Estimation of scour below spillways using neural networks

Information on the depth of the scour hole formed downstream of a ski-jump bucket type of energy dissipator is necessary in determining the safety of dams and adjoining structures. Traditional formulae available to predict scour depth Suffer from many limitations including one arising out of the technique of data analysis commonly employed. namely, statistical regression. This paper presents an alternative to regression in the form of neural networks. A feed for-ward network is developed to predict the depth of the Scour hole below ski-jump spillways from the specified values of head and discharge intensity. Field measurements collected from a variety of published literature are used to train the network. The validation of the developed network using observations that were not involved in the training indicated the usefulness of the neural network approach for the prediction problem under consideration. Network-yielded values are found to be more accurate than those given by the traditional equations. A matrix of weights and bias values for general use in any location is specified.