Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O3) Concentrations Prediction

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dc.contributor.authorNur Nazmi Liyana Mohd Napi
dc.contributor.authorMohammad Syazwan Noor Mohamed
dc.contributor.authorSamsuri Abdullah
dc.contributor.authorAmalina Abu Mansor
dc.contributor.authorAli Najah Ahmed
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T02:53:42Z
dc.date.available2024-05-10T02:53:42Z
dc.date.issued2020
dc.description.abstractRapid economic growth has led to an increase in ozone (O3) concentration which significantly affecting human health and environment. The prediction of O3 is complicated due to the redundancy of influencing parameters which introduce the multicollinearity problem. The aim of this study is to assess the best prediction model for O3 concentration which is Multiple Linear Regression (MLR) and Principle Component Regression (PCR). Data from2012 to 2014 were used including O3, nitrogen dioxide (NO2), nitrogen oxide (O2), temperature, relative humidity and wind speed on hourly basis. Principle Component Analysis (PCA) was used in order to reduce multicollinearity problem, prior to the implementation of MLR. The hybrid model of PCR was selected as best -fitted models as it had higher correlation coefficient, R2 values compared with MLR model.
dc.identifier.citationNapi, N. N. L. M., Mohamed, M. S. N., Abdullah, S., Mansor, A. A., Ahmed, A. N., & Ismail, M. (2020). Multiple linear regression (MLR) and principal component regression (PCR) for ozone (O3) concentrations prediction. In IOP Conference Series: Earth and Environmental Science (Vol. 616, No. 1, p. 012004). IOP Publishing.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/481
dc.language.isoen
dc.publisherIOP Publishing
dc.publisher.alternativeIOP Conference Series: Earth and Environmental Science
dc.titleMultiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O3) Concentrations Prediction
dc.typeJournal

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