Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions

Puscasu, Ruslan M. (2014). Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions. In: David Abramson, Michael Lees, Valeria Krzhizhanovskaya, Jack Dongarra and Peter M. A. Sloot, ICCS 2014. 14th International Conference on Computational Science. 14th Annual International Conference on Computational Science, ICCS 2014, Cairns, QLD, Australia, (1156-1170). 10-12 June 2014. doi:10.1016/j.procs.2014.05.104


Author Puscasu, Ruslan M.
Title of paper Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions
Conference name 14th Annual International Conference on Computational Science, ICCS 2014
Conference location Cairns, QLD, Australia
Conference dates 10-12 June 2014
Proceedings title ICCS 2014. 14th International Conference on Computational Science   Check publisher's open access policy
Journal name Procedia Computer Science   Check publisher's open access policy
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2014
Sub-type Fully published paper
DOI 10.1016/j.procs.2014.05.104
Open Access Status DOI
ISSN 1877-0509
Editor David Abramson
Michael Lees
Valeria Krzhizhanovskaya
Jack Dongarra
Peter M. A. Sloot
Volume 29
Start page 1156
End page 1170
Total pages 15
Language eng
Abstract/Summary In this paper, an implementation study was undertaken to employ Artificial Neural Networks (ANN) in third-generation ocean wave models for direct mapping of wind-wave spectra into exact nonlinear interactions. While the investigation expands on previously reported feasibility studies of Neural Network Interaction Approximations (NNIA), it focuses on a new robust neural network that is implemented in Wavewatch III (WW3) model. Several idealistic and real test scenarios were carried out. The obtained results confirm the feasibility of NNIA in terms of speeding-up model calculations and is fully capable of providing operationally acceptable model integrations. The ANN is able to emulate the exact nonlinear interaction for single- And multimodal wave spectra with a much higher accuracy then Discrete Interaction Approximation (DIA). NNIA performs at least twice as fast as DIA and at least two hundred times faster than exact method (Web-Resio-Tracy, WRT) for a well trained dataset. The accuracy of NNIA is network configuration dependent. For most optimal network configurations, the NNIA results and scatter statistics show good agreement with exact results by means of growth curves and integral parameters. Practical possibilities for further improvements in achieving fast and highly accurate emulations using ANN for emulating time consuming exact nonlinear interactions are also suggested and discussed.
Keyword Artificial neural networks
Data assimilation
Exact nonlinear interaction
Nonlinear wave-wave interaction
Numerical climate and weather prediction
Ocean wave forecast
Oceanic models
Wind-wave models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: W.H. Bryan Mining Geology Research Centre
Official 2015 Collection
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 08 Jul 2014, 12:46:16 EST by System User on behalf of WH Bryan Mining and Geology Centre