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Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory

Author

Listed:
  • Junhao Wu

    (Shanghai Ocean University)

  • Zhaocai Wang

    (Shanghai Ocean University)

  • Yuan Hu

    (Shanghai Ocean University)

  • Sen Tao

    (East China University of Science and Technology)

  • Jinghan Dong

    (Shanghai Ocean University)

Abstract

Water resources matters considerably in maintaining the biological survival and sustainable socio-economic development of a region. Affected by a combination of factors such as geographic characteristics of the basin and climate change, runoff variability is non-linear and non-stationary. Runoff forecasting is one of the important engineering measures to prevent flood disasters. The improvement of its accuracy is also a difficult problem in the research of water resources management. To this end, an ensemble deep learning model was hereby developed to forecast daily river runoff. First, variational mode decomposition (VMD) was used to decompose the original daily runoff series data set into discrete internal model function (IMF) and distinguish signals with different frequencies. Then, for each IMF, a convolutional neural network (CNN) was introduced to extract the features of each IMF component. Subsequently, a bi-directional long short-term memory network (BiLSTM) based on an attention mechanism (AM) was used for prediction. A Bayesian optimization algorithm (BOA) was also introduced to optimize the hyperparameters of the BiLSTM, thereby further improving the estimation precision of the VMD-CNN-AM-BOA-BiLSTM model. The model was applied to the daily runoff data from January 1, 2010 to November 30, 2021 at the Wushan and Weijiabao Hydrological Stations in the Wei River Basin, and the RMSEs of 3.54 and 15.23 were obtained for the test set data at the two stations respectively, which were much better than those of EEMD-VMD-SVM and CNN-BiLSTM-AM models. Additionally, the hereby proposed model is proven to have better peak flood prediction capability and adaptability under different hydrological environments. Based on this sound performance, the model becomes an effective data-driven tool in hydrological forecasting practice, and can also provide some reference and practical application guidance for water resources management and flood warning.

Suggested Citation

  • Junhao Wu & Zhaocai Wang & Yuan Hu & Sen Tao & Jinghan Dong, 2023. "Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 937-953, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03414-8
    DOI: 10.1007/s11269-022-03414-8
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    References listed on IDEAS

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    1. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
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    3. 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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    1. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).

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