Prediction of time variation of scour depth around spur dikes using neural networks

The maximum depth of scouring around spur dikes plays an important role in the hydraulic design process. There have been many studies on the maximum depth of scouring, but there is little information available on the time variation of scour depth. In this paper, the time variation of scouring around the first spur dike in a series was investigated experimentally. Experiments were carried out in four different bed materials under different flow intensities (U/U_cr). To achieve a time development of scouring around the first spur dike, more than 750 sets of experimental data were collected. The results showed that 70–90% of the equilibrium scour depths were occurring during the initial 20% of the overall time of scouring. Based on the data analysis, a regression model and artificial neural networks (ANNs) were developed. The models were compared with other empirical equations in the literature. However, the results showed that the developed regression model is quite accurate and more practical, but the ANN models by feed forward back propagation and radial basis function provide a better prediction of observation. Finally, by sensitivity analysis, the most and the least effective parameters, which affected time variation of scouring, were determined.