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Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks

Author

Listed:
  • Xingsheng Shu

    (Dalian University of Technology)

  • Yong Peng

    (Dalian University of Technology)

  • Wei Ding

    (Dalian University of Technology)

  • Ziru Wang

    (Dalian University of Technology)

  • Jian Wu

    (Dalian University of Technology)

Abstract

Many hydrological applications related to water resource planning and management primarily rely on a succession of streamflow forecasts with extensive lead times. In this study, two innovative models, termed as DirCNN and DRCNN, are proposed for multi-step-ahead (MSA) monthly streamflow forecasting based on the direct (Dir) and direct-recursive (DR) strategies and using the convolutional neural network (CNN) to automatically extract input variables. Compared to traditional MSA forecasting models, DirCNN and DRCNN can automatically extract input variables and predict streamflow for multiple lead times simultaneously. Xiangjiaba Hydropower Station, Huanren Reservoir, and Fengman Reservoir in China were included as case studies, and three artificial neural networks based models are used as comparative models. The most important results are highlighted below. First, the proposed DirCNN and DRCNN exhibit comparable prediction performances but outperform the comparison models. Second, with the increase in lead time, DirCNN and DRCNN demonstrate good consistency in forecasting accuracy. Third, the stacking order of candidate sequences has little effect on the DirCNN and DRCNN forecasting accuracy. These results suggest that DirCNN and DRCNN could be ahead of MSA monthly streamflow forecasting and thus would be helpful in the judicious use of water resources.

Suggested Citation

  • Xingsheng Shu & Yong Peng & Wei Ding & Ziru Wang & Jian Wu, 2022. "Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 3949-3964, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03165-6
    DOI: 10.1007/s11269-022-03165-6
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    References listed on IDEAS

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    1. Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
    2. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    3. Hakan Tongal & Martijn Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
    4. Hakan Tongal & Martijn J. Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
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    Cited by:

    1. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
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