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Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

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dc.citedby36
dc.contributor.authorJumin E.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216831084en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:13:32Z
dc.date.available2023-05-29T08:13:32Z
dc.date.issued2020
dc.description.abstractHigh level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia. � 2020, � 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2020.1758792
dc.identifier.epage725
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85084835158
dc.identifier.spage713
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084835158&doi=10.1080%2f19942060.2020.1758792&partnerID=40&md5=333f6763da33364053ddf6f0c5a1df34
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25733
dc.identifier.volume14
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleMachine learning versus linear regression modelling approach for accurate ozone concentrations predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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