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Generalized regression neural network for prediction of peak outflow from dam breach

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...

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Hauptverfasser: Sammen, Saad Shauket, Mohammad, Thamer Ahmad, Ghazali, Abdul Halim, Ahmed El-Shafie, Ahmed Hussein Kamel, Mohd Sidek, Lariyah
Format: Artikel
Sprache:English
Veröffentlicht: Springer 2017
Online Zugang:http://psasir.upm.edu.my/id/eprint/61947/1/Generalized%20regression%20neural%20network%20for%20prediction%20of%20peak%20outflow%20from%20dam%20breach.pdf
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spelling oai:psasir.upm.edu.my:61947 http://psasir.upm.edu.my/id/eprint/61947/ Generalized regression neural network for prediction of peak outflow from dam breach Sammen, Saad Shauket Mohammad, Thamer Ahmad Ghazali, Abdul Halim Ahmed El-Shafie, Ahmed Hussein Kamel Mohd Sidek, Lariyah 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. Springer 2017-01 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/61947/1/Generalized%20regression%20neural%20network%20for%20prediction%20of%20peak%20outflow%20from%20dam%20breach.pdf Sammen, Saad Shauket and Mohammad, Thamer Ahmad and Ghazali, Abdul Halim and Ahmed El-Shafie, Ahmed Hussein Kamel and Mohd Sidek, Lariyah (2017) Generalized regression neural network for prediction of peak outflow from dam breach. Water Resources Management, 31 (1). pp. 549-562. ISSN 0920-4741; ESSN: 1573-1650 https://link.springer.com/article/10.1007/s11269-016-1547-8 10.1007/s11269-016-1547-8
institution UPM IR
collection UPM IR
language English
description 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.
format Article
author Sammen, Saad Shauket
Mohammad, Thamer Ahmad
Ghazali, Abdul Halim
Ahmed El-Shafie, Ahmed Hussein Kamel
Mohd Sidek, Lariyah
spellingShingle Sammen, Saad Shauket
Mohammad, Thamer Ahmad
Ghazali, Abdul Halim
Ahmed El-Shafie, Ahmed Hussein Kamel
Mohd Sidek, Lariyah
Generalized regression neural network for prediction of peak outflow from dam breach
author_facet Sammen, Saad Shauket
Mohammad, Thamer Ahmad
Ghazali, Abdul Halim
Ahmed El-Shafie, Ahmed Hussein Kamel
Mohd Sidek, Lariyah
author_sort Sammen, Saad Shauket
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
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/61947/1/Generalized%20regression%20neural%20network%20for%20prediction%20of%20peak%20outflow%20from%20dam%20breach.pdf
_version_ 1819298147319414784
score 13.4562235

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