Abstract
Short-term water demand forecasting is crucial for constructing intelligent water supply system. Plenty of useful models have been built to address this issue. However, there are still many challenging problems, including that the accuracies of the models are not high enough, the complexity of the models makes them hard for wide use in reality and the capabilities of models to catch peaks still have much room for improvement. In order to solve these problems, we proposed an ensemble deep learning model named STL-Ada-LSTM for daily water demand forecast by combining STL method with AdaBoost-LSTM model. After data preprocessing, the smoothed series is decomposed by STL to gain three input series. Then, several LSTM models are integrated by the AdaBoost algorithm to construct the ensemble deep learning model for water demand forecast. At last, the superiority of the proposed model is demonstrated by comparing with other state-of-the-art models. The proposed method is applied for water demand forecast using daily datasets from two representative water plants located in Yiwu, East China. All models are assessed by mean absolute scaled error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R2) and Akaike information criterion (AIC). The results show that the proposed model not only improves the accuracy of the forecast, but also enhances the stability and conciseness. It is proven as a practical model with good accuracy and can be further applied in daily water demand forecast in other regions.
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Acknowledgements
The authors would like to thank Zhejiang Provincial Hydrological Management Center for providing the data used in this study. Editor and reviewers are greatly acknowledged for their constructive comments.
Funding
This study was funded by the Major Project of the Natural Science Foundation of Zhejiang (grant number LZ20E090001) and National Key Research and Development Program (grant number 2019YFC0408805).
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Yue-Ping Xu: Conceptualization, Supervision, Reviewing and Editing; Jing Liu: Conceptualization, Methodology, Original draft writing and Visualization; Xin-Lei Zhou: Data analysis, Methodology and Visualization; Lu-Qi Zhang: Data analysis, Visualization, Reviewing and Editing.
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Liu, J., Zhou, XL., Zhang, LQ. et al. Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System. Water Resour Manage 37, 2991–3012 (2023). https://doi.org/10.1007/s11269-023-03471-7
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DOI: https://doi.org/10.1007/s11269-023-03471-7