Multihazard Risk Assessment from Qualitative Methods to Bayesian Networks: Reviewing Recent Contributions and Exploring New Perspectives

  • John Tsiplakidis
  • Yorgos N. PhotisEmail author
Part of the Key Challenges in Geography book series (KCHGE)


Natural processes are interacting components of natural systems. Under certain circumstances, they can be transformed into threats for humanity, environment, and development. Examples such as the 2006 Pangandaran earthquake–tsunami and the 2011 Tohoku earthquake–tsunami–flood–nuclear catastrophe point out the necessity for an integrated multihazard risk assessment tool. This paper presents the critical steps and improvements in approaches to multihazard risk management. From the first qualitative, semiquantitative techniques with which risk is calculated through individual processes to more powerful techniques which try to capture and evaluate the interactions (trigger, cascade effect) among the natural hazards, such as Event Tree (ET) and Bayesian Networks (BNs). Especially Bayesian Networks and recently, their extensions as Dynamic Bayesian Networks (DBNs) and Hybrid Bayesian Networks (HBNs) offer a great opportunity for a more realistic and flexible multihazard risk assessment.


Risks assessment Hazards Models Network 


  1. Aguilera PA, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian networks in environmental modelling. Environ Model Softw 26(12):1376–1388CrossRefGoogle Scholar
  2. Aguilera PA, Fernández A, Reche F, Rumí R (2010) Hybrid Bayesian network classifiers: application to species distribution models. Environ Model Softw 25(12):1630–1639. Scholar
  3. Apivatanagul P, Davidson R, Blanton B, Nozick L (2011) Long-term regional hurricane hazard analysis for wind and storm surge. Coast Eng 58(6):499–509. Scholar
  4. Arnold M, Dilley M, Deichmann U, Chen RS, Lerner-Lam AL (2005) Natural disaster hotspots: a global risk analysis. Disaster Risk Manag 5:1–145Google Scholar
  5. Asimakopoulou E, Bessis N (2011) Towards an integrated multi-hazard prevention assessment for community threatsGoogle Scholar
  6. Aspinall WP, Woo G (2014) Santorini unrest 2011–2012: an immediate Bayesian belief network analysis of eruption scenario probabilities for urgent decision support under uncertainty. J Appl Volcanol 3(1):1–12CrossRefGoogle Scholar
  7. Bartel P, Muller J (2007) Horn of Africa natural hazard probability and risk analysis. US Department of State–Humanitarian Information UnitGoogle Scholar
  8. Bayraktarli YY (2006) Application of Bayesian probabilistic networks for liquefaction of soil. Paper presented at the 6th international Ph.D. symposium in civil engineeringGoogle Scholar
  9. Bayraktarli YY, Ulfkjaer J.-P., Yazgan, U., & Faber, M. H. (2005). On the application of Bayesian probabilistic networks for earthquake risk management. Paper presented at the 9th international conference on structural safety and reliability, Italy, RomeGoogle Scholar
  10. Bell R, Glade T (2004) Multi-hazard analysis in natural risk assessmentsGoogle Scholar
  11. Bell RG, Reese S, King AB (2007) Regional RiskScape: a multi-hazard loss modelling tool. Proc Coastal Communities Nat Disasters 17:18Google Scholar
  12. Ben‐Gal I (2007) Bayesian networks. In: Encyclopedia of statistics in quality and reliabilityGoogle Scholar
  13. Bennett JE, Racine-Poon A, Wakefield JC (1996) MCMC for nonlinear hierarchical models. In: Markov chain Monte Carlo in practice, Springer, pp 339–357Google Scholar
  14. Blaser L, Ohrnberger M, Riggelsen C, Babeyko A, Scherbaum F (2011) Bayesian networks for tsunami early warning. Geophys J Int 185(3):1431–1443CrossRefGoogle Scholar
  15. Buzna L, Peters K, Ammoser H, Kühnert C, Helbing D (2007) Efficient response to cascading disaster spreading. Phys Rev E 75(5):056107CrossRefGoogle Scholar
  16. Cai B, Liu Y, Liu Z, Tian X, Zhang Y, Ji R (2013) Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations. Risk Anal 33(7):1293–1311CrossRefGoogle Scholar
  17. CAPRA (2008–2012) CAPRA initiative: integrating disaster risk information into development policies and programs in Latin America and the Caribbean. Probabilistic Risk Assessment (CAPRA) InitiativeGoogle Scholar
  18. Cardona OD, Ordaz Schroder MG, Reinoso E, Yamín L, Barbat Barbat HA (2010) Comprehensive approach for probabilistic risk assessment (CAPRA): international initiative for disaster risk management effectivenessGoogle Scholar
  19. Carpignano A, Golia E, Di Mauro C, Bouchon S, Nordvik JP (2009) A methodological approach for the definition of multi-risk maps at regional level: first application. J Risk Res 12(3–4):513–534CrossRefGoogle Scholar
  20. Carrara A (1993) Uncertainty in evaluating landslide hazard and risk. In: Prediction and perception of natural hazards. Springer, pp 101–109Google Scholar
  21. Castillo E, Gutiérrez JM, Hadi AS (1998) Modeling probabilistic networks of discrete and continuous variables. J Multivar Anal 64(1):48–65CrossRefGoogle Scholar
  22. Charniak E (1991) Bayesian networks without tears. AI Mag 12(4):50Google Scholar
  23. Chen SH, Pollino CA (2012) Good practice in Bayesian network modelling. Environ Model Softw 37:134–145. Scholar
  24. Chib S (2001) Markov chain Monte Carlo methods: computation and inference. In: Handbook of econometrics, vol 5, pp 3569–3649CrossRefGoogle Scholar
  25. Chongfu H (1996) Fuzzy risk assessment of urban natural hazards. Fuzzy Sets Syst 83(2):271–282CrossRefGoogle Scholar
  26. Ciscar JC, Feyen L, Soria A, Lavalle C, Raes F, Perry M, Dosio A (2014) Climate impacts in Europe the JRC PESETA II projectGoogle Scholar
  27. Cobb BR, Rumi R, Salmerón A (2007) Bayesian network models with discrete and continuous variables. In: Advances in probabilistic graphical models. Springer, pp 81–102Google Scholar
  28. Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Smith JT (2013) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Env 73(2):209–263. Scholar
  29. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87CrossRefGoogle Scholar
  30. De Pippo T, Donadio C, Pennetta M, Petrosino C, Terlizzi F, Valente A (2008) Coastal hazard assessment and mapping in Northern Campania, Italy. Geomorphology 97(3):451–466CrossRefGoogle Scholar
  31. De Pippo T, Donadio C, Pennetta M, Terlizzi F, Valente A (2009) Application of a method to assess coastal hazard: the cliffs of the Sorrento Peninsula and Capri (southern Italy). Geol Soc London Spec Publ 322(1):189–204CrossRefGoogle Scholar
  32. Delmonaco G, Margottini C, Spizzichino D (2006) ARMONIA methodology for multi-risk assessment and the harmonisation of different natural risk maps. Deliverable 3.1.1, ARMONIAGoogle Scholar
  33. Dlamini WM (2011) Application of Bayesian networks for fire risk mapping using GIS and remote sensing data. GeoJournal 76(3):283–296CrossRefGoogle Scholar
  34. Dragicevic S, Filipovic D, Kostadinov S, Ristic R, Novkovic I, Zivkovic N et al. (2011) Natural hazard assessment for land-use planning in Serbia. Int J Environ Res 5(2):371–380Google Scholar
  35. Durham K (2003) Treating the risks in Cairns. Nat Hazards 30(2):251–261CrossRefGoogle Scholar
  36. Einstein H, Sousa R, Karam K, Manzella I, Kveldsvik V (2010) Rock slopes from mechanics to decision making. Chapter 1:3–13Google Scholar
  37. El Morjani Zel A, Ebener S, Boos J, Abdel Ghaffar E, Musani A (2007) Modelling the spatial distribution of five natural hazards in the context of the WHO/EMRO atlas of disaster risk as a step towards the reduction of the health impact related to disasters. Int J Health Geogr 6:8. Scholar
  38. European (2011) Risk assessment and mapping guidelines for disaster managementGoogle Scholar
  39. Faes C, Ormerod JT, Wand MP (2011) Variational Bayesian inference for parametric and nonparametric regression with missing data. J Am Stat Assoc 106(495)CrossRefGoogle Scholar
  40. Fall M, Azzam R, Noubactep C (2006) A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping. Eng Geol 82(4):241–263. Scholar
  41. Fausto Guzzetti AC, Cardinali M, Reichenbach P (1997) <Landslide hazard evaluation_a review of current techniques and_10032014.pdf>Google Scholar
  42. FEMA (2011) Multi-hazard loss estimation methodology: flood model. HAZUS-MH. Technical manual. U.S. Department of Homeland Security, Federal Emergency Management AgencyGoogle Scholar
  43. Fenton N, Littlewood B, Neil M, Strigini L, Wright D, Courtois P-J (1997) Bayesian belief network model for the safety assessment of nuclear computer-based systemsGoogle Scholar
  44. Fragiadakis M, Christodoulou SE (2014) Seismic reliability assessment of urban water networks. Earthquake Eng Struct Dynam 43(3):357–374. Scholar
  45. Friedman N, Goldszmidt M (1996) Building classifiers using Bayesian networksGoogle Scholar
  46. Frigerio S, van Westen CJ (2010) RiskCity and WebRiskCity: data collection, display, and dissemination in a multi-risk training package. Cartography Geogr Inf Sci 37(2):119–135CrossRefGoogle Scholar
  47. Garcia-Aristizabal A, Selva J, Fujita E (2013a) Integration of stochastic models for long-term eruption forecasting into a Bayesian event tree scheme: a basis method to estimate the probability of volcanic unrest. Bull Volc 75(2):1–13Google Scholar
  48. Garcia-Aristizabal A, Selva J, Fujita E (2013b) Integration of stochastic models for long-term eruption forecasting into a Bayesian event tree scheme: a basis method to estimate the probability of volcanic unrest. Bull Volcanol 75(2).
  49. García-Herrero S, Mariscal M, Gutiérrez JM, Toca-Otero A (2013) Bayesian network analysis of safety culture and organizational culture in a nuclear power plant. Saf Sci 53:82–95CrossRefGoogle Scholar
  50. Ghahramani Z (1998) Learning dynamic Bayesian networks. In: Adaptive processing of sequences and data structures. Springer, pp 168–197Google Scholar
  51. GIS, p. e. a. r. a. l. B. n. t. a., Grêt-Regamey A, Straub D (2006) Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS. Nat Hazards Earth Syst Sci 6(6):911–926Google Scholar
  52. Glade T (2012) Multi-hazard exposure analyses with multiriskGoogle Scholar
  53. Goodchild A, Jessup E, McCormack E, Andreoli D, Rose S, Ta C, Ivanov B (2009) Development and analysis of a GIS-based statewide freight data flow network. Washington State Department of TransportationGoogle Scholar
  54. Granger K, Jones TG, Leiba M, Scott G (1999) Community risk in Cairns: a multi-hazard risk assessment. Aust J Emerg Manag 14(2):25Google Scholar
  55. Greiving S, Fleischhauer M (2012) National climate change adaptation strategies of European states from a spatial planning and development perspective. Eur Plan Stud 20(1):27–48CrossRefGoogle Scholar
  56. Greiving S, Fleischhauer M, Lückenkötter J (2006) A methodology for an integrated risk assessment of spatially relevant hazards. J Environ Plann Manage 49(1):1–19CrossRefGoogle Scholar
  57. Gutierrez BT, Plant NG, Thieler ER (2011) A Bayesian network to predict coastal vulnerability to sea level rise. J Geophys Res Earth Surface 116(F2)Google Scholar
  58. Hapke C, Plant N (2010) Predicting coastal cliff erosion using a Bayesian probabilistic model. Mar Geol 278(1):140–149CrossRefGoogle Scholar
  59. Heinl M, Neuenschwander A, Sliva J, Vanderpost C (2006) Interactions between fire and flooding in a southern African floodplain system (Okavango Delta, Botswana). Landscape Ecol 21(5):699–709. Scholar
  60. Hong E-S, Lee I-M, Shin H-S, Nam S-W, Kong J-S (2009) Quantitative risk evaluation based on event tree analysis technique: application to the design of shield TBM. Tunn Undergr Space Technol 24(3):269–277CrossRefGoogle Scholar
  61. Huang C, Ruan D (2008) Fuzzy risks and an updating algorithm with new observations. Risk Anal 28(3):681–694CrossRefGoogle Scholar
  62. IEC/FDIS (2009) Risk management—risk assessment techniquesGoogle Scholar
  63. Isabella Bovolo C, Abele SJ, Bathurst JC, Caballero D, Ciglan M, Eftichidis G, Simo B (2009) A distributed framework for multi-risk assessment of natural hazards used to model the effects of forest fire on hydrology and sediment yield. Comput Geosci 35(5):924–945CrossRefGoogle Scholar
  64. Jensen FV (2001) Bayesian networks and decision graphs. In: Statistics for engineering and information science, vol 32. Springer, p 34Google Scholar
  65. Ji Z, Li N, Xie W, Wu J, Zhou Y (2013) Comprehensive assessment of flood risk using the classification and regression tree method. Stoch Env Res Risk Assess 27(8):1815–1828. Scholar
  66. Jiao Y, Hudson JA (1995) The fully-coupled model for rock engineering systemsGoogle Scholar
  67. Kappes MS, Gruber K, Frigerio S, Bell R, Keiler M, Glade T (2012a) The MultiRISK platform: the technical concept and application of a regional-scale multihazard exposure analysis tool. Geomorphology 151:139–155CrossRefGoogle Scholar
  68. Kappes MS, Gruber K, Frigerio S, Bell R, Keiler M, Glade T (2012b) The MultiRISK platform: the technical concept and application of a regional-scale multihazard exposure analysis tool. Geomorphology 151–152:139–155. Scholar
  69. Kappes MS, Keiler M, von Elverfeldt K, Glade T (2012c) Challenges of analyzing multi-hazard risk: a review. Nat Hazards 64(2):1925–1958. Scholar
  70. Kappes MS, Papathoma-Köhle M, Keiler M (2012d) Assessing physical vulnerability for multi-hazards using an indicator-based methodology. Appl Geogr 32(2):577–590CrossRefGoogle Scholar
  71. Khakzad N (2015) Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab Eng Syst Saf 138:263–272CrossRefGoogle Scholar
  72. Khakzad N, Khan F, Amyotte P (2013a) Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf Environ Prot 91(1–2):46–53. Scholar
  73. Khakzad N, Khan F, Amyotte P, Cozzani V (2013) Risk management of domino effects considering dynamic consequence analysis. Risk Anal. Scholar
  74. Khakzad N, Khan F, Amyotte P, Cozzani V (2014) Risk management of domino effects considering dynamic consequence analysis. Risk Anal 34(6):1128–1138CrossRefGoogle Scholar
  75. Langseth H, Nielsen TD, Rumí R, Salmerón A (2009) Inference in hybrid Bayesian networks. Reliab Eng Syst Saf 94(10):1499–1509. Scholar
  76. Langseth H, Nielsen TD, Salmerón A (2010) Parameter estimation and model selection for mixtures of truncated exponentials. Int J Approximate Reasoning 51(5):485–498CrossRefGoogle Scholar
  77. Lauritzen SL (1995) The EM algorithm for graphical association models with missing data. Comput Stat Data Anal 19(2):191–201CrossRefGoogle Scholar
  78. Lee C-J, Lee KJ (2006) Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal. Reliab Eng Syst Saf 91(5):515–532CrossRefGoogle Scholar
  79. Lerner UN (2002) Hybrid Bayesian networks for reasoning about complex systemsGoogle Scholar
  80. Leroi E (1997) Landslide risk mapping: problems, limitations and developments. In: Landslide risk assessment. Balkema, Rotterdam, pp 239–250CrossRefGoogle Scholar
  81. Liang W-J, Zhuang D-F, Jiang D, Pan J-J, Ren H-Y (2012) Assessment of debris flow hazards using a Bayesian Network. Geomorphology 171:94–100CrossRefGoogle Scholar
  82. Liu B, Siu YL, Mitchell G, Xu W (2013) Exceedance probability of multiple natural hazards: risk assessment in China’s Yangtze River Delta. Nat Hazards 69(3):2039–2055. Scholar
  83. Liu Z, Nadim F, Vangelsten BV, Eidsvig U, Kalsnes B (2014) Quantitative multi-risk modelling and management using Bayesian networks. In: Landslide science for a safer geoenvironment. Springer, pp 773–779Google Scholar
  84. Livingstone DJ, Salt DW (2005) Judging the significance of multiple linear regression models. J Med Chem 48(3):661–663CrossRefGoogle Scholar
  85. Lung T, Lavalle C, Hiederer R, Dosio A, Bouwer LM (2013) A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change. Glob Environ Change 23(2):522–536. Scholar
  86. Mahendra RS, Mohanty PC, Bisoyi H, Kumar TS, Nayak S (2011) Assessment and management of coastal multi-hazard vulnerability along the Cuddalore-Villupuram, east coast of India using geospatial techniques. Ocean Coast Manag 54(4):302–311CrossRefGoogle Scholar
  87. Malet J-P, Glade T, Casagli N (2010). Mountain risks: bringing science to society. CERG StrasbourgGoogle Scholar
  88. Marzocchi W (2009) Principles of multi-risk assessment: interaction amongst natural and man-induced risks. EUR-OPGoogle Scholar
  89. Marzocchi W, Garcia-Aristizabal A, Gasparini P, Mastellone ML, Di Ruocco A (2012) Basic principles of multi-risk assessment: a case study in Italy. Nat Hazards 62(2):551–573. Scholar
  90. Marzocchi W, Sandri L, Gasparini P, Newhall C, Boschi E (2004) Quantifying probabilities of volcanic events: the example of volcanic hazard at Mount Vesuvius. J Geophys Res Solid Earth (1978–2012), 109(B11)Google Scholar
  91. Matellini DB, Wall AD, Jenkinson ID, Wang J, Pritchard R (2013) Modelling dwelling fire development and occupancy escape using Bayesian network. Reliab Eng Syst Saf 114:75–91CrossRefGoogle Scholar
  92. MATRIX (2010–13) New Multi-HAzard and MulTi-RIsK assessment MethodS for Europe. (ENV.2010.1.3.4-1)Google Scholar
  93. Mediero L, Garrote L, Martin-Carrasco F (2007) A probabilistic model to support reservoir operation decisions during flash floods. Hydrol Sci J 52(3):523–537CrossRefGoogle Scholar
  94. Molina J-L, Pulido-Velázquez D, García-Aróstegui JL, Pulido-Velázquez M (2013) Dynamic Bayesian networks as a decision support tool for assessing climate change impacts on highly stressed groundwater systems. J Hydrol 479:113–129CrossRefGoogle Scholar
  95. Money ES, Reckhow KH, Wiesner MR (2012) The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. Sci Total Environ 426:436–445CrossRefGoogle Scholar
  96. Moral S, Rumí R, Salmerón A (2001) Mixtures of truncated exponentials in hybrid Bayesian networks. In: Symbolic and quantitative approaches to reasoning with uncertainty. Springer, pp 156–167Google Scholar
  97. Murphy K (2001) The bayes net toolbox for matlab. Comput Sci Stat 33(2):1024–1034Google Scholar
  98. Murphy KP (2002) Dynamic bayesian networks. In: Jordan M (ed) Probabilistic graphical modelsGoogle Scholar
  99. Nadejda Komendantova AS (2013)  <Multi-risk approach in centralized and decentralized.pdf>Google Scholar
  100. Nadim F, Liu Z (2013a) Quantitative risk assessment for earthquake-triggered landslides using Bayesian network. Paper presented at the proceedings of the 18th international conference on soil mechanics and geotechnical engineering, ParisGoogle Scholar
  101. Nadim F, Liu ZQ (2013b) Quantitative risk assessment for earthquake-triggered landslides using Bayesian networkGoogle Scholar
  102. Neil M, Fenton N, Tailor M (2005) Using Bayesian networks to model expected and unexpected operational losses. Risk Anal 25(4):963–972CrossRefGoogle Scholar
  103. Neri A, Aspinall WP, Cioni R, Bertagnini A, Baxter PJ, Zuccaro G, et al (2008) Developing an event tree for probabilistic hazard and risk assessment at Vesuvius. J Volcanol Geotherm Res 178(3):397–415CrossRefGoogle Scholar
  104. Neri M, Le Cozannet G, Thierry P, Bignami C, Ruch J (2013) A method for multi-hazard mapping in poorly known volcanic areas: an example from Kanlaon (Philippines). Nat Hazards Earth Syst Sci 13(8):1929–1943. Scholar
  105. Newhall C, Hoblitt R (2002) Constructing event trees for volcanic crises. Bull Volc 64(1):3–20CrossRefGoogle Scholar
  106. Nyberg JB, Marcot BG, Sulyma R (2006) Using Bayesian belief networks in adaptive management. Can J For Res 36(12):3104–3116. Scholar
  107. Pagano A, Giordano R, Portoghese I, Fratino U, Vurro M (2014) A Bayesian vulnerability assessment tool for drinking water mains under extreme events. Nat Hazards 74(3):2193–2227CrossRefGoogle Scholar
  108. Papakosta P, Straub D (2013) A Bayesian network approach to assessing wildfire consequences. Paper presented at the proceedings of ICOSSARGoogle Scholar
  109. Peng M, Zhang L (2012) Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks. Nat Hazards 64(1):903–933CrossRefGoogle Scholar
  110. Pollino CA, Woodberry O, Nicholson A, Korb K, Hart BT (2007) Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environ Model Softw 22(8):1140–1152CrossRefGoogle Scholar
  111. Qiu J, Wang Z, Ye X, Liu L, Dong L (2014) Modeling method of cascading crisis events based on merging Bayesian Network. Decis Support Syst 62:94–105CrossRefGoogle Scholar
  112. Reese S, King A, Bell R, Schmidt J (2007) Regional RiskScape: a multi-hazard loss modelling toolGoogle Scholar
  113. Ritchey T (1991) Analysis and synthesis: on scientific method-based on a study by Bernhard Riemann. Syst Res 8(4):21–41CrossRefGoogle Scholar
  114. Ronchetti F, Corsini A, Kollarits S, Leber D, Papez J, Plunger K, et al (2013) Improve information provision for disaster management: MONITOR II, EU project. In: Landslide science and practice. Springer, pp 47–54Google Scholar
  115. Rowe JP, Lester JC (2010) Modeling user knowledge with dynamic Bayesian networks in interactive narrative environmentsGoogle Scholar
  116. Rumí R, Salmerón A, Moral S (2006) Estimating mixtures of truncated exponentials in hybrid Bayesian networks. Test 15(2):397–421CrossRefGoogle Scholar
  117. Sandri L, Thouret J-C, Constantinescu R, Biass S, Tonini R (2014) Long-term multi-hazard assessment for El Misti volcano (Peru). Bull Volcanol 76(2).
  118. Schmidt-Thomé P, Kallio H, Jarva J, Tarvainen T, Greiving S (2006) The spatial effects and management of natural and technological hazards in Europe-ESPON 1.3.1 executive summary. Geological Survey of FinlandGoogle Scholar
  119. Schmidt J, Matcham I, Reese S, King A, Bell R, Henderson R, Heron D (2011) Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling. Nat Hazards 58(3):1169–1192. Scholar
  120. Smith AFM, Gelfand AE (1992) Bayesian statistics without tears: a sampling–resampling perspective. Am Stat 46(2):84–88Google Scholar
  121. Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199CrossRefGoogle Scholar
  122. Špačková O, Straub D (2011) Probabilistic risk assessment of excavation performance in tunnel projects using Bayesian networks: a case study. Paper presented at the proceedings of the 3rd international symposium on geotechnical safety and riskGoogle Scholar
  123. Straub D, Grêt-Regamey A (2006) A Bayesian probabilistic framework for avalanche modelling based on observations. Cold Reg Sci Technol 46(3):192–203CrossRefGoogle Scholar
  124. Syphard AD, Keeley JE, Massada AB, Brennan TJ, Radeloff VC (2012) Housing arrangement and location determine the likelihood of housing loss due to wildfire. PLoS ONE 7(3):e33954CrossRefGoogle Scholar
  125. Tarvainen T, Jarva J, Greiving S (2006) Spatial pattern of hazards and hazard interactions in Europe. Spec Paper Geol Surv Finland 42:83Google Scholar
  126. Tate E, Cutter SL, Berry M (2010) Integrated multihazard mapping. Environ Plann B Plann Des 37(4):646CrossRefGoogle Scholar
  127. Thierry P, Stieltjes L, Kouokam E, Nguéya P, Salley PM (2007) Multi-hazard risk mapping and assessment on an active volcano: the GRINP project at Mount Cameroon. Nat Hazards 45(3):429–456. Scholar
  128. Ullah A, Wang H (2013) Parametric and nonparametric frequentist model selection and model averaging. Econometrics 1(2):157–179. Scholar
  129. UNDHA (1992) Internationally agreed glossary of basic terms related to disaster management. Glossary (DNA/93/36). GenevaGoogle Scholar
  130. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 203(3):312–318CrossRefGoogle Scholar
  131. van Westen, C. J. (2013) 3.10 remote sensing and GIS for natural hazards assessment and disaster risk management. In: Shroder JF (ed) Treatise on geomorphology. Academic Press, San Diego, pp. 259–298Google Scholar
  132. van Westen C, Kappes MS, Luna BQ, Frigerio S, Glade T, Malet J-P (2014) Medium-scale multi-hazard risk assessment of gravitational processes. In: Mountain risks: from prediction to management and governance. Springer, pp 201–231Google Scholar
  133. van Westen CJ, Montoya L, Boerboom L (2002) <MULTI hazard risk costa rica westen.pdf> Google Scholar
  134. van Westen CJ, Quan Luna B, Vargas Franco R, Malet JP, Jaboyedoff M, Kappes MS, Sterlacchini S (2010) Development of training materials on the use of geo-information for multi-hazard risk assessment in a mountainous environmentGoogle Scholar
  135. van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414CrossRefGoogle Scholar
  136. Venkatesan M, Thangavelu A, Prabhavathy P (2013) An improved Bayesian classification data mining method for early warning landslide susceptibility model using GIS. Paper presented at the proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012)Google Scholar
  137. Wang J, Gu X, Huang T (2013) Using Bayesian networks in analyzing powerful earthquake disaster chains. Nat Hazards 68(2):509–527CrossRefGoogle Scholar
  138. Weber P, Medina-Oliva G, Simon C, Iung B (2012) Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng Appl Artif Intell 25(4):671–682CrossRefGoogle Scholar
  139. Westen CJ, Montoya L, Boerboom L, Badilla Coto E (2002) Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa RicaGoogle Scholar
  140. White GF, Kates RW, Burton I (2001) Knowing better and losing even more: the use of knowledge in hazards management. Glob Environ Change Part B Environ Hazards 3(3–4):81–92. Scholar
  141. Wipulanusat W, Nakrod S, Prabnarong P (2011) Multi-hazard risk assessment using GIS and RS applications: a case study of Pak Phanang Basin. Walailak J Sci Technol (WJST) 6(1):109–125Google Scholar
  142. Wu X, Liu H, Zhang L, Skibniewski MJ, Deng Q, Teng J (2015) A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliab Eng Syst Saf 134:157–168CrossRefGoogle Scholar
  143. Yates M, Cozannet GL (2012) Brief communication “evaluating European coastal evolution using Bayesian networks”. Nat Hazards Earth Syst Sci 12(4):1173–1177Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Geography and Regional PlanningNational Technical University of AthensAthensGreece

Personalised recommendations