Hydrological Forecasting Using Hybrid Data-Driven Approach

Abstract

This study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs and residue, respectively. The final forecasted time series is produced by the ensemble aggregation of the forecasted IMFs and residue. The efficiency of EEMD-FANN model is assessed based on the comparison with that of single Adaptive Neuro-Fuzzy Inference System (ANFIS) and FANN to demonstrate the applicability of the hybrid approach in daily river stage forecasting. As a result, it is found that the EEMD-FANN model utilizing time series decomposition by the EEMD and ensemble aggregation produces better performance than the single ANFIS and FANN models using original river stage time series as inputs. The results of this study also signify that the approach coupling the EEMD and FANN can significantly enhance the forecasting ability of the single FANN model and can be utilized as an effective modeling methodology to forecast river stage precisely.