Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia
At 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 Regressi...
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my.uniten.dspace-367512025-03-03T15:44:24Z Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia Latif S.D. Lai V. Hahzaman F.H. Ahmed A.N. Huang Y.F. Birima A.H. El-Shafie A. 57216081524 57204919704 58872567300 57214837520 55807263900 23466519000 16068189400 Air pollution Errors Landforms Learning systems Mean square error Ozone Regression analysis Support vector machines Case-studies Gaussian process regression Ground-level ozone Klang valley Machine-learning Malaysia Ozone concentration Ozone concentration forecasting Regression machine learning Regression modelling Forecasting At 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 Authors Final 2025-03-03T07:44:24Z 2025-03-03T07:44:24Z 2024 Article 10.1016/j.rineng.2024.101872 2-s2.0-85184516524 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184516524&doi=10.1016%2fj.rineng.2024.101872&partnerID=40&md5=b1d8816f6efb4c86d088969520ccae40 https://irepository.uniten.edu.my/handle/123456789/36751 21 101872 All Open Access; Gold Open Access Elsevier B.V. Scopus |
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Air pollution Errors Landforms Learning systems Mean square error Ozone Regression analysis Support vector machines Case-studies Gaussian process regression Ground-level ozone Klang valley Machine-learning Malaysia Ozone concentration Ozone concentration forecasting Regression machine learning Regression modelling Forecasting |
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Air pollution Errors Landforms Learning systems Mean square error Ozone Regression analysis Support vector machines Case-studies Gaussian process regression Ground-level ozone Klang valley Machine-learning Malaysia Ozone concentration Ozone concentration forecasting Regression machine learning Regression modelling Forecasting Latif S.D. Lai V. Hahzaman F.H. Ahmed A.N. Huang Y.F. Birima A.H. El-Shafie A. Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| description |
At 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 Authors |
| author2 |
57216081524 |
| author_facet |
57216081524 Latif S.D. Lai V. Hahzaman F.H. Ahmed A.N. Huang Y.F. Birima A.H. El-Shafie A. |
| format |
Article |
| author |
Latif S.D. Lai V. Hahzaman F.H. Ahmed A.N. Huang Y.F. Birima A.H. El-Shafie A. |
| author_sort |
Latif S.D. |
| title |
Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| title_short |
Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| title_full |
Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| title_fullStr |
Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| title_full_unstemmed |
Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia |
| title_sort |
ozone concentration forecasting utilizing leveraging of regression machine learnings: a case study at klang valley, malaysia |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816118647848960 |
| score |
13.244109 |