Modelling the dynamics of the evapotranspiration process using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Kamban Parasuraman and Amin Elshorbagy and 
                 Sean K. Carey",
  title =        "Modelling the dynamics of the evapotranspiration
                 process using genetic programming",
  journal =      "Hydrological Sciences Journal",
  year =         "2007",
  volume =       "52",
  number =       "3",
  pages =        "563--578",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0262-6667",
  URL =          "",
  DOI =          "doi:10.1623/hysj.52.3.563",
  size =         "16 pages",
  abstract =     "Evapotranspiration constitutes one of the major
                 components of the hydrological cycle and hence its
                 accurate estimation is of vital importance to assess
                 water availability and requirements. This study
                 explores the utility of genetic programming (GP) to
                 model the evapotranspiration process. An important
                 characteristic of GP is that both the model structure
                 and coefficients are simultaneously optimized. The
                 method is applied in modelling eddy-covariance
                 (EC)-measured latent heat (LE) as a function of net
                 radiation (NR), ground temperature (GT), air
                 temperature (AT), wind speed (WS) and relative humidity
                 (RH). Two case studies having different climatic and
                 topographic conditions are considered. The performance
                 of the GP model is compared with artificial neural
                 network (ANN) models and the traditional
                 Penman-Monteith (PM) method. Results from the study
                 indicate that both the datadriven models, GP and ANNs,
                 performed better than the PM method. However,
                 performance of the GP model is comparable with that of
                 the ANN model. The GP-evolved models are dominated by
                 NR and GT, indicating that these two inputs can
                 represent most of the variance in LE. The results show
                 that the GP-evolved equations are parsimonious and
                 understandable, and are well suited to modelling the
                 dynamics of the evapotranspiration process.",
  notes =        "Journal of the International Association of
                 Hydrological Sciences",

Genetic Programming entries for Kamban Parasuraman Amin Elshorbagy Sean K Carey