In this paper the capability of PSO is employed to deal with the ANFIS inherent shortcomings to extract optimum fuzzy If-Then rules in noisy area arisen from application of nondimentional variables to estimate scouring depth. In the model, a PSO algorithm is employed to optimize the clustering parameters controls fuzzy If-Then rules in subtractive clustering while another PSO algorithm is employed to tune the fuzzy rules parameters associated with the fuzzy If-Then rules. The PSO models objective function is RMSE by which the model attempts to minimize the error of scouring depth estimation with respect to its generalization capability. To evaluate the model performance, the experimental data sets are used as training, checking and testing data sets. In the dimensional model the mean current velocity, mean grain size, water depth, pipe diameter, shear boundary velocity while in the nondimensional model the pipe, boundary Reynolds numbers, Froude number and normalized depth of water are set as the as input variables. The results show that the model provides an alternative approach to the conventional empirical formulas. It is evident that the PSO-FIS-SO is superior to ANFIS model in the noisy area that the input and output variables slightly related to each other.