Publikacje

Abstract:

The aim of this study is to employ a Time Lagged Recurrent Neural Network (TLRNN) model for
forecasting near future reference evapotranspiration (ETo) values by using climate data taken from
meteorological station located in Velestino, a village near the city of Volos, in Thessaly, centre of Greece.
TLRNN is Multilayer Perceptron Neural Network (MLP-NN) with locally recurrent connections and short-term
memory structures that can learn temporal variations from the dataset. The network topology is using input
layer, hidden layer and a single output with the ETo values. The network model was trained using the back
propagation through time algorithm. Performance evaluations of the network model done by comparing the
Mean Bias Error (MBE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Index of
Agreement (IA). The evaluation of the results showed that the developed TLRNN model works properly and
the forecasting ETo values approximate the FAO-56 PM values. A good proximity of predictions with the
experimental data was noticed, achieving coefficients of determination (R2) greater than 75% and root mean
square error (RMSE) values less than 1.0 mm/day. The forecasts range up to three days ahead and can be
helpful to farmers for irrigation scheduling.

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Forecasting reference evapotranspiration using time lagged recurrent neural network