The use of Bayesian networks to facilitate implementation of water demand management strategies

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.