Comparison of three data-driven techniques in modelling the evapotranspiration process

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "I. El-Baroudy and A. Elshorbagy and S. K. Carey and 
                 O. Giustolisi and D. Savic",
  title =        "Comparison of three data-driven techniques in
                 modelling the evapotranspiration process",
  journal =      "Journal of Hydroinformatics",
  year =         "2010",
  volume =       "12",
  number =       "4",
  pages =        "365--379",
  keywords =     "genetic algorithms, genetic programming, EPR, actual
                 evapotranspiration, data driven techniques, eddy
                 covariance, evolutionary polynomial regression, neural
  ISSN =         "1464-7141",
  URL =          "",
  DOI =          "doi:10.2166/hydro.2010.029",
  size =         "15 pages",
  abstract =     "Evapotranspiration is one of the main components of
                 the hydrological cycle as it accounts for more than
                 two-thirds of the precipitation losses at the global
                 scale. Reliable estimates of actual evapotranspiration
                 are crucial for effective watershed modelling and water
                 resource management, yet direct measurements of the
                 evapotranspiration losses are difficult and expensive.
                 This research explores the utility and effectiveness of
                 data-driven techniques in modelling actual
                 evapotranspiration measured by an eddy covariance
                 system. The authors compare the Evolutionary Polynomial
                 Regression (EPR) performance to Artificial Neural
                 Networks (ANNs) and Genetic Programming (GP).
                 Furthermore, this research investigates the effect of
                 previous states (time lags) of the meteorological input
                 variables on characterising actual evapotranspiration.
                 The models developed using the EPR, based on the two
                 case studies at the Mildred Lake mine, AB, Canada
                 provided comparable performance to the models of GP and
                 ANNs. Moreover, the EPR provided simpler models than
                 those developed by the other data-driven techniques,
                 particularly in one of the case studies. The inclusion
                 of the previous states of the input variables slightly
                 enhanced the performance of the developed model, which
                 in turn indicates the dynamic nature of the
                 evapotranspiration process.",

Genetic Programming entries for Ibrahim El-Baroudy Amin Elshorbagy Sean K Carey Orazio Giustolisi Dragan Savic