Application of ensemble learning techniques to model the atmospheric concentration of SO2

Результат исследований: Вклад в журналСтатьяНаучно-исследовательскаярецензирование

Язык оригиналаАнглийский
Страницы (с-по)309-318
Число страниц10
ЖурналGlobal Journal of Environmental Science and Management
Том5
Номер выпуска3
DOI
СостояниеОпубликовано - 1 янв 2019

Отпечаток

learning
sulfur dioxide
prediction
model validation
atmospheric pollution
pollution
modeling
voting
support vector machine
machine learning

Ключевые слова

    Предметные области ASJC Scopus

    • Environmental Science(all)

    Предметные области WoS

    • Науки об окружающей среде

    Цитировать

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    title = "Application of ensemble learning techniques to model the atmospheric concentration of SO2",
    keywords = "Air Pollution Modeling, Bagging, Ensemble Learning Techniques, Multilayer Perceptron (MLP), Random forest, Sulphur Dioxide (SO), Support Vector Machine (SVM), Voting, SUPPORT VECTOR MACHINES, URBAN AIR, NO2 CONCENTRATIONS, Sulphur Dioxide (SO2), LEVEL, Random Forest, PREDICTION, NEURAL-NETWORKS, AREA, POLLUTION, OZONE, PM10",
    author = "A. Masih",
    year = "2019",
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    issn = "2383-3572",
    publisher = "Iran Solid Waste Association",
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    }

    Application of ensemble learning techniques to model the atmospheric concentration of SO2. / Masih, A.

    В: Global Journal of Environmental Science and Management, Том 5, № 3, 01.01.2019, стр. 309-318.

    Результат исследований: Вклад в журналСтатьяНаучно-исследовательскаярецензирование

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