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Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia

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dc.citedby6
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorLai V.en_US
dc.contributor.authorHahzaman F.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid58872567300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:44:24Z
dc.date.available2025-03-03T07:44:24Z
dc.date.issued2024
dc.description.abstractAt Klang Valley, ground-level ozone is a significant source of air pollution. Ozone (O3) concentration is affected by meteorological conditions and air pollutants. Linear Regression Models (LRM), Regression Trees (RT), Support Vector Machines (SVM), Ensembles of Trees (ET), Gaussian Process Regression (GPR), and Neural Networks (NN) are utilized in a thorough analysis to determine the accuracy of various machine learning in forecasting the ground level O3 concentration. The primary associated contributions from this research are comparisons of regression statistical model performance based on indicators of root mean squared error (RMSE), coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), prediction speed, and training time of regression models. Overall, exponential GPR outperformed other regression models in scenario 1 (S-1), scenario 2 (S-2), scenario (S-3), and scenario 4 (S-4) by incorporating multiple number of lags into respective scenarios and new method of testing ?re-substitution? performed more reliable and consistent than applying identical datasets to 20 % of model testing. The findings showed that GPR performed accurate results with R2 = 0.98, 0.95, 0.96, and 0.96 for S-1, S-2, S-3 and S-4 respectively. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101872
dc.identifier.doi10.1016/j.rineng.2024.101872
dc.identifier.scopus2-s2.0-85184516524
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184516524&doi=10.1016%2fj.rineng.2024.101872&partnerID=40&md5=b1d8816f6efb4c86d088969520ccae40
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36751
dc.identifier.volume21
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleResults in Engineering
dc.subjectAir pollution
dc.subjectErrors
dc.subjectLandforms
dc.subjectLearning systems
dc.subjectMean square error
dc.subjectOzone
dc.subjectRegression analysis
dc.subjectSupport vector machines
dc.subjectCase-studies
dc.subjectGaussian process regression
dc.subjectGround-level ozone
dc.subjectKlang valley
dc.subjectMachine-learning
dc.subjectMalaysia
dc.subjectOzone concentration
dc.subjectOzone concentration forecasting
dc.subjectRegression machine learning
dc.subjectRegression modelling
dc.subjectForecasting
dc.titleOzone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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