Prediction of Scour Downstream of Grade-Control Structures Using Neural Networks

Abstract

A new approach for predicting local scour downstream of grade-control structures based on neural networks is presented. An explicit neural networks formulation (ENNF) is developed using a transfer function (sigmoid) and optimal weights obtained from a training process. A genetic algorithm was used to optimize the neural network architecture and the optimal weights for input and output parameters were obtained using the Levenberg–Marquardt back-propagation algorithm. Experimental data available in the literature, including large-scale results were used for training and validation of the proposed model. The predictive performance of the ENNF was found superior to other regression-based equations and the robustness of ENNF was evaluated using field data.