Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events
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The availability and the quality of drinking water are key requirements for the well-being and the safety of a community, both in ordinary conditions and in case of disasters. Providing safe drinking water in emergency contributes to limit the intensity and the duration of crises, and is thus one of the main concerns for decision-makers, who operate under significant uncertainty. The present work proposes a Decision Support System for the emergency management of drinking water supply systems, integrating: i) a vulnerability assessment model based on Bayesian Belief Networks with the related uncertainty assessment model; ii) a model for impact, and related uncertainty assessment, based on Bayesian Belief Networks. The results of these models are jointly analyzed, providing decision-makers with a ranking of the priority of intervention. A GIS interface (G-Net) is developed to manage both input spatial information and results. The methodology is implemented in L’Aquila case study, discussing the potentialities associated to the use of the tool dealing with information and data uncertainty.
KeywordsEmergency management Drinking water supply systems Bayesian belief networks Uncertainty analysis Decision support system
The present research activity was developed within a research project funded by the Italian Department of Civil Protection (‘Intesa Operativa del 19.12.2006 tra DPC e IRSA—Rep. 618).
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- Das B (1999) Representing uncertainties using Bayesian networks. DSTO-TR-0918, DSTO Electronics and Surveillance Research Laboratory, AustraliaGoogle Scholar
- EPA (2015) Systems measures of water distribution system resilience. EPA 600/R-14/383Google Scholar
- Landuyt D, Van der Biest K, Broekx S, Staes J, Meire P, Goethals PLM (2015) A GIS plug-in for Bayesian belief networks: towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping. Environ Model Softw 71:30–38. https://doi.org/10.1016/j.envsoft.2015.05.002 CrossRefGoogle Scholar
- Liu R, Chen Y, Wu J, Gao L, Barrett D, Xu T, Li L, Huang C, Yu J (2016) Assessing spatial likelihood of flooding hazard using naive Bayes and GIS: a case study in Bowen Basin, Australia. Stochastic Environ Res Risk Assess 30(6):1575–1590. https://doi.org/10.1007/s00477-015-1198-y CrossRefGoogle Scholar
- Pagano A, Giordano R, Portoghese I, Vurro M, Fratino U (2014b) Emergency management of drinking water infrastructures based on a Bayesian decision support system. Vulnerability, uncertainty, and risk: quantification, mitigation, and management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014, p 2012–2021Google Scholar
- Pearl J (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San FranciscoGoogle Scholar
- Perng SY, Buscher M (2015) Uncertainty and transparency: augmenting modelling and prediction for crisis response. Proceedings of the ISCRAM 2015 Conference, Kristiansand, May 24–27, Palen, Büscher, Comes & Hughes edsGoogle Scholar
- Tateosian L (2015) Python for ArcGIS. Springer. https://doi.org/10.1007/978-3-319-18398-5
- van der Keur P, van Bers C, Henriksen HJ, Nibanupudi HK, Yadav S, Wijaya R, Subiyono A, Mukerjee N, Hausmann HJ, Hare M, van Scheltinga CT, Pearn G, Jaspers F (2016) Identification and analysis of uncertainty in disaster risk reduction and climate change adaptation in South and Southeast Asia. Int J Disaster Risk Reduct 16:208–214. https://doi.org/10.1016/j.ijdrr.2016.03.002 CrossRefGoogle Scholar