Please use this identifier to cite or link to this item:
https://repository.cihe.edu.hk/jspui/handle/cihe/2982
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Leung, Andrew Yee Tak | en_US |
dc.contributor.other | Lu, W. Z. | - |
dc.contributor.other | Wang, W. J. | - |
dc.contributor.other | Wang, X. K. | - |
dc.date.accessioned | 2022-04-08T08:56:37Z | - |
dc.date.available | 2022-04-08T08:56:37Z | - |
dc.date.issued | 2003 | - |
dc.identifier.uri | https://repository.cihe.edu.hk/jspui/handle/cihe/2982 | - |
dc.description.abstract | Forecasting 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.iso | en | en_US |
dc.title | Prediction of air pollutant levels using support vector machines: An effective tool | en_US |
dc.type | conference paper | en_US |
dc.relation.conference | The 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering | en_US |
dc.contributor.affiliation | School of Computing and Information Sciences | en_US |
dc.cihe.affiliated | No | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.grantfulltext | none | - |
item.openairetype | conference paper | - |
crisitem.author.dept | Yam Pak Charitable Foundation School of Computing and Information Sciences | - |
Appears in Collections: | CIS Publication |
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