Publication:
Ozone prediction based on support vector machine

Simple item page
dc.citedby5
dc.contributor.authorTanaskuli M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorBorhana A.A.en_US
dc.contributor.authorMardhiah N.A.en_US
dc.contributor.authorMathivananen_US
dc.contributor.authorid57211856363en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid55212152300en_US
dc.contributor.authorid57211856548en_US
dc.contributor.authorid57211853165en_US
dc.date.accessioned2023-05-29T07:28:45Z
dc.date.available2023-05-29T07:28:45Z
dc.date.issued2019
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v17.i3.pp1461-1466
dc.identifier.epage1466
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85075133960
dc.identifier.spage1461
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075133960&doi=10.11591%2fijeecs.v17.i3.pp1461-1466&partnerID=40&md5=b3a3d2aca313a2b2889a69fba0a1761c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24914
dc.identifier.volume17
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleOzone prediction based on support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS