An Ensemble Neural Network Model to Forecast Drinking Water Consumption
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 5
Abstract
A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This study was partially funded by the project “TESES-Urb–Techno-Economic Methodologies to Investigate Sustainable Energy Scenarios at the Urban Level” of the Free University of Bozen-Bolzano. The authors would like to thank the anonymous reviewers for their valuable contribution. They would also like to thank Novareti S.p.A. for providing the data for this study.
References
Abadi, M., et al. 2016. “Tensorflow: A system for large-scale machine learning.” In Proc., 12th Symp. on Operating Systems Design and Implementation 16, 265–283. Berkeley, CA: USENIX.
Adamowski, J., H. Fung 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 Resour. Res. 48 (1): W01528. https://doi.org/10.1029/2010WR009945.
Adamowski, J., and C. Karapataki. 2010. “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms.” J. Hydrol. Eng. 15 (10): 729–743. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000245.
Altunkaynak, A., and T. A. Nigussie. 2018. “Monthly water demand prediction using wavelet transform, first-order differencing and linear detrending techniques based on multilayer perceptron models.” Urban Water J. 15 (2): 177–181. https://doi.org/10.1080/1573062X.2018.1424219.
Ambrosio, J. K., B. M. Brentan, M. Herrera, E. Luvizotto, L. Ribeiro, and J. Izquierdo. 2019. “Committee machines for hourly water demand forecasting in water supply systems.” Math. Problems Eng. 2019 (1): 1–11. https://doi.org/10.1155/2019/9765468.
Bakker, M., J. H. G. Vreeburg, L. J. Palmen, V. Sperber, G. Bakker, and L. C. Rietveld. 2013a. “Better water quality and higher energy efficiency by using model predictive flow control at water supply systems.” J. Water Supply Res. Technol. AQUA 62 (1): 1–13. https://doi.org/10.2166/aqua.2013.063.
Bakker, M., J. H. G. Vreeburg, K. M. van Schagen, and L. C. Rietveld. 2013b. “A fully adaptive forecasting model for short-term drinking water demand.” Environ. Modell. Software 48 (Oct): 141–151. https://doi.org/10.1016/j.envsoft.2013.06.012.
Bata, M. H., R. Carriveau, and D. S.-K. Ting. 2020. “Short-term water demand forecasting using nonlinear autoregressive artificial neural networks.” J. Water Resour. Plann. Manage. 146 (3): 04020008. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001165.
Bengio, Y., P. Simard, P. Frasconi. 1994. “Learning long-term dependencies with gradient descent is difficult.” IEEE Trans. Neural Networks 5 (2): 157–166.
Bennett, N. D., et al. 2013. “Characterising performance of environmental models.” Environ. Modell. Software 40 (Feb): 1–20. https://doi.org/10.1016/j.envsoft.2012.09.011.
Billings, R. B., and C. V. Jones. 2011. Forecasting urban water demand. Denver: American Water Works Association.
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 (Jan): 926–933. https://doi.org/10.1016/j.proeng.2014.11.526.
Brentan, B. M., E. Luvizotto, 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 (Jan): 532–541. https://doi.org/10.1016/j.cam.2016.02.009.
Candelieri, A. 2017. “Clustering and support vector regression for water demand forecasting and anomaly detection.” Water 9 (3): 224. https://doi.org/10.3390/w9030224.
Cho, K., B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” Preprint, submitted on June 3, 2014. http://arxiv.org/abs/1406.1078.
Chollet, F. 2015. “Keras.” Accessed December 19, 2020. https://github.com/fchollet/keras.
Donkor, E. A., T. A. Mazzuchi, R. Soyer, and J. Alan Roberson. 2014. “Urban water demand forecasting: Review of methods and models.” J. Water Resour. Plann. Manage. 140 (2): 146–159. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000314.
Elman, J. L. 1990. “Finding structure in time.” Cogn. Sci. 14 (2): 179–211. https://doi.org/10.1207/s15516709cog1402_1.
Gardiner, V., and P. Herrington. 1986. Water demand forecasting. London: CRC Press.
Gargano, R., C. Tricarico, G. del Giudice, and F. Granata. 2016. “A stochastic model for daily residential water demand.” Water Supply 16 (6): 1753–1767. https://doi.org/10.2166/ws.2016.102.
Ghalehkhondabi, I., E. Ardjmand, W. A. Young, and G. R. Weckman. 2017. “Water demand forecasting: Review of soft computing methods.” Environ. Monit. Assess. 189 (7): 313. https://doi.org/10.1007/s10661-017-6030-3.
Ghiassi, M., D. K. Zimbra, and H. Saidane. 2008. “Urban water demand forecasting with a dynamic artificial neural network model.” J. Water Resour. Plann. Manage. 134 (2): 138–146. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(138).
Graves, A. 2014. “Generating sequences with recurrent neural networks.” Preprint, submitted August 4, 2013. http://arxiv.org/abs/1308.0850.
Guo, G., S. Liu, Y. Wu, J. Li, R. Zhou, and X. Zhu. 2018. “Short-term water demand forecast based on deep learning method.” J. Water Resour. Plann. Manage. 144 (12): 04018076. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992.
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.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780.
House-Peters, L. A., and H. Chang. 2011. “Urban water demand modeling: Review of concepts, methods, and organizing principles: Review.” Water Resour. Res. 47 (5): 11. https://doi.org/10.1029/2010WR009624.
Jordan, M. I. 1997. “Serial order: A parallel distributed processing approach.” In Vol. 121 of Advances in psychology, 471–495. Amsterdam, Netherlands: Elsevier.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. http://arxiv.org/abs/1412.6980.
Menapace, A., A. Zanfei, M. Felicetti, D. Avesani, M. Righetti, and R. Gargano. 2020. “Burst detection in water distribution systems: The issue of dataset collection.” Appl. Sci. 10 (22): 8219. https://doi.org/10.3390/app10228219.
Menapace, A., A. Zanfei, and M. Righetti. 2021. “Tuning ANN hyperparameters for forecasting drinking water demand.” Appl. Sci. 11 (9): 4290. https://doi.org/10.3390/app11094290.
Mouatadid, S., and J. Adamowski. 2017. “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.
Msiza, I. S., F. V. Nelwamondo, and T. Marwala. 2007. “Artificial neural networks and support vector machines for water demand time series forecasting.” In Proc., 2007 IEEE Int. Conf. on Systems, Man and Cybernetics, 638–643. New York: IEEE.
Mu, L., F. Zheng, R. Tao, Q. Zhang, and Z. Kapelan. 2020. “Hourly and daily urban water demand predictions using a long short-term memory based model.” J. Water Resour. Plann. Manage. 146 (9): 05020017. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001276.
Nair, V., and G. E. Hinton. 2010. “Rectified linear units improve restricted Boltzmann machines.” Accessed June 21, 2010. https://dl.acm.org/doi/10.5555/3104322.3104425.
Nunes Carvalho, T. M., F. de Souza Filho, and V. C. Porto. 2021. “Urban water demand modeling using machine learning techniques: Case study of Fortaleza, Brazil.” J. Water Resour. Plann. Manage. 147 (1): 05020026. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001310.
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 (Jan): 177–185. https://doi.org/10.1016/j.proeng.2017.03.225.
Pandey, P., N. D. Bokde, S. Dongre, and R. Gupta. 2021. “Hybrid models for water demand forecasting.” J. Water Resour. Plann. Manage. 147 (2): 04020106. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001331.
Pascanu, R., C. Gulcehre, K. Cho, and Y. Bengio. 2014. “How to construct deep recurrent neural networks.” Preprint, submitted December 20, 2013. http://arxiv.org/abs/1312.6026.
Petneházi, G. 2018. “Recurrent neural networks for time series forecasting.” Preprint, submitted January 1, 2019. http://arxiv.org/abs/1901.00069.
Polebitski, A. S., and R. N. Palmer. 2010. “Seasonal residential water demand forecasting for census tracts.” J. Water Resour. Plann. Manage. 136 (1): 27–36. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000003.
Santopietro, S., R. Gargano, F. Granata, and G. de Marinis. 2020. “Generation of water demand time series through spline curves.” J. Water Resour. Plann. Manage. 146 (11): 04020080. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001282.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Networks 61 (Jan): 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Sebri, M. 2016. “Forecasting urban water demand: A meta-regression analysis.” J. Environ. Manage. 183 (Dec): 777–785. https://doi.org/10.1016/j.jenvman.2016.09.032.
Wei, S., A. Lei, and S. N. Islam. 2010. “Modeling and simulation of industrial water demand of Beijing municipality in China.” Front. Environ. Sci. Eng. China 4 (1): 91–101. https://doi.org/10.1007/s11783-010-0007-6.
Xenochristou, M., and Z. Kapelan. 2020. “An ensemble stacked model with bias correction for improved water demand forecasting.” Urban Water J. 17 (3): 212–223. https://doi.org/10.1080/1573062X.2020.1758164.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 17, 2021
Accepted: Dec 21, 2021
Published online: Mar 10, 2022
Published in print: May 1, 2022
Discussion open until: Aug 10, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Hauwa Mohammed Mustafa, Gasim Hayder, S. I. Abba, Abeer D. Algarni, Mohammed Mnzool, Abdurahman H. Nour, Performance Evaluation of Hydroponic Wastewater Treatment Plant Integrated with Ensemble Learning Techniques: A Feature Selection Approach, Processes, 10.3390/pr11020478, 11, 2, (478), (2023).
- Ariele Zanfei, Andrea Menapace, Maurizio Righetti, An artificial intelligence approach for managing water demand in water supply systems, IOP Conference Series: Earth and Environmental Science, 10.1088/1755-1315/1136/1/012004, 1136, 1, (012004), (2023).
- Volkan Yilmaz, Mehmet Alpars, An Investigation of the Temporal Interaction of Urban Water Consumption in the Framework of Settlement Characteristics, Water Resources Management, 10.1007/s11269-023-03447-7, (2023).
- Xiaoying Pan, Kai Cai, Lifeng Wu, Using a Grey Niche Model to Predict the Water Consumption in 31 Regions of China, Water, 10.3390/w14121883, 14, 12, (1883), (2022).
- Andrea Menapace, Ariele Zanfei, Alberto De Luca, David Di Pauli, Maurizio Righetti, Towards a Digital Twin Model for the Management of the Laives Aqueduct, EWaS5, 10.3390/environsciproc2022021070, (70), (2022).
- Ariele Zanfei, Bruno Melo Brentan, Andrea Menapace, Maurizio Righetti, A short-term water demand forecasting model using multivariate long short-term memory with meteorological data, Journal of Hydroinformatics, 10.2166/hydro.2022.055, 24, 5, (1053-1065), (2022).
- Ariele Zanfei, Andrea Menapace, Bruno Melo Brentan, Maurizio Righetti, How Does Missing Data Imputation Affect the Forecasting of Urban Water Demand?, Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0001624, 148, 11, (2022).
- Ariele Zanfei, Bruno M. Brentan, Andrea Menapace, Maurizio Righetti, Manuel Herrera, Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting, Water Resources Research, 10.1029/2022WR032299, 58, 7, (2022).
- Ariele Zanfei, Andrea Menapace, Bruno M. Brentan, Maurizio Righetti, Manuel Herrera, Novel approach for burst detection in water distribution systems based on graph neural networks, Sustainable Cities and Society, 10.1016/j.scs.2022.104090, 86, (104090), (2022).