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  1. Artikel: Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models.

    Majumder, Reek / Pollard, Jacquan / Salek, M Sabbir / Werth, David / Comert, Gurcan / Gale, Adrian / Khan, Sakib Mahmud / Darko, Samuel / Chowdhury, Mashrur

    Environmental health insights

    2024  Band 18, Seite(n) 11786302241227307

    Abstract: The environmental impacts of global warming driven by methane ( ... ...

    Abstract The environmental impacts of global warming driven by methane (CH
    Sprache Englisch
    Erscheinungsdatum 2024-02-27
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2452946-1
    ISSN 1178-6302
    ISSN 1178-6302
    DOI 10.1177/11786302241227307
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Buch ; Online: Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

    Majumder, Reek / Pollard, Jacquan / Salek, M Sabbir / Werth, David / Comert, Gurcan / Gale, Adrian / Khan, Sakib Mahmud / Darko, Samuel / Chowdhury, Mashrur

    2023  

    Abstract: The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) ... ...

    Abstract The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-12-17
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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