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

Bing Yan, Qing He Zhang, Wing Hong Onyx Wai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1655-1664
Number of pages10
JournalComputers and Geosciences
Issue number12
Publication statusPublished - 1 Dec 2008


  • Artificial neural network
  • Sand ripple prediction
  • Wave

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

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