Real-Time Water Quality Monitoring: Assessment of Multisensor Data Using Bayesian Belief Networks
Publication: Journal of Water Resources Planning and Management
Volume 138, Issue 1
Abstract
Real-time sensing in water distribution systems provides a potentially powerful analytical tool for providing water security. Through monitoring surrogate parameters (e.g., pH, turbidity, and residual chlorine) over time, the natural variations of a distribution system’s parameters are established, allowing rapid detection of changes in water quality. However, the level of performance that water quality event detection algorithms have exhibited to date is insufficient for real-world utilization. Bayesian belief networks (BBNs) offer a formalized method of reasoning under uncertainty and are well suited to the analysis of multiple sources of information. The application of a BBN to water quality event detection is described. Surrogate parameters (pH, conductivity, and turbidity) were monitored during an experimental E. coli contamination. Difference filtration using a 60-s moving window of observations identified rapid rates of change present in the surrogate parameter signals, demonstrated as responsive to contamination as simulated in bench-scale studies. A BBN was constructed to assimilate the surrogate parameter variation and compute temporal probability distributions about the contamination of an experimental system. The BBN topology, probability distributions and data transformation techniques applied were validated through successful identification of contaminant injections.
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Acknowledgments
The writers are grateful to the Natural Sciences and Engineering Research Council of Canada strategic grant program for financial support of this research.
References
ASCE. (2004). Interim voluntary guidelines for designing an outline contaminant monitoring system. 558.
Brosnan, T. M., ed. (1999). “Early warning monitoring to detect hazardous events in water supplies.” Risk Sciences Institute Workshop Rep., Int. Life Sciences Institute, Washington, DC.
Charniak, E. (1991). “Bayesian networks without tears.” AI Magazine, 12(4), 50–63.
Dawsey, W. J., Minsker, B. S., and Amir, E. (2007). “Real time assessment of drinking water systems using a dynamic Bayesian network.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA, 507.
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. Div., 132(4), 234–241.
Ghazali, M., and McBean, E. (2009). “Current technologies for on-line monitoring of drinking water distribution systems.” Conceptual modeling of urban water systems, Monograph 17, W. James et al., eds., CHI, Guelph, Ontario, Canada, 381–398.
Ghazali, M., McBean, E., Whalen, P., and Journal, K. (2010). “Supporting a drinking water contaminant warning system using the adenosine triphosphate test.” Can. J. Civ. Eng., 37, 1423–1431.
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.
Hargesheimer, E. E., Conio, O., and Popovicova, J. (2002). Online monitoring for drinking water utilities, AWWA Research Foundation and American Water Works Association, Denver, 1–63.
Hrudey, S. E., and Leiss, W. (2003). “Risk management and precaution: Insights on the cautious use of evidence.” Environ. Health Perspect., 111(13), 1577.
Janke, R., Murray, R., Uber, J., and Taxon, T. (2006). “Comparison of physical sampling and real-time monitoring strategies for designing a contamination warning system in a drinking water distribution system.” J. Water Resour. Plann. Manage., 132(4), 310–313.
Jensen, F. V. (1996). An introduction to Bayesian networks. UCL, 178.
Kang, C. W., and Golay, M. W. (1999). “A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems.” Expert Syst. Appl., 17(1), 21–32.
McKenna, S. A., Hart, D., Klise, K., Cruz, V., and Wilson, M. (2007). “Event detection from water quality time series.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA, 518.
Ostfeld, A., and Salomons, E. (2004). “Optimal layout of early warning detection stations for water distribution systems security.” J. Water Resour. Plann. Manage., 134(6), 556–568.
U.S. Environmental Protection Agency (USEPA). (2008). “Cincinnati pilot post-implementation system status.” Rep. No. EPA 817-R-08-004, Washington, DC.
U.S. Environmental Protection Agency (USEPA). (2005). “Technologies and techniques for early warning systems to monitor and evaluate drinking water quality: A state-of-the-art review.” Rep. No. EPA/600/R-05/156, Office of Water, Washington, DC.
Wagner, M. M. et al. (2001). “The emerging science of very early detection of disease outbreaks.” J. Public Health Manage. Practice, 7(6), 51.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 138 • Issue 1 • January 2012
Pages: 63 - 70
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Aug 20, 2010
Accepted: Apr 30, 2011
Published online: May 13, 2011
Published in print: Jan 1, 2012
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