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Environmental decision-making using Bayesian networks: creating an environmental report card

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dc.contributor Australian Institute Of Marine Science
dc.contributor Queensland Univ Technol
dc.contributor Queensland University Of Technology (qut)
dc.contributor Univ Melbourne
dc.contributor Australian Inst Marine Sci
dc.contributor University Of Melbourne
dc.contributor Gladstone Hlth Harbour Partnership
dc.contributor Environmetr Pty Ltd
dc.contributor.author MENGERSEN, KERRIE
dc.contributor.author JOHNSON, SANDRA
dc.contributor.author LOGAN, MURRAY
dc.contributor.author FOX, DAVID
dc.contributor.author KIRKWOOD, JOHN
dc.contributor.author PINTO, UTHPALA
dc.date.accessioned 2017-10-15T18:42:59Z
dc.date.accessioned 2017-10-15T18:42:59Z
dc.date.accessioned 2019-05-09T01:09:06Z
dc.date.available 2017-10-15T18:42:59Z
dc.date.available 2017-10-15T18:42:59Z
dc.date.available 2019-05-09T01:09:06Z
dc.date.issued 2017-07-01
dc.identifier.citation Johnson S, Logan M, Fox D, Kirkwood J, Pinto U, Mengersen K (2017) Environmental decision-making using Bayesian networks: creating an environmental report card. Applied Stochastic Models in Business and Industry 33(4): 335-347
dc.identifier.issn 1524-1904
dc.identifier.uri http://epubs.aims.gov.au/11068/11382
dc.description.abstract Environmental report cards are popular mechanisms for summarising the overall status of an environmental system of interest. This paper describes the development of such a report card in the context of a study for Gladstone Harbour in Queensland, Australia. The harbour is within the World Heritage-protected Great Barrier Reef and is the location of major industrial development, hence the interest in developing a way of reporting its health in a statistically valid, transparent and sustainable manner. A Bayesian network (BN) approach was used because of its ability to aggregate and integrate different sources of information, provide probabilistic estimates of interest and update these estimates in a natural manner as new information becomes available. BN modelling is an iterative process, and in the context of environmental reporting, this is appealing as model development can be initiated while quantitative knowledge is still under development, and subsequently refined as more knowledge becomes available. Moreover, the BN model helps build the maturity of the quantitative information needed and helps target investment in monitoring and/or process modelling activities to inform the approach taken. The model is able to incorporate spatial and temporal information and may be structured in such a way that new indicators of relevance to the underlying environmental gradient being monitored may replace less informative indicators or be added to the model with minimal effort. The model described here focuses on the environmental component, but has the capacity to also incorporate social, cultural and economic components of the Gladstone Harbour Report Card. Copyright (c) 2016 John Wiley & Sons, Ltd.
dc.description.sponsorship This project was funded by the Gladstone Healthy Harbour Partnership. The scientific and methodological contributions of the GHHP Science Team and Independent Science Panel to this project are gratefully acknowledged.
dc.language English
dc.subject Environmental Report Card
dc.subject Tool
dc.subject Operations Research & Management Science
dc.subject Mathematics, Interdisciplinary Applications
dc.subject Bayesian Network
dc.subject Healthy Harbour
dc.subject Statistics & Probability
dc.subject Mathematics
dc.title Environmental decision-making using Bayesian networks: creating an environmental report card
dc.type journal article
dc.identifier.doi 10.1002/asmb.2190
dc.identifier.wos WOS:000407654400001


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