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Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

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  • Xue-hua Zhao
  • Xu Chen

Abstract

Annual runoff forecasting remains a difficult task due to its complicated non-stationary characteristics. To solve this difficulty and improve the prediction accuracy, this paper proposes a novel hybrid model for annual runoff forecasting. This model is based on the ensemble empirical mode decomposition (EEMD) and Auto-Regressive (AR). And it is suitable for non-stationary time series. The proposed model is tested using the annual runoff data from four hydrologic stations in the upper reaches of the Fenhe River basin in China. The non-stationary original annual runoff time series is first decomposed into a limited number of intrinsic mode functions (IMFs) and one trend term using EEMD technique for making the time series stationary. Then, these IMFs are forecasted by establishing corresponding optimum AR models only stationary processes, and trend term is predicted by quadratic polynomial equation. At last, the prediction results of the modeled IMFs and trend term are summed to formulate an ensemble forecast for the original runoff series. The performance of the EEMD-AR hybrid model is compared with EMD-AR and single AR models. Results indicate that EEMD-AR hybrid model gives better accuracy in predicting annual runoff in the study area when compared to other models. Copyright Springer Science+Business Media Dordrecht 2015

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  • Xue-hua Zhao & Xu Chen, 2015. "Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2913-2926, June.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:8:p:2913-2926
    DOI: 10.1007/s11269-015-0977-z
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    2. José-Luis Molina & Santiago Zazo, 2017. "Causal Reasoning for the Analysis of Rivers Runoff Temporal Behavior," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4669-4681, November.
    3. Jiang Wu & Jianzhong Zhou & Lu Chen & Lei Ye, 2015. "Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5091-5108, November.
    4. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
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    6. Li, He & Liu, Pan & Guo, Shenglian & Zuo, Qiting & Cheng, Lei & Tao, Jie & Huang, Kangdi & Yang, Zhikai & Han, Dongyang & Ming, Bo, 2022. "Integrating teleconnection factors into long-term complementary operating rules for hybrid power systems: A case study of Longyangxia hydro-photovoltaic plant in China," Renewable Energy, Elsevier, vol. 186(C), pages 517-534.
    7. Xike Zhang & Qiuwen Zhang & Gui Zhang & Zhiping Nie & Zifan Gui & Huafei Que, 2018. "A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 15(5), pages 1-23, May.

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