Prediction of sand ripple geometry under waves using an artificial neural network

Authors:
Bing Yan;Qing-He Zhang;Onyx W. H. Wai
Affiliations:
School of Civil Engineering, Tianjin University and Key Laboratory of Harbor and Ocean Engineering, Ministry of Education, Tianjin 300072, PR China;School of Civil Engineering, Tianjin University and Key Laboratory of Harbor and Ocean Engineering, Ministry of Education, Tianjin 300072, PR China;Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hong Kong, PR China
Venue:
Computers & Geosciences
Year:
2008

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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.