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Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

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dc.citedby7
dc.contributor.authorYafouz A.en_US
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57221981418en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:37:26Z
dc.date.available2023-05-29T09:37:26Z
dc.date.issued2022
dc.descriptionForecasting; Learning algorithms; Machine learning; Monitoring; Neural networks; Ozone; Public health; Regression analysis; Air quality monitoring; Artificial neural network modeling; Gaussian process regression; Hyper-parameter; Hyper-parameter optimizations; Machine learning models; Ozone concentration; Ozone concentrations predictions; Quality monitoring system; Support vector regressions; Air qualityen_US
dc.description.abstractOzone (O3) is one of the common air pollutants. An increase in the ozone concentration can adversely affect public health and the environment such as vegetation and crops. Therefore, atmospheric air quality monitoring systems were found to monitor and predict ozone concentration. Due to complex formation of ozone influenced by precursors of ozone (O3) and meteorological conditions, there is a need to examine and evaluate various machine learning (ML) models for ozone concentration prediction. This study aims to utilize various ML models including Linear Regression (LR), Tree Regression (TR), Support Vector Regression (SVR), Ensemble Regression (ER), Gaussian Process Regression (GPR) and Artificial Neural Networks Models (ANN) to predict tropospheric (O3) using ozone concentration dataset. The dataset was created by observing hourly average data from air quality monitoring systems in 3 different stations including Putrajaya, Kelang, and KL in 3 sites in Peninsular Malaysia. The prediction models have been trained on this dataset and validated by optimizing their hyperparameters. Additionally, the performance of models was evaluated in terms of RMSE, MAE, R2, and training time. The results indicated that LR, SVR, GPR and ANN were able to give the highest R2 (83 % and 89 %) with specific hyperparameters in stations Kelang and KL, respectively. On the other hand, SVR and ER outweigh other models in terms of R2 (79 %) in Putrajaya station. Overall, regardless slightly performance differences, several developed models were able to learn patterns well and provide good prediction performance in terms of R2, RMSE and MAE. Ensemble regression models were found to balance between high prediction accuracy in terms of R2 and low training time and thus considered as a feasible solution for application of Ozone concentration prediction using the data in hourly scenario. � 2021 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2021.10.021
dc.identifier.epage4622
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85117767086
dc.identifier.spage4607
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85117767086&doi=10.1016%2fj.aej.2021.10.021&partnerID=40&md5=f6438886e4ba4c42c136394976541809
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26874
dc.identifier.volume61
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.titleComprehensive comparison of various machine learning algorithms for short-term ozone concentration predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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