首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Near-shore swell estimation from a global wind-wave model: Spectral process,linear, and artificial neural network models
Authors:Matthew Browne  Bruno Castelle  Darrell Strauss  Rodger Tomlinson  Michael Blumenstein  Chris Lane
Institution:1. Griffith Centre for Coastal Management, Gold Coast campus, Griffith University, PMB 50 Gold Coast Mail Centre QLD 9726, Australia;2. School of Information and Communications Technology, Griffith University, Australia;3. CoastalWatch Australia, Suite 3, 66 Appel Street, Surfers Paradise QLD 4217, Australia
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 NOAA WaveWatch3 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.
Keywords:Artificial neural networks  Near-shore wave transformation  Wave modeling  Wave estimation
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号