Modeling of local scour depth downstream hydraulic structures in trapezoidal channel using GEP and ANNs

Local scour downstream stilling basins is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour depth. Lack estimation of local scour can endanger to stability of hydraulic structure and can cause risk of failure. This paper presents Gene expression program (GEP) and artificial neural network (ANNs), to simulate local scour depth downstream hydraulic structures. The experimental data is collected from the literature for the scour depth downstream the stilling basin through a trapezoidal channel. Using GEP approach gives satisfactory results compared with artificial neural network (ANN) and multiple linear regression (MLR) modeling in predicting the scour depth downstream of hydraulic structures.