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PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS

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dc.citedby0
dc.contributor.authorAhmad A.N.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorMansor A.A.en_US
dc.contributor.authorDom N.C.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorIsmail N.A.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorid57810266500en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57211858557en_US
dc.contributor.authorid57217286875en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57014388900en_US
dc.contributor.authorid57210403363en_US
dc.date.accessioned2024-10-14T03:20:11Z
dc.date.available2024-10-14T03:20:11Z
dc.date.issued2023
dc.description.abstractOzone is one of the major challenges for the air quality community due to its adverse impact on the environment and human health. This study seeks to improve the understanding of underlying mechanisms for several developed models for ozone prediction. We aim to establish a robust prediction model for ozone concentration up to the next four hours. Three years dataset including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), particulate matter (PM10, PM2.5), wind speed, solar radiation, temperature, and relative humidity (RH) were used in this study. The data were analyzed by using Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Cluster-Multiple Linear Regression (CMLR) in predicting the next hours of O3 concentration. Results show that the MLR models executed high accuracy for O3t+1 (R2= 0.313), O3,t+2 (R2= 0.265), O3,t+3 (R2= 0.227) and O3,t+4 (R2= 0.217) as the best fitted-model. In conclusion, the MLR model is suitable for the next hour's O3 concentration prediction. � 2006-2023 Asian Research Publishing Network (ARPN). All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.59018/0623162
dc.identifier.epage1269
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85170659879
dc.identifier.spage1258
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85170659879&doi=10.59018%2f0623162&partnerID=40&md5=026d21d5a776b182860c8efe57fc8411
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34497
dc.identifier.volume18
dc.pagecount11
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleARPN Journal of Engineering and Applied Sciences
dc.subjectcluster
dc.subjectmultiple linear regression
dc.subjectPrediction
dc.subjectprincipal component analysis
dc.titlePREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELSen_US
dc.typeArticleen_US
dspace.entity.typePublication
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