Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events

  • Alessandro Pagano
  • Irene Pluchinotta
  • Raffaele Giordano
  • Anna Bruna Petrangeli
  • Umberto Fratino
  • Michele Vurro
Article
  • 15 Downloads

Abstract

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.

Keywords

Emergency management Drinking water supply systems Bayesian belief networks Uncertainty analysis Decision support system 

Notes

Acknowledgments

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).

Compliance with Ethical Standards

Conflict of Interest

None.

References

  1. Das B (1999) Representing uncertainties using Bayesian networks. DSTO-TR-0918, DSTO Electronics and Surveillance Research Laboratory, AustraliaGoogle Scholar
  2. Diao K, Sweetapple C, Farmani R, Fu G, Ward S, Butler D (2016) Global resilience analysis of water distribution systems. Water Res 106:383–393.  https://doi.org/10.1016/j.watres.2016.10.011 CrossRefGoogle Scholar
  3. Eidsvig UMK, Kristensen K, Vangelsten BV (2017) Assessing the risk posed by natural hazards to infrastructures. Nat Hazards Earth Syst Sci 17:481–504.  https://doi.org/10.5194/nhess-17-481-2017 CrossRefGoogle Scholar
  4. EPA (2015) Systems measures of water distribution system resilience. EPA 600/R-14/383Google Scholar
  5. Fragiadakis M, Christodoulou SE, Vamvatsikos D (2013) Reliability assessment of urban water distribution networks under seismic loads. Water Resour Manag 27:3739–3764.  https://doi.org/10.1007/s11269-013-0378-0 CrossRefGoogle Scholar
  6. Francis RA, Guikema SD, Henneman L (2014) Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliab Eng Syst Saf 130:1–11.  https://doi.org/10.1016/j.ress.2014.04.024 CrossRefGoogle Scholar
  7. Gaudard L, Romerio F (2015) Natural hazard risk in the case of an emergency: the real options’ approach. Nat Hazards 75(1):473–488.  https://doi.org/10.1007/s11069-014-1330-1 CrossRefGoogle Scholar
  8. Gonzalez-Redin J, Luque S, Poggio L, Smith R, Gimona A (2016) Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes. Environ Res 144:15–26.  https://doi.org/10.1016/j.envres.2015.11.009 CrossRefGoogle Scholar
  9. John A, Yang Z, Riahi R, Wang J (2016) A risk assessment approach to improve the resilience of a seaport system using Bayesian networks. Ocean Eng 111:136–147.  https://doi.org/10.1016/j.oceaneng.2015.10.048 CrossRefGoogle Scholar
  10. Johnson S, Low-Choy S, Mengersen K (2011) Integrating Bayesian networks and geographic information systems: good practice examples. Integr Environ Assess Manag 8(3):473–479.  https://doi.org/10.1002/ieam.262 CrossRefGoogle Scholar
  11. Kabir G, Demissie G, Sadiq R, Tesfamariam S (2015) Integrating failure prediction models for water mains: Bayesian belief network based data fusion. Knowl Based Syst 85:159–169.  https://doi.org/10.1016/j.knosys.2015.05.002 CrossRefGoogle Scholar
  12. Kabir G, Sadiq R, Tesfamariam S (2016) A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines. Struct Infrastruct Eng 12(8):874–889.  https://doi.org/10.1080/15732479.2015.1053093 CrossRefGoogle Scholar
  13. Karimi HA, Houston BH (1996) Evaluating strategies for integrating environmental models with GIS: current trends and future needs. Comput Environ Urban Syst 20(6):413–425.  https://doi.org/10.1016/S0198-9715(97)00006-9 CrossRefGoogle Scholar
  14. Kongar I, Esposito S, Giovinazzi S (2017) Post-earthquake assessment and management for infrastructure systems: learning from the Canterbury (New Zealand) and L’Aquila (Italy) earthquakes. Bull Earthq Eng 15(2):589–620.  https://doi.org/10.1007/s10518-015-9761-y CrossRefGoogle Scholar
  15. 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
  16. 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
  17. Mala-Jetmarova H, Sultanova N, Savic D (2017) Lost in optimisation of water distribution systems? A literature review of system operation. Environ Model Softw 93:209–254.  https://doi.org/10.1016/j.envsoft.2017.02.009 CrossRefGoogle Scholar
  18. Marcot BG (2012) Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol Model 230:50–62.  https://doi.org/10.1016/j.ecolmodel.2012.01.013 CrossRefGoogle Scholar
  19. McCormick S (2016) New tools for emergency managers: an assessment of obstacles to use and implementation. Disasters 40(2):207–225.  https://doi.org/10.1111/disa.12141 CrossRefGoogle Scholar
  20. Molina JL, Farmani R, Bromley J (2011) Aquifers management through evolutionary bayesian networks:the Altiplano case study (SE Spain). Water Resour Manag 25(14):3883–3909.  https://doi.org/10.1007/s11269-011-9893-z CrossRefGoogle Scholar
  21. Molina JL, Zazo S, Rodríguez-Gonzálvez P, González-Aguilera D (2016) innovative analysis of runoff temporal behavior through Bayesian networks. Water 8(11):484.  https://doi.org/10.3390/w8110484 CrossRefGoogle Scholar
  22. Pagano A, Giordano R, Portoghese I, Fratino U, Vurro M (2014a) A Bayesian vulnerability assessment tool for drinking water mains under extreme events. Nat Hazards 74(3):2193–2227.  https://doi.org/10.1007/s11069-014-1302-5 CrossRefGoogle Scholar
  23. 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
  24. Pagano A, Pluchinotta I, Giordano R, Vurro M (2017) Drinking water supply in resilient cities: notes from L’Aquila earthquake case study. Sustain Cities Soc 28:435–449.  https://doi.org/10.1016/j.scs.2016.09.005 CrossRefGoogle Scholar
  25. Pearl J (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San FranciscoGoogle Scholar
  26. 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
  27. Phan TD, Smart JCR, Capon SJ, Hadwen WL, Sahin O (2016) Applications of Bayesian belief networks in water resource management: a systematic review. Environ Model Softw 85:98–111.  https://doi.org/10.1016/j.envsoft.2016.08.006 CrossRefGoogle Scholar
  28. Sobradelo R, Martı J, Kilburn C, Lopez C (2015) Probabilistic approach to decision-making under uncertainty during volcanic crises: retrospective application to the El Hierro (Spain) 2011 volcanic crisis. Nat Hazards 76:979–998.  https://doi.org/10.1007/s11069-014-1530-8 CrossRefGoogle Scholar
  29. Tanyimboh TT (2017) Informational entropy: a failure tolerance and reliability surrogate for water distribution networks. Water Resour Manag 31:3189–3204.  https://doi.org/10.1007/s11269-017-1684-8 CrossRefGoogle Scholar
  30. Tateosian L (2015) Python for ArcGIS. Springer.  https://doi.org/10.1007/978-3-319-18398-5
  31. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modeling. Ecol Model 203(3–4):312–318.  https://doi.org/10.1016/j.ecolmodel.2006.11.033 CrossRefGoogle Scholar
  32. Uusitalo L, Lehikoinen A, Helle I, Myrberg K (2015) An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ Model Softw 63:24–31.  https://doi.org/10.1016/j.envsoft.2014.09.017 CrossRefGoogle Scholar
  33. 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
  34. Wu J, Zhou R, Xu S, Wu Z (2017) Probabilistic analysis of natural gas pipeline network accident based on Bayesian network. J Loss Prev Process Ind 46:126–136.  https://doi.org/10.1016/j.jlp.2017.01.025 CrossRefGoogle Scholar
  35. Zhao X, Cai H, Chen Z, Gong H, Feng Q (2016) Assessing urban lifeline systems immediately after seismic disaster based on emergency resilience. Struct Infrastruct Eng 12(12):1634–1649.  https://doi.org/10.1080/15732479.2016.1157609 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Water Research Institute – National Research Council (IRSA-CNR)BariItaly
  2. 2.LAMSADE – CNRS, Univ. Paris-Dauphine, PSL Research UnivParisFrance
  3. 3.DICATEChPolitecnico di BariBariItaly

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