Abstract
Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.
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Acknowledgments
This work was funded by the Scientific Research Foundation for High-level Talents of Beibu Gulf University (Grant No. 2019KYQD22) and Guangxi Province Education Department (Grant No 2021KY0438) .
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Some or all data and models that support the findings of this study are available from the corresponding author upon reasonable request.
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Some or all code that support the findings of this study are available from the corresponding author upon reasonable request.
Funding
This work was funded by the Scientific Research Foundation for High-level Talents of Beibu Gulf University (Grant No. 2019KYQD22) and Guangxi Province Education Department (Grant No 2021KY0438).
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Haidong Huang contributed to the conception of the study and manuscript preparation;
Zhixiong Zhang helped to perform data analyses;
Fengxuan Song helped to write the manuscript.
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Huang, H., Zhang, Z. & Song, F. An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting. Water Resour Manage 35, 1757–1773 (2021). https://doi.org/10.1007/s11269-021-02808-4
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DOI: https://doi.org/10.1007/s11269-021-02808-4