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A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting

Author

Listed:
  • Yutao Qi

    (Xidian University)

  • Zhanao Zhou

    (Xidian University)

  • Lingling Yang

    (Xidian University)

  • Yining Quan

    (Xidian University)

  • Qiguang Miao

    (Xidian University)

Abstract

Reservoir inflow forecasting is one of the most important issues in delicacy water resource management at reservoirs. Considering the non-linearity and of daily reservoir inflow data, a decomposition-ensemble learning model based on the long short-term memory neural network (DEL-LSTM) is developed in this paper for daily reservoir inflow forecasting. DEL-LSTM employs the logarithmic transformation based preprocessing method to cope with the non-stationary of the inflow data. Then, the ensemble empirical mode decomposition and Fourier spectrum methods are used to decompose the inflow data into the trend term, period term, and random term. For each decomposed term, a regression model based on the LSTM neural network is built to obtain the corresponding prediction result. Finally, the prediction results of the three items are integrated to get the final prediction result. Case studies on the Ankang reservoir in China have been conducted by using data from 1/1/1943 to 12/31/1971. Experimental results illustrated the superiority of the decomposition-ensemble framework and the LSTM neural network in forecasting daily reservoir inflow with big fluctuations. Comparing with some representative models, the proposed DEL-LSTM performs better in prediction accuracy, the average absolute percentage error is reduced to 13.11%, and the normalized mean square error is reduced by 4%, the coefficient of determination was increased by 5%.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:12:d:10.1007_s11269-019-02345-1
    DOI: 10.1007/s11269-019-02345-1
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    References listed on IDEAS

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    1. 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.
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    4. Saman Razavi & Shahab Araghinejad, 2009. "Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(1), pages 39-55, January.
    5. R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    6. 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.
    7. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
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    Cited by:

    1. Fugang LI & Guangwen MA & Shijun CHEN & Weibin HUANG, 2021. "An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2941-2963, July.
    2. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3227-3241, June.
    3. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    4. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.

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