Potential of Bayesian networks for adaptive management in water recycling

Denise Beaudequin; F Harden; Anne H Roiko; K Mengersen

doi:10.1016/j.envsoft.2017.01.016

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Potential of Bayesian networks for adaptive management in water recycling

Journal article

Peer reviewed

Potential of Bayesian networks for adaptive management in water recycling

Denise Beaudequin, F Harden, Anne H Roiko and K Mengersen

Environmental Modelling and Software, Vol.91, pp.251-270

2017

DOI: https://doi.org/10.1016/j.envsoft.2017.01.016

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Abstract

Bayesian network

exposure modeling

public open space

quantitative microbial risk assessment

water recycling

waterborne pathogens

Water recycling is an important solution to increasing water scarcity. However, universal wastewater treatment standards deter uptake of recycling schemes. Lack of data also impedes fit-for-purpose water recycling and water managers are challenged by decision making under uncertain conditions. Bayesian networks (BNs) are increasingly recognised as a valuable tool for decision making under uncertainty. In this study BNs are used to model health risks associated with wastewater irrigation of a public open space. Three BNs based on quantitative microbial risk assessment model risk reduction potential along a treatment chain and at the site of reuse. The BNs simulate multiple exposure profiles and scenarios, providing conditional probability of infection or illness, comparable with health-based targets. Study findings highlight the significant impact of post treatment risk mitigation, despite challenging conditions. BNs provide a transparent, defensible evidence base for mapping and quantifying risk pathways, comparing decision options and predicting outcomes of management policies.

Details

Title: Potential of Bayesian networks for adaptive management in water recycling
Authors: Denise Beaudequin (Author) - Queensland University of Technology
F Harden (Author) - Queensland University of Technology
Anne H Roiko (Author) - Griffith University
K Mengersen (Author) - Queensland University of Technology
Publication details: Environmental Modelling and Software, Vol.91, pp.251-270
Publisher: Pergamon
Date published: 2017
DOI: 10.1016/j.envsoft.2017.01.016
ISSN: 1364-8152
Organisation Unit: University of the Sunshine Coast, Queensland; Thompson Institute; Sustainability Research Cluster
Language: English
Record Identifier: 99450727902621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Sciences; Water Resources

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