Genetic Programming: A New Paradigm in Rainfall Runoff Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{me22,
  title =        "Genetic Programming: A New Paradigm in Rainfall Runoff
                 Modeling",
  author =       "Shie-Yui Liong and Tirtha Raj Gautam and 
                 Soon Thiam Khu and Vladan Babovic and Maarten Keijzer and 
                 Nitin Muttil",
  journal =      "Journal of American Water Resources Association",
  year =         "2002",
  volume =       "38",
  number =       "3",
  pages =        "705--718",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming,
                 Rainfall-runoff relationships, Runoff forecasting,
                 Rainfall-runoff models, Algorithms, Singapore, Upper
                 Bukit Timah catchment",
  DOI =          "doi:10.1111/j.1752-1688.2002.tb00991.x",
  size =         "14 pages",
  abstract =     "Genetic Programming (GP) is a domain-independent
                 evolutionary programming technique that evolves
                 computer programs to solve, or approximately solve,
                 problems. To verify GP's capability, a simple example
                 with known relation in the area of symbolic regression,
                 is considered first. GP is then used as a flow
                 forecasting tool. A catchment in Singapore with a
                 drainage area of about 6 km2 is considered in this
                 study. Six storms of different intensities and
                 durations are used to train GP and then verify the
                 trained GP. Analysis of the GP induced rainfall and
                 runoff relationship shows that the cause and effect
                 relationship between rainfall and runoff is consistent
                 with the hydrologic process. The result shows that the
                 runoff prediction accuracy of symbolic regression based
                 models, measured in terms of root mean square error and
                 correlation coefficient, is reasonably high. Thus, GP
                 induced rainfall runoff relationships can be a viable
                 alternative to traditional rainfall runoff models.",
  notes =        "AWRA Paper Number 00146",
}

Genetic Programming entries for Shie-Yui Liong Tirtha Raj Gautam Soon-Thiam Khu Vladan Babovic Maarten Keijzer Nitin Muttil

Citations