Sedaei, N.

Solaimani, K.

English

Wageningen UR (University & Research Centre): Publication System

Estimation of the monthly average discharge based on Artificial Neural 
Network (A case study; Talar and Kasilian watersheds) 
N. Sedaei1, K.Solaimani2  
1Dept. of Natural resources, Ph.D. student in Watershed Management, Sari University of Agric. & Natural 
Resources, P.o.Box 737, Sari-Iran, e-mail: nsdaei@yahoo.com  Tel: +989-387683352         
2Dept. of Natural resources, Professor, GIS Centre, Sari University of Agric. & Natural Resources, P.o.Box 737, 
Sari-Iran, e-mail: solaimani2001@yahoo.co.uk Tel: +98-9111521858        
 
ABSTRACT 
Over the past 15 years, artiﬁcial neural networks (ANNs) have been used increasingly for prediction and forecasting 
in water resources and environmental engineering. However, despite this high level of research activity, methods for 
developing ANN models are not yet well established. To obtain a snapshot of current practice, ANN development 
methods are assessed based on the taxonomies  for  journal papers  that  were  published  from  1999  to  2007  and  
focus  on  the prediction of water resource variables in river systems and also flow discharges. Artificial Neural 
Network is one of the intelligence and flexible techniques for any complex processes which is able to recognize the 
relation between input and output. Estimation of the monthly average discharge is one of the main challenges 
encountered in water, river engineering and also has a basic role in planning, management, sustainable and optimal 
operation. There are various parameters affecting the input discharge value. They are not fully known, and their 
relationship with input discharge is so complex. Thus, giving mathematical relationship of this concern is difficult. 
Spatial and temporal characteristics which influence discharge of the rivers clarify the complicacy of relations. 
Artificial Neural Networks have high ability to overcome this complexity and due to their properties, they can 
simulate complicated relations. In this study ANN model was employed for average discharge estimation in Talar 
and Kasilian Rivers in the Northern flank of Alborz, Iran. Also two hydrometric stations, one evaporimeter station 
and one gauging station were chosen. The statistical period for this study was about 15 years. To test the 
homogeneity of data Double-mass curve method was applied. Before entering the input data to the model they were 
separated to three parts: Training, Validation and Test data. To design the network in model, Cross Validation 
method has been used. Also it  was  attempted  to  design  multi-layer  Perceptron  with  Back Propagation learning 
rule for recovering the non-linear relationship between dependent and independent variables. Although multilayer 
perceptron is still the most popular model architecture, other model architectures are also used extensively. 
Application of this relation would be resulted in prediction of monthly average input discharge in stream. For 
validation of the proposed model the gained results from Artificial Neural Network model were compared with the 
field measurements. In relation to model calibration, gradient based methods are used almost exclusively. The 
results from the study showed that there is an acceptable overlapping between predicted values from Artificial 
Neural Networks and observed data, with root mean square error (RMSE) of 0.0122. Finally, results specified that 
ANN is a suitable method for monthly discharge estimation in the selected watershed with these networks being also 
of the capability to confirm basin response to spatial and temporal characteristics. 
 
Keywords: Discharge, ANN, Kasilian and Talar watersheds, Iran. 


Estimation of the monthly average discharge based on Artificial Neural Network (A case study; Talar and Kasilian watersheds)

https://library.wur.nl/ojs/index.php/AE2012/article/download/12427/11931

Estimation of the monthly average discharge based on Artificial Neural Network (A case study; Talar and Kasilian watersheds)

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