Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition

Environ Res. 2015 May;139:46-54. doi: 10.1016/j.envres.2015.02.002. Epub 2015 Feb 12.


Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.

Keywords: Artificial neural network; Decomposition and ensemble; Ensemble empirical mode decomposition (EEMD); Hydrologic time series; Medium and long-term runoff forecasting.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Forecasting / methods*
  • Hydrology / methods*
  • Hydrology / statistics & numerical data
  • Hydrology / trends
  • Models, Statistical*
  • Neural Networks, Computer*
  • Time Factors
  • Water Resources / analysis*
  • Water Resources / statistics & numerical data