On The Effect of Preprocessing Techniques For Evapotranspiration Estimation Using Soft Computing Methods

Implementing a reliable computational model for predicting the reference evapotranspiration (ET 0 ) process is essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, two artificial intelligence (AI) models, artificial neural network (ANN) and model tree (MT), were investigated for modelling ET 0 . To validate model performance, five climatic stations such as Urmia, Mahabad, Takab, Khoy, and sardasht in West Azerbaijan Province of Iran. In the next step and to improve the model's accuracy, a novel preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was coupled with those AI models to remove the trends or noise in the time series dataset. The extracted results indicated that the EEMD-MT model for all five stations outperformed other standalone and hybrid models.