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A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network

Author

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  • Shengwen Zhou

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Digital Manufacturing Key Laboratory, Wuhan 430070, China)

  • Shunsheng Guo

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Digital Manufacturing Key Laboratory, Wuhan 430070, China)

  • Baigang Du

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Digital Manufacturing Key Laboratory, Wuhan 430070, China)

  • Shuo Huang

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Digital Manufacturing Key Laboratory, Wuhan 430070, China)

  • Jun Guo

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Digital Manufacturing Key Laboratory, Wuhan 430070, China)

Abstract

Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R 2 ).

Suggested Citation

  • Shengwen Zhou & Shunsheng Guo & Baigang Du & Shuo Huang & Jun Guo, 2022. "A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11086-:d:907097
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    References listed on IDEAS

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    Cited by:

    1. Dongsu Kim & Yongjun Lee & Kyungil Chin & Pedro J. Mago & Heejin Cho & Jian Zhang, 2023. "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    2. Jie Yang & Guihong Ren & Yaxin Wang & Qi Liu & Jiamin Zhang & Wenqi Wang & Lingzhi Li & Wuping Zhang, 2024. "Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost," Sustainability, MDPI, vol. 16(5), pages 1-17, February.

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