Comparison of Efficiency for ‎Hydrological Models (AWBM & ‎SimHyd) and Neural Network (MLP & ‎RBF) in Rainfall–Runoff Simulation ‎(Case study: Bar Aryeh Watershed ‎‌-‌Neyshabur)‎

For suitable programming and management of water resources, access to perfect information from the discharge at the watershed outlet is essential. In most watersheds, the hydrometric station is not available; then, different models are used to simulate the discharge within watersheds without data. The selection of preferred model for rainfall- runoff simulation depends to the purpose of modeling and available data. In this research the conceptual rainfall- runoff models, SimHyd and AWBM and neural network models, MLP and RBF (by regard the less need to measured data) were used to model of rainfall- runoff process in Bar-Aryeh watershed of Nishabur. The length of data was 30 years (1983-2012) and the length of calibration and validation periods was 5 and 7 years, respectively. RRL and SPSS programs software were used for simulation of runoff. Nash - Sutcliff (ENS), coefficient of determination (R2), the root mean square error (RMSE) used to evaluate the models.  Results showed that hydrological models simulate rainfall- runoff process in Bar Aryeh watershed of Neyshabur better than neural network models. Between mentioned models, the SimHyd with ENS, R2 and RMSE equal to 0.632, 0.8 and 0.02 respectively in the calibration period and 0.541, 0.74 and 0.08 in the validation period has better performance than other models which used in this research. The results showed that the Rosenbrock's search optimizer for the hydrological models and the function of tangent hyperbolic for the neural network models have more accurate operations than other optimizers. In addition, used models simulate the minimum and average values of the flow with an acceptable accuracy but the simulation of maximum values did not do well. Because these models do not regard the type and intensity of precipitation, lag time from snowmelt and concentration time of watershed.