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

Shengwen Zhou, Shunsheng Guo, Baigang Du (), Shuo Huang and Jun Guo
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Shengwen Zhou: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Shunsheng Guo: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Baigang Du: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Shuo Huang: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Jun Guo: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Sustainability, 2022, vol. 14, issue 17, 1-22

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 ).

Keywords: water demand forecasting; multivariate time series; convolutional neural network; long short-term memory; attention mechanism; encoder-decoder network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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