Genetic programming to predict ski-jump bucket spill-way scour

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "H. Md Azamathulla and A. {Ab. Ghani} and 
                 N. A. Zakaria and S. H. Lai and C. K. Chang and C. S. Leow and 
                 Z. Abuhasan",
  title =        "Genetic programming to predict ski-jump bucket
                 spill-way scour",
  journal =      "Journal of Hydrodynamics, Ser. B",
  volume =       "20",
  number =       "4",
  pages =        "477--484",
  year =         "2008",
  ISSN =         "1001-6058",
  DOI =          "doi:10.1016/S1001-6058(08)60083-9",
  URL =          "",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, spillway scour, ski-jump bucket",
  abstract =     "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

Genetic Programming entries for Hazi Mohammad Azamathulla Aminuddin Ab Ghani Nor Azazi Zakaria Sai Hin Lai Chun Kiat Chang Cheng Siang Leow Zorkeflee Abu Hasan