Daily evapotranspiration modeling using regression and intelligent models(Case Study)

Evapotranspiration plays an imperative role in management of regional water resources, climate change and agricultural production. In this study efficiency of some data-driven techniques, including support vector machine (SVM), artificial neural networks (ANN) and its hybrid with wavelet transform (WANN), multi linear regression (MLR) and decision tree (DT) for predicting Evapotranspiration rates at Scottsbluff Station in Nebraska have been monitored. For this purpose, 5 meteorological parameters utilized as inputs for the models. Daily meteorological information, data used in this study, were between 2005 - 2013 years to train and test the models. In order to implement each of the models 8 scenarios were considered according to combination of input parameters. For evaluate performance of the studied techniques, three different statistical indices were used which included root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). In addition, Taylor charts were used to test similarity between observation and prediction data. The results showed that at the Scottsbluff station, WANN8 (is the eighth scenario for the WANN model) according to the root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe equal to 0.097, 0.999 and 0.999 performed better than ANN, SVM, MLR and DT. The SVM and ANN models also showed excellent accuracy, and the DT and MLR models performed worse than the other models despite their acceptable accuracy. As a conclusion, the results of the present study were proved that WANN provides reasonable procedures for modeling Scottsbluff at the Scottsbluff station.