Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized F...

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Main Authors:	Salarpour, Mohsen, Zulkifli Yusop, Jajarmizadeh, Milad, Fadhilah Yusof
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2014
Online Access:	http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/1/07_Mohsen.pdf

id	ukm-8146
recordtype	eprints
spelling	ukm-81462016-12-14T06:46:21Z http://journalarticle.ukm.my/8146/ Development of generalized feed forward network for predicting annual flood (depth) of a tropical river Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof, The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable. Universiti Kebangsaan Malaysia 2014-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/8146/1/07_Mohsen.pdf Salarpour, Mohsen and Zulkifli Yusop, and Jajarmizadeh, Milad and Fadhilah Yusof, (2014) Development of generalized feed forward network for predicting annual flood (depth) of a tropical river. Sains Malaysiana, 43 (12). pp. 1865-1871. ISSN 0126-6039 http://www.ukm.my/jsm/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable.
format	Article
author	Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof,
spellingShingle	Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof, Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
author_facet	Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof,
author_sort	Salarpour, Mohsen
title	Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
title_short	Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
title_full	Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
title_fullStr	Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
title_full_unstemmed	Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
title_sort	development of generalized feed forward network for predicting annual flood (depth) of a tropical river
publisher	Universiti Kebangsaan Malaysia
publishDate	2014
url	http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/1/07_Mohsen.pdf
first_indexed	2023-09-18T19:51:37Z
last_indexed	2023-09-18T19:51:37Z
_version_	1777406257152393216

Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

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