Short-term Streamflow Forecasting with Global Climate Change Implications - A Comparative Study between Genetic Programming and Neural Network Models

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

@Article{Makkeasorn:2008:JH,
  author =       "A. Makkeasoyrn and Ni-Bin Chang and Xiaobing Zhou",
  title =        "Short-term Streamflow Forecasting with Global Climate
                 Change Implications - A Comparative Study between
                 Genetic Programming and Neural Network Models",
  journal =      "Journal of Hydrology",
  volume =       "352",
  number =       "3-4",
  pages =        "336--354",
  year =         "2008",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2008.01.023",
  URL =          "http://www.sciencedirect.com/science/article/B6V6C-4RRFNK3-2/2/26f7ea5d045a8c5457038f4c4d0b73e5",
  keywords =     "genetic algorithms, genetic programming, ANN,
                 Streamflow forecasting, Neural network, Global climate
                 change, NEXRAD, Sea surface temperature",
  abstract =     "Summary Sustainable water resources management is a
                 critically important priority across the globe. While
                 water scarcity limits the uses of water in many ways,
                 floods may also result in property damages and the loss
                 of life. To more efficiently use the limited amount of
                 water under the changing world or to resourcefully
                 provide adequate time for flood warning, the issues
                 have led us to seek advanced techniques for improving
                 stream flow forecasting on a short-term basis. This
                 study emphasizes the inclusion of sea surface
                 temperature (SST) in addition to the spatio-temporal
                 rainfall distribution via the Next Generation Radar
                 (NEXRAD), meteorological data via local weather
                 stations, and historical stream data via USGS gage
                 stations to collectively forecast discharges in a
                 semi-arid watershed in south Texas. Two types of
                 artificial intelligence models, including genetic
                 programming (GP) and neural network (NN) models, were
                 employed comparatively. Four numerical evaluators were
                 used to evaluate the validity of a suite of forecasting
                 models. Research findings indicate that GP-derived
                 streamflow forecasting models were generally favored in
                 the assessment in which both SST and meteorological
                 data significantly improve the accuracy of forecasting.
                 Among several scenarios, NEXRAD rainfall data were
                 proven its most effectiveness for a 3-day forecast, and
                 SST Gulf-to-Atlantic index shows larger impacts than
                 the SST Gulf-to-Pacific index on the stream-flow
                 forecasts. The most forward looking GP-derived models
                 can even perform a 30-day streamflow forecast ahead of
                 time with an r-square of 0.84 and RMS error 5.4 in our
                 study.",
}

Genetic Programming entries for A Makkeasoyrn Ni-Bin Chang Xiaobing Zhou

Citations