Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks
Publication: Journal of Water Resources Planning and Management
Volume 137, Issue 4
Abstract
This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system–aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work has been supported by the National Research Foundation of Singapore (NRF) and the Singapore—MIT Alliance for Research and Technology (SMART) through the Center for Environmental Modeling and Sensing.
References
Alvisi, S., Franchini, M., and Marinelli, A. (2007). “A short-term, pattern-based model for water-demand forecasting.” J. Hydroinf., 9(1), 39–50.
Andersen, J. H., and Powell, R. S. (2000). “Implicit state-estimation technique for water network monitoring.” Urban Water, 2(2), 123–130.
Anderson, E. J., and Al-Jamal, K. H. (1995). “Hydraulic-network simplification.” J. Water Resour. Plann. Manage., 121(3), 235–240.
Clark, C., and Wu, Z. Y. (2006). “Integrated hydraulic model and genetic algorithm optimization for informed analysis of a real water system.” Proc. 8th Annual Water Distribution Systems Analysis Symp. (CD-ROM), ASCE, Reston, VA.
Datta, R. S. N., and Sridharan, K. (1994). “Parameter estimation in water-distribution systems by least squares.” J. Water Resour. Plann. Manage., 120(4), 405–422.
Davidson, J. W., and Bouchart, F. J.-C. (2006). “Adjusting nodal demands in SCADA constrained real-time water distribution network models.” J. Hydraul. Eng., 132(1), 102–110.
EPANET [Computer software]. U.S. EPA, Washington, DC, 〈www.epa.gov/ORD/NRMRL/wswrd/epanet.html〉.
Ghiassi, M., Zimbra, D. K., and Saidane, H. (2008). “Urban water demand forecasting with a dynamic artificial neural network model.” J. Water Resour. Plann. Manage., 134(2), 138–146.
Greco, M., and Del Guidice, G. (1999). “New approach to water distribution network calibration.” J. Hydraul. Eng., 125(8), 849–854.
Hamberg, D., and Shamir, U. (1988). “Schematic models for distribution systems design. I: Combination concept.” J. Water Resour. Plann. Manage., 114(2), 129–140.
Hammerlin, G., and Hoffman, K. H. (1991). “Numerical mathematics.” Graduate texts in mathematics, Springer, NewYork.
Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI.
Huber, P. J. (1973). “Robust regression: Asymptotics, conjectures, and Monte Carlo.” Ann. Stat., 1(5), 799–821.
Jonkergouw, P. M. R., Khu, S.-T., Kapelan, Z. S., and Savic, D. A. (2008). “Water quality model calibration under unknown demands.” J. Water Resour. Plann. Manage., 134(4), 326–336.
Kapelan, Z. S., Savic, D. A., and Walters, G. A. (2002). “Hybrid GA for calibration of water distribution system hydraulic models.” Proc., 1st Annual Environmental & Water Resources Systems Analysis (EWRSA) Symp. (CD-ROM), Environmental and Water Resources Institute (ASCE), Reston, VA.
Kapelan, Z. S., Savic, D. A., and Walters, G. A. (2007). “Calibration of water distribution hydraulic models using a Bayesian-type procedure.” J. Hydraul. Eng., 133(8), 927–936.
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.
Lansey, K. E., and Basnet, C. (1991). “Parameter estimation for water distribution networks.” J. Water Resour. Plann. Manage., 117(1), 126–144.
Lingireddy, S., and Ormsbee, L. (1998). “Neural networks in optimal calibration of water distribution systems.” Artificial neural networks for civil engineers: Advanced features and applications, ASCE, Reston, VA.
Maidment, D. R., and Miaou, S. P. (1986). “Daily water use in nine cities.” Water Resour. Res., 22(6), 845–851.
Ormsbee, L. E. (1989). “Implicit network calibration.” J. Water Resour. Plann. Manage., 115(2), 243–257.
Ostfeld, A., et al. (2008). “The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms.” J. Water Resour. Plann. Manage., 134(6), 556–568.
Perelman, L., and Ostfeld, A. (2008). “Water distribution system aggregation for water quality analysis.” J. Water Resour. Plann. Manage., 134(3), 303–309.
Preis, A., Whittle, A. J., and Ostfeld, A. (2009a). “On-line hydraulic state prediction for water distribution systems.” Proc. 11th Water Distribution Systems Analysis Symp. (WDSA09), World Environmental & Water Resources Congress, Kansas City, MO.
Preis, A., Whittle, A. J., Ostfeld, A., and Perelman, L. (2009b). “On-line hydraulic state estimation in urban water networks using reduced models.” Integrating Water Systems: Proc. Tenth Int. Conf. on Computing and Control in the Water Industry (CCWI 2009), J. Boxall and C. Maksimovic, eds., CRC Press, Boca Raton, FL.
Quinlan, J. R. (1992). “Learning with continuous classes.” Proc. 5th Australian Joint Conf. on Artificial Intelligence, World Scientific, Singapore, 343–348.
Reddy, P. V. N., Sridharan, K., and Rao, P. V. (1996). “WLS method for parameter estimation in water distribution networks.” J. Water Resour. Plann. Manage., 122(3), 157–164.
Savic, D. A., and Walters, G. A. (1995). “Genetic algorithm techniques for calibrating network models.” Rep. No. 95/12, Centre for Systems and Control Engineering, Univ. of Exeter, Exeter, UK, 41.
Shamir, U., and Salomons, E. (2008). “Optimal real-time operation of urban water distribution systems using reduced models.” J. Water Resour. Plann. Manage., 134(2), 181–185.
Shang, F., Uber, J., van Bloemen Waanders, B., Boccelli, D., and Janke, R. (2006). “Real time water demand estimation in water distribution system.” Proc. 8th Annual Water Distribution Systems Analysis Symp. (CD-ROM), ASCE, Reston, VA.
Ulanicki, B., Zehnpfund, A., and Martinez, F. (1996). “Simplification of water distribution network models.” Proc., 2nd Int. Conf. on Hydroinformatics, Balkema, Rotterdam, Netherlands, 493–500.
U.S. EPA. (2005). “Water distribution system analysis: Field studies modeling and management. A reference guide for utilities.” Rep. No. EPA/600/R-06/028, Office of Research and Development National Risk Management Research Laboratory Water Supply and Water Resources Division, Cincinnati, OH.
Walski, T. M., Chase, D. V., Savic, D. A., Grayman, W., Beckwith, S., and Koelle, E. (2003). Advanced water distribution modeling and management, Haestad Methods, Inc., Waterbury, CT.
Wu, Z. Y., and Simpson, A. R. (2001). “Competent genetic algorithm optimization of water distribution systems.” J. Comput. Civ. Eng., 15(2), 89–101.
Wu, Z. Y., Walski, T. M., Mankowski, R., Herrin, G., Gurrieri, R., and Tryby, M. (2002). “Calibrating water distribution models via genetic algorithms.” Proc. AWWA Information Management Technology Conf., American Water Works Association (AWWA), Denver.
Zhou, S. L., McMahon, T. A., and Lewis, W. J. (2000). “Forecasting daily urban water demand: A case study of Melbourne.” J. Hydrol. (Amsterdam), 236(3–4), 153–164.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 137 • Issue 4 • July 2011
Pages: 343 - 351
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jan 31, 2010
Accepted: Jul 26, 2010
Published online: Jul 31, 2010
Published in print: Jul 1, 2011
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.