Abstract
Predicting urban water demand is important in rationalizing water allocation and building smart cities. Influenced by multifarious factors, water demand is with high-frequency noise and complex patterns. It is difficult for a single learner to predict the nonlinear water demand time series. Therefore, ensemble learning is introduced in this work to predict water demand. A model (Word-embedded Temporal Feature Network, WE-TFN) for predicting water demand influenced by complex factors is proposed as a base learner. Besides, the seasonal time series model and the Principal Component Analysis and Temporal Convolutional Network (PCA-TCN) are combined with WE-TFN for ensemble learning. Based on the water demand data set provided by the Shenzhen Open Data Innovation Contest (SODIC), WE-TFN is compared with some typical models. The experimental results show that WE-TFN performs well in fitting local extreme values and predicting volatility. The ensemble learning method declines by approximately 68.73% on average on the Root Mean Square Error (RMSE) compared with a single base learner. Overall, WE-TFN and the ensemble learning method outperform baselines and perform well in water demand prediction.
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Acknowledgments
Z Xu thanks three anonymous reviewers from The University of Helsinki (Finland) for their constructive comments and three native English speakers from University College London (The United Kingdom) for helpful discussions.
Funding
This research was supported in part by National Key Research and Development Plan Key Special Projects [grant number 2018YFB2100303]; Shandong Province colleges and universities youth innovation technology plan innovation team project [grant number 2020KJN011]; Shandong Provincial Natural Science Foundation [grant number ZR2020MF060]; Program for Innovative Postdoctoral Talents in Shandong Province [grant number 40618030001]; National Natural Science Foundation of China [grant number 61802216]; and Postdoctoral Science Foundation of China [grant number 2018M642613].
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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Z Xu, Z Lv, J Li, and A Shi. The first draft of the manuscript was written by Z Xu. All authors read and approved the final manuscript.
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Xu, Z., Lv, Z., Li, J. et al. A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning. Water Resour Manage 36, 4293–4312 (2022). https://doi.org/10.1007/s11269-022-03255-5
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DOI: https://doi.org/10.1007/s11269-022-03255-5