Genetic Programming: A New Paradigm in Rainfall Runoff Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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