Genetic Programming For Cellular Automata Urban Inundation Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Gibson:2014:HIC,
  author =       "Mike J. Gibson and Edward C. Keedwell and 
                 Dragan A. Savic",
  title =        "Genetic Programming For Cellular Automata Urban
                 Inundation Modelling",
  booktitle =    "11th International Conference on Hydroinformatics",
  year =         "2014",
  address =      "New York, USA",
  month =        aug # " 17-21",
  organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-692-28129-1",
  URL =          "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1650/1723.pdf",
  size =         "8 pages",
  abstract =     "Recent advances in Cellular Automata (CA) represent a
                 new, computationally efficient method of simulating
                 flooding in urban areas. A number of recent
                 publications in this field have shown that CAs can be
                 much more computationally efficient than methods that
                 use standard shallow water equations (Saint
                 Venant/Navier-Stokes equations). CAs operate using
                 local state-transition rules that determine the
                 progression of the flow from one cell in the grid to
                 another cell, and in a number of publications the
                 Manning's Formula is used as a simplified local state
                 transition rule. Through the distributed interactions
                 of the CA, computationally simplified urban flooding
                 can be simulated, although these methods are limited by
                 the approximation represented by the Manning's
                 formula.

                 An alternative approach is to learn the state
                 transition rule using an artificial intelligence
                 approach. One such approach is Genetic Programming (GP)
                 that has the potential to be used to optimise state
                 transition rules to maximise accuracy and minimise
                 computation time. In this paper we present some
                 preliminary findings on the use of genetic programming
                 (GP) for deriving these rules automatically. The
                 experimentation compares GP-derived rules with human
                 created solutions based on the Manning's formula and
                 findings indicate that the GP rules can improve on
                 these approaches.",
  notes =        "http://www.hic2014.org/xmlui/",
}

Genetic Programming entries for Mike J Gibson Ed Keedwell Dragan Savic

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