Article

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