Bayesian Network Models for
Integrated Catchment-scale Management of Salinity
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Addressing salinity management problem, at catchment scales requires an integrated modelling approach, in which key bio-physical and socio-economic drivers, processes and impacts are all considered. The problems are also characterised by data and knowledge that is sparse, scattered and of varying type and quality. Therefore, the approach must have the ability to use and integrate both qualitative and quantitative data and information extracted from a range of pre-existing sources. This book presents and evaluates the use of a Bayesian Decision Network model as an integrated approach for consid...
Addressing salinity management problem, at catchment scales requires an integrated modelling approach, in which key bio-physical and socio-economic drivers, processes and impacts are all considered. The problems are also characterised by data and knowledge that is sparse, scattered and of varying type and quality. Therefore, the approach must have the ability to use and integrate both qualitative and quantitative data and information extracted from a range of pre-existing sources. This book presents and evaluates the use of a Bayesian Decision Network model as an integrated approach for considering the trade-offs associated with the management of dryland salinity, a major environmental problem in Australia. The ability and effectiveness of the BDN approach in building integrated catchment assessment and management tools are demonstrated through a case study in the Little River catchment in the upper Macquarie River basin, NSW, Australia.
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