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Decomposition-ANN Methods for Long-Term Discharge Prediction Based on Fisher’s Ordered Clustering with MESA

Author

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  • Fang-Fang Li

    (China Agricultural University)

  • Zhi-Yu Wang

    (Shandong Water Conservancy Vocational College)

  • Xiao Zhao

    (China Agricultural University)

  • En Xie

    (China Agricultural University)

  • Jun Qiu

    (Tsinghua University)

Abstract

Precise and reliable long-term streamflow prediction contributes to water resources planning and management. Artificial neural network (ANN) have shown its remarkable ability in forecasting non-linear hydrological processes without involvement of complex, dynamic, hydrological and hydro-climatologic physical process in the water shed. To improve its non-stationary responses, decomposition methods are adopted as pre-processing methods in this study including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Seasonal-Trend decomposition using Loess (STL). The original time sequence is decomposed to several components, which are then taken as the inputs of the ANN model. EMD and EEMD are data- adaptable methods, and thus the number of Intrinsic Mode Functions (IMFs) might differ for different sequences, leading to the discrepancy of the input number for ANN model in training and predicting. Fisher’s ordered clustering is thus used to classify the IMFs into a determined number of classes based on their frequency spectrum resulting from Maximum Entropy Spectral Analysis (MESA). The proposed methodology is applied on four important hydrological stations on the upper stream of the Yellow River and the Yangtze River in China, respectively, to forecast the streamflow of the next whole year with the historical daily data of the past 6 years. The Nash-Sutcliffe efficiencies of the monthly prediction are higher than 0.85 for all of the four cases, and various indicators indicates that the proposed hybrid method of STL-ANN performs better than other compared methods. The highlights of this study lies in that only historical daily streamflow data is used to derive an accurate long-term prediction by data mining based on decomposition technology and mapping relationships between the decomposed components and the original sequence in the future.

Suggested Citation

  • Fang-Fang Li & Zhi-Yu Wang & Xiao Zhao & En Xie & Jun Qiu, 2019. "Decomposition-ANN Methods for Long-Term Discharge Prediction Based on Fisher’s Ordered Clustering with MESA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3095-3110, July.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02295-8
    DOI: 10.1007/s11269-019-02295-8
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    3. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    4. Norden E. Huang & Man‐Li Wu & Wendong Qu & Steven R. Long & Samuel S. P. Shen, 2003. "Applications of Hilbert–Huang transform to non‐stationary financial time series analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(3), pages 245-268, July.
    5. 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.
    6. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    7. Zaw Latt & Hartmut Wittenberg, 2014. "Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2109-2128, June.
    8. Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
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    1. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
    2. Shengli Liao & Huan Wang & Benxi Liu & Xiangyu Ma & Binbin Zhou & Huaying Su, 2023. "Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1539-1555, March.
    3. Fang-Fang Li & Han Cao & Chun-Feng Hao & Jun Qiu, 2021. "Daily Streamflow Forecasting Based on Flow Pattern Recognition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4601-4620, October.

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