Evapotranspiration Modeling Using Linear Genetic Programming Technique
Publication: Journal of Irrigation and Drainage Engineering
Volume 136, Issue 10
Abstract
The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration modeling. 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 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 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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Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 136 • Issue 10 • October 2010
Pages: 715 - 723
Copyright
© 2010 ASCE.
History
Received: Nov 5, 2009
Accepted: Feb 24, 2010
Published online: Mar 16, 2010
Published in print: Oct 2010
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