Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12015
Title: Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting
Authors: Zhang, ZX
Li, CW 
Qi, YQ
Li, YS 
Keywords: Artificial neural networks
Data assimilation
Wave forecast
Wind-wave model
Issue Date: 2006
Publisher: International Water Association Publishing
Source: Journal of hydroinformatics, 2006, v. 8, no. 1, p. 65-76 How to cite?
Journal: Journal of hydroinformatics 
Abstract: Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period by definition To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.
URI: http://hdl.handle.net/10397/12015
ISSN: 1464-7141
DOI: 10.2166/jh.2006.005
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

18
Last Week
0
Last month
Citations as of Apr 10, 2016

WEB OF SCIENCETM
Citations

17
Last Week
0
Last month
0
Citations as of Aug 8, 2017

Page view(s)

30
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.