Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration

Abstract

Evapotranspiration is one of the most important components of hydrologic cycle for optimal management of water resources, especially in arid and semi-arid regions such as Iran. The main objective of the present research is to investigate the performance of empirical equations and soft computing approaches including gene expression programming (GEP), two types of support vector machine (SVM) namely SVM-polynomial (SVM-Poly) and SVM-radial basis function (SVM-RBF), as well as multivariate adaptive regression splines (MARS) in estimating monthly mean reference evapotranspiration (ETo) in Iran. In the present study, 16 empirical equations from temperature-based, mass transfer-based, radiation-based and meteorological parameters-based categories were utilized. Monthly mean data of 44 stations in the study region was used to estimate the monthly mean ETo. 50% of the data (22 stations) for the calibration/training step and the remaining 50% of the data (22 stations) were applied for the validation/testing stage of the empirical equations/soft computing methods. At first, 16 empirical equations were locally calibrated on the basis of FAO-56 Penman-Monteith method (as standard method). The results revealed that the calibration process improved the performance of equations in comparison with the original form of them. Then, the capability of the GEP, SVM-Poly, SVM-RBF and MARS models was evaluated for estimation of the monthly mean ETo. The selection of models’ inputs was conducted based on the used parameters in the empirical equations. It was found that the MARS and SVM-RBF methods generally performed better than GEP and SVM-Poly. At the end part of study, the accuracy of empirical equations and soft computing methods was compared. Overall, the performance of the MARS and SVM-RBF was better than used empirical equations.