Incident wave run-up prediction using the response surface methodology and neural networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rehman, Khawar | - |
dc.contributor.author | Khan, Hammad | - |
dc.contributor.author | Cho, Yong-Sik | - |
dc.contributor.author | Hong, Seung Ho | - |
dc.date.accessioned | 2022-07-18T01:33:40Z | - |
dc.date.available | 2022-07-18T01:33:40Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1436-3240 | - |
dc.identifier.issn | 1436-3259 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/108234 | - |
dc.description.abstract | Submerged breakwaters (SBs) protect coastal areas from intense wave actions, such as inundation and erosion, by controlling the wave run up. The effective regulation of wave run-up heights depends on the accuracy of predictions made by the forecasting model and on understanding the relation between incident wave characteristics, SBs' geometry, and their configuration. This paper proposes models based on the Artificial neural network (ANN) and the Response surface methodology (RSM) to predict the maximum wave run-up heights over a series of rubble mound and caisson-type SBs under varying incident wave conditions. The data for the ANN and RSM models are obtained through physical modeling in the laboratory flume. The objectives of the study are to (1) provide robust tools for the prediction of the maximum wave run up under complex wave-structure interactions; (2) explore the optimum conditions for reducing the maximum run-up height and examine the interdependence of wave-structure characteristics; and (3) investigate the run-up prediction efficacy of the ANN and RSM models. Assessment of the prediction quality of the ANN and RSM models reveals that both techniques establish powerful tools for wave run-up prediction; however, the former offers a slightly better statistical performance. Well-trained ANN model such as Multi-layer perceptron and well-tested statistical methods have considerable potential for application in the development of climate adaptive coastal resilience plans because of their rapid and robust predictive capability. | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Verlag | - |
dc.title | Incident wave run-up prediction using the response surface methodology and neural networks | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1007/s00477-021-02076-z | - |
dc.identifier.scopusid | 2-s2.0-85113143592 | - |
dc.identifier.wosid | 000686854500001 | - |
dc.identifier.bibliographicCitation | Stochastic Environmental Research and Risk Assessment, v.36, no.1, pp 17 - 32 | - |
dc.citation.title | Stochastic Environmental Research and Risk Assessment | - |
dc.citation.volume | 36 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 17 | - |
dc.citation.endPage | 32 | - |
dc.type.docType | Article | - |
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 | Mathematics | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordAuthor | Solitary waves | - |
dc.subject.keywordAuthor | Physical modeling | - |
dc.subject.keywordAuthor | Artificial neural network (ANN) | - |
dc.subject.keywordAuthor | Submerged breakwaters | - |
dc.subject.keywordAuthor | Wave run-up | - |
dc.subject.keywordAuthor | Response surface methodology (RSM) | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00477-021-02076-z | - |
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.