Modelling rainfall-runoff using genetic programming

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

@Article{Whigham:2001:MCM,
  author =       "P. A. Whigham and P. F. Crapper",
  title =        "Modelling rainfall-runoff using genetic programming",
  journal =      "Mathematical and Computer Modelling",
  volume =       "33",
  pages =        "707--721",
  year =         "2001",
  number =       "6-7",
  month =        mar # "-" # apr,
  keywords =     "genetic algorithms, genetic programming, Rainfall
                 runoff",
  ISSN =         "0895-7177",
  DOI =          "doi:10.1016/S0895-7177(00)00274-0",
  URL =          "http://www.sciencedirect.com/science/article/B6V0V-42R1KRY-G/1/226d0ab4c2f13472b01ada47c8473fbf",
  abstract =     "Genetic programming is an inductive form of machine
                 learning that evolves a computer program to perform a
                 task defined by a set of presented (training) examples
                 and has been successfully applied to problems that are
                 complex, nonlinear and where the size, shape, and
                 overall form of the solution are not explicitly known
                 in advance. We describe the application of a
                 grammatically-based genetic programming system to
                 discover rainfall-runoff relationships for two vastly
                 different catchments. A context-free grammar is used to
                 define the search space for the mathematical language
                 used to express the evolving programs. A daily time
                 series of rainfall-runoff is used to train the evolving
                 population. A deterministic lumped parameter model,
                 based on the unit hydrograph, is compared with the
                 results of the evolved models on an independent data
                 set. The favourable results of the genetic programming
                 approach show that machine learning techniques are
                 potentially a useful tool for developing hydrological
                 models, especially when surface water movement and
                 water losses are poorly understood.",
}

Genetic Programming entries for Peter Alexander Whigham Peter F Crapper

Citations