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Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Australian Rivers Institute, Griffith School of Environment and Science, Griffith University, Nathan, Queensland 4111, Australia
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Griffith School of Engineering and Built Environment, Griffith University, Gold Coast, Queensland 4215, Australia
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Griffith Business School, Griffith University, Nathan, Queensland 4111, Australia
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Department of Agriculture and Fisheries, Toowoomba, Queensland 4350, Australia
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Author to whom correspondence should be addressed.
Water 2019, 11(12), 2642; https://doi.org/10.3390/w11122642
Received: 29 October 2019 / Revised: 4 December 2019 / Accepted: 11 December 2019 / Published: 14 December 2019
(This article belongs to the Section Water Resources Management, Policy and Governance)
Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Text
Keywords: climate change; decision support tools; optimization-based approaches; scenario-based approaches; management measure categories; socio-economic stressors climate change; decision support tools; optimization-based approaches; scenario-based approaches; management measure categories; socio-economic stressors
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Phan, T.D.; Smart, J.C.R.; Stewart-Koster, B.; Sahin, O.; Hadwen, W.L.; Dinh, L.T.; Tahmasbian, I.; Capon, S.J. Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal. Water 2019, 11, 2642.

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