Scour depth modelling by a multi-objective evolutionary paradigm

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Laucelli:2011:EMS,
  author =       "Daniele Laucelli and Orazio Giustolisi",
  title =        "Scour depth modelling by a multi-objective
                 evolutionary paradigm",
  journal =      "Environmental Modelling \& Software",
  year =         "2011",
  volume =       "26",
  number =       "4",
  pages =        "498--509",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 polynomial regression, Evolutionary computation,
                 Regression analysis, Multi-objective optimisation,
                 Local scouring",
  ISSN =         "1364-8152",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364815210002859",
  DOI =          "doi:10.1016/j.envsoft.2010.10.013",
  size =         "12 pages",
  abstract =     "Local scour modelling is an important issue in
                 environmental engineering in order to prevent
                 degradation of river bed and safeguard the stability of
                 grade-control structures. Many empirical formulations
                 can be retrieved from literature to predict the
                 equilibrium scour depth, which is usually assumed as
                 representative of the phenomenon. These empirical
                 equations have been mostly constructed in some ways by
                 leveraging regression procedures on experimental data,
                 usually laboratory observations (thus from small/medium
                 scale experiments). Laboratory data are more accurate
                 measurements but generally not completely
                 representative of the actual conditions in real-world
                 cases, that are often much more complex than those
                 schematised by the laboratory equipment. This is the
                 main reason why some of the literature expressions were
                 not adequate when used for practical applications in
                 large-scale examples. This work deals with the
                 application of an evolutionary modelling paradigm,
                 named Evolutionary Polynomial Regression (EPR), to such
                 problem. Such a technique was originally presented as a
                 classical approach, used to achieve a single model for
                 each analysis, and has been recently updated by
                 implementing a multi-modelling approach (i.e., to
                 obtain a set of optimal candidate solutions/models)
                 where a multi-objective genetic algorithm is used to
                 get optimal models in terms of parsimony of
                 mathematical expressions vs. fitting to data. A wide
                 database of field and laboratory observations is used
                 for predicting the equilibrium scour depth as a
                 function of a set of variables characterising the flow,
                 the sediments and the dimension of the grade-control
                 structure. Results are discussed considering two
                 regressive models available in literature that have
                 been trained on the same data used for EPR. The
                 proposed modelling paradigm proved to be a useful tool
                 for data analysis and, in the particular case study,
                 able to find feasible explicit models featured by an
                 appreciable generalisation performance.",
}

Genetic Programming entries for Daniele B Laucelli Orazio Giustolisi

Citations