Prediction of extremal precipitation by quantile regression forests: from SNU Multiscale Team
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seoncheol | - |
dc.contributor.author | Kwon, Junhyeon | - |
dc.contributor.author | Kim, Joonpyo | - |
dc.contributor.author | Oh, Hee-Seok | - |
dc.date.accessioned | 2023-09-18T07:18:28Z | - |
dc.date.available | 2023-09-18T07:18:28Z | - |
dc.date.created | 2023-07-07 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 1386-1999 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190966 | - |
dc.description.abstract | This paper considers the problem of spatio-temporal extreme value prediction of precipitation data. This work presents some methods that predict monthly extremes over the next 20 years corresponding to 0.998 quantile at several stations over a certain region. The proposed methods are based on a novel combination of quantile regression forests and circular transformation. As the core of the methodology, quantile regression forests by combining many decorrelated bootstrapping trees may improve prediction performance, and circular transformation is used for building circular transformed predictors of months, which are put into the quantile regression forests model for prediction. The empirical performance of the proposed methods are evaluated through real data analysis, which demonstrates promising results of the proposed approaches. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Prediction of extremal precipitation by quantile regression forests: from SNU Multiscale Team | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Seoncheol | - |
dc.identifier.doi | 10.1007/s10687-018-0323-y | - |
dc.identifier.scopusid | 2-s2.0-85046762724 | - |
dc.identifier.wosid | 000445371300013 | - |
dc.identifier.bibliographicCitation | EXTREMES, v.21, no.3, pp.463 - 476 | - |
dc.relation.isPartOf | EXTREMES | - |
dc.citation.title | EXTREMES | - |
dc.citation.volume | 21 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 463 | - |
dc.citation.endPage | 476 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Circular transform | - |
dc.subject.keywordAuthor | Extreme precipitation | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Quantile regression forests | - |
dc.subject.keywordAuthor | Spatio-temporal extremes | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s10687-018-0323-y | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.