Abstract
Routine and rapid estimation of evapotranspiration (ET) at regional scale is of great significance for agricultural, hydrological and climatic studies. A large number of empirical or semi-empirical equations have been developed for assessing ET from meteorological data. The FAO-56 PM is one of the most important methods used to estimate evapotranspiration. The advantage of FAO-56 PM is a physically based method that requires a large number of climatic parameter data. In this paper, the potential of two types of neuro-fuzzy system, including ANFIS based on subtractive clustering (S_ANFIS), ANFIS based on the fuzzy C-means clustering method (F_ANFIS), and multiple linear regression (MLR), were used in modelling daily evapotranspiration (ET0). For this purpose various daily climate data – air temperature (T), relative humidity (RH), wind speed (U) and insolation duration (ID) – from Dar El Beidain Algiers, Algeria, were used as inputs for the ANFIS and MLR models to estimate the ET0 obtained by FAO-56 based on the Penman-Monteith equation. The obtained results show that the performances of S_ANFIS model yield superior to those of F_ANFIS and MLR models. It can be judged from results of the Nash-Sutcliffe efficiency coefficient (EC) where S_ANFIS (EC = 94.01%) model can improve the performances of F_ANFIS (EC = 93.00%) and MLR (EC = 92.12%) during the test period, respectively.
Highlights
Evapotranspiration is an important component of the hydrological cycle and influenced by many meteorological parameters
Neurofuzzy provides an efficient way of handling the uncertainty for complex systems without sufficient data or with only vague information (Ross, 1995; Cox, 1999), and clustering processes can be a very effective technique to identify natural groupings in data from a large dataset, thereby allowing concise representation of relationships embedded in the data
The effect of number of cluster changes on the quality of the results from F_ANFIS is shown in Fig. 5; from Fig. 5 it was observed that the F_ANFIS model having 7 clusters estimated the minimum value of mean absolute relative error (MARE)
Summary
Evapotranspiration is an important component of the hydrological cycle and influenced by many meteorological parameters. These NF use daily climatic data (air temperature, relative humidity, wind speed and the insolation duration) as inputs, and ET0 values estimated by the Penman-Monteith formula as outputs. The PM equation for estimating reference evaporation used in this study is based on most recent FAO-56 PM model described in the FAO’s Irrigation and Drainage Paper No 56 (Allen et al, 1998).
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call