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.

Afiliación

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 en En | MEDLINE | ID: mdl-36820858

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chem Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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