Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration

• Two different fuzzy genetic (FG) models are compared in modeling evapotranspiration.
• Sugeno FG (SGF) model is found to be better than the Mamdani FG (MFG) model.
• MFG models have much more computational times for training than the SFG models.
• The optimal FG models are also compared with commonly used empirical equations.
• SFG models perform better than the corresponding empirical equations.

SummaryThis study investigates the applicability of Mamdani and Sugeno fuzzy genetic approaches in modeling reference evapotranspiration (ET0). The daily air temperature, solar radiation, relative humidity and wind speed data from Adana and Antalya stations, Turkey, were used as inputs to the fuzzy genetic models for estimating ET0 obtained using the standard FAO-56 Penman–Monteith equation. Comparison of two different fuzzy genetic methods indicated that the Sugeno fuzzy genetic (SFG) method was faster and had a better accuracy than the Mamdani fuzzy genetic (MFG) method in modeling daily ET0. SGF and MFG models were also compared with the recently proposed Valiantzas’s equations and following empirical models: Hargreaves–Samani and Priestley–Taylor methods. Root mean-squared errors (RMSE), mean-absolute errors (MAE) and determination coefficient (R2) were used for the evaluation of the models’ performances. Results revealed that the SFG and MFG models were performed better than the empirical models in modeling daily ET0 process. Comparison of the two different fuzzy genetic approaches indicated that the SFG had a better accuracy than the MFG. For the Adana and Antalya stations, the SFG1 model with RMSE = 0.219 and 111 mm/day, MAE = 0.097 and 0.080 mm/day and R2 = 0.983 and 0.998 in validation period was found to be superior in modeling daily ET0 than the other models, respectively.