Publication:
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting

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dc.citedby1
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorDom N.C.en_US
dc.contributor.authorNapi N.N.L.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorZulkifli M.F.R.en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57217286875en_US
dc.contributor.authorid57224902975en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57221126643en_US
dc.date.accessioned2023-05-29T09:09:09Z
dc.date.available2023-05-29T09:09:09Z
dc.date.issued2021
dc.description.abstractThe increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area. � 2021 WITPress. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18280/ijdne.160107
dc.identifier.epage59
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85103438754
dc.identifier.spage53
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103438754&doi=10.18280%2fijdne.160107&partnerID=40&md5=06231a5c328f50d7e319f8e3359defa5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26328
dc.identifier.volume16
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Design and Nature and Ecodynamics
dc.titleThree-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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