Hybrid Models for Water Demand Forecasting
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 2
Abstract
An accurate prediction of future water consumption is necessary to create a satisfactory design for a water distribution system. In this study, two new hybrid approaches are proposed for accurately predicting future hourly and monthly water demands. The first approach is based on the hybridization of ensemble empirical mode decomposition (EEMD) and difference pattern sequence forecasting (DPSF), and the second is based on the hybridization of EEMD with DPSF and autoregressive integrated moving average (ARIMA). Historical hourly water consumption datasets of southeastern Spain and monthly datasets of Nagpur, India are used for assessing the performance of the proposed approaches. The performance of the EEMD-DPSF approach is checked using the root mean square error (RMSE), mean absolute error (MAE), and mean percentage absolute error (MAPE). Further, the results are compared with those obtained using PSF, ARIMA, DPSF, their hybrid models, and various other ANN models. The proposed EEMD-DPSF method is found to perform significantly better than the other state-of-the-art methods in terms of prediction accuracy without compromising time and memory complexities. The comparison between the two proposed models demonstrates that the EEMD-DPSF approach provides better results, whereas the EEMD-DPSF-ARIMA approach requires shorter computational time.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code used during the study are proprietary or confidential in nature and may only be provided with restrictions. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
Neeraj Bokde was supported by the R&D project work undertaken under the Visvesvaraya Ph.D. Scheme of the Ministry of Electronics and Information Technology, Government of India, implemented by the Digital India Corporation. We also acknowledge Dr. Manuel Herrera for providing the Spanish dataset and Mr. Sanjoy Roy, Chief Officer, Orange City Water Pvt. Ltd, Nagpur, for Indian dataset used in this study. Corrections made by Dr. B. S. Murty, Professor, IIT Chennai, India and Dr. Tiku Tanyimboh, Professor, University of Witwatersrand, Johannesburg, South Africa are gratefully acknowledged by the authors.
References
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.
Agenis, M., and N. Bokde. 2019. “GuessCompx: Empirically estimates algorithm complexity.” Accessed November 19, 2019. https://CRAN.R-project.org/package=GuessCompx.
Agenis-Nevers, M., N. D. Bokde, Z. M. Yaseen, and M. Shende. Forthcoming. “An empirical estimation for time and memory algorithm complexities: Newly developed R package.” Multimedia Tools Appl. 1–19. https://doi.org/10.1007/s11042-020-09471-8.
Al-Saati, N. 1998. Forecasting water consumption by Box-Jenkins ARIMA modeling. Baghdad, Iraq: Foundation of Technical Education.
Altunkaynak, A., and T. Nigussie. 2017. “Monthly water consumption prediction using season algorithm and wavelet transform–based models.” J. Water Resour. Plann. Manage. 143 (6): 04017011. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000761.
Alvarez, F. M., A. Troncoso, J. C. Riquelme, and J. S. A. Ruiz. 2011. “Energy time series forecasting based on pattern sequence similarity.” IEEE Trans. Knowl. Data Eng. 23 (8): 1230–1243. https://doi.org/10.1109/TKDE.2010.227.
Anele, A., Y. Hamam, A. Abu-Mahfouz, and E. Todini. 2017. “Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction.” Water 9 (11): 887. https://doi.org/10.3390/w9110887.
Anele, A., E. Todini, Y. Hamam, and A. Abu-Mahfouz. 2018. “Predictive uncertainty estimation in water demand forecasting using the model conditional processor.” Water 10 (4): 475. https://doi.org/10.3390/w10040475.
Arandia, E., A. Ba, B. Eck, and S. McKenna. 2016. “Tailoring seasonal time series models to forecast short-term water demand.” J. Water Resour. Plann. Manage. 142 (3): 04015067. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000591.
Azadeh, A., N. Neshat, and H. Hamidipour. 2012. “Hybrid fuzzy regression–artificial neural network for improvement of short-term water consumption estimation and forecasting in uncertain and complex environments: Case of a large metropolitan city.” J. Water Resour. Plann. Manage. 138 (1): 71–75. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000152.
Bai, Y., P. Wang, C. Li, J. Xie, and Y. Wang. 2014a. “Dynamic forecast of daily urban water consumption using a variable-structure support vector regression model.” J. Water Resour. Plann. Manage. 141 (3): 04014058. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000457.
Bai, Y., P. Wang, C. Li, J. Xie, and Y. Wang. 2014b. “A multi-scale relevance vector regression approach for daily urban water demand forecasting.” J. Hydrol. 517 (Sep): 236–245. https://doi.org/10.1016/j.jhydrol.2014.05.033.
Bokde, N., G. Asencio-Cortes, and F. Martinez-Alvarez. 2017a. PSF: Forecasting of univariate time series using the pattern sequence-based forecasting (PSF) algorithm. Vienna, Austria: R Foundation for Statistical Computing.
Bokde, N., G. Asencio-Cortés, F. Martínez-Álvarez, and K. Kulat. 2017b. “PSF: Introduction to R package for pattern sequence based forecasting algorithm.” R J. 9 (1): 324–333. https://doi.org/10.32614/RJ-2017-021.
Bokde, N., A. Feijóo, N. Al-Ansari, S. Tao, and Z. M. Yaseen. 2020a. “The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling.” Energies 13 (7): 1666. https://doi.org/10.3390/en13071666.
Bokde, N., A. Feijoo, and K. Kulat. 2018. “Analysis of differencing and decomposition preprocessing methods for wind speed prediction.” Appl. Soft Comput. 71 (Oct): 926–938. https://doi.org/10.1016/j.asoc.2018.07.041.
Bokde, N., A. Feijóo, D. Villanueva, and K. Kulat. 2019. “A review on hybrid empirical mode decomposition models for wind speed and wind power prediction.” Energies 12 (2): 254. https://doi.org/10.3390/en12020254.
Bokde, N. D., and G. B. Andersen. 2020. “ForecastTB: Test bench for the comparison of forecast methods.” Accessed May 19, 2020. https://CRAN.R-project.org/package=ForecastTB.
Bokde, N. D., Z. M. Yaseen, and G. B. Andersen. 2020b. “ForecastTB—An R package as a test-bench for time series forecasting—Application of wind speed and solar radiation modeling.” Energies 13 (10): 2578. https://doi.org/10.3390/en13102578.
Box, G., and G. Jenkins. 1973. “Some comments on a paper by Chatfield and Prothero and on a review by Kendall.” J. R. Stat. Soc., Ser. A 136 (3): 337–352. https://doi.org/10.2307/2344995.
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 (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.
Candelieri, A., D. Soldi, and F. Archetti. 2015. “Short-term forecasting of hourly water consumption by using automatic metering readers data.” Procedia Eng. 119 (1): 844–853. https://doi.org/10.1016/j.proeng.2015.08.948.
Chen, G., T. Long, J. Xiong, and Y. Bai. 2017. “Multiple random forests modelling for urban water consumption forecasting.” Water Resour. Manage. 31 (15): 4715–4729. https://doi.org/10.1007/s11269-017-1774-7.
Dong, J., W. Dai, L. Tang, and L. Yu. 2019. “Why do EMD-based methods improve prediction? A multiscale complexity perspective.” J. Forecasting 38 (7): 714–731. https://doi.org/10.1002/for.2593.
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.
Fujimoto, Y., and Y. Hayashi. 2012. “Pattern sequence-based energy demand forecast using photovoltaic energy records.” In Proc., Int. Conf. on Renewable Energy Research and Applications (ICRERA), 1–6. New York: IEEE.
Gagliardi, F., S. Alvisi, M. Franchini, and M. Guidorzi. 2017. “A comparison between pattern-based and neural network short-term water demand forecasting models.” Water Sci. Technol. Water Supply 17 (5): 1426–1435. https://doi.org/10.2166/ws.2017.045.
Guo, G., and S. Liu. 2018. “Short-term water demand forecast based on deep neural network.” In Vol. 1 of Proc., WDSA/CCWI Joint Conf. Kingston, ON, Canada: Queen’s Univ.
Gupta, A., N. Bokde, and K. Kulat. 2018. “Hybrid leakage management for water network using PSF algorithm and soft computing techniques.” Water Resour. Manage. 32 (3): 1133–1151. https://doi.org/10.1007/s11269-017-1859-3.
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.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. R. Soc. London, Ser. A 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Hyndman, R. J., and Y. Khandakar. 2007. Automatic time series for forecasting: The forecast package for R. Clayton, Australia: Dept. of Econometrics and Business Statistics, Monash Univ.
Kavasseri, R. G., and K. Seetharaman. 2009. “Day-ahead wind speed forecasting using f-ARIMA models.” Renew. Energy 34 (5): 1388–1393. https://doi.org/10.1016/j.renene.2008.09.006.
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.
Kwiatkowski, D., P. C. Phillips, P. Schmidt, and Y. Shin. 1992. “Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?” J. Econom. 54 (1–3): 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y.
Laio, F., and S. Tamea. 2007. “Verification tools for probabilistic forecasts of continuous hydrological variables.” Hydrol. Earth Syst. Sci. 11 (4): 1267–1277.
Liu, H., C. Chen, H.-Q. Tian, and Y.-F. Li. 2012a. “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks.” Renew. Energy 48 (Dec): 545–556. https://doi.org/10.1016/j.renene.2012.06.012.
Liu, H., H.-Q. Tian, and Y.-F. Li. 2012b. “Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction.” Appl. Energy 98 (Oct): 415–424. https://doi.org/10.1016/j.apenergy.2012.04.001.
Majidpour, M., C. Qiu, P. Chu, R. Gadh, and H. R. Pota. 2014. “Modified pattern sequence-based forecasting for electric vehicle charging stations.” In Proc., Int. Conf. on Smart Grid Communications (SmartGridComm), 710–715. New York: IEEE.
Martnez-Álvarez, F., A. Troncoso, J. C. Riquelme, and J. S. Aguilar-Ruiz. 2008. “LBF: A labeled-based forecasting algorithm and its application to electricity price time series.” In Proc., IEEE Int. Conf. on Data Mining, 453–461. New York: IEEE.
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.
Punmia, B. 1977. Water supply engineering, 24. New Delhi, India: Standard Book House.
Romano, M., and Z. Kapelan. 2014. “Adaptive water demand forecasting for near real-time management of smart water distribution systems.” Environ. Modell. Software 60 (Oct): 265–276. https://doi.org/10.1016/j.envsoft.2014.06.016.
Rousseeuw, P. J. 1987. “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” J. Comput. Appl. Math. 20: 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
Seo, Y., S. Kwon, and Y. Choi. 2018. “Short-term water demand forecasting model combining variational mode decomposition and extreme learning machine.” Hydrology 5 (4): 54. https://doi.org/10.3390/hydrology5040054.
Shabri, A., and R. Samsudin. 2014. “A new approach for water demand forecasting based on empirical mode decomposition.” In Proc., 8th Malaysian Software Engineering Conf. (MySEC), 284–288. New York: IEEE.
Shabri, A., R. Samsudin, and U. Teknologi. 2015. “Empirical mode decomposition–least squares support vector machine based for water demand forecasting.” Int. J. Adv. Soft Comput. Appl. 7 (2): 38–53.
Wang, S., N. Zhang, L. Wu, and Y. Wang. 2016. “Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method.” Renew. Energy 94 (Aug): 629–636. https://doi.org/10.1016/j.renene.2016.03.103.
Wu, Z., and N. E. Huang. 2009. “Ensemble empirical mode decomposition: A noise-assisted data analysis method.” Adv. Adapt. Data Anal. 1 (1): 1–41. https://doi.org/10.1142/S1793536909000047.
Wu, Z., N. E. Huang, and X. Chen. 2009. “The multi-dimensional ensemble empirical mode decomposition method.” Adv. Adapt. Data Anal. 1 (3): 339–372. https://doi.org/10.1142/S1793536909000187.
Xu, Y., J. Zhang, Z. Long, and Y. Chen. 2018. “A novel dual-scale deep belief network method for daily urban water demand forecasting.” Energies 11 (5): 1068. https://doi.org/10.3390/en11051068.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jul 18, 2019
Accepted: Sep 17, 2020
Published online: Dec 4, 2020
Published in print: Feb 1, 2021
Discussion open until: May 4, 2021
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.