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

Abstract

In the present study, an attempt has been made to compare the reference evapotranspiration (ETo), 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 Tmean 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.