Improving Runoff Forecasting by Input Variable Selection in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{muttil:76,
  author =       "N. Muttil and S. Y. Liong",
  editor =       "Don Phelps and Gerald Sehlke",
  title =        "Improving Runoff Forecasting by Input Variable
                 Selection in Genetic Programming",
  publisher =    "ASCE",
  year =         "2001",
  booktitle =    "World Water Congress 2001",
  volume =       "111",
  pages =        "76--76",
  address =      "Orlando, Florida, USA",
  month =        "20-24 " # may,
  keywords =     "genetic algorithms, genetic programming, Forecasting,
                 Runoff, Rainfall-runoff, relationships, Watersheds",
  isbn13 =       "978-0-7844-0569-7",
  DOI =          "doi:10.1061/40569(2001)76",
  size =         "7 pages",
  abstract =     "Determining the relationship between rainfall and
                 runoff for a watershed is one of the most important
                 problems faced by hydrologists and engineers. This
                 relationship is known to be highly complex with strong
                 correlation between the model parameters. In any model
                 development process, the selection of appropriate model
                 inputs is extremely important. Many authors in the past
                 have attempted to address the issue of selecting the
                 most relevant parameters of a given data set based on
                 sensitivity analysis, yet the effect of interaction of
                 variables is not clearly expatiated. In this study, we
                 use the Group Method of Data Handling (GMDH) technique
                 for selecting the significant variables to be used as
                 input to Genetic Programming, which leads to improved
                 runoff forecasting. The main advantage of GMDH
                 technique is that it considers the interaction amongst
                 the variables while selecting the ones that are
                 significant.",
  notes =        "number = 40569",
}

Genetic Programming entries for Nitin Muttil Shie-Yui Liong

Citations