Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 2
Abstract
This paper presents the development of a dynamic artificial neural network model (DAN2) for comprehensive urban water demand forecasting. Accurate short-, medium-, and long-term demand forecasting provides water distribution companies with information for capacity planning, maintenance activities, system improvements, pumping operations optimization, and the development of purchasing strategies. We examine the effects of including weather information in the forecasting models and show that such inclusion can improve accuracy. However, we demonstrate that by using time series water demand data, DAN2 models can provide excellent fit and forecasts without reliance upon the explicit inclusion of weather factors. All models are validated using data from an actual water distribution system. The monthly, weekly, and daily models produce forecasting accuracies above 99%, and the hourly models above 97%. The excellent model accuracy demonstrates the effectiveness of DAN2 in forecasting urban water demand across all time horizons. Finally, we compare our results with those of an autoregressive integrated moving average model and a traditional artificial neural network model.
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Acknowledgments
The writers would like to thank the San Jose Water Company for providing the data for this research. In particular, we thank Curtis Rayer, Andrew Gere, and George Belhumeur of SJW for their support. Finally, the writers thank Demian Lee for his contributions to this paper.
References
Aly, A. H., and Wanakule, N. (2004). “Short-term forecasting for urban water consumption.” J. Water Resour. Plann. Manage., 130(5), 405–410.
Billings, R. B., and Agthe, D. E. (1998). “State-space versus multiple regression for forecasting urban water demand.” J. Water Resour. Plann. Manage., 124(2), 113–117.
Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, New York.
Ghiassi, M., and Saidane, H. (2005). “A dynamic architecture for artificial neural networks.” Neurocomputing, 63, 397–413.
Ghiassi, M., Saidane, H., and Zimbra, D. K. (2005). “A dynamic artificial neural network model for forecasting time series events.” Int. J. Forecast., 21(2), 341–362.
Ghiassi, M., Zimbra, D. K., and Saidane, H. (2006). “Medium-term system load forecasting with a dynamic artificial neural network model.” Electr. Power Syst. Res., 76(5), 302–316.
Hippert, H. S., Pedreira, C. E., and Souza, R. C. (2001). “Neural networks for short-term load forecasting: A review and evaluation.” IEEE Trans. Power Syst., 16(1), 44–55.
Homwongs, C., Sastri, T., and Foster, J. W., III. (1994). “Adaptive forecasting of hourly municipal water consumption.” J. Water Resour. Plann. Manage., 120(6), 888–905.
Jowitt, P. W., and Xu, C. (1992). “Demand forecasting for water distribution systems.” Civ. Eng. Syst., 9, 105–121.
Kenward, T. C., and Howard, C. D. (1999). “Forecasting for urban water demand management.” Proc., 26th Annual Water Resources Planning and Management Conf., ASCE, Reston, Va.
Maidment, D. R., and Miaou, S. P. (1986). “Daily water use in nine cities.” J. Water Resour. Plann. Manage., 110(1), 90–106.
Makridakis, S. G., Wheelwright, S. C., and Hyndman, R. J. (1998). Forecasting: Methods and applications, 3rd Ed., Wiley, New York.
Nitivattananon, V., Sadowski, E. C., and Quimpo, R. G. (1996). “Optimization of water supply system operation.” J. Water Resour. Plann. Manage., 122(5), 374–384.
Perry, P. F. (1981). “Demand forecasting in water supply networks.” J. Hydr. Div., 107(9), 1077–1087.
Saleba, G. S. (1985). “Water demand forecasting.” Proc., Demand Forecasting and Financial Risk Assessment, AWWA, Denver.
San Jose Census. (2000). ⟨http://www.bayareacensus.ca.gov/cities/SanJose.htm⟩ (Feb. 2, 2006).
Shvarster, L., Shamir, U., and Feldman, M. (1993). “Forecasting hourly water demands by pattern recognition approach.” J. Water Resour. Plann. Manage., 119(6), 611–627.
SPSS. (1998). Clementine data mining system, SPSS Inc., Chicago.
SPSS. (2004). SPSS Trends v13.0, SPSS Inc, Chicago.
United States Census Bureau (USCB). (2005). ⟨http://www.factfinder.census.gov⟩ (Feb. 2, 2006).
Weather Underground. (2005). ⟨http://www.wunderground.com⟩ (Feb. 2, 2006).
Zhang, G., Patuwo, E. B., and Hu, M. Y. (1998). “Forecasting with artificial neural network: The state of the art.” Int. J. Forecast., 14, 35–62.
Zhou, S. L., McMahon, T. A., and Lewis, W. J. (2000). “Forecasting daily urban water demand: A case study of Melbourne.” J. Hydrol., 236, 153–164.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 2 • March 2008
Pages: 138 - 146
Copyright
© 2008 ASCE.
History
Received: Feb 14, 2006
Accepted: Jun 8, 2007
Published online: Mar 1, 2008
Published in print: Mar 2008
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