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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universiti Kebangsaan Malaysia
2014
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| Online Access: | http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/1/07_Mohsen.pdf |
| _version_ | 1848811708193177600 |
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| author | Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof, |
| author_facet | Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof, |
| author_sort | Salarpour, Mohsen |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T23:50:41Z |
| format | Article |
| id | oai:generic.eprints.org:8146 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T23:50:41Z |
| publishDate | 2014 |
| publisher | Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org: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/ |
| spellingShingle | Salarpour, Mohsen Zulkifli Yusop, Jajarmizadeh, Milad Fadhilah Yusof, Development of generalized feed forward network for predicting annual flood (depth) of a tropical river |
| title | 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_short | 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 |
| url | http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/ http://journalarticle.ukm.my/8146/1/07_Mohsen.pdf |