Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach
Dams; 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; Trai...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Springer Netherlands
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-23488 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-234882023-05-29T14:40:56Z Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach Sammen S.S. Mohamed T.A. Ghazali A.H. El-Shafie A.H. Sidek L.M. 57192093108 7006371182 57211811043 16068189400 35070506500 Dams; 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; smoothing Several 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. Final 2023-05-29T06:40:55Z 2023-05-29T06:40:55Z 2017 Article 10.1007/s11269-016-1547-8 2-s2.0-84997501222 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997501222&doi=10.1007%2fs11269-016-1547-8&partnerID=40&md5=97db82bb06839a3de91e948fe2699fec https://irepository.uniten.edu.my/handle/123456789/23488 31 1 549 562 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 |
Dams; 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; smoothing |
| author2 |
57192093108 |
| author_facet |
57192093108 Sammen S.S. Mohamed T.A. Ghazali A.H. El-Shafie A.H. Sidek L.M. |
| format |
Article |
| author |
Sammen S.S. Mohamed T.A. Ghazali A.H. El-Shafie A.H. Sidek L.M. |
| spellingShingle |
Sammen S.S. Mohamed T.A. Ghazali A.H. El-Shafie A.H. Sidek L.M. Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| author_sort |
Sammen S.S. |
| title |
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| title_short |
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| title_full |
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| title_fullStr |
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| title_full_unstemmed |
Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach |
| title_sort |
generalized regression neural network for prediction of peak outflow from dam breach |
| publisher |
Springer Netherlands |
| publishDate |
2023 |
| _version_ |
1806428359183302656 |