Modelling evapotranspiration using discrete wavelet transform and neural networks

This study combines wavelet transforms and feed-forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub-time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre-processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi-linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied Successfully for evapotranspiration modelling from climatic data. Copyright (C) 2009 John Wiley & Sons, Ltd.