Rainfall-Runoff Modelling Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "A. W. Jayawardena and N. Muttil and 
                 T. M. K. G. Fernando",
  title =        "Rainfall-Runoff Modelling Using Genetic Programming",
  booktitle =    "International Congress on Modelling and Simulation,
                 MODSIM 2005",
  year =         "2005",
  editor =       "Andre Zerger and Robert M. Argent",
  month =        dec,
  organisation = "Modelling and Simulation Society of Australia and New
  keywords =     "genetic algorithms, genetic programming,
                 rainfall-runoff modelling, data-driven models,
                 evolutionary algorithms",
  isbn13 =       "0-9758400-2-9",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://www.mssanz.org.au/modsim05/papers/jayawardena.pdf",
  abstract =     "The problem of accurately determining river flows from
                 rainfall, evaporation and other factors, occupies an
                 important place in hydrology. The rainfall-runoff
                 process is believed to be highly non-linear, time
                 varying, spatially distributed and not easily described
                 by simple models. Practitioners in water resources have
                 embraced data-driven modelling approaches
                 enthusiastically, as they are perceived to overcome
                 some of the difficulties associated with physics-based
                 approaches. Such approaches have proved to be an
                 effective and efficient way to model the rainfall
                 runoff process in situations where enough data on
                 physical characteristics of catchment is not available
                 or when it is essential to predict the flow in the
                 shortest possible time to enable sufficient time for
                 notification and evacuation procedures. In the recent
                 past, an evolutionary based data driven modelling
                 approach, genetic programming (GP) has been used for
                 rainfall-runoff modelling. In this study, GP has been
                 applied for predicting the runoff from three catchments
                 -- a small steeply sloped catchment in Hong Kong (Hok
                 Tau catchment) and two relatively bigger catchments",
  notes =        "University of Hong Kong",

Genetic Programming entries for A W Jayawardena Nitin Muttil T M K G Fernando