Synthetic generation of hydrologic time series based on nonparametric random generation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Tae-Woong | - |
dc.contributor.author | Valdes, Juan B. | - |
dc.date.accessioned | 2021-06-23T23:04:48Z | - |
dc.date.available | 2021-06-23T23:04:48Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2005-09 | - |
dc.identifier.issn | 1084-0699 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/45762 | - |
dc.description.abstract | Synthetic hydrologic time series can be used to quantify the uncertainty of a water resources system. Conventional parametric models, such as autoregressive moving average or Markovian models, assume that the variable under consideration is Gaussian. This assumption, however, is a shortcoming of parametric models and motivates the development of nonparametric approaches. Nonparametric models based on a kernel function have an innate low-order structure and are restricted to highly persistent variables. This study presented a seminonparametric (SNP) model that takes advantage of both parametric and nonparametric models to generate monthly precipitation and temperature in the Conchos River Basin in Mexico. By adopting a consistent and robust scheme from the Markovian model and a nonparametric mechanism to generate a distribution-free random, component, the SNP model reliably reproduced sample properties such as mean, variance, correlation, and multimodality in the probability density function. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | American Society of Civil Engineers | - |
dc.title | Synthetic generation of hydrologic time series based on nonparametric random generation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Tae-Woong | - |
dc.identifier.doi | 10.1061/(ASCE)1084-0699(2005)10:5(395) | - |
dc.identifier.scopusid | 2-s2.0-24944507808 | - |
dc.identifier.wosid | 000231403900006 | - |
dc.identifier.bibliographicCitation | Journal of Hydrologic Engineering - ASCE, v.10, no.5, pp.395 - 404 | - |
dc.relation.isPartOf | Journal of Hydrologic Engineering - ASCE | - |
dc.citation.title | Journal of Hydrologic Engineering - ASCE | - |
dc.citation.volume | 10 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 395 | - |
dc.citation.endPage | 404 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | STOCHASTIC GENERATION | - |
dc.subject.keywordPlus | STREAMFLOW SIMULATION | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | FREQUENCY | - |
dc.subject.keywordPlus | DROUGHTS | - |
dc.subject.keywordAuthor | Hydrologic models | - |
dc.subject.keywordAuthor | Precipitation | - |
dc.subject.keywordAuthor | Random variables | - |
dc.subject.keywordAuthor | Temperature | - |
dc.subject.keywordAuthor | Time series analysis | - |
dc.identifier.url | https://ascelibrary.org/doi/10.1061/%28ASCE%291084-0699%282005%2910%3A5%28395%29 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
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.