Genetic Programming‐Based Empirical Model for Daily Reference Evapotranspiration Estimation

TLDR

The study introduces genetic programming as a novel method to estimate reference evapotranspiration using daily atmospheric data from the CIMIS database. The GP model uses daily solar radiation, mean temperature, relative humidity, and wind speed, and its performance is evaluated against seven standard reference evapotranspiration models using statistical metrics such as mean square error and determination coefficient. The GP-derived equation yields satisfactory results, performing comparably to conventional models and offering a viable alternative for reference evapotranspiration estimation.

Abstract

Abstract Genetic programming (GP) is presented as a new tool for the estimation of reference evapotranspiration by using daily atmospheric variables obtained from the California Irrigation Management Information System (CIMIS) database. The variables employed in the model are daily solar radiation, daily mean temperature, average daily relative humidity and wind speed. The results obtained are compared to seven conventional reference evapotranspiration models including: (1) the Penman‐Monteith equation modified by CIMIS, (2) the Penman‐Monteith equation modified by the Food and Agricultural Organization (FAO 56), (3) the Hargreaves‐Samani equation, (4) the solar radiation‐based ET 0 equation, (5) the Jensen‐Haise equation, (6) the Jones‐Ritchie equation, and (7) the Turc method. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient are used to measure the performance of the model developed by employing GP. Statistics and scatter plots indicate that the new equation produces quite satisfactorily results and can be used as an alternative to the conventional models.