Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Tanaskuli M., Ahmed A.N., Zaini N., Abdullah S., Borhana A.A., Mardhiah N.A., Mathivanan
Other Authors: 57211856363
Format: Article
Published: Institute of Advanced Engineering and Science 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-24914
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
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/
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.
author2 57211856363
author_facet 57211856363
Tanaskuli M.
Ahmed A.N.
Zaini N.
Abdullah S.
Borhana A.A.
Mardhiah N.A.
Mathivanan
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_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
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806426043002650624
score 13.222552

Similar Items