Prediction of wave ripple characteristics using genetic programming

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@Article{Goldstein:2013:CSR,
  author =       "Evan B. Goldstein and Giovanni Coco and 
                 A. Brad Murray",
  title =        "Prediction of wave ripple characteristics using
                 genetic programming",
  journal =      "Continental Shelf Research",
  year =         "2013",
  volume =       "71",
  month =        "1 " # dec,
  pages =        "1--15",
  ISSN =         "0278-4343",
  DOI =          "doi:10.1016/j.csr.2013.09.020",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0278434313003166",
  keywords =     "genetic algorithms, genetic programming, geology,
                 Ripples, Bedforms, Machine learning, Data driven
                 prediction, Symbolic regression",
  abstreact =    "We integrate published data sets of field and
                 laboratory experiments of wave ripples and use genetic
                 programming, a machine learning paradigm, in an attempt
                 to develop a universal equilibrium predictor for ripple
                 wavelength, height, and steepness. We train our genetic
                 programming algorithm with data selected using a
                 maximum dissimilarity selection routine. Thanks to this
                 selection algorithm; we use less data to train the
                 genetic programming software, allowing more data to be
                 used as testing (i.e., to compare our predictor vs.
                 common prediction schemes). Our resulting predictor is
                 smooth and physically meaningful, different from other
                 machine learning derived results. Furthermore our
                 predictor incorporates wave orbital ripples that were
                 previously excluded from empirical prediction schemes,
                 notably ripples in coarse sediment and long wavelength,
                 low height ripples (hummocks). This new predictor shows
                 ripple length to be a weakly nonlinear function of both
                 bottom orbital excursion and grain size. Ripple height
                 and steepness are both nonlinear functions of grain
                 size and predicted ripple length (i.e., bottom orbital
                 excursion and grain size). We test this new prediction
                 scheme against common (and recent) predictors and the
                 new predictors yield a lower normalised root mean
                 squared error using the testing data. This study
                 further demonstrates the applicability of machine
                 learning techniques to successfully develop well
                 performing predictors if data sets are large in size,
                 extensive in scope, multidimensional, and nonlinear.",
}

Genetic Programming entries for Evan B Goldstein Giovanni Coco A Brad Murray

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