Alternative neural networks to estimate the scour below spillways

Artificial neural networks (ANN's) are associated with difficulties like lack of success in a given problem and unpredictable level of accuracy that could be achieved. In every new application it therefore becomes necessary to check their usefulness vis-a-vis the traditional methods and also to ascertain their performance by trying out different combinations of network architectures and learning schemes. The present study was oriented in this direction and it pertained to the problem of scour depth prediction for ski-jump type of spillways. It evaluates performance of different network configurations and learning mechanisms. The network architectures considered are the usual feed forward back propagation trained using the standard error back propagation as well as the cascade correlation training schemes, relatively less used configurations of radial basis function and adaptive neuro-fuzzy inference system. The network inputs were characteristic head and discharge intensity over the spillways while the output was the predicted scour depth at downstream of the bucket. The performance of different schemes was tested using error criteria of correlation coefficient, average error, average absolute deviation, and mean square error. It was found that the traditional formulae of Veronese, Wit, Martins and Incyth as well as a new regression formula derived by authors failed to predict the scour depths satisfactorily and that the neuro-fuzzy scheme emerged as the most satisfactory one for the problem under consideration. This study showed that the traditional equation-based methods of predicting design scour downstream of a ski-jump bucket could better be replaced by one of the soft computing schemes. (C) 2007 Elsevier Ltd. All rights reserved.