Evaluation of different data management scenarios for estimating daily reference evapotranspiration

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ETo) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ETo estimation equations. Two data management scenarios were evaluated for estimating ETo: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves-Samani, Makkink and Turc ETo equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.