2026-09-18T20:14:35Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/29783 2023-07-10 col_2142_5131 col_2142_14770 com_2142_5130 com_2142_3518 com_2142_234

Minsker, Barbara S. Dawsey, Wesley J. 2012-02-06T20:16:33Z 2012-02-06T20:16:33Z 2011-12 2012-02-06T20:16:33Z 2011-12 A Bayesian belief network (BBN) methodology is proposed for combining evidence to better characterize contamination events and reduce false positive sensor detections in drinking water distribution systems. A BBN is developed that integrates sensor data with other validating evidence of contamination scenarios. This network is used to graphically express the causal relationships between events such as operational changes or a true contaminant release and consequent observable evidence in an example distribution system. In the BBN methodology proposed here, multiple computer simulations of contaminant transport are used to estimate the prior probabilities of a positive sensor detection. These simulations are run over multiple combinations of possible source locations and initial mass injections for a conservative solute. This approach provides insight into the effect of uncertainties in source mass and location on the detection probability of the sensors. In addition, the simulations identify the upstream nodes that are more likely to result in positive detections. The BBN incorporates the probabilities that result from these simulations, and the network is updated to reflect three demonstration scenarios – a false positive and two true positive sensor detections. Item withdrawn by Alexis Thompson (athmpsn1@illinois.edu) on 2011-12-02T19:34:28Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Dawsey_Wesley.pdf: 185257 bytes, checksum: 15ae1e0d3c547cb919d07d4661a590f6 (MD5) Made available in DSpace on 2012-02-06T20:16:33Z (GMT). No. of bitstreams: 2 Dawsey_Wesley.pdf: 185257 bytes, checksum: 15ae1e0d3c547cb919d07d4661a590f6 (MD5) license.txt: 4061 bytes, checksum: 234f058b15a19e7a29ebd521319537b4 (MD5) http://hdl.handle.net/2142/29783 en Copyright 2011 Wesley J. Dawsey Sensors Security Water Distribution Drinking Water Bayesian Networks Bayesian belief networks to integrate monitoring evidence of water distribution system contamination Dissertation / Thesis text Civil and Environmental Engineering 1251 Environmental Engineering in Civil Engineering 0231 University of Illinois at Urbana-Champaign Thesis M.S. MS:Env Engr Civil Engr -UIUC 10KS0231MS