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Monthly Streamflow Forecasting Using Convolutional Neural Network

Author

Listed:
  • Xingsheng Shu

    (Dalian University of Technology)

  • Wei Ding

    (Dalian University of Technology)

  • Yong Peng

    (Dalian University of Technology)

  • Ziru Wang

    (Dalian University of Technology)

  • Jian Wu

    (Dalian University of Technology)

  • Min Li

    (Dalian University of Technology)

Abstract

Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:15:d:10.1007_s11269-021-02961-w
    DOI: 10.1007/s11269-021-02961-w
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    References listed on IDEAS

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    Cited by:

    1. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    2. Li, Baozhu & Lv, Xiaotian & Chen, Jiaxin, 2024. "Demand and supply gap analysis of Chinese new energy vehicle charging infrastructure: Based on CNN-LSTM prediction model," Renewable Energy, Elsevier, vol. 220(C).
    3. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    4. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
    5. 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.
    6. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
    7. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    8. Shu, Xingsheng & Ding, Wei & Peng, Yong & Wang, Ziru, 2024. "Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region," Energy, Elsevier, vol. 298(C).
    9. Wenxin Xu & Jie Chen & Xunchang J. Zhang, 2022. "Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3609-3625, August.

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