Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 9
Abstract
This case study uses a long short-term memory (LSTM)–based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from to . Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX’s prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request ([email protected]).
Acknowledgments
This work is funded by the National Natural Science Foundation of China (Grant No. 51922096), Excellent Youth Natural Science Foundation of Zhejiang Province in China (LR19E080003), Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 51761145022), and National Science and Technology Major Project for Water Pollution Control and Treatment (2017ZX07201004).
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Received: Sep 29, 2019
Accepted: Apr 13, 2020
Published online: Jun 29, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 29, 2020
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