Estimating
of river discharge is one of the more important parameters in the water resources
management. In recent years, due to increasing population, increased water consumption
in industrial, agricultural and health sections, thus water shortge becomes a global
problem. Accurate estimation of the river discharge is one of the most important
parameters in surface water resources management, especially in order to determine
appropriate values in flood, drought, drinking, agricultural and industral topics.
The case study in this research is Mahabad River that is located in west Azarbaijan
province in west north of Iran. In this study, we used 70%, 15% and 15% data in
order to train, validate and test, respectively. In this study, data of Kawtar and
Baitas stations were used in order to determine Mahabad River discharge. In each
ststion, several different networks were prepared using NeuroSolutions V.6.0 software.
The neural models included Multilayer Perceptron (MLP), Generalized Feed
Forward, Jordan/Elman, Radial Basis Functions (RBF) and Principle Component
Analysis (PCA), and different transfer functions included Tanh, Sigmoid, Linear
Tanh, Linear Sigmoid and the number of hidden layers of.The different number of
nodesin layers with different learning algorithms (Momentum, Levenberg Marquardt,
Quickprop, DeltaBarDelta, Conjugate Gradient) and different networks were compared.
The results showed the artificial neural networks. They predicted the river
discharge with 10.67 and 0.94 (
m
3/s)
2 and the
high value of correlation coefficient with 0.88 and 0.75 for Kawtar and Baitas
stations respectivly.