Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17479
Title: Prediction of sand ripple geometry under waves using an artificial neural network
Authors: Yan, B
Zhang, QH
Wai, OWH 
Keywords: Artificial neural network
Sand ripple prediction
Wave
Issue Date: 2008
Publisher: Pergamon Press
Source: Computers & geosciences, 2008, v. 34, no. 12, p. 1655-1664 How to cite?
Journal: Computers & geosciences 
Abstract: The length and height of a sand ripple in the seabed are the two basic parameters used to estimate the bottom shear stress and predict the transport of sand by wave action. These values are currently obtained with the help of many empirical equations. A different estimation method, in the form of an artificial neural network, is presented in this paper. The network is trained by measurements collected in the laboratory and in-situ under different forcing conditions. Validation of the present neural network results with different measurements shows that the new method can predict the ripple length and height much more accurately than the conventional empirical equations.
URI: http://hdl.handle.net/10397/17479
ISSN: 0098-3004
EISSN: 1873-7803
DOI: 10.1016/j.cageo.2008.03.002
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