Evapotranspiration Modeling Using Linear Genetic Programming Technique

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ozgur Kisi and Aytac Guven",
  title =        "Evapotranspiration Modeling Using Linear Genetic
                 Programming Technique",
  journal =      "Journal of Irrigation and Drainage Engineering",
  year =         "2010",
  volume =       "136",
  number =       "10",
  pages =        "715--723",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming,
                 Evapotranspiration, Computer programming, ANN, Neural
                 networks, Evapotranspiration modelling, Linear genetic
                 programming, SVM, Support vector regression",
  publisher =    "American Society of Civil Engineers",
  ISSN =         "0733-9437",
  DOI =          "doi:10.1061/(ASCE)IR.1943-4774.0000244",
  size =         "9 pages",
  abstract =     "The study investigates the accuracy of linear genetic
                 programming (LGP), which is an extension to genetic
                 programming (GP) technique, in daily reference
                 evapotranspiration (ET0) modelling. The daily climatic
                 data, solar radiation, air temperature, relative
                 humidity, and wind speed from three stations, Windsor,
                 Oakville, and Santa Rosa, in central California, are
                 used as inputs to the LGP to estimate ET0 obtained
                 using the FAO-56 Penman-Monteith equation. The accuracy
                 of the LGP is compared with those of the support vector
                 regression (SVR), artificial neural network (ANN), and
                 those of the following empirical models: the California
                 irrigation management system Penman, Hargreaves,
                 Ritchie, and Turc methods. The root-mean-square errors,
                 mean-absolute errors, and determination coefficient
                 (R2) statistics are used for evaluating the accuracy of
                 the models. Based on the comparison results, the LGP is
                 found to be superior alternative to the SVR and ANN
  notes =        "1Dept. of Civil Engineering, Hydraulics Div., Erciyes
                 Univ., 38039 Kayseri, Turkey (corresponding author).
                 2Dept. of Civil Engineering, Hydraulics Div., Gaziantep
                 Univ., 27310 Gaziantep, Turkey.",

Genetic Programming entries for Ozgur Kisi Aytac Guven