Urban Water Demand Forecasting: Review of Methods and Models
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 2
Abstract
This paper reviews the literature on urban water demand forecasting published from 2000 to 2010 to identify methods and models useful for specific water utility decision making problems. Results show that although a wide variety of methods and models have attracted attention, applications of these models differ, depending on the forecast variable, its periodicity and the forecast horizon. Whereas artificial neural networks are more likely to be used for short-term forecasting, econometric models, coupled with simulation or scenario-based forecasting, tend to be used for long-term strategic decisions. Much more attention needs to be given to probabilistic forecasting methods if utilities are to make decisions that reflect the level of uncertainty in future demand forecasts.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study was funded by the National Oceanic and Atmospheric Administration’s (NOAA) Climate Program Office through the American Water Works Association, as part of the project “Decreasing climate induced risk for municipal water demand forecasting.” We appreciate the feedback received from participants of two AWWA workshops designed to discuss outputs of this study and from three anonymous reviewers of this paper. Our citing of commercial software products and consulting entities is for illustrative purposes only and not an endorsement of their preference over other equally good alternatives.
References
Adamowski, J., and Karapataki, C. (2010). “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms.” J. Hydrol. Eng., 729–743.
Alhumoud, J. (2008). “Freshwater consumption in Kuwait: Analysis and forecasting.” J. Water Supply: Res. Technol.—AQUA, 57(4), 279–288.
Altunkaynak, A., Özger, M., and Çakmakc, M. (2005). “Water consumption prediction of Istanbul city by using fuzzy logic approach.” Water Resour. Manage., 19(5), 641–654.
Alvisi, S., Franchini, M., and Marinelli, A. (2007). “A short-term, pattern-based model for water-demand forecasting.” J. Hydroinf., 9(1), 39–50.
Aly, A., and Wanakule, N. (2004). “Short-term forecasting for urban water consumption.” J. Water Resour. Plann. Manage., 405–410.
Arbués, F., Garcıa-Valiñas, M. Á., and Martınez-Espiñeira, R. (2003). “Estimation of residential water demand: A state-of-the-art review.” J. Soc. Econ., 32(1), 81–102.
Billings, B., and Jones, C. (2008). Forecasting urban water demand, 2nd Ed., American Waterworks Association, Denver, CO.
Bougadis, J., Adamowski, K., and Diduch, R. (2005). “Short-term municipal water demand forecasting.” Hydrolog. Process., 19(1), 137–148.
Box, G. E. P., Pierce, D. A., and Newbold, P. (1987). “Estimating trend and growth rates in seasonal time series.” J. Am. Stat. Assoc., 82(397), 276–282.
Brekke, L., Larsen, M., Ausburn, M., and Takaichi, L. (2002). “Suburban water demand modeling using stepwise regression.” J. Am. Water Works Assoc., 94(10), 65–75.
Burney, N., Mukhopadhyay, A., Al-Mussallam, N., Akber, A., and Al-Awadi, E. (2001). “Forecasting of freshwater demand in Kuwait.” Arabian J. Sci. Eng., 26(2B), 99–113.
Caiado, J. (2010). “Performance of combined double seasonal univariate time series models for forecasting water demand.” J. Hydrol. Eng., 215–222.
Clemen, R. (1989). “Combining forecasts: A review and annotated bibliography.” Int. J. Forecasting, 5(4), 559–583.
Coomes, P., Rockaway, T., Rivard, J., and Kornstein, B. (2010). North America residential water usage trends since 1992, Water Research Foundation, Denver.
Cutore, P., Campisano, A., Kapelan, Z., Modica, C., and Savic, D. (2008). “Probabilistic prediction of urban water consumption using the SCEM-UA algorithm.” Urban Water J., 5(2), 125–132.
Feng, S., Li, L., Duan, Z., and Zhang, J. (2007). “Assessing the impacts of south-to-north water transfer project with decision support systems.” Decis. Support Syst., 42(4), 1989–2003.
Firat, M., Yurdusev, M., and Turan, M. (2009). “Evaluation of artificial neural network techniques for municipal water consumption modeling.” Water Resour. Manage., 23(4), 617–632.
Fischer, C., Herrnstadt, E., and Morgenstern, R. (2009). “Understanding errors in EIA projections of energy demand.” Resour. Energy Econ., 31(3), 198–209.
Froukh, M. (2001). “Decision-support system for domestic water demand forecasting and management.” Water Resour. Manage., 15(6), 363–382.
Gardiner, V., and Herrington, P. (1990). Water demand forecasting, 1st Ed., Spon Press, Norwich, U.K.
Gardner, E. (2006). “Exponential smoothing: The state of the art-part II.” Int. J. Forecasting, 22(4), 637–666.
Gato, S., Jayasuriya, N., and Roberts, P. (2007a). “Forecasting residential water demand: Case study.” J. Water Resour. Plann. Manage., 309–319.
Gato, S., Jayasuriya, N., and Roberts, P. (2007b). “Temperature and rainfall thresholds for base use urban water demand modelling.” J. Hydrol., 337(3–4), 364–376.
Ghiassi, M., Zimbra, D., and Saidane, H. (2008). “Urban water demand forecasting with a dynamic artificial neural network model.” J. Water Resour. Plann. Manage., 138–146.
Goodchild, C. (2003). “Modelling the impact of climate change on domestic water demand.” Water Environ. J., 17(1), 8–12.
Hagen, E., Holmes, K., Kiang, J., and Steiner, R. (2005). “Benefits of iterative water supply forecasting in the Washington, DC, metropolitan area.” J. Am. Water Resour. Assoc., 41(6), 1417–1430.
Hazen, Sawyer and PMCL. (2004). “The Tampa Bay water long-term demand forecasting model.” 〈http://www.tampabaywater.org/documents/conservation/LongtermDemandForecastingModelJune2003.pdf〉 (Jun. 04, 2012).
Herrera, M., Torgo, L., Izquierdo, J., and Perez-Garcia, R. (2010). “Predictive models for forecasting hourly urban water demand.” J. Hydrol., 387(1–2), 141–150.
Hyndman, R., Koehler, A., Ord, J., and Snyder, R. (2005). “Prediction intervals for exponential smoothing using two new classes of state-space models.” J. Forecasting, 24(1), 17–37.
Jain, A., and Ormsbee, L. (2001). “A decision support system for drought characterization and management.” Civ. Eng. Environ. Syst., 18(2), 105–140.
Jain, A., and Ormsbee, L. (2002). “Short-term water demand forecast modeling techniques—conventional methods versus AI.” J. Am. Water Works Assoc., 94(7), 64–72.
Jain, A., Varshney, A., and Joshi, U. (2001). “Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks.” Water Resour. Manage., 15(5), 299–321.
Jentgen, L., Kidder, H., Hill, R., and Conrad, S. (2007). “Energy management strategies use short-term water consumption forecasting to minimize cost of pumping operations.” J. Am. Water Works Assoc., 99(6), 86–94.
Lee, S., Wentz, E., and Gober, P. (2010). “Space-time forecasting using soft geostatistics: A case study in forecasting municipal water demand for Phoenix, Arizona.” Stochastic Environ. Res. Risk Assess., 24(2), 283–295.
Levin, E., Maddaus, W., Sandkulla, N., and Pohl, H. (2006). “Forecasting wholesale demand and conservation savings.” J. Am. Water Works Assoc., 98(2), 102–111.
Martinez-Espineira, R. (2007). “An estimation of residential water demand using co-integration and error correction techniques.” J. Appl. Econ., 10(1), 161–184.
Miaou, S. P. (1990). “A stepwise time series regression procedure for water demand model identification.” Water Resour. Res., 26(9), 1887–1897.
Mohamed, M., and Al-Mualla, A. (2010a). “Water demand forecasting in Umm Al-Quwain (UAE) using the IWR-MAIN specify forecasting model.” Water Resour. Manage., 24(14), 4093–4120.
Mohamed, M., and Al-Mualla, A. (2010b). “Water demand forecasting in Umm Al-Quwain using the constant rate model.” Desalination, 259(1–3), 161–168.
Mun, J. (2010). Modeling risk: Applying Monte Carlo risk simulation, strategic real options, stochastic forecasting, and portfolio optimization, 2nd Ed., Wiley, Hoboken, NJ.
Palisade Inc. (n.d). “Case study: ADWEC uses @RISK from Palisade to forecast water demand in Abu Dhabi—Palisade.” 〈http://www.palisade.com/cases/adwec.asp〉 (Jun. 4, 2012).
Polebitski, A., and Palmer, R. (2010). “Seasonal residential water demand forecasting for census tracts.” J. Water Resour. Plann. Manage., 27–36.
Polebitski, A., Palmer, R., and Waddell, P. (2011). “Evaluating water demands under climate change and transitions in the urban environment.” J. Water Resour. Plann. Manage., 249–257.
Shlyakhter, A., Kammen, D., Broido, C., and Wilson, R. (1994). “Quantifying the credibility of energy projections from trends in past data—The United-States energy sector.” Energy Policy, 22(2), 119–130.
Strzepek, K., et al. (1999). “New methods of modeling water availability for agriculture under climate change: The US cornbelt.” J. Am. Water Resour. Assoc., 35(6), 1639–1655.
Taylor, S. (1981). “Initialization of exponential smoothing forecasts.” AIIE Trans., 13(3), 199–205.
Wang, X., Sun, Y., Song, L., and Mei, C. (2009). “An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. II: Model application and results.” J. Environ. Manage., 90(8), 2612–2619.
Wei, S., Lei, A., and Islam, S. (2010). “Modeling and simulation of industrial water demand of Beijing municipality in China.” Frontiers of environmental science and engineering in China, 4(1), 91–101.
Wei, W. (2005). Time series analysis: Univariate and multivariate methods, 2nd Ed., Addison Wesley, Boston, MA.
Williamson, P., Mitchell, G., and McDonald, A. (2002). “Domestic water demand forecasting: A static microsimulation approach.” Water Environ. J., 16(4), 243–248.
Wu, L., and Zhou, H. (2010). “Urban water demand forecasting based on HP filter and fuzzy neural network.” J. Hydroinf., 12(2), 172–184.
Zhou, S., McMahon, T., Walton, A., and Lewis, J. (2000). “Forecasting daily urban water demand: A case study of Melbourne.” J. Hydrol., 236(3–4), 153–164.
Zhou, S., McMahon, T., Walton, A., and Lewis, J. (2002). “Forecasting operational demand for an urban water supply zone.” J. Hydrol., 259(1–4), 189–202.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 2 • February 2014
Pages: 146 - 159
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Mar 27, 2012
Accepted: Sep 10, 2012
Published online: Sep 13, 2012
Discussion open until: Feb 13, 2013
Published in print: Feb 1, 2014
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.