Evapotranspiration prediction using adaptive neuro-fuzzy inference system and Penman FAO 56 equation for St. Johns, FL, USA

Abstract

Evapotranspiration (ET) estimation is a primary problem for irrigation engineers and hydraulic designers because it is an important part of hydrologic cycle. 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 values are used to train ANFIS model and 385 daily values are used to test it. ANFIS model determination coefficient with daily observed ET values found as 0.909. 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 found as 0.857. For the comparison of the ANFIS model 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. © 2017 Mustafa Mamak, Fatih Üneş, Yunus Ziya Kaya, Mustafa Demirci. Published by VGTU Press. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY-NC 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.