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Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia

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dc.citedby2
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAlmalayih M.en_US
dc.contributor.authorYafouz A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorIrwan D.en_US
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid58645168000en_US
dc.contributor.authorid57221981418en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55937632900en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:37Z
dc.date.available2024-10-14T03:17:37Z
dc.date.issued2023
dc.description.abstractInsidious toxin carbon monoxide (CO) can imitate a wide range of different disease states. Clinicians have, and will continue to have, serious concerns about the impact of CO imbalances on the human body. Carbon monoxide concentration has been exceeding the allowable levels in Malaysia. Owing to this, the main objective of this research is to propose a carbon monoxide (CO) prediction model based on machine learning techniques. Three years of historical data were used as input to develop the proposed models to predict carbon monoxide concentrations on a 12-hour and 24-hour basis. Four different machine learning technique models were used for the prediction which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Automated Neural Network � Multi-Layer Perceptron (ANN-MLP). The input parameters used are wind speed, humidity, Ozone (O3), Nitric oxide (NOx), Sulfur dioxide (SO2), and Nitrogen Dioxide (NO2). For each location, in this study, the uncertainty of the models utilized has been implemented to ensure the robustness of the performance. Furthermore, Taylor Diagram has been constructed to distinguish the performance of each model. The results indicate that ANN-MLP outperformed the all-other models involved in this study and showed efficiency in predicting Carbone monoxide concentration. By using ANN-MLP, the highest determination coefficient R2 were achieved which are 0.7190, 0.8914 and 0.7441 for the first station (S1), second station (S2) and the third station (S3) respectively by using 24-hour dataset. Meanwhile, by using a 12-hour dataset, 0.7490 for S1, 0.8942 for S2 and 0.8127 for S3. The uncertainty analysis of the ANN-MLP has 0.99 of confidence level and the lowest D-factor achieved, at S2 by using 12-hour dataset, is 0.000250455. These results ensure the effectiveness and robustness of ANN-MLP to predict carbon monoxide in the tropospheric layer. Code availability: Not applicable. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103387
dc.identifier.doi10.1016/j.eti.2023.103387
dc.identifier.scopus2-s2.0-85174052448
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174052448&doi=10.1016%2fj.eti.2023.103387&partnerID=40&md5=a83b48e8c7e29ee37c4a4bd95c0a4c67
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34000
dc.identifier.volume32
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEnvironmental Technology and Innovation
dc.subjectAir Quality
dc.subjectCarbon monoxide concentration
dc.subjectMachine learning
dc.subjectPredictive model
dc.subjectUncertainty analysis
dc.subjectAir quality
dc.subjectAtmospheric humidity
dc.subjectDecision trees
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMultilayer neural networks
dc.subjectNitric oxide
dc.subjectNitrogen oxides
dc.subjectQuality control
dc.subjectSulfur dioxide
dc.subjectSupport vector machines
dc.subjectUncertainty analysis
dc.subjectWind
dc.subjectAtmospheric carbon monoxide
dc.subjectCarbon monoxide concentration
dc.subjectMachine learning algorithms
dc.subjectMachine learning techniques
dc.subjectMachine-learning
dc.subjectMalaysia
dc.subjectMultilayers perceptrons
dc.subjectNeural-networks
dc.subjectPerformance
dc.subjectPredictive models
dc.subjectCarbon monoxide
dc.titlePrediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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