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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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Main Authors: Latif, Sarmad Dashti, Lai, Vivien, Hahzaman, Farah Hazwani, Ahmed, Ali Najah, Huang, Yuk Feng, Birima, Ahmed H., El-Shafie, Ahmed
Format: Article
Published: Elsevier B.V. 2024
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/44780/
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spelling my.um.eprints.447802024-07-15T07:51:54Z http://eprints.um.edu.my/44780/ Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia Latif, Sarmad Dashti Lai, Vivien Hahzaman, Farah Hazwani Ahmed, Ali Najah Huang, Yuk Feng Birima, Ahmed H. El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) 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 Elsevier B.V. 2024 Article PeerReviewed Latif, Sarmad Dashti and Lai, Vivien and Hahzaman, Farah Hazwani and Ahmed, Ali Najah and Huang, Yuk Feng and Birima, Ahmed H. and El-Shafie, Ahmed (2024) Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia. Results in Engineering, 21. ISSN 2590-1230, DOI https://doi.org/10.1016/j.rineng.2024.101872 <https://doi.org/10.1016/j.rineng.2024.101872>. 10.1016/j.rineng.2024.101872
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Latif, Sarmad Dashti
Lai, Vivien
Hahzaman, Farah Hazwani
Ahmed, Ali Najah
Huang, Yuk Feng
Birima, Ahmed H.
El-Shafie, Ahmed
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
format Article
author Latif, Sarmad Dashti
Lai, Vivien
Hahzaman, Farah Hazwani
Ahmed, Ali Najah
Huang, Yuk Feng
Birima, Ahmed H.
El-Shafie, Ahmed
author_facet Latif, Sarmad Dashti
Lai, Vivien
Hahzaman, Farah Hazwani
Ahmed, Ali Najah
Huang, Yuk Feng
Birima, Ahmed H.
El-Shafie, Ahmed
author_sort Latif, Sarmad Dashti
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 2024
url http://eprints.um.edu.my/44780/
_version_ 1805881166479228928
score 13.211869

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