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

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

Insidious 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 thi...

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Main Authors:	Latif S.D., Almalayih M., Yafouz A., Ahmed A.N., Zaini N., Irwan D., AlDahoul N., Sherif M., El-Shafie A.
Other Authors:	57216081524
Format:	Article
Published:	Elsevier B.V. 2024
Subjects:	Air Quality Carbon monoxide concentration Machine learning Predictive model Uncertainty analysis Air quality Atmospheric humidity Decision trees Forecasting Learning algorithms Learning systems Multilayer neural networks Nitric oxide Nitrogen oxides Quality control Sulfur dioxide Support vector machines Wind Atmospheric carbon monoxide Machine learning algorithms Machine learning techniques Machine-learning Malaysia Multilayers perceptrons Neural-networks Performance Predictive models Carbon monoxide
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spelling	my.uniten.dspace-340002024-10-14T11:17:37Z Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia Latif S.D. Almalayih M. Yafouz A. Ahmed A.N. Zaini N. Irwan D. AlDahoul N. Sherif M. El-Shafie A. 57216081524 58645168000 57221981418 57214837520 56905328500 55937632900 56656478800 7005414714 16068189400 Air Quality Carbon monoxide concentration Machine learning Predictive model Uncertainty analysis Air quality Atmospheric humidity Decision trees Forecasting Learning algorithms Learning systems Multilayer neural networks Nitric oxide Nitrogen oxides Quality control Sulfur dioxide Support vector machines Uncertainty analysis Wind Atmospheric carbon monoxide Carbon monoxide concentration Machine learning algorithms Machine learning techniques Machine-learning Malaysia Multilayers perceptrons Neural-networks Performance Predictive models Carbon monoxide Insidious 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 Authors Final 2024-10-14T03:17:37Z 2024-10-14T03:17:37Z 2023 Article 10.1016/j.eti.2023.103387 2-s2.0-85174052448 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174052448&doi=10.1016%2fj.eti.2023.103387&partnerID=40&md5=a83b48e8c7e29ee37c4a4bd95c0a4c67 https://irepository.uniten.edu.my/handle/123456789/34000 32 103387 All Open Access Gold Open Access Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Air Quality Carbon monoxide concentration Machine learning Predictive model Uncertainty analysis Air quality Atmospheric humidity Decision trees Forecasting Learning algorithms Learning systems Multilayer neural networks Nitric oxide Nitrogen oxides Quality control Sulfur dioxide Support vector machines Uncertainty analysis Wind Atmospheric carbon monoxide Carbon monoxide concentration Machine learning algorithms Machine learning techniques Machine-learning Malaysia Multilayers perceptrons Neural-networks Performance Predictive models Carbon monoxide
spellingShingle	Air Quality Carbon monoxide concentration Machine learning Predictive model Uncertainty analysis Air quality Atmospheric humidity Decision trees Forecasting Learning algorithms Learning systems Multilayer neural networks Nitric oxide Nitrogen oxides Quality control Sulfur dioxide Support vector machines Uncertainty analysis Wind Atmospheric carbon monoxide Carbon monoxide concentration Machine learning algorithms Machine learning techniques Machine-learning Malaysia Multilayers perceptrons Neural-networks Performance Predictive models Carbon monoxide Latif S.D. Almalayih M. Yafouz A. Ahmed A.N. Zaini N. Irwan D. AlDahoul N. Sherif M. El-Shafie A. Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
description	Insidious 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 Authors
author2	57216081524
author_facet	57216081524 Latif S.D. Almalayih M. Yafouz A. Ahmed A.N. Zaini N. Irwan D. AlDahoul N. Sherif M. El-Shafie A.
format	Article
author	Latif S.D. Almalayih M. Yafouz A. Ahmed A.N. Zaini N. Irwan D. AlDahoul N. Sherif M. El-Shafie A.
author_sort	Latif S.D.
title	Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
title_short	Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
title_full	Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
title_fullStr	Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
title_full_unstemmed	Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia
title_sort	prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: a case study in kuala lumpur, malaysia
publisher	Elsevier B.V.
publishDate	2024
_version_	1814061098995810304
score	13.211869

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

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