The present study investigates the potential of Radial Basis Function (RBF) neural networks for the prediction of reference evapotraspiration (ETo). The study utilizes daily climatic data of temperature, relative humidity, sunshine hours, wind speed, and rainfall for five years collected from Mosul meteorological station, north of Iraq. Thirteen RBF networks each using varied input combination of climatic variables have been trained and tested. The network output is compared with estimated daily Penman-Monteith ETo values. To evaluate the performance of RBF networks, the same networks in the studied cases were re-trained using the well-known feedforward-backpropagation (FF-BP) networks. In addition, the effect of including a time index within the inputs of considered networks is investigated.
The study shows that the RBF network is seen to emulate the FF-BP in its performance and can be effectively used for ETo prediction. Besides, it is much easier to built and much faster to train. It is noticed that the networks’ output are very highly correlated to estimated ETo, especially when concerning all the climatic parameters. The study results reveal that adding a time index to the inputs highly improves the ETo prediction of the studied cases.
Keywords: Radial Basis Networks, reference evapotranspiration, climatic data.