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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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Main Authors: Tanaskuli, M., Ahmed, A.N., Zaini, N., Abdullah, S., Borhana, A.A., Mardhiah, N.A., Mathivanan
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-132082020-07-03T07:59:23Z Ozone prediction based on support vector machine Tanaskuli, M. Ahmed, A.N. Zaini, N. Abdullah, S. Borhana, A.A. Mardhiah, N.A. Mathivanan 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. 2020-02-03T03:31:06Z 2020-02-03T03:31:06Z 2019 Article 10.11591/ijeecs.v17.i3.pp1461-1466 en
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/
language English
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
author Tanaskuli, M.
Ahmed, A.N.
Zaini, N.
Abdullah, S.
Borhana, A.A.
Mardhiah, N.A.
Mathivanan
spellingShingle Tanaskuli, M.
Ahmed, A.N.
Zaini, N.
Abdullah, S.
Borhana, A.A.
Mardhiah, N.A.
Mathivanan
Ozone prediction based on support vector machine
author_facet Tanaskuli, M.
Ahmed, A.N.
Zaini, N.
Abdullah, S.
Borhana, A.A.
Mardhiah, N.A.
Mathivanan
author_sort Tanaskuli, M.
title Ozone prediction based on support vector machine
title_short 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_sort ozone prediction based on support vector machine
publishDate 2020
_version_ 1672614214970114048
score 13.19449

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