Applications of Bayesian belief networks in water resource management: A systematic review |
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| Affiliation: | 1. Australian Rivers Institute, Griffith School of Environment, Griffith University, Nathan, Queensland, Australia;2. Griffith Climate Change Response Program, Australia;3. Griffith School of Engineering, Griffith University, Gold Coast, Queensland, Australia;1. Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China;2. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China;1. CENSUI, Comillas, 39520, Cantabria, Spain;2. Basque Centre for Climate Change (BC3), Sede Building 1, UPV Scientific Campus, 48940, Leioa, Spain;1. Griffith School of Engineering, Griffith University, Queensland, Australia;2. School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia;3. Menzies Health Institute, Griffith University, Queensland, Australia;1. Department of Engineering Management and Systems Engineering, 1776 G St., NW #159, The George Washington University, Washington, DC 20006, USA;2. Department of Geography and Environmental Engineering, The Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA;1. Nicholas School of the Environment, Duke University, Durham, NC 27708, USA;2. Department of Environmental Sciences, The University of Toledo, Toledo, OH 43606, USA;3. Institute of Marine Sciences, The University of North Carolina at Chapel Hill, Morehead City, NC 28557, USA |
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| 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 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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| Keywords: | Bayesian belief networks Climate change Dynamically complex system Integrated modelling framework Spatial geographic distributions |
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