Daily pan evaporation modeling using linear genetic programming technique

This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modeling. The daily climatic data, air temperature, solar radiation, wind speed, pressure and humidity of three automated weather stations, Fresno, Los Angeles and San Diego in California, are used as inputs to the LGP to estimate pan evaporation. The LGP estimates are compared with those of the Gene-expression programming (GEP), which is another branch of GP, multilayer perceptrons (MLP), radial basis neural networks (RBNN), generalized regression neural networks (GRNN) and Stephens–Stewart (SS) models. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the LGP technique could be employed successfully in modeling evaporation process from the available climatic data.