Evaluation of CEEMD- GPR hybrid model in temporal and spatial daily stream flow forecasting

Document Type : Research Paper


1 M.S.c Student. Water Resources Management, Department of water engineering, Faculty of civil Engineering, Tabriz university, Tabriz, Iran

2 Professor, Department of Water Engineering, Faculty of civil engineering, Tabriz university, Tabriz, Iran


Accurate prediction of river daily discharge is a suitable tool for water resources planning and management. In this paper, cross station discharge of the Arkansas River in U.S.A, were examined using Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and complete ensemble empirical mode decomposition combined models. For this Purpose, in the first step, the daily and monthly discharge was predicted via GPR and ELM models. Then, the discharge time series were broken up by CEEMD method into cages, and these subclasses were introduced into the Gaussian process regression end ELM modeling to simulate discharge. Furthermore, direct correlation (DC), Root Mean Square Error (RMSE), correlation coefficient (R) and Mean Absolute Percentage Error (MAPE) were used to evaluate the efficiency of the models. The results showed that the CEEMD approach improved the performance of the above mentioned models dramatically. For instance, the values of MAPE correspond to GPR hybrid model in forecasting discharge in the first, second and third station with CEEMD pre-processing were reduced by 34, 27and 32 percent, respectively, as compared to those in the GPR model without pre-processing. Also, the effect of each of the sub-series of ensemble empirical mode decomposition model (Residual and IMFs) was studied to improve predictive outcomes. It was observed that the most inefficient subseries in the complete ensemble empirical mode decomposition model is the residual subseries. The CEEMD- ELM model can be used in watershed management and flood control in Iran.


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