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Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Abstract

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

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Acknowledgments

This research was supported by Central Research Grant of Hong Kong Polytechnic University (4-ZZAD), Program for Science & Technology Innovation Talents in Universities of Henan Province (13HASTIT034), Science and technology innovation team in Colleges and universities in Henan Province (14IRTSTHN028) and the foundation for University Backbone Teacher of Henan Province (2012GGJS-099). We gratefully acknowledge the thorough and insightful comments by the editor and anonymous reviewers.

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Correspondence to Kwok-wing Chau.

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Wang, Wc., Chau, Kw., Xu, Dm. et al. Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition. Water Resour Manage 29, 2655–2675 (2015). https://doi.org/10.1007/s11269-015-0962-6

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Keywords

  • Annual runoff forecasting
  • Hydrologic time series
  • Auto-regressive integrated moving average (ARIMA)
  • Ensemble empirical mode decomposition (EEMD)
  • Decomposition and ensemble