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Generation mean analysis for forage yield and quality in Kenaf

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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Main Authors: Noori, Zahra, Saleh, Ghizan, Foroughi, Majid, Behmaram, Rahmatollah, Kashiani, Pedram, Zare, Mahdi, Abd Halim, Mohd Ridzwan, Alimon, Abdul Razak, Siraj, Siti Shapor
Format: Article
Language:English
Published: Imam Khomeini International University and Iranian Biotechnology Society 2016
Online Access:http://psasir.upm.edu.my/id/eprint/61948/1/Generation%20mean%20analysis%20for%20forage%20yield%20and%20quality%20in%20Kenaf.pdf
http://psasir.upm.edu.my/id/eprint/61948/
http://ijgpb.journals.ikiu.ac.ir/article_1168.html
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spelling my.upm.eprints.619482019-11-28T00:41:51Z http://psasir.upm.edu.my/id/eprint/61948/ Generation mean analysis for forage yield and quality in Kenaf Noori, Zahra Saleh, Ghizan Foroughi, Majid Behmaram, Rahmatollah Kashiani, Pedram Zare, Mahdi Abd Halim, Mohd Ridzwan Alimon, Abdul Razak Siraj, Siti Shapor 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. Imam Khomeini International University and Iranian Biotechnology Society 2016-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/61948/1/Generation%20mean%20analysis%20for%20forage%20yield%20and%20quality%20in%20Kenaf.pdf Noori, Zahra and Saleh, Ghizan and Foroughi, Majid and Behmaram, Rahmatollah and Kashiani, Pedram and Zare, Mahdi and Abd Halim, Mohd Ridzwan and Alimon, Abdul Razak and Siraj, Siti Shapor (2016) Generation mean analysis for forage yield and quality in Kenaf. Iranian Journal of Genetics and Plant Breeding, 5 (2). pp. 23-31. ISSN 2251-9610; ESSN: 2676-346X http://ijgpb.journals.ikiu.ac.ir/article_1168.html
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
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 Noori, Zahra
Saleh, Ghizan
Foroughi, Majid
Behmaram, Rahmatollah
Kashiani, Pedram
Zare, Mahdi
Abd Halim, Mohd Ridzwan
Alimon, Abdul Razak
Siraj, Siti Shapor
spellingShingle Noori, Zahra
Saleh, Ghizan
Foroughi, Majid
Behmaram, Rahmatollah
Kashiani, Pedram
Zare, Mahdi
Abd Halim, Mohd Ridzwan
Alimon, Abdul Razak
Siraj, Siti Shapor
Generation mean analysis for forage yield and quality in Kenaf
author_facet Noori, Zahra
Saleh, Ghizan
Foroughi, Majid
Behmaram, Rahmatollah
Kashiani, Pedram
Zare, Mahdi
Abd Halim, Mohd Ridzwan
Alimon, Abdul Razak
Siraj, Siti Shapor
author_sort Noori, Zahra
title Generation mean analysis for forage yield and quality in Kenaf
title_short Generation mean analysis for forage yield and quality in Kenaf
title_full Generation mean analysis for forage yield and quality in Kenaf
title_fullStr Generation mean analysis for forage yield and quality in Kenaf
title_full_unstemmed Generation mean analysis for forage yield and quality in Kenaf
title_sort generation mean analysis for forage yield and quality in kenaf
publisher Imam Khomeini International University and Iranian Biotechnology Society
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/61948/1/Generation%20mean%20analysis%20for%20forage%20yield%20and%20quality%20in%20Kenaf.pdf
http://psasir.upm.edu.my/id/eprint/61948/
http://ijgpb.journals.ikiu.ac.ir/article_1168.html
_version_ 1651869083582332928
score 13.211869

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