Journal of Hydrodynamics

, Volume 20, Issue 4, pp 477–484 | Cite as

Genetic Programming to Predict Ski-Jump Bucket Spill-Way Scour

  • H. AzamathullaEmail author
  • A. Ghani
  • N. A. Zakaria
  • S. H. Lai
  • C. K. Chang
  • C. S. Leow
  • Z. Abuhasan


Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.

Key words

Genetic Programming (GP) neural networks spillway scour ski-jump bucket 


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Copyright information

© China Ship Scientific Research Center 2008

Authors and Affiliations

  • H. Azamathulla
    • 1
    Email author
  • A. Ghani
    • 1
  • N. A. Zakaria
    • 1
  • S. H. Lai
    • 1
  • C. K. Chang
    • 1
  • C. S. Leow
    • 1
  • Z. Abuhasan
    • 1
  1. 1.River Engineering and Urban Drainage Research CentreUniversity Sains MalaysiaNibong Tebal, Pulau PinangMalaysia

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