IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i8p2913-2926.html
   My bibliography  Save this article

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

Author

Listed:
  • 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

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-0977-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-0977-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chesheng Zhan & Sidong Zeng & Shanshan Jiang & Huixiao Wang & Wen Ye, 2014. "An Integrated Approach for Partitioning the Effect of Climate Change and Human Activities on Surface Runoff," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3843-3858, September.
    2. Adlul Islam & Alok Sikka & B. Saha & Anamika Singh, 2012. "Streamflow Response to Climate Change in the Brahmani River Basin, India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1409-1424, April.
    3. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2014. "On infimum Dickey–Fuller unit root tests allowing for a trend break under the null," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 235-242.
    4. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    5. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    6. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    7. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA 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. 25(11), pages 2683-2703, September.
    8. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    9. Chongli Di & Xiaohua Yang & Xiaochao Wang, 2014. "A Four-Stage Hybrid Model for Hydrological Time Series Forecasting," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-18, August.
    10. Jaydip Makwana & Mukesh Tiwari, 2014. "Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4857-4873, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Lijun Jiao & Ruimin Liu & Linfang Wang & Lin Li & Leiping Cao, 2021. "Evaluating Spatiotemporal Variations in the Impact of Inter-basin Water Transfer Projects in Water-receiving Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5409-5429, December.
    5. Muzi Zhang & Junyi Li & Bing Pan & Gaojun Zhang, 2018. "Weekly Hotel Occupancy Forecasting of a Tourism Destination," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    6. Reza Rezaiy & Ani Shabri, 2024. "An innovative hybrid W-EEMD-ARIMA model for drought forecasting using the standardized precipitation index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13513-13542, November.
    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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Reza Rezaiy & Ani Shabri, 2024. "An innovative hybrid W-EEMD-ARIMA model for drought forecasting using the standardized precipitation index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13513-13542, November.
    2. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    3. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
    4. Jianzhu Li & Guoqing Li & Shuhan Zhou & Fulong Chen, 2016. "Quantifying the Effects of Land Surface Change on Annual Runoff Considering Precipitation Variability by SWAT," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1071-1084, February.
    5. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    6. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    7. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    8. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    9. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    10. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    11. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    12. Ke Yan & Yuting Dai & Meiling Xu & Yuchang Mo, 2019. "Tunnel Surface Settlement Forecasting with Ensemble Learning," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    13. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    14. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    15. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    16. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    17. Milan Stojković & Srđan Kostić & Stevan Prohaska & Jasna Plavšić & Vesna Tripković, 2017. "A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1089-1103, March.
    18. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    19. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).
    20. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:8:p:2913-2926. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.