Bayesian Network and system thinking modelling to manage water-related health risks from extreme events

Bertone, E., Sahin, O., Richards, R. and Roiko, R. A. (2016). Bayesian Network and system thinking modelling to manage water-related health risks from extreme events. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015, Singapore, Singapore, (1272-1276). 6-9 December 2015. doi:10.1109/IEEM.2015.7385852


Author Bertone, E.
Sahin, O.
Richards, R.
Roiko, R. A.
Title of paper Bayesian Network and system thinking modelling to manage water-related health risks from extreme events
Conference name IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015
Conference location Singapore, Singapore
Conference dates 6-9 December 2015
Convener IEEE
Proceedings title 2015 IEEE International Conference on Industrial Engineering and Engineering Management
Journal name 2015 Ieee International Conference On Industrial Engineering and Engineering Management (Ieem)
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/IEEM.2015.7385852
Open Access Status Not Open Access
ISBN 9781467380669
ISSN 2157-362X
Volume 2016
Start page 1272
End page 1276
Total pages 5
Collection year 2017
Language eng
Abstract/Summary A combination of Bayesian Network (BN), System Dynamics (SD) and participatory modelling to develop a risk assessment tool for managing water-related health risks associated with extreme events has been developed. The risk assessment tool is applied to the Prospect water filtration plant system, main source of potable water for the Sydney metropolitan region. Conceptual models were developed by the stakeholders around the key indicator parameters of turbidity, water colour and cryptosporidium. These three conceptual models were and used for developing separate BN and SD models. Here we present the development of a BN designed to understand the risk of extreme events on the ability to provide drinking water of a desired quality. The model has undergone development and preliminary parameterization via two participatory workshops. However, its development is an ongoing process with the next stage involving supplementing the 'expert opinion' used to parameterize the model so far with 'hard' data.
Keyword Bayesian networks
Extreme events
Water quality
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
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School of Agriculture and Food Sciences
 
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