Publication:
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction

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dc.citedby5
dc.contributor.authorMohd Napi N.N.L.en_US
dc.contributor.authorNoor Mohamed M.S.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorid57224902975en_US
dc.contributor.authorid57194940680en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57210403363en_US
dc.date.accessioned2023-05-29T08:06:36Z
dc.date.available2023-05-29T08:06:36Z
dc.date.issued2020
dc.descriptionAir quality; Economics; Environmental technology; Forecasting; Linear regression; Nitrogen oxides; Ozone; Wind; Air quality levels; Correlation coefficient; Influencing parameters; Multicollinearity; Multiple linear regressions; Principal component regression; Principle component analysis; Principle component regression; Predictive analyticsen_US
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 from 2012 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. In conclusion, the information from best-fitted prediction model can be used by local authorities to plan the precaution measure in combating and preserve the better air quality level. � 2020 Institute of Physics Publishing. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12004
dc.identifier.doi10.1088/1755-1315/616/1/012004
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85100068898
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100068898&doi=10.1088%2f1755-1315%2f616%2f1%2f012004&partnerID=40&md5=ed57f25c19b55bca5cb37068c7a5b69d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25060
dc.identifier.volume616
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleMultiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations predictionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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