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Daily Streamflow Forecasting Based on Flow Pattern Recognition

Author

Listed:
  • Fang-Fang Li

    (China Agricultural University)

  • Han Cao

    (China Agricultural University)

  • Chun-Feng Hao

    (China Institute of Water Resources and Hydropower Research)

  • Jun Qiu

    (Tsinghua University)

Abstract

Accurate streamflow prediction is of great significance for water resource management. In recent years, data-driven models such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely used in the field of flow prediction. However, traditional data-driven models neglect the extraction and utilization of the data's own characteristics. This study proposes a daily flow prediction model based on the pattern recognition of flow sequences. Based on the input number of the prediction model derived from the partial autocorrelation function, the flow sequence was divided into subsequences. Five patterns of flow subsequences, including monotonic rising, monotonic falling, monotonic stable, concave, and convex, were then identified, which helped to explore the characteristics of the flow subsequences. For each pattern, traditional ANN and SVM models were applied to predict the flow. A comparison with the traditional ANN and SVM models shows that the hybrid models of the pattern recognition method (PRM) and the traditional ANN and SVM have higher accuracy. The Nash efficiency coefficient (NSE) of the hybrid PRM-SVM model was as high as 0.9815, and the mean absolute percentage error (MAPE) was only 6.75%. In addition, the prediction accuracy of the flood peak also improved. The average relative error of the peak flood derived from the hybrid PRM-ANN and PRM-SVM models were reduced by 0.12% and 0.40%, respectively, compared with the traditional ANN and SVM models. Graphical Abstract

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:13:d:10.1007_s11269-021-02971-8
    DOI: 10.1007/s11269-021-02971-8
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    References listed on IDEAS

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    1. Y. Li & G. Huang & S. Nie, 2009. "Water Resources Management and Planning under Uncertainty: an Inexact Multistage Joint-Probabilistic Programming Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2515-2538, September.
    2. 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.
    3. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    4. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
    5. 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.
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    1. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).

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