Comparative study of reference evapotranspiration estimation methods including Artificial Neural Network for dry sub-humid agro-ecological region

In the present study, an attempt has been made to compare the reference evapotranspiration (ET_o), computed by eight different methods, namely, Penman-Monteith, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas, ANN and FAO(24) model for the dry subhumid agro-ecological region (Varanasi). An attempt was also made to find out utility of artificial neural networks (ANN) for estimation of ET₀ with minimum input. Feed forward network has been used for prediction of ETo using resilient back-propagation method and the architecture 2–21(having parameters T_mean and solar radiation) was found to be the best one. The average annual evapotranspiration (by Penman-Monteith method) for Varanasi was found to be 1447.4 mm. When compared among the different methods for estimation of reference evapotranspiration with Penman-Monteith method, the FAO-24 and Hargraves-Samani (3) under estimate, however, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas over-estimate and ANN closely estimates reference evapotranspiration.