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Evaluation for long term PM10 concentration forecasting using multi linear regression (MLR) and principal component regression (PCR) models

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dc.citedby22
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorFong S.Y.en_US
dc.contributor.authorAhmed A.M.A.N.en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57189591438en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T06:11:42Z
dc.date.available2023-05-29T06:11:42Z
dc.date.issued2016
dc.descriptionair quality; atmospheric pollution; computer simulation; forecasting method; multiple regression; numerical model; particulate matter; policy implementation; pollution control; principal component analysis; Kuala Terengganu; Malaysia; Terengganu; West Malaysiaen_US
dc.description.abstractAir pollution in Peninsular Malaysia is dominated by particulate matter which is demonstrated by having the highest Air Pollution Index (API) value compared to the other pollutants at most part of the country. Particulate Matter (PM10) forecasting models development is crucial because it allows the authority and citizens of a community to take necessary actions to limit their exposure to harmful levels of particulates pollution and implement protection measures to significantly improve air quality on designated locations. This study aims in improving the ability of MLR using PCs inputs for PM10 concentrations forecasting. Daily observations for PM10 in Kuala Terengganu, Malaysia from January 2003 till December 2011 were utilized to forecast PM10 concentration levels. MLR and PCR (using PCs input) models were developed and the performance was evaluated using RMSE, NAE and IA. Results revealed that PCR performed better than MLR due to the implementation of PCA which reduce intricacy and eliminate data multi-collinearity. � 2007, Thai Society of Higher Eduation Institutes on Environment. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage110
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84973345523
dc.identifier.spage101
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84973345523&partnerID=40&md5=ecb39a107812ad9f6e7b9affd3169328
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22697
dc.identifier.volume9
dc.publisherThai Society of Higher Eduation Institutes on Environmenten_US
dc.sourceScopus
dc.sourcetitleEnvironmentAsia
dc.titleEvaluation for long term PM10 concentration forecasting using multi linear regression (MLR) and principal component regression (PCR) modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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