Accurate prediction of sediment scour hole dimensions downstream of hydraulic structures, e.g. the outlet bucket, is a complex and not straight forward engineering problem encountered worldwide. Because of the complexities of the study, its comprehensive, simultaneous including water flow, sediment and applying all of the effective variables involved in scouring it is not easy possible. Dimensions of a scour hole are usually determined by empirical equations which their validation is limited by experimental conditions. As constructing physical models has its own difficulty, determining of scour hole parameters has been applied in this paper for a collection of previous experimental studies. Two artificial intelligence techniques (ANN & ANFIS) are used and the results are compared with empirical equation for maximum scour holes using nonlinear regression method. Artificial Neural Network (ANN) simply represents interconnection of neurons, each of which carries out the task of combining the input, determining its strength by comparing the combination and finding out the result. On the other hand, ANFIS is a hybrid scheme which uses the learning capability of the ANN to derive the fuzzy rules with membership functions. The results showed that maximum error caused by applying ANFIS techniques in estimating scour hole dimensions was 5.2 percent while the error in neural network model was 10.38 percent. The significance of different parameters was discussed and a simple, innovative formula was proposed. This formula is an interesting tool for the engineering community due to its preferences for estimating the parameters of complex phenomena like erosion procedures. It has been established that scour estimations could be improved if soft computation is used in place of the traditional formulae. Manuscript profile