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Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting

Author

Listed:
  • Chuan Li

    (Dongguan University of Technology)

  • Yun Bai

    (Dongguan University of Technology)

  • Bo Zeng

    (Dongguan University of Technology)

Abstract

Inflow forecasting applies data supports for the operations and managements of reservoirs. To better accommodate the sophisticated characteristics of the daily reservoir inflow, two deep feature learning architectures, i.e., deep restricted Boltzmann machine (DRBM) and stack Autoencoder (SAE), respectively, are introduced in this paper. This study sheds light on the application of deep learning architectures for daily reservoir inflow forecasting, which has been attracting much attention in various areas for its ability to extract and learn useful features from a large number of data. Evaluations are made comparing the basic feed forward neural network (FFNN), the autoregressive integrated moving average (ARIMA), and two categories deep neural networks (DNNs) constructed by the integrations the FFNN with two deep feature learning architectures, named DRBM-based NN and stack SAE-based NN, respectively. Two daily inflow series of the Three Gorges reservoir (1/1/2000–31/12/2014) and the Gezhouba reservoir (1/1/1992–31/12/2014), China, are applied for four modeling exercises, respectively. The results show that, the two DNN models overwhelm the FFNN and the ARIMA models in terms of mean absolute percentage error, normalized root-mean-square error, and threshold statistic criteria.

Suggested Citation

  • Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:14:d:10.1007_s11269-016-1474-8
    DOI: 10.1007/s11269-016-1474-8
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    References listed on IDEAS

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    2. Yun Bai & Nejc Bezak & Klaudija Sapač & Mateja Klun & Jin Zhang, 2019. "Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4783-4797, November.
    3. Yutao Qi & Zhanao Zhou & Lingling Yang & Yining Quan & Qiguang Miao, 2019. "A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4123-4139, September.
    4. 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.
    5. Yun Bai & Nejc Bezak & Bo Zeng & Chuan Li & Klaudija Sapač & Jin Zhang, 2021. "Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1167-1181, March.
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