Publication:
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach

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dc.citedby27
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorMohamed T.A.en_US
dc.contributor.authorGhazali A.H.en_US
dc.contributor.authorEl-Shafie A.H.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid7006371182en_US
dc.contributor.authorid57211811043en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid35070506500en_US
dc.date.accessioned2023-05-29T06:40:55Z
dc.date.available2023-05-29T06:40:55Z
dc.date.issued2017
dc.descriptionDams; Digital storage; Disasters; Failure (mechanical); Mean square error; Neural networks; Regression analysis; Breach outflow; Dam failure; Dam safety; Generalized regression artificial neural networks; Generalized regression neural networks; Reservoir characteristic; Root mean square errors; Training and testing; Reservoirs (water); artificial neural network; dam failure; flow field; outflow; peak discharge; peak flow; prediction; regression analysis; smoothingen_US
dc.description.abstractSeveral techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash � Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods. � 2016, Springer Science+Business Media Dordrecht.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-016-1547-8
dc.identifier.epage562
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84997501222
dc.identifier.spage549
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84997501222&doi=10.1007%2fs11269-016-1547-8&partnerID=40&md5=97db82bb06839a3de91e948fe2699fec
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23488
dc.identifier.volume31
dc.publisherSpringer Netherlandsen_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.titleGeneralized Regression Neural Network for Prediction of Peak Outflow from Dam Breachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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