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  • Applications of Bayesian belief networks in water resource management: A systematic review

    Author(s)
    Phan, Thuc D
    Smart, James CR
    Capon, Samantha J
    Hadwen, Wade L
    Sahin, Oz
    Griffith University Author(s)
    Capon, Samantha J.
    Hadwen, Wade L.
    Sahin, Oz
    Smart, Jim C.
    Phan, Thuc D.
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of ...
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    Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools.
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    Journal Title
    Environmental Modelling & Software
    Volume
    85
    DOI
    https://doi.org/10.1016/j.envsoft.2016.08.006
    Subject
    Natural resource management
    Publication URI
    http://hdl.handle.net/10072/100139
    Collection
    • Journal articles

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