Modelling of daily reference evapotranspiration using deep neural network in different climates
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Precise and reliable estimation of reference evapotranspiration (ET o ) is an essential for the irrigation and water resources management. ET o is difficult to predict due to its complex processes. This complexity can be solved using machine learning methods. This study investigates the performance of artificial neural network (ANN) and deep neural network (DNN) models for estimating daily ET o . Previously proposed ANN and DNN methods have been realized, and their performances have been compared. Six input data including maximum air temperature (T max ), minimum air temperature (T min ), solar radiation (R n ), maximum relative humidity (RH max ), minimum relative humidity (RH min ) and wind speed (U 2 ) are used from 4 meteorological stations (Adana, Aksaray, Isparta and Niğde) during 1999-2018 in Turkey. The results have shown that our proposed DNN models achieves satisfactory accuracy for daily ET o estimation compared to previous ANN and DNN models. The best performance has been 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) of 0.1590, respectively. Therefore, the P-DNN-SeLU model could be recommended for estimation of ET o in other climate zones of the world.
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