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Centre for Mathematical Modelling and Flow
Analysis
Wave
Overtopping Prediction Using Global-Local Artificial Neural Networks
by David Wedge: March 2006
Abstract
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.
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