Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration

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dc.contributor.authorWan Nur Shaziayani
dc.contributor.authorAhmad Zia Ul-Saufie
dc.contributor.authorHasfazilah Ahmat
dc.contributor.authorDhiya Al-Jumeily
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:19Z
dc.date.available2024-05-10T03:25:19Z
dc.date.issued2021
dc.description.abstractAir pollution is currently becoming a significant global environmental issue. The sources of air pollution in Malaysia are mobile or stationary. Motor vehicles are one of the mobile sources. Stationary sources originated from emissions caused by urban development, quarrying and power plants and petrochemical. The most noticeable contaminant in the Peninsular of Malaysia is the particulate matter (PM10), the highest contributor of Air Pollution Index (API) compared to other pollution parameters. The aim of this study is to determine the best loss function between quantile regression (QR) and ordinary least squares (OLS) using boosted regression tree (BRT) for the prediction of PM10 concentration in Alor Setar, Klang and Kota Bharu, Malaysia.
dc.identifier.citationShaziayani, W. N., Ul-Saufie, A. Z., Ahmat, H., & Al-Jumeily, D. (2021). Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration. Air Quality, Atmosphere & Health, 14(10), 1647-1663.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/541
dc.language.isoen
dc.publisherSpringer
dc.publisher.alternativeAir Quality, Atmosphere & Health
dc.subjectParticulate matter (PM10)
dc.subjectQuantile regression
dc.subjectOrdinary least squares (OLS)
dc.subjectBoosted regression tree
dc.titleCoupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration
dc.typeJournal

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