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

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


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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.

Item Type:Article
Keywords:Annual flood; artificial neural networks; cross validation; generalized feed forward; Johor River; predictive uncertainty
Journal:Sains Malaysiana
ID Code:8146
Deposited By: ms aida -
Deposited On:04 Jan 2015 12:34
Last Modified:14 Dec 2016 06:46

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