A comparison between artificial neural network algorithms and empirical equations applied to submerged weir scour evolution prediction

Estimating the time evolution of a local scour hole downstream of submerged weirs can help determine the maximum scour depth and length and is essential to designing submerged weir foundations. In the current study, artificial neural networks with a backpropagation learning algorithm were used to estimate the temporal variation of scour profiles downstream of submerged weirs under clear water conditions. Physical factors, such as the flow condition, weir size, and sediment characteristics, are general scour considerations. Two sets of data combinations, namely original and non-dimensional data, were utilized in developing the backpropagation network (BPN) model. Using the data combinations and a trial-and-error method, the appropriate number of hidden neurons in the model architecture was determined. The results indicated that using non-dimensional variables as inputs in estimating the time evolution of scour holes downstream of a submerged weir is more suitable for the BPN model. Comparisons of the prediction results of the BPN model and an empirical regression formula revealed that the BPN model was more accurate and more direct in predicting the temporal variation of the local scour hole profile. In addition, sensitivity analysis showed that the dimensionless time exerted the most significant impact on the time evolution of the non-dimensional scour hole profile.