Abstarct: Fuzzy concepts and related inferences have been proposed a new approach to human modeling and calculation methods. Although, different powerful fuzzy modeling methods have been developed up to now, but some of these methods are different with real human modeling method, because of utilized mathematics and exact calculations in their constructions. Active Learning Method (ALM) is one of the fuzzy modeling methods which uses a very basic level of mathematics. ALM has been innovated in 1997. ALM has very simple algorithm that avoids of mathematical complexity. In this study, ALM with has been utilized for the simulation of daily runoff in Karoon basin (one of the most important basins in Iran). Hence, the daily discharge data of Karoon River in Pol-e-Shaloo station from 1991 until 1999 were gathered for modeling. The first five years (1991-1995) were used for the training of ALM model and the residual data were used for the test of trained model. The input data used for ALM modeling were daily precipitation, temperature, humidity and vapor pressure with different time lags. In addition, the impacts of changing the fuzzification points and the resolution of grid on the simulation results were investigated and the best parameters were selected for ALM. In this study, several statistical criteria were used for the evaluation of the modeling results. The Nash-Sutcliffe, R2, Root mean square error (cms) and Percent of total volume error values of the tested ALM model with 32 fuzzy rules for daily runoff modeling were 0.29, 0.31, 265 and 23, respectively. For further evaluations, the ALM results was compared with ANN (Artificial Neural Networks) and ANFIS (Adaptive Neuro-Fuzzy Inference Systems) modeling results. This comparison demonstrated that the ALM has better performance than ANN and ANFIS. In addition, the training of an ALM model is easier than the training of ANN and ANFIS. Also, ALM has the ability to identify and rank the relevant variables of the system under investigation. Hence, According to the ALM abilities, it has merit to be introduced as a new and appropriate modeling method for the runoff simulation. Karoon basin is one of the most important basins in Iran. Hence, simulation and prediction of its stream flow seems vital. In this study, HEC-HMS model is used for the deterministic simulation of daily stream flow in Karoon basin. In addition, SMA (Soil Moisture Account) module was utilized for the loss calculation in HEC-HMS model, because among of different loss functions in HEC-HMS, only SMA is able for the continuous runoff modeling. SMA is one of the newest modules for loss calculation. The daily discharge data of Karoon River in Pol-e-Shaloo station from 1991 until 1999 were used for modeling in this study. The first five years (1991-1995) were used for the calibration of model and the residual data were used for the model validation. In addition, the effect of utilizing of warm up in the modeling was evaluated and the results showed the model improvement when warm up is utilized in the modeling. In this study, several statistical criteria were used for the evaluation of the modeling results. The Nash-Sutcliffe, Bias/mean volume, R2, Mean percent of absolute error and Percent of total volume error values of validated model for daily runoff modeling were 0.8, 0.12, 0.84, 36.5 and 11.3, respectively. These results for monthly runoff modeling were 0.91, 0.11, 0.93, 24.6 and 11.3, respectively. These proper results demonstrated the ability of HEC-HMS model for the runoff modeling. For further investigation, the developed HEC-HMS model was compared with the developed run off models in the other studies. This comparison verified the merit of HEC-HMS with SMA module for the runoff modeling in the Karoon basin. This is the abstract of Saudi Arabia conference.