Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste.

Lan, Dong-Ying; He, Pin-Jing; Qi, Ya-Ping; Wu, Ting-Wei; Xian, Hao-Yang; Wang, Rui-Heng; Lü, Fan; Zhang, Hua

Lan, Dong-Ying; He, Pin-Jing; Qi, Ya-Ping; Wu, Ting-Wei; Xian, Hao-Yang; Wang, Rui-Heng; Lü, Fan; Zhang, Hua.

Affiliation

Lan DY; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
He PJ; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Qi YP; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
Wu TW; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Xian HY; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Wang RH; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Lü F; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Zhang H; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Anal Chem ; 95(9): 4412-4420, 2023 Mar 07.

Article de En | MEDLINE | ID: mdl-36820858

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies Langue: En Journal: Anal Chem Année: 2023 Type de document: Article Pays d'affiliation: Chine Pays de publication: États-Unis d'Amérique

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