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Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)
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Daily reference evapotranspiration (ET0), as a dependent variable, was estimated for two weather stations in South Korea, using 8 years (1985–1992) of measurements of independent variables of air temperature, sunshine hours, wind speed and relative humidity. The model uses the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for estimating daily ET0. In the first part of the study, the applied models were trained, tested and validated using various combinations of the recorded independent variables, which corresponded to the Hargreaves–Samani, Priestly–Taylor and FAO56-PM equations. The goodness of fit for the models was evaluated in terms of the coefficient of determination (
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