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Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738


ZAKHROUF, Mousaab; BOUCHELKIA, Hamid  and  STAMBOUL, Madani. Neuro-fuzzy systems to estimate reference evapotranspiration. Water SA [online]. 2019, vol.45, n.2, pp.232-238. ISSN 1816-7950.

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.

Keywords : modelling; FAO-56 PM evapotranspiration; S-ANFIS; F-ANFIS; MLR; semi-arid regions; Algeria.

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