Applications of Bayesian belief networks in water resource management: A systematic review. (November 2016) - Phan, Thuc D.; Smart, James C.R.; Capon, Samantha J.; Hadwen, Wade L.; Sahin, Oz
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. Highlights: The application of BBNs to water resource management was rarely applied in developing countries and in tropical regions. Only 8% reviewed papers explored potential impacts of climate change on water resources. Only 11% and 6% of reviewed articles applied influence diagrams and Object-Oriented Bayesian Networks respectively. Most reviewed articles applied BBNs in strategic decision-making contexts (52%) for water resource management. Results from BBN models were rarely compared or tested against other modelling approaches to validate their performance.