Advertisement

Assessment of flood risk in Mediterranean catchments: an approach based on Bayesian networks

  • M. Julia Flores
  • Rosa F. RoperoEmail author
  • Rafael Rumí
Original Paper
  • 26 Downloads

Abstract

National and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm events are the main reasons for compensation according to the National Insurance Consortium. The aim of this paper is to model the risk of flooding in a Mediterranean catchment in the South of Spain. A hybrid dynamic object-oriented Bayesian network (OOBN) was learnt based on mixture of truncated exponential models, a scenario of rainfall event was included, and the final model was validated. OOBN structure allows the catchment to be divided into five different units and models each of them independently. It transforms a complex problem into a simple and easily interpretable model. Results show that the model is able to accurately watch the evolution of river level, by predicting its increase and the time the river needs to recover normality, which can be defined as the river resilience.

Keywords

Flood risk assessment Dynamic Bayesian networks Object-oriented Bayesian networks Mediterranean watershed 

Notes

Acknowledgements

This study was supported by the Spanish Ministry of Economy and Competitiveness through Projects TIN2016-77902-C3-1-P and TIN2016-77902-C3-3-P, and by the Regional Government of Andalusia through project P12-TIC-2541.

References

  1. AEMET (2008) Generación de escenarios regionalizados de cambio climático en España. Informe técnico. Technical report, Ministerio de Economía, Industria y CompetitividadGoogle Scholar
  2. Aguilera PA, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian networks in environmental modelling. Environ Model Softw 26:1376–1388CrossRefGoogle Scholar
  3. Bolle A, das Neves L, Smets S, Mollaert J, Buitrago S (2018) An impact-oriented early warning and Bayesian-based decision support system for flood risks in Zeebrugge harbour. Coast Eng 134:191–202CrossRefGoogle Scholar
  4. CCS (2017) Estadística de Riesgos Extraordinarios. Serie 1971–2016. Technical report, Consorcio de Compensación de SegurosGoogle Scholar
  5. Chan TU, Hart BT, Kennard MJ, Pusey BJ, Shenton W, Douglas MM, Valentine E, Patel S (2012) Bayesian network models for environmental flow decision making in the Daly river, Northern territory, Australia. River Res Appl 28:283–301CrossRefGoogle Scholar
  6. Cobb BR, Rumí R, Salmerón A (2007) Bayesian networks models with discrete and continuous variables. In: Lucas P, Gámez JA, Salmerón A (eds) Advances in probabilistic graphical models. Studies in fuzziness and soft computing. Springer, Berlin, pp 81–102CrossRefGoogle Scholar
  7. Commission E (2007) European Commission, 2007. Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risksGoogle Scholar
  8. Dlamini WM (2010) A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland. Environ Model Softw 25:199–208CrossRefGoogle Scholar
  9. Elvira-Consortium (2002) Elvira: an environment for creating and using probabilistic graphical models. In: Proceedings of the first European workshop on probabilistic graphical models, pp 222–230. http://www.ia.uned.es/investig/proyectos/elvira/
  10. Gine-Garriga R, Requejo D, Molina J, Perez-Foguet A (2018) A novel planning approach for the water, sanitation and hygiene (wash) sector: the use of object-oriented Bayesian networks. Environ Model Softw 103:1–15CrossRefGoogle Scholar
  11. Guadalquivir Plan (2007) Plan especial de actuación en situaciones de alerta y eventual sequía de la cuenca hidrográfica del Guadalquivir. Technical report, Ministerio de Medio AmbienteGoogle Scholar
  12. Jager W, Christie E, Hanea A, den Heijer C, Spencer T (2018) A Bayesian network approach for coastal risk analysis and decision making. Coast Eng 134:48–61CrossRefGoogle Scholar
  13. Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs. Springer, BerlinCrossRefGoogle Scholar
  14. Keshtkar AR, Slajegheh A, Sadoddin A, Allan MG (2013) Application of Bayesian networks for sustainability assessment in catchment modeling and management (case study: the Hablehrood river catchment). Ecol Model 268:48–54CrossRefGoogle Scholar
  15. Kim K, Lee S, Jin Y (2018) Forecasting quarterly inflow to reservoirs combining a copula-based Bayesian network method with drought forecasting. Water 10:233CrossRefGoogle Scholar
  16. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, CambridgeGoogle Scholar
  17. Korb KB, Nicholson AE (2011) Bayesian artificial intelligence. CRC Press, Boca RatonGoogle Scholar
  18. Koski T, Noble J (2011) Bayesian networks: an introduction. Wiley, New YorkGoogle Scholar
  19. Landuyt D, Broekx S, D'hondt R, Engelen G, Aertsens J, Geothals P (2013) A review of Bayesian belief networks in ecosystem service modelling. Environ Model Softw.  https://doi.org/10.1016/j.envsoft.2013.03.011 CrossRefGoogle Scholar
  20. Langseth H, Bangsø O (2001) Parameter learning in object-oriented Bayesian networks. Ann Math Artif Intell 32(1):221–243CrossRefGoogle Scholar
  21. Langseth H, Nielsen TD, Rumí R, Salmerón A (2009) Inference in hybrid Bayesian networks. Reliab Eng Syst Saf 94:1499–1509CrossRefGoogle Scholar
  22. Lauritzen SL (1992) Propagation of probabilities, means and variances in mixed graphical association models. J Am Stat Assoc 87:1098–1108CrossRefGoogle Scholar
  23. Liu Q, Peres F, Tchangani T (2016a) Object-oriented Bayesian network for complex system risk assessment. IFAC 49:31–36Google Scholar
  24. Liu Q, Tchangani A, Pérès F (2016b) Modelling complex large scale systems using object oriented Bayesian networks (OOBN). IFAC-PapersOnLine 49(12):127–132CrossRefGoogle Scholar
  25. Maldonado A, Aguilera P, Salmerón A (2016) Continuous Bayesian networks for probabilistic environmental risk mapping. Stoch Environ Res Risk Assess 30(5):1441–1455.  https://doi.org/10.1007/s00477-015-1133-2 CrossRefGoogle Scholar
  26. Malekmohammadi B, Moghadam N (2018) Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran. Environ Monit Assess 190:1–17CrossRefGoogle Scholar
  27. Marcot BG, Penman T (2019) Advances in Bayesian network modelling: integration of modelling technologies. Environ Model Softw 111:386–393CrossRefGoogle Scholar
  28. Molina JL, Pulido-Veláquez D, García-Aróstegui J, Pulido-Velázquez M (2013) Dynamic Bayesian network as a decision support tool for assessing climate change impacts on highly stressed groundwater systems. J Hydrol 479:113–129CrossRefGoogle Scholar
  29. Moral S, Rumí R, Salmerón A (2001) Mixtures of truncated exponentials in hybrid Bayesian networks. In: ECSQARU’01. Lecture notes in artificial intelligence, vol 2143. Springer, Berlin, pp 156–167Google Scholar
  30. Mortera J, Vicard P, Vergari C (2013) Object-oriented Bayesian networks for a decision support system for antitrust enforcement. Ann Appl Stat 7:714–738CrossRefGoogle Scholar
  31. Murphy KP (2002) Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, BerkeleyGoogle Scholar
  32. Nicholson A, Flores J (2011) Combining state and transition models with dynamic Bayesian networks. Ecol Model 222:555–566CrossRefGoogle Scholar
  33. Papacharalampous G, Tyralis H, Koutsoyiannis D (2019) Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Stoch Environ Res Risk Assess 33(2):481–514CrossRefGoogle Scholar
  34. Paprotny D, Morales-Napoles O (2017) Estimating extreme river discharges in Europe through a Bayesian networks. Hydrol Earth Syst Sci 21:2615–2636CrossRefGoogle Scholar
  35. Pearl J (1988) Probabilistic reasoning in intelligent systems: network of plausible inference. Morgan Kaufmann, San MateoGoogle Scholar
  36. Pérez-Ramiréz PA, Bouwer-Utne I (2015) Use of dynamic Bayesian networks for life extension assessment of ageing systems. Reliab Eng Syst Saf 133:119–136CrossRefGoogle Scholar
  37. Provan GM (1993) Tradeoffs in constructing and evaluating temporal influence diagrams. In: Proceedings of the 9th conference of the uncertainty in artificial intelligence, pp 40–47CrossRefGoogle Scholar
  38. Ropero RF (2016) Hybrid Bayesian networks: a statistical tool in ecology and environmental sciences. Ph.D. thesis, Department of Biology and Geology, University of AlmeríaGoogle Scholar
  39. Ropero RF, Nicholson A, Rumí R, Aguilera P (2018) Learning and inference methodologies for hybrid dynamic Bayesian networks: a case study for a water reservoir system in Andalusia, Spain. Stoch Environ Res Risk Assess 32(11):3117–3135.  https://doi.org/10.1007/s00477-018-1566-5 CrossRefGoogle Scholar
  40. Rumí R (2003) Modelos de redes bayesianas con variables discretas y continuas. Ph.D. thesis, Universidad de AlmeríaGoogle Scholar
  41. Rumí R, Salmerón A (2007) Approximate probability propagation with mixtures of truncated exponentials. Int J Approx Reason 45:191–210CrossRefGoogle Scholar
  42. Rumí R, Salmerón A, Moral S (2006) Estimating mixtures of truncated exponentials in hybrid Bayesian networks. Test 15:397–421CrossRefGoogle Scholar
  43. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B (Methodol) 36(2):111–147Google Scholar
  44. Voinov A, Bousquet F (2010) Modelling with stakeholders. Environ Model Softw 24:1268–1281CrossRefGoogle Scholar
  45. Wang X, Zhu J, Ma F, Li C, Cai Y, Yang Z (2016) Bayesian network-based risk assessment for hazmat transportation on the Middle Route of the South-to-North Water Transfer Project in China. Stoch Environ Res Risk Assess 30:841–857CrossRefGoogle Scholar
  46. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San MateoGoogle Scholar
  47. Yu J, Xu L, Xie X, Hou D, Huang P, Zhang G, Zhang H (2017) Contamination event detection method using multi-stations temporal–spatial information based on Bayesian network in water distribution systems. Water 9:894CrossRefGoogle Scholar
  48. Yung EC, Wilkinson L, Nicholson A, Quintana-Ascencio P, Fauth J, Hall D, Ponzio K, Rumpff L (2016) Modelling spatial and temporal changes with GIS and spatial and dynamic Bayesian networks. Environ Model Softw 82:108–120CrossRefGoogle Scholar
  49. Zhu X, Zhang G, Yuan K, Ling H, Xu H (2018) Evaluation of agricultural water pricing in an irrigation district based on a Bayesian network. Water 10:768CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computing Systems Department, SIMD I3AUniversity of Castilla-La ManchaAlbaceteSpain
  2. 2.Informatics and Environmental Research Group, Department of Biology and GeologyUniversity of AlmeríaAlmeríaSpain
  3. 3.Department of MathematicsUniversity of AlmeríaAlmeríaSpain

Personalised recommendations