Genetic Programming and Its Application in Real-Time Runoff Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{khu:2001:JASRA,

• 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 significantly.",
• notes = "AWRA Paper Number 99178",

}

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

Citations