Ozone prediction based on support vector machine
The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been...
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Institute of Advanced Engineering and Science
2023
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| author | Tanaskuli M. Ahmed A.N. Zaini N. Abdullah S. Borhana A.A. Mardhiah N.A. Mathivanan |
| author2 | 57211856363 |
| author_facet | 57211856363 Tanaskuli M. Ahmed A.N. Zaini N. Abdullah S. Borhana A.A. Mardhiah N.A. Mathivanan |
| author_sort | Tanaskuli M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model. � 2020 Institute of Advanced Engineering and Science. |
| format | Article |
| id | my.uniten.dspace-24914 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | my.uniten.dspace-249142023-05-29T15:28:45Z Ozone prediction based on support vector machine Tanaskuli M. Ahmed A.N. Zaini N. Abdullah S. Borhana A.A. Mardhiah N.A. Mathivanan 57211856363 57214837520 56905328500 56509029800 55212152300 57211856548 57211853165 The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model. � 2020 Institute of Advanced Engineering and Science. Final 2023-05-29T07:28:45Z 2023-05-29T07:28:45Z 2019 Article 10.11591/ijeecs.v17.i3.pp1461-1466 2-s2.0-85075133960 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075133960&doi=10.11591%2fijeecs.v17.i3.pp1461-1466&partnerID=40&md5=b3a3d2aca313a2b2889a69fba0a1761c https://irepository.uniten.edu.my/handle/123456789/24914 17 3 1461 1466 All Open Access, Gold, Green Institute of Advanced Engineering and Science Scopus |
| spellingShingle | Tanaskuli M. Ahmed A.N. Zaini N. Abdullah S. Borhana A.A. Mardhiah N.A. Mathivanan Ozone prediction based on support vector machine |
| title | Ozone prediction based on support vector machine |
| title_full | Ozone prediction based on support vector machine |
| title_fullStr | Ozone prediction based on support vector machine |
| title_full_unstemmed | Ozone prediction based on support vector machine |
| title_short | Ozone prediction based on support vector machine |
| title_sort | ozone prediction based on support vector machine |
| url_provider | http://dspace.uniten.edu.my/ |