Please use this identifier to cite or link to this item: http://hdl.handle.net/11607/2661
Title: Neural networks for predicting flow discharge in the balarood river (Iran)
Authors: Emamgholizadeh, S.
Department of Water and Soil, Agriculture Collage, Shahrood University of Technology
Keywords: Water Management
Discharge Predicting
Artificial Neural Networks
Balarood River
Rain
Temperature
Issue Date: 2008
Publisher: Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi
Citation: Emamgholizadeh, S. (2008). Neural networks for predicting flow discharge in the balarood river (Iran). International Meeting on Soil Fertility Land Management and Agroclimatology, Special Issue, 289-295.
Abstract: In this study an artificial neural networks (ANNs) model, multi-layer perception using back-propagation algorithm (MLP/BP) was used for predicting flow discharge in the Balarood River which located in Khozestan province, Iran. The rain and temperature data as monthly collected at the five meteorology stations near the Balarood basin, and corresponding them the measured discharge at the Dokohe hydrometric station on the Balarood river were used to train and validate the ANN model. The ANN model was performed by varying the network parameters to minimize the prediction error and determine the optimum network configuration. The results show that the best architecture for the MLP/BP model comprised of 10 neurons in the hidden layer and a learning rate of 0.01. Overall, the performance of the MLP/BP neural network was good in predicting the discharge of Balarood River. This information can be used for proper water management studies in that area.
URI: http://hdl.handle.net/11607/2661
Appears in Collections:2009 Özel Sayı

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