Forecasting of Reference Evapotranspiration by Artificial Neural Networks

In recent years, artificial neural networks (ANNs) have been applied to forecasting in many areas of engineering. In this note, a sequentially adaptive radial basis function network is applied to the forecasting of reference evapotranspiration (ETo). The sequential adaptation of parameters and structure is achieved using an extended Kalman filter. The criterion for network growing is obtained from the Kalman filter’s consistency test, while the criteria for neuron/connection pruning are based on the statistical parameter significance test. The weather parameter data (air temperature, relative humidity, wind speed, and sunshine) were available at Nis, Serbia and Montenegro, from January 1977 to December 1996. The monthly reference evapotranspiration data were obtained by the Penman-Monteith method, which is proposed as the sole standard method for the computation of reference evapotranspiration. The network learned to forecast ETo,t+1 based on ETo,t?11 and ETo,t?23. The results show that ANNs can be used for forecasting reference evapotranspiration with high reliability.