Evapotranspiration Modeling Using Linear Genetic Programming Technique
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- @Article{Kisi:2010:JIDE,
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author = "Ozgur Kisi and Aytac Guven",
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title = "Evapotranspiration Modeling Using Linear Genetic
Programming Technique",
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journal = "Journal of Irrigation and Drainage Engineering",
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year = "2010",
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volume = "136",
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number = "10",
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pages = "715--723",
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month = oct,
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keywords = "genetic algorithms, genetic programming,
Evapotranspiration, Computer programming, ANN, Neural
networks, Evapotranspiration modelling, Linear genetic
programming, SVM, Support vector regression",
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publisher = "American Society of Civil Engineers",
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ISSN = "0733-9437",
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DOI = "doi:10.1061/(ASCE)IR.1943-4774.0000244",
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size = "9 pages",
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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
techniques.",
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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
Citations