Seyedeh Ameneh Sajjadi - Department of Agronomy, Rasht Branch, Islamic Azad University, Rasht, Iran.
Naser peivasteh kenari - Master of Hydrology Engineering, Department of Hydrology, Shushtar Branch, Islamic Azad University,Shushtar ,Iran
Seyed Saber Sajadi - Master of Architecture, Oxford Brookes University, Oxford,UK

It is of great economic prominence to estimate the flow rate of stream due to itsinfluence on water resource management. There are several ways to calculate theoutput runoff of a basin, each of which has its own advantages and disadvantages.Recently, Artificial Neural Network (ANN) has attracted scientists’ interest as oneof the methods of dynamic systems analysis in a variety of hydrology engineeringfields. The present study uses MATLAB software package for analysis in whichthe model is fed with the hydrometrical data and monthly temperature of Babolrudbasin in gauging stations, namely, Qrantalaar and Koshtargah, and flow rate ofhydrometric station on the Babolrud River within a statistical period of 33 years,9128-8295 .The results of this modeling showed that the ANN would accuratelyestimate the flow rate of the Babolrud River.

Artificial Neural Network, Meteorological Parameters, RBF