Short-Term Water Demand Forecast Based on Deep Learning Method
Publication: Journal of Water Resources Planning and Management
Volume 144, Issue 12
Abstract
Short-time water demand forecasting is essential for optimal control in a water distribution system (WDS). Current methods (e.g., time-series models and conventional artificial neural networks) have limited power in practice due to the nonlinear nature of changes in water demand. In particular, 15-min time-step forecasting may not be accurate when using conventional models. To tackle this problem, this paper investigates the potential of deep learning in short-term water demand forecasting, developing a gated recurrent unit network (GRUN) model to forecast water demand 15 min and 24 h into the future with a 15-min time step. The performance of GRUN was compared with a conventional artificial neural network (ANN) model and seasonal autoregressive integrated moving average (SARIMA) model. A correction module was used to reduce the cumulative error. The results show that the deep learning method improves the performance of water demand prediction. The correction module enhances the performance of ANN and GRUN models. In general, deep neural network models like GRUN outperform the ANN and SARIMA models for both 15-min and 24-h forecasts. These findings can provide more flexible and effective solutions for water demand forecasting.
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Acknowledgments
This work was jointly supported by the National Key Research and Development Program of China for International Science & Innovation Cooperation Major Project between Governments (2016YFE0118800) and the Water Major Program (2017ZX07201002, 2017ZX07108002).
References
Adamowski, J., H. F. Chan, S. O. Prasher, B. Ozga-Zielinski, and A. Sliusarieva. 2012. “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada.” Water Res. Res. 48 (1): 273–279. https://doi.org/10.1029/2010WR009945.
Adamowski, J. F. 2008. “Peak daily water demand forecast modeling using artificial neural networks.” J. Water Res. Plann. Manage. 134 (2): 119–128. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(119).
Al-Mutaz, I., A. H. Ajbar, and E. Ali. 2011. “Long term water demand forecast for the city of Riyadh, Saudi Arabia.” In Proc., Int. Conf. on Water, Energy, and the Environment. Sharjah, UAE: American Univ. of Sharjah.
Arandia, E., A. Ba, B. Eck, and S. McKenna. 2016. “Tailoring seasonal time series models to forecast short-term water demand.” J. Water Res. Plann. Manage. 142 (3): 04015067. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000591.
Bakker, M., K. van Schagen, and J. Timmer. 2003. “Flow control by prediction of water demand.” J. Water Supply Res. Technol. 52 (6): 417–424. https://doi.org/10.2166/aqua.2003.0038.
Bakker, M., J. H. G. Vreeburg, K. M. van Schagen, and L. C. Rietveld. 2013. “A fully adaptive forecasting model for short-term drinking water demand.” Environ. Modell. Software 48 (5): 141–151. https://doi.org/10.1016/j.envsoft.2013.06.012.
Banjac, G., M. Vasak, and M. Baotic. 2015. “Adaptable urban water demand prediction system.” Water Sci. Technol. Water Supply 15 (5): 958–964. https://doi.org/10.2166/ws.2015.048.
Bansal, T., D. Belanger, and A. McCallum. 2016. “Ask the GRU: Multi-task learning for deep text recommendations.” In Proc., 10th ACM Conf. on Recommender Systems, 107–114. New York: ACM.
Bennett, N. D., et al. 2013. “Characterising performance of environmental models.” Environ. Modell. Software 40 (40): 1–20. https://doi.org/10.1016/j.envsoft.2012.09.011.
Bougadis, J., K. Adamowski, and R. Diduch. 2005. “Short-term municipal water demand forecasting.” Hydrol. Process. 19 (1): 137–148. https://doi.org/10.1002/hyp.5763.
Braun, M., T. Bernard, O. Piller, and F. Sedehizade. 2014. “24-hours demand forecasting based on SARIMA and support vector machines.” Procedia Eng. 89: 926–933. https://doi.org/10.1016/j.proeng.2014.11.526.
Brentan, B. M., E. Luvizotto Jr., M. Herrera, J. Izquierdo, and R. Pérez-García. 2017. “Hybrid regression model for near real-time urban water demand forecasting.” J. Comput. Appl. Math. 309: 532–541. https://doi.org/10.1016/j.cam.2016.02.009.
Cho, K., B. Van Merrienboer, D. Bahdanau, and Y. Bengio. 2014. “On the properties of neural machine translation: Encoder-decoder approaches.” Preprint, submitted September 3, 2014. http://arxiv.org/abs/1409.1259.
Chollet, F. 2015. “Keras.” Accessed September, 2017. https://github.com/fchollet/keras.
Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” Preprint, submitted December 11, 2014. http://arxiv.org/abs/1412.3555.
Cutore, P., A. Campisano, Z. Kapelan, C. Modica, and D. Savic. 2008. “Probabilistic prediction of urban water consumption using the SCEM-UA algorithm.” Urban Water J. 5 (2): 125–132. https://doi.org/10.1080/15730620701754434.
Dahl, G. E., T. N. Sainath, and G. E. Hinton. 2013. “Improving deep neural networks for LVCSR using rectified linear units and dropout.” In Vol. 26 of Proc., IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 8609–8613. Vancouver, BC: IEEE.
Dey, R., and F. M. Salemt. 2017. “Gate-variants of Gated Recurrent Unit (GRU) neural networks.” In Proc., 2017 IEEE 60th Int. Midwest Symp. on Circuits and Systems (MWSCAS), 1597–1600. Boston: IEEE.
Do, N. C., A. R. Simpson, J. W. Deuerlein, and O. Piller. 2017. “Particle filter–based model for online estimation of demand multipliers in water distribution systems under uncertainty.” J. Water Res. Plann. Manage. 143 (11): 04017065. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000841.
Donkor, E. A., T. A. Mazzuchi, R. Soyer, and J. A. Roberson. 2014. “Urban water demand forecasting: Review of methods and models.” J. Water Res. Plann. Manage. 140 (2): 146–159. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000314.
Fan, C., F. Xiao, and Y. Zhao. 2017. “A short-term building cooling load prediction method using deep learning algorithms.” Appl. Energy 195: 222–233. https://doi.org/10.1016/j.apenergy.2017.03.064.
Glorot, X., A. Bordes, and Y. Bengio. 2011. “Domain adaptation for large-scale sentiment classification: A deep learning approach.” In Proc., Int. Conf. on Machine Learning, 513–520. Bellevue, WA: Omnipress.
Gouws, S. 2012. “Deep unsupervised feature learning for natural language processing.” In Proc., 2012 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, 48–53. Stroudsburg, PA: Association for Computational Linguistics.
Herrera, M., L. Torgo, J. Izquierdo, and R. Pérez-García. 2010. “Predictive models for forecasting hourly urban water demand.” J. Hydrol. 387 (1–2): 141–150. https://doi.org/10.1016/j.jhydrol.2010.04.005.
Hutton, C., and Z. Kapelan. 2015. “Real-time burst detection in water distribution systems using a Bayesian demand forecasting methodology.” Procedia Eng. 119 (1): 13–18. https://doi.org/10.1016/j.proeng.2015.08.847.
Hutton, C. J., Z. Kapelan, L. Vamvakeridou-Lyroudia, and D. A. Savic. 2012. “Real-time demand estimation in water distribution systems under uncertainty.” In Proc., WDSA 2012: 14th Water Distribution Systems Analysis Conf. Adelaide, Australia: Engineers Australia.
Kofinas, D., N. Mellios, E. Papageorgiou, and C. Laspidou. 2014. “Urban water demand forecasting for the island of Skiathos.” Procedia Eng. 89: 1023–1030. https://doi.org/10.1016/j.proeng.2014.11.220.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
Maier, H. R., A. Jain, G. C. Dandy, and K. P. Sudheer. 2010. “Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions.” Environ. Modell. Software 25 (8): 891–909. https://doi.org/10.1016/j.envsoft.2010.02.003.
Mouatadid, S., and J. Adamowski. 2016. “Using extreme learning machines for short-term urban water demand forecasting.” Urban Water J. 14 (6): 630–638. https://doi.org/10.1080/1573062X.2016.1236133.
Nair, V., and G. E. Hinton. 2010. “Rectified linear units improve restricted Boltzmann machines.” In Proc., 27th Int. Conf. on Machine Learning, 807–814. Bellevue, WA: Omnipress.
Oliveira, P. J., J. L. Steffen, and P. Cheung. 2017. “Parameter estimation of seasonal ARIMA models for water demand forecasting using the harmony search algorithm.” Procedia Eng. 186: 177–185. https://doi.org/10.1016/j.proeng.2017.03.225.
Polebitski, A. S., and R. N. Palmer. 2010. “Seasonal residential water demand forecasting for census tracts.” J. Water Res. Plann. Manage. 136 (1): 27–36. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000003.
Preis, A., A. Whittle, and A. Ostfeld. 2009. “Online hydraulic state prediction for water distribution systems.” Am. Soc. Civ. Eng. 37 (342): 323–345. https://doi.org/10.1061/41036(342)32.
Romano, M., and Z. Kapelan. 2014. “Adaptive water demand forecasting for near real-time management of smart water distribution systems.” Environ. Modell. Software 60 (7): 265–276. https://doi.org/10.1016/j.envsoft.2014.06.016.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Networks 61: 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Shumway, R. H., and D. S. Stoffer. 2017. “ARIMA models.” In Time series analysis and its applications. Cham, Switzerland: Springer.
Sun, Y., Y. Chen, X. Wang, and X. Tang. 2014. “Deep learning face representation by joint identification-verification.” Preprint, submitted June 18, 2014. https://arxiv.org/abs/1406.4773.
Theano Development Team. 2016. “Theano: A Python framework for fast computation of mathematical expressions.” Preprint, submitted May 9, 2016. http://arxiv.org/abs/1605.02688.
Tiwari, M. K., and J. F. Adamowski. 2015. “An ensemble wavelet bootstrap machine learning approach to water demand forecasting: A case study in the city of Calgary, Canada.” Urban Water J. 14 (2): 185–201. https://doi.org/10.1080/1573062X.2015.1084011.
Wong, J. S., Q. Zhang, and Y. D. Chen. 2010. “Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast.” Water Res. Res. 16 (3): 335–343. https://doi.org/10.1016/0043-1354(82)90194-4.
Zhou, S. L., T. A. McMahon, A. Walton, and J. Lewis. 2000. “Forecasting daily urban water demand: A case study of Melbourne.” J. Hydrol. 236 (3–4): 153–164. https://doi.org/10.1016/S0022-1694(00)00287-0.
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Published In
Journal of Water Resources Planning and Management
Volume 144 • Issue 12 • December 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Sep 22, 2017
Accepted: May 16, 2018
Published online: Sep 27, 2018
Published in print: Dec 1, 2018
Discussion open until: Feb 27, 2019
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