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Conference Paper: Modelling of river discharges using neural networks derived from support vector regression

TitleModelling of river discharges using neural networks derived from support vector regression
Authors
KeywordsComputers
Artificial intelligence mathematics
Issue Date2003
PublisherIEEE.
Citation
IEEE International Fuzzy Systems Conference Proceedings, St. Louis, Missouri, USA, 25-28 May 2003, v. 2, p. 1321-1326 How to Cite?
AbstractNeural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls.
Persistent Identifierhttp://hdl.handle.net/10722/46674
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChoy, KYen_HK
dc.contributor.authorChan, CWen_HK
dc.date.accessioned2007-10-30T06:55:37Z-
dc.date.available2007-10-30T06:55:37Z-
dc.date.issued2003en_HK
dc.identifier.citationIEEE International Fuzzy Systems Conference Proceedings, St. Louis, Missouri, USA, 25-28 May 2003, v. 2, p. 1321-1326en_HK
dc.identifier.issn1544-5615en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46674-
dc.description.abstractNeural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls.en_HK
dc.format.extent374189 bytes-
dc.format.extent5145 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE International Conference on Fuzzy Systemsen_HK
dc.rights©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputersen_HK
dc.subjectArtificial intelligence mathematicsen_HK
dc.titleModelling of river discharges using neural networks derived from support vector regressionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1544-5615&volume=2&spage=1321&epage=1326&date=2003&atitle=Modelling+of+river+discharges+using+neural+networks+derived+from+support+vector+regressionen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/FUZZ.2003.1206622en_HK
dc.identifier.scopuseid_2-s2.0-0038170239en_HK
dc.identifier.hkuros79465-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0038170239&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2en_HK
dc.identifier.spage1321en_HK
dc.identifier.epage1326en_HK
dc.identifier.scopusauthoridChoy, KY=7005477038en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK

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