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An evaluation of existent methods for estimation of embankment dam breach parameters

artificial neural network; dam failure; embankment dam; peak discharge; uncertainty analysis

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Main Authors: Sammen S.S., Mohamed T.A., Ghazali A.H., Sidek L.M., El-Shafie A.
Other Authors: 57192093108
Format: Article
Published: Springer Netherlands 2023
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spelling my.uniten.dspace-232442023-05-29T14:38:44Z An evaluation of existent methods for estimation of embankment dam breach parameters Sammen S.S. Mohamed T.A. Ghazali A.H. Sidek L.M. El-Shafie A. 57192093108 7006371182 57211811043 35070506500 16068189400 artificial neural network; dam failure; embankment dam; peak discharge; uncertainty analysis The study of dam-break analysis is considered important to predict the peak discharge during dam failure. This is essential to assess economic, social and environmental impacts downstream and to prepare the emergency response plan. Dam breach parameters such as breach width, breach height and breach formation time are the key variables to estimate the peak discharge during dam break. This study presents the evaluation of existing methods for estimation of dam breach parameters. Since all of these methods adopt regression analysis, uncertainty analysis of these methods becomes necessary to assess their performance. Uncertainty was performed using the data of more than 140 case studies of past recorded failures of dams, collected from different sources in the literature. The accuracy of the existing methods was tested, and the values of mean absolute relative error were found to be ranging from 0.39 to 1.05 for dam breach width estimation and from 0.6 to 0.8 for dam failure time estimation. In this study, artificial neural network (ANN) was recommended as an alternate method for estimation of dam breach parameters. The ANN method is proposed due to its accurate prediction when it was applied to similar other cases in water resources. � 2017, Springer Science+Business Media Dordrecht. Final 2023-05-29T06:38:44Z 2023-05-29T06:38:44Z 2017 Article 10.1007/s11069-017-2764-z 2-s2.0-85011309625 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011309625&doi=10.1007%2fs11069-017-2764-z&partnerID=40&md5=4728002d7c29f8de7650b1480e91e79b https://irepository.uniten.edu.my/handle/123456789/23244 87 1 545 566 All Open Access, Green Springer Netherlands Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description artificial neural network; dam failure; embankment dam; peak discharge; uncertainty analysis
author2 57192093108
author_facet 57192093108
Sammen S.S.
Mohamed T.A.
Ghazali A.H.
Sidek L.M.
El-Shafie A.
format Article
author Sammen S.S.
Mohamed T.A.
Ghazali A.H.
Sidek L.M.
El-Shafie A.
spellingShingle Sammen S.S.
Mohamed T.A.
Ghazali A.H.
Sidek L.M.
El-Shafie A.
An evaluation of existent methods for estimation of embankment dam breach parameters
author_sort Sammen S.S.
title An evaluation of existent methods for estimation of embankment dam breach parameters
title_short An evaluation of existent methods for estimation of embankment dam breach parameters
title_full An evaluation of existent methods for estimation of embankment dam breach parameters
title_fullStr An evaluation of existent methods for estimation of embankment dam breach parameters
title_full_unstemmed An evaluation of existent methods for estimation of embankment dam breach parameters
title_sort evaluation of existent methods for estimation of embankment dam breach parameters
publisher Springer Netherlands
publishDate 2023
_version_ 1806426710160179200
score 13.214268

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