Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree

In the current study, an ability of a novel regression-based method is evaluated in modeling daily reference evapotranspiration (ET0), which is an important issue in water resources management and planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM), and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (Nash and Sutcliffe efficiency, NES > 0.997) followed by the MLPNN (NES > 0.990), and M5Tree (NES > 0.945) in modeling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modeling scenario revealed that taking into account of all variables would considerably increase models' accuracies for the three stations.

Automated summary

In this study, a new regression-based method called radial basis M5 model tree (RM5Tree) was evaluated for modeling daily reference evapotranspiration (ET0). Results showed that RM5Tree performed the best followed by MLPNN and M5Tree in modeling daily ET0. Solar radiation was identified as the most influential parameter on ET0.