Streamflow prediction based on hybrid Empirical Mode Decomposition and artificial intellegance methods

The correct and accurate estimation of the river flow using different models is a significant issue in water resources research. In this research, two hydrometric stations of Sari-Qomish and Nizam-Abad located in West Azarbaijan province were used to accurately estimate the daily flow of Zarineh-Rood River. To reach this aim, Empirical Mode Decomposition (EMD) preprocessing algorithm was used to deal with the complexity and instability of time series data. EMD is a data analysis method for extracting signals in data generation through non-linear and non-stationary operations. In this research, the method of gene expression programming model and artificial neural network model were used. The results of the research showed that the performance of the gene expression programming model was equal and sometimes less than the performance of the artificial neural network. However, the combination of the two mentioned models with the technique (EMD) increased the accuracy of the model and reduced the error in simulating the river flow in Sari-Qomish and Nizam-Abad stations.