Abstract:
Bayesian networks have received increasing recognition in recent years as a
potentially effective tool in supporting water management decisions. Despite a
number of reports of their use, no formal evaluation of the effectiveness of Bayesian
networks in facilitating water resources management exists. This study improves
understanding of the strengths and weaknesses of Bayesian networks through their
application in a water-stressed region in Europe where domestic sector water
demand management is considered as a mitigation measure. The fieldwork results
provide a comprehensive technical and end-user evaluation of the use of Bayesian
networks in water demand management implementation which, to our knowledge, is
the first of its kind to be reported in the academic literature. For the technical
evaluation, expert knowledge was first used to generate the structure of Bayesian
network models which were then populated with data collected in the case study
region. The model development supported the examination of several research
questions regarding the technical suitability of Bayesian network modelling to
facilitate implementation of water demand management strategies. For the end-user
evaluation a survey was used to record the experiences of practitioners who applied
Bayesian network models to a number of water demand management problems
during a one-day workshop. Evaluation indicators included the effectiveness of
Bayesian networks in facilitating strategic planning, technical support, transparency
of data, learning among and between stakeholders, organisational receptivity,
reliance on decision, and a comparison of experiences of decision conflict, effort and
decision confidence. Results from the end-user evaluation provide evidence that
Bayesian networks are particularly effective in terms of technical suitability and
transparency, and policy-makers perceived effectiveness scores were significantly
higher than individuals from other professions. Conclusions from the technical
evaluation found that Bayesian networks can provide support in achieving cost-
effectiveness in terms of sampling and data collection by focusing resources on
collecting relevant data to reduce uncertainty. Conclusions from the end-user
evaluation found that, for cross-sectoral planning in the context of managing water
scarcity, their transparent representation of strengths of causes and effects between
variables makes Bayesian networks an effective tool for facilitating dialogue and
collaboration across science-policy interfaces.
