A forecast of surface ozone using analytical models
In this study, several analytical models were tested to forecast the surface ozone concentration using Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Time Series Regression (TSR). Four study areas were selected in this study, namely Seberang Jaya in Penang, Shah Alam in Selang...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/21408/ http://journalarticle.ukm.my/21408/1/JKSI_4.pdf |
| _version_ | 1848815344752263168 |
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| author | Firdaus Mohamad Hamzah, Haliza Othman, Ahmad Nazri Tajul Ariffin, Norshariani Abd Rahman, Mohd Khairul Amri Kamarudin, Mohd Saifullah Rusiman, |
| author_facet | Firdaus Mohamad Hamzah, Haliza Othman, Ahmad Nazri Tajul Ariffin, Norshariani Abd Rahman, Mohd Khairul Amri Kamarudin, Mohd Saifullah Rusiman, |
| author_sort | Firdaus Mohamad Hamzah, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | In this study, several analytical models were tested to forecast the surface ozone concentration using Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Time Series Regression (TSR). Four study areas were selected in this study, namely Seberang Jaya in Penang, Shah Alam in Selangor, Larkin in Johor and Kota Bharu in Kelantan. The main objective of this study is to determine the appropriate analytical models MLR and ANN for surface ozone forecasting in some zones of peninsular Malaysia, to forecast surface ozone concentration with TSR model in several zones of peninsular Malaysia and to compare the performance of each model by the performance index. The performance index that will be shown in this study for the model comparison are root mean square error (RMSE), mean square error (MSE) and determination of coefficient (R2). The ANN model showed better performance compared to the MLR and TSR models in the model comparison in each station. The station in Larkin, Johor provides high accuracy in forecasting surface ozone concentrations for each model with minimum MSE, 0.000009 ppm and RMSE, 0.0042 ppm compared to other stations. The value of R2 is 0.33 which is highest compared to station in Seberang Jaya and Kota Bharu. |
| first_indexed | 2025-11-15T00:48:30Z |
| format | Article |
| id | oai:generic.eprints.org:21408 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:48:30Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:214082023-04-05T02:06:32Z http://journalarticle.ukm.my/21408/ A forecast of surface ozone using analytical models Firdaus Mohamad Hamzah, Haliza Othman, Ahmad Nazri Tajul Ariffin, Norshariani Abd Rahman, Mohd Khairul Amri Kamarudin, Mohd Saifullah Rusiman, In this study, several analytical models were tested to forecast the surface ozone concentration using Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Time Series Regression (TSR). Four study areas were selected in this study, namely Seberang Jaya in Penang, Shah Alam in Selangor, Larkin in Johor and Kota Bharu in Kelantan. The main objective of this study is to determine the appropriate analytical models MLR and ANN for surface ozone forecasting in some zones of peninsular Malaysia, to forecast surface ozone concentration with TSR model in several zones of peninsular Malaysia and to compare the performance of each model by the performance index. The performance index that will be shown in this study for the model comparison are root mean square error (RMSE), mean square error (MSE) and determination of coefficient (R2). The ANN model showed better performance compared to the MLR and TSR models in the model comparison in each station. The station in Larkin, Johor provides high accuracy in forecasting surface ozone concentrations for each model with minimum MSE, 0.000009 ppm and RMSE, 0.0042 ppm compared to other stations. The value of R2 is 0.33 which is highest compared to station in Seberang Jaya and Kota Bharu. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/21408/1/JKSI_4.pdf Firdaus Mohamad Hamzah, and Haliza Othman, and Ahmad Nazri Tajul Ariffin, and Norshariani Abd Rahman, and Mohd Khairul Amri Kamarudin, and Mohd Saifullah Rusiman, (2022) A forecast of surface ozone using analytical models. Jurnal Kejuruteraan, 34 (SI5(2)). pp. 35-45. ISSN 0128-0198 https://www.ukm.my/jkukm/si-5-2-2022/ |
| spellingShingle | Firdaus Mohamad Hamzah, Haliza Othman, Ahmad Nazri Tajul Ariffin, Norshariani Abd Rahman, Mohd Khairul Amri Kamarudin, Mohd Saifullah Rusiman, A forecast of surface ozone using analytical models |
| title | A forecast of surface ozone using analytical models |
| title_full | A forecast of surface ozone using analytical models |
| title_fullStr | A forecast of surface ozone using analytical models |
| title_full_unstemmed | A forecast of surface ozone using analytical models |
| title_short | A forecast of surface ozone using analytical models |
| title_sort | forecast of surface ozone using analytical models |
| url | http://journalarticle.ukm.my/21408/ http://journalarticle.ukm.my/21408/ http://journalarticle.ukm.my/21408/1/JKSI_4.pdf |