Development of a flood-damage-based flood forecasting technique
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Eui Hoon | - |
dc.contributor.author | Kim, Joong Hoon | - |
dc.date.accessioned | 2021-09-02T07:57:32Z | - |
dc.date.available | 2021-09-02T07:57:32Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-08 | - |
dc.identifier.issn | 0022-1694 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/73836 | - |
dc.description.abstract | Flood forecasting is a pre-emptive non-structural measure used to mitigate inundation. Most current flood forecasting techniques incorporate complex processes, such as training and optimization, before the technique can be applied. Conventional flood forecasting techniques, based on flood volume, provide alerts even if there is no significant risk of flood damage. In this study, a new flood forecasting technique has been developed based on likely flood damage using the multi-dimensional flood damage analysis method. This new flood forecasting technique overcomes the drawbacks of current flood forecasting techniques because it can be easily applied using rainfall data. The studied drainage area was divided into subareas, and the damage functions were obtained for each subarea using the flood volumes and damage information. Using these damage functions, the rainfall intensity when the flood damage initially occurred was calculated for each duration and subarea. The damage graph produced for flood forecasting in each subarea identified the rainfall intensities and durations that resulted from the initial occurrence of flood damage. This new flood forecasting technique could be used to save lives, valuable assets, and manage drainage areas. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | MODEL | - |
dc.subject | RIVER | - |
dc.subject | RAINFALL | - |
dc.title | Development of a flood-damage-based flood forecasting technique | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Joong Hoon | - |
dc.identifier.doi | 10.1016/j.jhydrol.2018.06.003 | - |
dc.identifier.scopusid | 2-s2.0-85048576071 | - |
dc.identifier.wosid | 000441492700016 | - |
dc.identifier.bibliographicCitation | JOURNAL OF HYDROLOGY, v.563, pp.181 - 194 | - |
dc.relation.isPartOf | JOURNAL OF HYDROLOGY | - |
dc.citation.title | JOURNAL OF HYDROLOGY | - |
dc.citation.volume | 563 | - |
dc.citation.startPage | 181 | - |
dc.citation.endPage | 194 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | RIVER | - |
dc.subject.keywordPlus | RAINFALL | - |
dc.subject.keywordAuthor | Flood forecasting | - |
dc.subject.keywordAuthor | Flood volume | - |
dc.subject.keywordAuthor | Flood damage | - |
dc.subject.keywordAuthor | Damage graph | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.