Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models

File Size Format
49454_1.pdf 2092Kb Adobe PDF View
Title Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models
Author Browne, Matthew; Castelle, Bruno Olivier; Strauss, Darrell; Tomlinson, Rodger Benson; Blumenstein, Michael Myer; Lane, Chris
Journal Name Coastal Engineering
Editor H F Burcharth
Year Published 2007
Place of publication Netherlands
Publisher Elsevier BV
Abstract Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAAWaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.
Peer Reviewed Yes
Published Yes
Publisher URI
Alternative URI
Copyright Statement Copyright 2007 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 54
Issue Number 5
Page from 445
Page to 460
ISSN 0378-3839
Date Accessioned 2008-02-29
Language en_AU
Research Centre Griffith Centre for Coastal Management; Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Environmental Engineering Modelling
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

Show simple item record

Griffith University copyright notice