Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor.

Heltzel, Robert S; Johnson, Derek R; Zaki, Mohammed T; Gebreslase, Aron K; Abdul-Aziz, Omar I

Heltzel, Robert S; Johnson, Derek R; Zaki, Mohammed T; Gebreslase, Aron K; Abdul-Aziz, Omar I.

Afiliación

Heltzel RS; West Virginia University, Mechanical and Aerospace Engineering Department, Center for Alternative Fuels, Engines, and Emissions, 263 Engineering Sciences Building, Morgantown, WV 26506, United States.
Johnson DR; West Virginia University, Mechanical and Aerospace Engineering Department, Center for Alternative Fuels, Engines, and Emissions, 263 Engineering Sciences Building, Morgantown, WV 26506, United States.
Zaki MT; West Virginia University, Mechanical and Aerospace Engineering Department, Center for Alternative Fuels, Engines, and Emissions, 263 Engineering Sciences Building, Morgantown, WV 26506, United States.
Gebreslase AK; West Virginia University, Mechanical and Aerospace Engineering Department, Center for Alternative Fuels, Engines, and Emissions, 263 Engineering Sciences Building, Morgantown, WV 26506, United States.
Abdul-Aziz OI; West Virginia University, Mechanical and Aerospace Engineering Department, Center for Alternative Fuels, Engines, and Emissions, 263 Engineering Sciences Building, Morgantown, WV 26506, United States.

Heliyon ; 8(12): e11962, 2022 Dec.

Article en En | MEDLINE | ID: mdl-36578421

Palabras clave

Controlled releases; Eddy covariance; Indirect quantification; Machine learning; Methane emissions; Neural network; OTM 33A; Random forest

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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