Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)

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dc.contributor.authorAhmad Zia Ul-Saufie
dc.contributor.authorAhmad Shukri Yahaya
dc.contributor.authorNor Azam Ramli
dc.contributor.authorNorrimi Rosaida
dc.contributor.authorHazrul Abdul Hamid
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:34Z
dc.date.available2024-05-10T03:25:34Z
dc.date.issued2013
dc.description.abstractFuture PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia.
dc.identifier.citationUl-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., & Hamid, H. A. (2013). Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmospheric Environment, 77, 621-630.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/579
dc.language.isoen
dc.publisherElsevier
dc.publisher.alternativeAtmospheric Environment
dc.subjectPrinciple component analysis
dc.subjectFeedforward backpropagation
dc.subjectMultiple linear regression
dc.subjectFuture prediction
dc.subjectPM10 concentrations
dc.titleFuture daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)
dc.typeJournal

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