June 22, 2024
Abbas Rezaei

Abbas Rezaei

Academic rank: Assistant professor
Address:
Education: Ph.D in Electrical engineering
Phone: 083-38305001
Faculty: Faculty ofٍٍ Electrical Engineering

Research

Title
Prediction of equilibrium scour depth in uniform non-cohesive sediments downstream of an apron using computational intelligence
Type Article
Keywords
Researchers Afshin Eghbalzadeh، Mohsen HAYATI، Abbas Rezaei، Mitra Javan

Abstract

Accurate prediction of equilibrium scour depth downstream of hydraulic structures has an important role in their appropriate design. In this paper, the applicability of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models in prediction of equilibrium scour depth is studied. Multi-layer perceptron neural network structure is employed for the training of the ANN models. Experimen-tal data of published literature are used for training and testing the models. The equi-librium scour depth is predicted as a function of five input variables; apron length, sluice gate opening, issuing velocity of jet, tailwater depth and median sediment size. The comparison of results shows that ANN model provides a better prediction of scour depth than ANFIS as well as the previous empirical relationship obtained by regression analysis. However, the results indicate that both ANN and ANFIS models can successfully predict equilibrium scour depth. Finally, based on the sensitivity analysis, it was found that d50 has the greatest effect on equilibrium scour depth.