@inproceedings{c3a4ed08b57947b5a14b3dff4e4dd3f9,
title = "Reducing online contaminant monitoring uncertainty using a bayesian belief network",
abstract = "There is a great deal of uncertainty in real time characterization of water distribution system contamination events. Much of this uncertainty is due to the lack of targeted sensors which makes it necessary to use surrogate water quality parameters to indirectly measure the presence of a contaminant. A positive sensor detection can often be validated by pieces of evidence observed in a distribution system. This paper illustrates how Bayesian belief networks can be used to represent distribution system contamination scenarios. A framework was developed that integrated sensor data with other validating evidence of a contamination event. This framework was used to express causality between the events and observed evidence that comprise contamination scenarios. Copyright ASCE 2005.",
author = "Dawsey, {W. J.} and Minsker, {B. S.} and VanBlaricum, {V. L.}",
year = "2005",
doi = "10.1061/40792(173)43",
language = "English (US)",
isbn = "0784407924",
series = "World Water Congress 2005: Impacts of Global Climate Change - Proceedings of the 2005 World Water and Environmental Resources Congress",
pages = "43",
booktitle = "World Water Congress 2005",
note = "2005 World Water and Environmental Resources Congress ; Conference date: 15-05-2005 Through 19-05-2005",
}