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Dynamic Bayesian Network Approach for Modeling Trihalomethanes from Ontario Water Supply Systems

Zoe Jingyu Zhu, Jeff Kroes and Edward McBean (2010)
University of Guelph
DOI: https://doi.org/10.14796/JWMM.R236-28
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Abstract

A dynamic Bayesian network (DBN) approach is used to quantify relational knowledge for modeling relations or dependencies between variables from the dynamic system of disinfection byproduct (DBP) formation which changes over time. The DBN framework is used to assess causality between constituent parameters of water supply quality, based on data from communities in Ontario which rely on groundwater as their source of supply. The DBN models are used to assess probabilistic dimensions and to assist decision-making by identifying control options to decrease DBP formation.

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PAPER INFO

Identification

CHI ref #: R236-28 782
Volume: 18
DOI: https://doi.org/10.14796/JWMM.R236-28
Cite as: CHI JWMM 2010;R236-28

Publication History

Received: N/A
Accepted: N/A
Published: February 15, 2010

Status

# reviewers: 2
Version: Final published

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© 2010 CHI. Some rights reserved.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The Journal of Water Management Modeling is an open-access (OA) publication. Open access means that articles and papers are available without barriers to all who could benefit from them. Practically speaking, all published works will be available to a worldwide audience, free, immediately on publication. As such, JWMM can be considered a Diamond, Gratis OA journal.

All papers published in the JWMM are licensed under a Creative Commons Attribution 4.0 International License (CC BY).

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AUTHORS

Zoe Jingyu Zhu

University of Guelph, Guelph, ON, Canada
ORCiD:

Jeff Kroes

University of Guelph, Guelph, ON, Canada
ORCiD:

Edward McBean

University of Guelph, Guelph, ON, Canada
ORCiD:


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