Genetic Programming and Its Application in Real-Time Runoff Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Soon Thiam Khu and Shie-Yui Liong and 
                 Vladan Babovic and Henrik Madsen and Nitin Muttil",
  title =        "Genetic Programming and Its Application in Real-Time
                 Runoff Forecasting",
  journal =      "Journal of the American Water Resources Association",
  year =         "2001",
  volume =       "37",
  number =       "2",
  pages =        "439--451",
  month =        apr,
  publisher =    "American Water Resources Association",
  keywords =     "genetic algorithms, genetic programming, Runoff
                 forecasting, Rainfall-runoff models, Storms, NAM
                 rainfall-runoff simulation model, MIKE II hydrodynamic
                 model, NAMKAL, France, Orgeval River, Ru des Avenelles,
                 Ru de Bourgogne, Ru de Rognon",
  DOI =          "doi:10.1111/j.1752-1688.2001.tb00980.x",
  size =         "13 pages",
  abstract =     "Genetic programming (GP), a relatively new
                 evolutionary technique, is demonstrated in this study
                 to evolve codes for the solution of problems. First, a
                 simple example in the area of symbolic regression is
                 considered. GP is then applied to real-time runoff
                 forecasting for the Orgeval catchment in France. In
                 this study, GP functions as an error updating scheme to
                 complement a rainfall-runoff model, MIKE11/NAM. Hourly
                 runoff forecasts of different updating intervals are
                 performed for forecast horizons of up to nine hours.
                 The results show that the proposed updating scheme is
                 able to predict the runoff quite accurately for all
                 updating intervals considered and particularly for
                 updating intervals not exceeding the time of
                 concentration of the catchment. The results are also
                 compared with those of an earlier study, by the World
                 Meteorological Organization, in which autoregression
                 and Kalman filter were used as the updating methods.
                 Comparisons show that GP is a better updating tool for
                 real-time flow forecasting. Another important finding
                 from this study is that nondimensionalizing the
                 variables enhances the symbolic regression process
  notes =        "AWRA Paper Number 99178",

Genetic Programming entries for Soon-Thiam Khu Shie-Yui Liong Vladan Babovic Henrik Madsen Nitin Muttil