Evapotranspiration Modeling Using Linear Genetic Programming Technique

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 ) 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 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.