A Mixture-Density-Network Based Approach for Finding Rating Curves: Facing Multi-Modality and Unbalanced Data Distribution
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Chulsang | - |
dc.contributor.author | Park, Jooyoung | - |
dc.date.accessioned | 2021-09-08T04:56:39Z | - |
dc.date.available | 2021-09-08T04:56:39Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2010-03 | - |
dc.identifier.issn | 1226-7988 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/116934 | - |
dc.description.abstract | In this paper, the use of MDNs (Mixture Density Networks) is proposed for deciding rating curves. This method is beneficial especially when a single curve is developed when the relation between stage and discharge exhibits hysteresis. The computational analyses performed for the Han River and Mokkye stations showed that the MDN-based method yields more meaningful results than the conventional least squares approach. Of particular significance was the possible identification of the bi-modal characteristics of rating curves under the proposed method. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | STAGE | - |
dc.subject | HYSTERESIS | - |
dc.title | A Mixture-Density-Network Based Approach for Finding Rating Curves: Facing Multi-Modality and Unbalanced Data Distribution | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoo, Chulsang | - |
dc.contributor.affiliatedAuthor | Park, Jooyoung | - |
dc.identifier.doi | 10.1007/s12205-010-0243-0 | - |
dc.identifier.scopusid | 2-s2.0-77649207385 | - |
dc.identifier.wosid | 000275076600018 | - |
dc.identifier.bibliographicCitation | KSCE JOURNAL OF CIVIL ENGINEERING, v.14, no.2, pp.245 - 252 | - |
dc.relation.isPartOf | KSCE JOURNAL OF CIVIL ENGINEERING | - |
dc.citation.title | KSCE JOURNAL OF CIVIL ENGINEERING | - |
dc.citation.volume | 14 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 245 | - |
dc.citation.endPage | 252 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001426641 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | STAGE | - |
dc.subject.keywordPlus | HYSTERESIS | - |
dc.subject.keywordAuthor | rating curves | - |
dc.subject.keywordAuthor | neural networks | - |
dc.subject.keywordAuthor | mixture density networks | - |
dc.subject.keywordAuthor | multi-layer perceptrons | - |
dc.subject.keywordAuthor | scaled conjugate gradients algorithms | - |
dc.subject.keywordAuthor | multi-modality | - |
dc.subject.keywordAuthor | hysteresis | - |
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