Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/2982
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dc.contributor.authorLeung, Andrew Yee Taken_US
dc.contributor.otherLu, W. Z.-
dc.contributor.otherWang, W. J.-
dc.contributor.otherWang, X. K.-
dc.date.accessioned2022-04-08T08:56:37Z-
dc.date.available2022-04-08T08:56:37Z-
dc.date.issued2003-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/2982-
dc.description.abstractForecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here aims to examine the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series.en_US
dc.language.isoenen_US
dc.titlePrediction of air pollutant levels using support vector machines: An effective toolen_US
dc.typeconference paperen_US
dc.relation.conferenceThe 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineeringen_US
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.cihe.affiliatedNo-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.openairetypeconference paper-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
Appears in Collections:CIS Publication
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