Bayesian Approach for Real-Time Probabilistic Contamination Source Identification
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 8
Abstract
Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes’ rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to changes in sensor signals. In terms of intrusion events, the beta-binomial approach was more selective than the Bayes’ rule approach in identifying potential source node–time pairs and provided the flexibility to account for multiple possible injection locations.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors sincerely appreciate the help of Dr. Feng Shang in the implementation of the particle backtracking algorithm as well as Mr. Robert Janke in running sensor placement results using TEVA-SPOT. The authors also would like to gratefully acknowledge the funding support provided by the CMMI Directorate, Civil Infrastructure Systems (NSF) through grant number #0900713.
References
Allgeier, S. C., Pulz, J., and Murray, R. (2006). “Conceptual design of a contamination warning system.” Proc., Water Security Congress, AWWA, Washington, DC.
Berry, J., et al. (2010). “User’s manual: TEVA-SPOT toolkit 2.4.”, National Homeland Security Research Center, U.S. EPA, Washington, DC.
Berry, J., Hart, W. E., Phillips, C. A., Uber, J. G., and Watson, J.-P. (2006). “Sensor placement in municipal water networks with temporal integer programming models.” J. Water Resour. Plann. Manage., 218–224.
Boccelli, D. L., et al. (2004). “Tracer tests for network model calibration.” Proc., World Water and Environmental Resources Congress, G. Sehlke, D. F. Hayes and D. K. Stevens, eds., ASCE, Reston, VA.
Byer, D., and Carlson, K. H. (2005). “Real-time detection of intentional chemical contamination in the distribution system.” J. Am. Water Works Assoc., 97(7), 130–133.
Dawsey, W. J., Minsker, B. S., and VanBlaricum, V. L. (2006). “Bayesian belief networks to integrate monitoring evidence of water distribution system contamination.” J. Water Resour. Plann. Manage., 234–241.
De Sanctis, A. E., Boccelli, D. L., Shang, F., and Uber, J. G. (2008). “Probabilistic approach to characterize contamination sources with imperfect sensors.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
De Sanctis, A. E., Shang, F., and Uber, J. G. (2010). “Real-time identification of possible contamination sources using network backtracking methods.” J. Water Resour. Plann. Manage., 444–453.
Di Cristo, C., and Leopardi, A. (2008). “Pollution source identification of accidental contamination in water distribution networks.” J. Water Resour. Plann. Manage., 197–202.
Guan, J., Aral, M. M., Maslia, M. L., and Grayman, W. M. (2006). “Identification of contaminant sources in water distribution systems using simulation–coptimization method: Case study.” J. Water Resour. Plann. Manage., 252–262.
Hall, J., et al. (2007). “On-line water quality parameters as indicators of distribution system contamination.” J. Am. Water Works Assoc., 99(1), 66–77.
Hart, D. B., and McKenna, S. A. (2011). “CANARY user’s manual.”, National Homeland Security Research Center, U.S. EPA, Cincinnati, OH.
Helbling, D. E., and VanBriesen, J. M. (2008). “Continuous monitoring of residual chlorine concentrations in response to controlled microbial intrusions in a laboratory-scale distribution system.” Water Res., 42(12), 3162–3172.
Huang, J. J., and McBean, E. A. (2009). “Data mining to identify contaminant event locations in water distribution systems.” J. Water Resour. Plann. Manage., 466–474.
Janke, R., et al. (2010). “Threat ensemble vulnerability assessment-sensor placement optimization tool (TEVA-SPOT) graphical user interface user’s manual.”, National Homeland Security Research Center, U.S. EPA, Cincinnati, OH.
Kottegoda, N. T., and Rosso, R. (1997). Statistics, probability, and reliability for civil and environmental engineers, McGraw-Hill, New York.
Laird, C. D., Biegler, L. T., and van Bloemen Waanders, B. G. (2006). “Mixed-integer approach for obtaining unique solutions in source inversion of water networks.” J. Water Resour. Plann. Manage., 242–251.
Laird, C. D., Biegler, L. T., van Bloemen Waanders, B. G., and Bartlett, R. A. (2005). “Contamination source determination for water networks.” J. Water Resour. Plann. Manage., 125–134.
Liu, L., Sankarasubramanian, A., and Ranjithan, S. R. (2011). “Logistic regression analysis to estimate contaminant sources in water distribution systems.” J. Hydroinform., 13(3), 545–557.
McKenna, S. A., Hart, D., Klise, K., Cruz, V., and Wilson, M. (2007). “Event detection from water quality time series.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
McKenna, S. A., Wilson, M., and Klise, K. A. (2008). “Detecting changes in water quality data.” J. Am. Water Works Assoc., 100(1), 74–85.
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., 556–568.
Ostfeld, A., and Salomons, E. (2004). “Optimal layout of early warning detection stations for water distribution systems security.” J. Water Resour. Plann. Manage., 377–385.
Preis, A., and Ostfeld, A. (2006). “Contamination source identification in water systems: A hybrid model trees-linear programming scheme.” J. Water Resour. Plann. Manage., 263–273.
Preis, A., and Ostfeld, A. (2007). “A contamination source identification model for water distribution system security.” Eng. Optim., 39(8), 941–947.
Preis, A., and Ostfeld, A. (2008). “Genetic algorithm for contaminant source characterization using imperfect sensors.” Civ. Eng. Environ. Syst., 25(1), 29–39.
Preis, A., Whittle, A., and Ostfeld, A. (2008). “Multi-objective sensor network design for water distribution systems.” J. Water Resour. Plann. Manage., 366–377.
Propato, M. (2006). “Contamination warning in water networks: General mixed-integer linear models for sensor location design.” J. Water Resour. Plann. Manage., 225–233.
Propato, M., Sarrazy, F., and Tryby, M. (2010). “Linear algebra and minimum relative entropy to investigate contamination events in drinking water systems.” J. Water Resour. Plann. Manage., 483–492.
Rana, M., and Boccelli, D. L. (2013). “Contamination spread forecasting and identification of sampling locations in a water distribution network.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Rossman, L. A. (2000). EPANET2 user’s manual, Risk Reduction Engineering Laboratory, U.S. EPA, Cincinnati, OH.
Shang, F., Uber, J. G., and Polycarpou, M. M. (2002). “Particle backtracking algorithm for water distribution system analysis.” J. Environ. Eng., 441–450.
Shen, H., McBean, E. A., and Ghazal, M. (2009). “Multi-stage response to contaminant ingress into water distribution systems and probability quantification.” Can. J. Civ. Eng., 36(11), 1764–1772.
Vankayala, P., Sankarasubramanian, A., Ranjithan, S. R., and Mahinthakumar, G. (2009). “Contaminant source identification in water distribution networks under conditions of demand uncertainty.” Environ. Forensics, 10(3), 253–263.
Yang, X., and Boccelli, D. L. (2012). “Model-based event detection in distribution systems.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Yang, Y. J., Haught, R. C., and Goodrich, J. A. (2009). “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results.” J. Environ. Manage., 90(8), 2494–2506.
Zierolf, M. L., Polycarpou, M. M., and Uber, J. G. (1998). “Development and auto-calibration of an input-output model of chlorine transport in drinking water.” IEEE Trans. Control Syst. Technol., 6(4), 543–553.
Information & Authors
Information
Published In
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 18, 2012
Accepted: Jun 3, 2013
Published online: Jun 5, 2013
Published in print: Aug 1, 2014
Discussion open until: Sep 22, 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.