Methodology for Bayesian Belief Network Development to Facilitate Compliance with Water Quality Regulations
Publication: Journal of Infrastructure Systems
Volume 16, Issue 1
Abstract
Limited resources and drinking water quality requirements pose significant challenges to those managing small and rural drinking water distribution systems (WDSs). Real-time monitoring technologies could support regulatory compliance, if shortcomings such as false readings and data corruption could be overcome. Bayesian belief networks (BBNs) are proposed as a means to mitigate technological shortcomings and increase certainty about the state of a given WDS. This paper describes a methodology for the development of BBNs that integrates known system characteristics with real-time monitoring technologies to support the water quality compliance of small or rural WDSs. Expert judgment was used both in the development of the structure of the BBN and in quantifying the required probability relationships. The results of a case study application of this methodology suggest that it is useful in developing a BBN to support decision making for a WDS with limited use of real-time monitoring technology.
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Acknowledgments
The writers are grateful for the contributions of Dr. Bryan Karney in the development of this methodology. We thank the staff at SaskWater Inc. as well as the representatives of the towns of Martensville, Dalmeny, Osler, and Hague, Saskatchewan for their collaboration. Special thanks go to B. S. Jong for his preliminary research and development of Fig. 1. Finally, the present work would not have been possible without the financial support of the Canadian Water Network and the National Science and Engineering Research Council (NSERC).
References
Bristow, E., and Brumbelow, K. (2006). “Delay between sensing and response in water contamination events.” J. Infrastruct. Syst., 12(2), 87–95.
Clemen, R. T., and Winkler, R. L. (1999). “Combining probability distributions from experts in risk analysis.” Risk Anal., 19(2), 187–203.
Coupé, V. M. H., and Van Der Gaag, L. C. (2002). “Properties of sensitivity analysis of Bayesian belief networks.” Ann. Math. Artif. Intell., 32(4), 232–256.
Dawsey, W. J., Minsker, B. S., and Van Blaricum, V. L. (2006). “Bayesian belief networks to integrate monitoring evidence of water distribution system contamination.” J. Water Resour. Plan. Manage., 132(4), 234–241.
Hrudley, S., and Hrudley, E. (2004). Safe drinking water—Lessons from recent outbreaks in affluent nations, IWA, Cornwall, U.K.
Jensen, F. V. (1996). An introduction to Bayesian networks, Springer, New York.
Joseph, S. (2007). “The effectiveness of Bayesian belief networks and real-time monitoring technology in facilitating the implementation of water quality regulations.” MASc thesis, Univ. of Toronto, Toronto.
Keeney, R. L., and Von Winterfeld, D. (1991). “Eliciting probabilities from experts in complex technical problems.” IEEE Trans. Eng. Manage., 38(3), 191–201.
Lindley, T., and Buchberger, S. (2002). “Assessing intrusion susceptibility in distribution systems.” J. Am. Water Works Assoc., 94(6), 66–79.
McInnis, D. (2004). “A relative-risk framework for evaluating transient pathogen intrusion in distribution systems.” Urban Water, 1(2), 113–127.
Meyer, M. A., and Booker, J. M. (2001). Eliciting and analyzing expert judgement: A practical guide, SIAM, Philadelphia.
O’Leary, D. (1998). “Knowledge acquisition from multiple experts: An empirical study.” Manage. Sci., 44, 1049–1058.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference, Morgan Kaufmann, San Francisco.
Renooij, S. (2001). “Probability elicitation for belief networks: Issues to consider.” Knowl. Eng. Rev., 16(3), 255–269.
Robertson, W., Stanfield, G., Howard, G., and Bartram, J. (2003). “Chapter 6: Monitoring the quality of drinking water during storage and distribution.” Assessing microbial safety of drinking water, IN WHO, ed., IWA, Cornwall, U.K., 179–204.
Stiber, N. A., Small, M. J., and Pantazidou, M. (2004). “Site specific updating and aggregation of Bayesian belief network models for multiple experts.” Risk Anal., 24(6), 1529–1538.
Strauss, J. (2005). “Escape from Kashechewan.” Globe and Mail, Oct. 28, 2005.
Tang, Z. (2005). “Developing conditional probability tables from fractional data for Bayesian belief networks in engineering decision making.” Ph.D. thesis, Univ. of Toronto, Toronto, ⟨http://proquest.umi.com/pqdlink?did=1001862061&Fmt=7&clientId=12520&RQT=309&VName=PQD⟩ (Sept. 11, 2006).
Tang, Z., and McCabe, B. (2007). “Developing complete conditional probability tables from fractional data for Bayesian belief networks.” J. Comput. Civ. Eng., 21(4), 265–276.
Tversky, A., and Kahneman, D. (1974). “Judgements under uncertainty: Heuristics and biases.” Science, 185(4157), 1124–1131.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 16 • Issue 1 • March 2010
Pages: 58 - 65
Copyright
© 2010 ASCE.
History
Received: Jan 28, 2008
Accepted: Sep 3, 2009
Published online: Feb 12, 2010
Published in print: Mar 2010
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