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Precise and reliable estimation of reference evapotranspiration (ET o ) is anessential for the irrigation and water resources management. ET o is difficultto predict due to its complex processes. This complexity can be solved usingmachine learning methods. This study investigates the performance of artificialneural network (ANN) and deep neural network (DNN) models for estimating dailyET o . Previously proposed ANN and DNN methods have been realized, and theirperformances have been compared. Six input data including maximum airtemperature (T max ), minimum air temperature (T min ), solar radiation (R n ),maximum relative humidity (RH max ), minimum relative humidity (RH min ) andwind speed (U 2 ) are used from 4 meteorological stations (Adana, Aksaray,Isparta and Ni\u{g}de) during 1999-2018 in Turkey. The results have shown thatour proposed DNN models achieves satisfactory accuracy for daily ET oestimation compared to previous ANN and DNN models. The best performance hasbeen observed with the proposed model of DNN with SeLU activation function(P-DNN-SeLU) in Aksaray with coefficient of determination (R 2 ) of 0.9934,root mean square error (RMSE) of 0.2073 and mean absolute error (MAE) of0.1590, respectively. Therefore, the P-DNN-SeLU model could be recommended forestimation of ET o in other climate zones of the world.

Modelling of daily reference evapotranspiration using deep neural network in different climates