Wave overtopping prediction using global-local artificial neural networks
The construction of sea walls requires accurate predictions of hazard levels. These are commonly expressed in terms of wave overtopping rates. A large amount of data related to wave overtopping has recently become available. Use of this data has allowed the development of artificial neural networks, which have the aim of accurately predicting wave overtopping rates. The available data cover a wide range of structural configurations and sea conditions. The neural networks created therefore constitute a unified, generic approach to the problem of wave overtopping prediction. Neural network models are developed using two standard approaches: multi-layer perceptron (MLP) networks and radial basis function (RBF) networks. A novel hybrid approach is then developed. The hybrid networks combine the properties of MLP and RBF networks. This is achieved firstly through a hybrid architecture, which contains artificial neurons of the types used in both MLP and RBF networks. Secondly, the hybrid networks are trained using a hybrid algorithm which combines the gradient descent method usually associated with MLP networks with a more deterministic forward-selection-of-centres method commonly used by RBF networks. The hybrid networks are shown to have better generalisation properties with the overtopping dataset than have basic MLP or RBF networks. They have been named 'global-local artificial neural networks' (GL-ANNs) to reflect their ability to model both global and local variation in an input-output mapping. The properties of GL-ANNs are explored further through the use of a number of benchmark datasets. It is shown that GL-ANNs often contain fewer neurons than the corresponding RBF networks and have less need of regularisation when setting interneuronal weights. Some criteria for determining whether the GL-ANN approach is likely to be beneficial for a particular dataset are also developed. Such datasets are seen to be those that have inter-parameter relationships that operate on both a local and global level. The overtopping dataset used within this study is seen to be typical of such datasets.