Evapotranspiration (ET) estimation is a primary problem for irrigation engineers and hydraulic designers since it is an important part of hydrologic circle. Even it is non-negligible in hydraulic design calculations, it is not clear enough to estimate or calculate ET. There are some meteorological parameters which effect ET directly or indirectly such as Relative Humidity (RH), Solar Radiation (SR), Air Temperature (AT) and Wind Speed (U). In this study authors used Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of ET and results are compared with Penman FAO 56 empirical formula. 1158 daily AT, SR, RH and U statistics are used to train ANFIS model and 385 daily statistics are used to test it. The determination coefficient of ANFIS model with daily observed ET values is found as 0.954. Also test set values are used to calculate Penman FAO 56 formula and the determination coefficient of Penman FAO 56 with daily observed ET values is found as 0.926. For the comparison of the ANFIS model results and Penman FAO 56 formula results, Mean Square Error (MSE) and Mean Absolute Error (MAE) are computed. According to the comparison it is understood that ANFIS model has better performance than Penman FAO 56 empirical formula for the prediction of daily ET.

Keywords: Evapotranspiration, Penman FAO 56, Artificial Intelligent, Estimation Modelling.

DOI: https://doi.org/10.3846/enviro.2017.085