Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jin-Young | - |
dc.contributor.author | Choi, Changhyun | - |
dc.contributor.author | Kang, Doosun | - |
dc.contributor.author | Kim, Byung Sik | - |
dc.contributor.author | Kim, Tae-Woong | - |
dc.date.accessioned | 2021-06-22T04:45:22Z | - |
dc.date.available | 2021-06-22T04:45:22Z | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2073-4441 | - |
dc.identifier.issn | 2073-4441 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/770 | - |
dc.description.abstract | With recent increases of heavy rainfall during the summer season, South Korea is hit by substantial flood damage every year. To reduce such flood damage and cope with flood disasters, it is necessary to reliably estimate design floods. Despite the ongoing efforts to develop practical design practice, it has been difficult to develop a standardized guideline due to the lack of hydrologic data, especially flood data. In fact, flood frequency analysis (FFA) is impractical for ungauged watersheds, and design rainfall-runoff analysis (DRRA) overestimates design floods. This study estimated the appropriate design floods at ungauged watersheds by combining the DRRA and watershed characteristics using machine learning methods, including decision tree, random forest, support vector machine, deep neural network, the Elman recurrent neural network, and the Jordan recurrent neural network. The proposed models were validated using K-fold cross-validation to reduce overfitting and were evaluated based on various error measures. Even though the DRRA overestimated the design floods by 160%, on average, for our study areas the proposed model using random forest reduced the errors and estimated design floods at 99% of the FFA, on average. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/w12113022 | - |
dc.identifier.scopusid | 2-s2.0-85095974479 | - |
dc.identifier.wosid | 000594719100001 | - |
dc.identifier.bibliographicCitation | WATER, v.12, no.11, pp 1 - 15 | - |
dc.citation.title | WATER | - |
dc.citation.volume | 12 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 15 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordAuthor | design flood | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | rainfall | - |
dc.subject.keywordAuthor | ungauged watershed | - |
dc.subject.keywordAuthor | random forest | - |
dc.identifier.url | https://www.mdpi.com/2073-4441/12/11/3022 | - |
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.