Real-Time Guidance for Hydrant Flushing Using Sensor-Hydrant Decision Trees
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 6
Abstract
A utility may detect contaminant in a water distribution network through water quality sensor information, which indicates that a biological pathogen or chemical contaminant is present in the network. A utility manager should identify actions that can be taken to protect public health, and flushing a contaminant by opening a set of hydrants can be an effective response action. Hydrants should be selected and timed to flush the contaminant; however, accurately ascertaining the characteristics of the contaminant source may be impossible, which creates difficulties in developing a hydrant flushing strategy. This research develops a decision-making approach that is designed to select hydrant flushing strategies in response to sensor activations and does not require information about the characteristics of the contaminant source. A sensor-hydrant decision tree is introduced to provide a library of rules for opening and closing hydrants based on the order of activated sensors. Sensor-hydrant decision trees are developed for a wide range of contaminant events using a simulation-optimization methodology. Potential contamination events are generated using Monte Carlo simulation and are simulated using a water distribution system model. Events are classified based on the order of the activation of water quality sensors in the network, and a noisy genetic algorithm is used to identify hydrant strategies for each class of events. Three sensor-hydrant decision trees are developed to represent risky, risk-averse, and adaptive management strategies. A risk-averse strategy specifies immediate actions to achieve average performance over many events. A risky strategy specifies specialized actions based on the prediction of the plume movement or a decision to wait to receive more information. An adaptive strategy specifies the actions for opening hydrants as each sensor is activated. An adaptive approach does not require predictions of the plume movement, but may result in lower performance due to delays in taking actions. The methodology is demonstrated to develop sensor-hydrant decision trees for a virtual midsized municipality, Mesopolis.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research is funded in part by the National Science Foundation, Award 0927739. Any opinions and findings are those of the author and do not necessarily represent the views of the sponsor.
References
Alfonso, L., Jonoski, A., and Solomatine, D. (2010). “Multiobjective optimization of operational responses for contaminant flushing in water distribution networks.” J. Water Resour. Plann. Manage., 48–58.
Alvisi, S., Creaco, E., and Franchini, M. (2011). “Segment identification in water distribution systems.” Urban Water J., 8(4), 203–217.
Baranowski, T. M., and LeBoeuf, E. J. (2008). “Consequence management utilizing optimization.” J. Water Resour. Plann. Manage., 386–394.
Creaco, E., Franchini, M., and Alvisi, S. (2010). “Optimal placement of isolation valves in water distribution systems based on valve cost and weighted average demand shortfall.” Water Resour. Manage., 24(15), 4317–4338.
Drake, K. (2011). “Using niched co-evolution strategies to address non-uniqueness in characterizing sources of contamination in a water distribution system.” Master’s thesis, Texas A&M Univ., College Station, TX.
Drake, K., and Zechman, E. M. (2011). “Using niched co-evolution strategies to address non-uniqueness in characterizing the source of contamination in a water distribution system.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Ellison, D., Duranceau, S., Ancel, S., Deagle, G., and McCoy, R. (2003). Investigation of pipe cleaning methods, AWWA Research Foundation, Denver, CO.
Friedman, M., Kirmeyer, G. J., and Antoun, E. (2002). “Developing and implementing a distribution system flushing program.” Am. Water Works Assoc. J., 94(7), 48–56.
Gavanelli, M., Nonato, M., Peano, A., Alvisi, S., and Franchini, M. (2012). “Genetic algorithms for scheduling devices operation in a water distribution system in response to contamination events.” Evolutionary computation in combinatorial optimization, J.-K. Hao and M. Middendorf, eds., Vol. 7245, Lecture Notes in Computer Science, Springer, Berlin, 124–135.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Longman Publishing, Boston, MA.
Greick, P. (2006). “Water and terrorism.” Water Policy, 8(6), 481–503.
Guan, J., Aral, M., Maslia, M., and Grayman, W. (2006). “Identification of contaminant sources in water distribution systems using simulation–optimization method: Case study.” J. Water Resour. Plann. Manage., 252–262.
Guidorzi, M., Franchini, M., and Alvisi, S. (2009). “A multi-objective approach for detecting and responding to accidental and intentional contamination events in water distribution systems.” Urban Water J., 6(2), 115–135.
Hrudey, S., and Hrudey, E. (2004). Safe drinking water. Lessons from recent outbreaks in affluent nations, International Water Association Publishing, Cornwall, U.K.
Johnson, G., and Brumbelow, K. (2008). “Developing mesopolis- a virtual city for research in water distribution system and interdependent infrastructures.” 〈https://ceprofs.civil.tamu.edu/kbrumbelow/Mesopolis/index.htm〉.
Kroll, D. (2006). Security our water supply: Protecting a vulnerable resources, PennWell Publishers, Tulsa, OK.
Laird, C., Biegler, L., van Bloemen Waanders, B., and Bartlett, R. (2005). “Contamination source determination for water networks.” J. Water Resour. Plann. Manage., 125–134.
Liu, L., Ranjithan, S., and Mahinthakumar, G. (2011). “Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure.” J. Water Resour. Plann. Manage., 183–192.
Liu, L., Zechman, E. M., Mahinthakumar, G., and Ranji Ranjithan, S. (2012). “Identifying contaminant sources for water distribution systems using a hybrid method.” Civ. Eng. Environ. Syst., 29(2), 123–136.
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
Poulin, A., Mailhot, A., Grondin, P., Delorme, L., and Villeneuve, J.-P. (2006). “Optimization of operational response to contamination in water networks.” Water Distribution Systems Analysis Symp., Vol. 247, ASCE, Reston, VA, 1–15.
Preis, A., and Ostfeld, A. (2006). “Contamination source identification in water systems: A hybrid model treeslinear programming scheme.” J. Water Resour. Plann. Manage., 263–273.
Rasekh, A., and Brumbelow, K. (2013). “Probabilistic analysis and optimization to characterize critical water distribution system contamination scenarios.” J. Water Resour. Plann. Manage., 191–199.
Rasekh, A., and Brumbelow, K. (2014). “Drinking water distribution systems contamination management to reduce public health impacts and system service interruptions.” Environ. Modell. Softw., 51, 12–25.
Rossman, L. (2000). Epanet users manual, U.S. Environmental Protection Agency Risk Reduction Engineering Lab, Cincinnati, OH.
Shafiee, M. E. (2014). “Modeling sociotechnical water distribution system contamination events to evaluate and identify mitigation strategies.” Ph.D. dissertation, North Carolina State Univ., Raleigh, NC.
Shafiee, M. E., and Zechman, E. M. (2013). “An agent-based modeling framework for sociotechnical simulation of water distribution contamination events.” J. Hydroinf., 15(3), 862–880.
Shang, F., Uber, J. G., van Bloemen Waanders, B. G., Boccelli, D., and Janke, R. (2006). “Real time water demand estimation in water distribution system.” Proc. 8th Annual Water Distribution Systems Symp., ASCE, Reston, VA, 1–14.
Smalley, B., Minsker, B., and Goldberg, D. (2000). “Risk-based in situ bioremediation design using a noisy genetic algorithm.” Water Resour. Res., 36(10), 3043–3052.
U.S. EPA (USEPA). (2003). “Response protocol toolbox: Planning for and responding to drinking water contamination threats and incidentsoverview and application.”, Washington, DC.
U.S. EPA (USEPA). (2004a). “Response protocol toolbox: Planning for and responding to drinking water contamination threats and incidentsmodule 5: Public health response guide.”, Washington, DC.
U.S. EPA (USEPA). (2004b). “Response protocol toolbox: Planning for and responding to drinking water contamination threats and incidentsmodule 6: Remediation and recovery guide.”, Washington, DC.
Zechman, E. M. (2011). “Agent-based modeling to simulate contamination events and evaluate threat management strategies in water distribution systems.” Risk Anal., 31(5), 758–772).
Zechman, E. M. (2013). “Integrating evolution strategies and genetic algorithms with agent-based modeling for flushing a contaminated water distribution system.” J. Hydroinf., 15(3), 798–812.
Zechman, E. M., and Ranjithan, S. R. (2009). “Evolutionary computation-based methods for characterizing contaminant sources in a water distribution system.” J. Water Resour. Plann. Manage., 334–343.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 6 • June 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Feb 17, 2014
Accepted: Jul 2, 2014
Published online: Aug 11, 2014
Discussion open until: Jan 11, 2015
Published in print: Jun 1, 2015
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.