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Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

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dc.citedby0
dc.contributor.authorNapi N.N.L.M.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorGhazali N.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorDom N.C.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorid57702029500en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid26430938300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57217286875en_US
dc.contributor.authorid57210403363en_US
dc.date.accessioned2024-10-14T03:21:52Z
dc.date.available2024-10-14T03:21:52Z
dc.date.issued2023
dc.description.abstractMeteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions. � 2023 UTHM Publisher. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.30880/ijie.2023.15.01.010
dc.identifier.epage117
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85152670270
dc.identifier.spage106
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85152670270&doi=10.30880%2fijie.2023.15.01.010&partnerID=40&md5=c8852861b94efa67c188866ac16469e3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34702
dc.identifier.volume15
dc.pagecount11
dc.publisherPenerbit UTHMen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofBronze Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal of Integrated Engineering
dc.subjectgaseous pollutant
dc.subjectindustrial
dc.subjectmeteorological
dc.subjectmultiple linear regression
dc.subjectOzone
dc.titleDifferent Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Areaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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