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.
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© 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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