Modeling reference evapotranspiration using three different heuristic regression approaches

• The accuracies of LSSVM, MARS and M5Tree are compared in modeling reference evapotranspiration (ET0).
• LSSVM models perform better than the MARS and M5Tree models.
• The models are also compared in two different cross station applications.
• MARS perform better than the others when the local input data are not available.
• In the absence of local input and output data, however, M5Tree outperforms the others.

Modeling reference evapotranspiration (ET0) is important in reservoir management, planning regional water resources and evaluation of drinking-water supplies. The study investigates the ability of three different heuristic regression approaches, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) in modeling ET0. The first part of the study focused on testing the accuracy of the LSSVR, MARS and M5Tree models in estimating the ET0 data of Antalya and Isparta stations located in Mediterranean Region of Turkey. Cross-validation method was utilized in the applications. The LSSVR models were observed to be better than the MARS and M5Tree models in estimating ET0 of Antalya and Isparta stations with local input and output data. The accuracy of the applied methods was investigated in estimation of ET0 using air temperature, solar radiation, relative humidity and wind speed inputs from nearby station in the second part of the study (cross-station application without local input data). The results showed that the MARS models provided better accuracy than the LSSVR and M5Tree models with respect to SI, mean absolute error (MAE) and determination coefficient (R2). In the third part of the study, the accuracy of the applied models was investigated in ET0 estimation using input and output data from nearby station. The results showed that the M5Tree models outperformed the other models with respect to SI, MAE and R2. The overall results showed that the LSSVR could be successfully used in estimating ET0 by using local input and output data. In case of without local inputs, however, the MARS model performed better than the LSSVR and M5Tree models while the M5Tree was observed to be the best alternative for estimating ET0 in the absence of local input and output data.