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Reference evapotranspiration (ETo) is a primary determinant for making decisions on irrigation scheduling. For assessing water demand ahead of time, FAO-56 Penman–Monteith (FAO-56 PM) equation is the traditional empirical method applied to obtain ETo. In real time, the precise calculation of ETo is genuinely troublesome and complex due to its complex mix of attributes. Artificial Neural Networks (ANN)-based modelling techniques are employed to predict evapotranspiration. In this study, the viability of the deep learning neural network (DLNN) in predicting ETo on the data obtained in the command area of the Veeranam tank system during the period 1995–2016 in India is investigated. The predicted ETo obtained by DLNN is compared with ETo obtained by empirically calculated FAO-56 PM method. To show that DLNN is superior to other ANN methods, result of DLNN method is also compared with radial basis function neural network and multilayer perceptronas baseline methods in terms of various error metrics.

Modelling of Reference Evapotransipration using Climatic Parameters for Irrigation Scheduling using Machine learning