In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto-and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF. KeywordsHSPF Model, Artificial Neural Network (ANN), Runoff Simulation, Balkhichai River Watershed * Corresponding author. F. Amirhossien et al. 204 IntroductionStreamflow is one of the most important processes in the hydrological cycle and its prediction is vital for water resources management and planning [1]. Computer simulation models of watershed hydrology and artificial intelligent techniques are widely used for runoff simulation and forecasting. The use of watershed models is increasing due to the growing demands of improving runoff quantity.Over the last decades, artificial intelligent techniques have been introduced and widely applied in hydrological studies as powerful alternative modelling tools, such as Artificial Neural Network (ANN) [2]-[6], and fuzzy inference system (FIS) [7]- [9]. In addition, Shamseldin [10] (1997), Kumar et al. [11] and Mutlu et al. [12] compared ANNs with different input variables for runoff simulation. The comparisons showed that the ANN models applying both with rainfall and discharge as input variables gave better results than the models with rainfall as the input. When the model utilizes of rainfall values as the input variables, the simulated hydrographs do not match the measured hydrographs so well [13] [14]. Although better fits between the simulated and measured hydrographs have been reported in other studies, where additional variables such as temperature [15], evaporation [16] and, soil moisture [17] have been included as inputs for the ANN model.HSPF is a semi-distributed, conceptual model that combines spatially distributed physical attributes into the hydrologic response units. In this model, surface runoff is simula...