Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)

• We modeled the evapotranspiration from the temperature data.
• We utilized the artificial neural networks and the wavelet neural networks models.
• An adopted wavelet neural networks was proposed.
• The adopted wavelet neural networks performed the best.

Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate forecasting is essential in all water resources applications. In this study, artificial neural network (ANN) and wavelet neural network (WNN) were utilized to forecast daily ET from temperature and wind speed data. The WNN model used in this study is a neural network model with one hidden layer and a wavelet function as an activation function. The climatic data of Redesdale climatology station, Australia for the period 2009–2012 were utilized for the analysis. The daily reference values of ET were calculated by the FAO-PM56 method. The maximum temperature, minimum temperatures and wind speed data were used as the inputs and the reference values of ET data series was utilized as the output of the ANN and WNN models. In order to assess the effect of decomposing the input data by wavelet transform on the models efficiency, the original dataset and separately the decomposed time series were applied for calibrating and validating the models. The influence of using wind speed data as the third input on the performance of models was also investigated. The results showed that both the ANN and WNN models predicted ET at an acceptable accuracy level. However, the wavlet-WNN261 (2 inputs, 6 neurons in the hidden layer and one output) performed the best with the RMSE, APE, N.S. and R values of 1.03 mm/day, 22%, 0.79 and 0.89, respectively.