About Interfaces Between Machine Learning, Complex Networks, Survivability Analysis, and Disaster Risk Reduction

  • Leonardo Bacelar Lima SantosEmail author
  • Luciana R. Londe
  • Tiago José de Carvalho
  • Daniel S. Menasché
  • Didier A. Vega-Oliveros


Modern society strongly relies on critical infrastructures such as telecommunications, transport networks, and the supply of gas, water, and energy. Such infrastructures, which are often exposed to natural hazards, can cause significant damage when disrupted. Among the different strategies to prevent these disruptions and cope with preparedness, mathematical models can be used to support managers in several approaches, as classification and estimation problems using machine learning, vulnerability quantification on complex networks, and survivability analysis. Nevertheless, the assessment of these quantities demands a solid conceptual discussion. In this chapter, we explore concepts of non-linear dynamics, complex systems, machine learning, and survivability analysis in the context of disaster risk reduction.



This research is partially supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, grant 2015/50122-0) and DFG-GRTK (grant 1740/2). DAVO acknowledges FAPESP (grant 2016/23698-1) and LBLS acknowledges FAPESP (grant 2018/06205-7) for the financial support.


  1. 1.
    Abbatantuono, G., Lamonaca, S., La Scala, M., Stecchi, U.: Monitoring and emergency control of natural gas distribution urban networks. In: IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), 2016, pp. 1–6. IEEE, Piscataway (2016)Google Scholar
  2. 2.
    Abrahart, R., See, L.: Neural network modelling of non-linear hydrological relationships. Hydrol. Earth Syst. Sci. Discuss. 11(5), 1563–1579 (2007)CrossRefGoogle Scholar
  3. 3.
    Albert, R., Jeong, H., Barabasi, A.L.: Error and attack tolerance of complex networks (2000). arXiv:cond-mat/0008064v1Google Scholar
  4. 4.
    Alobaidi, I.A.: Dependability Analysis and Recovery Support for Smart Grids. Missouri University of Science and Technology, Rolla (2015)Google Scholar
  5. 5.
    Anderson, J.: An Introduction to Neural Networks. A Bradford Book. MIT Press, Cambridge (1995)CrossRefGoogle Scholar
  6. 6.
    Arianos, S., Bompard, E., Carbone, A., Xue, F.: Power grids vulnerability: a complex network approach (2009). arXiv:08105278 [physicssoc-ph]Google Scholar
  7. 7.
    Avritzer, A., Carnevali, L., Ghasemieh, H., Happe, L., Haverkort, B.R., Koziolek, A., Menasche, D., Remke, A., Sarvestani, S.S., Vicario, E.: Survivability evaluation of gas, water and electricity infrastructures. Electron. Notes Theor. Comput. Sci. 310, 5–25 (2015)CrossRefGoogle Scholar
  8. 8.
    AYuen, D., Kadlec, B.J., Bollig, E.F., Dzwinel, W., Garbow, Z.A., da Silva, C.R.S.: Clustering and visualization of earthquake data in a grid environment. Vis. Geosci. 10(1), 1–12 (2005)CrossRefGoogle Scholar
  9. 9.
    Banihabib, M.E.: Performance of conceptual and black-box models in flood warning systems. Cogent Eng. 3(1), 1127, 798 (2016)Google Scholar
  10. 10.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bell, M.G.H., Kanturska, U., Schmocker, J.D., Fonzone, A.: Attacker defender models and road network vulnerability. Philos. Trans. R. Soc. Lond. A: Math. Phys. Eng. Sci. 366, 1893–1906 (2008). MathSciNetCrossRefGoogle Scholar
  12. 12.
    Berdica, K.: An introduction to road vulnerability: what has been done, is done and should be done. Transp. Policy 9, 117–127 (2002)CrossRefGoogle Scholar
  13. 13.
    Berton, L., Vega-Oliveros, D.A., Valverde-Rebaza, J.C., da Silva, A.T., de Andrade Lopes, A.: The impact of network sampling on relational classification. In: 3rd Annual International Symposium on Information Management and Big Data - SIMBig, pp. 62–72 (2016)Google Scholar
  14. 14.
    Berton, L., de Andrade Lopes, A., Vega-Oliveros, D.A.: A comparison of graph construction methods for semi-supervised learning. In: 2018 International Joint Conference on Neural Networks (IJCNN), IJCNN’18, pp 1–8. IEEE, Piscataway (2018).
  15. 15.
    Biffis, E., Chavez, E.: Satellite data and machine learning for weather risk management and food security. Risk Anal. 37(8), 1508–1521 (2017). CrossRefGoogle Scholar
  16. 16.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)zbMATHGoogle Scholar
  17. 17.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)zbMATHGoogle Scholar
  18. 18.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)CrossRefGoogle Scholar
  19. 19.
    Cheng, T., Wang, J.: Applications of spatio-temporal data mining and knowledge for forest fire. In: ISPRS Technical Commission VII Mid Term Symposium, pp. 148–153 (2006)Google Scholar
  20. 20.
    Cheung, N.K.W.: At risk: natural hazards, people’s vulnerability and disasters. Geogr. J. 173, 189–190 (2007)CrossRefGoogle Scholar
  21. 21.
    Christiano Silva, T., Zhao, L.: Machine Learning in Complex Networks. Springer, Cham (2016)CrossRefGoogle Scholar
  22. 22.
    Cortez, P., Morais, AdJR: A data mining approach to predict forest fires using meteorological data. In: Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, Associação Portuguesa para a Inteligência Artificial (APPIA), pp. 512–523 (2007)Google Scholar
  23. 23.
    Davis, L. (ed.): Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  24. 24.
    De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems, Ph.D. thesis, Ann Arbor, MI, USA, aAI7609381 (1975)Google Scholar
  25. 25.
    de Lima, G.R.T., Santos, L.B.L., de Carvalho, T.J., Carvalho, A.R., Cortivo, F.D., Scofield, G.B., Negri, R.G.: An operational dynamical neuro-forecasting model for hydrological disasters. Model. Earth Syst. Environ. 2(2), 94 (2016)CrossRefGoogle Scholar
  26. 26.
    Draper, N., Smith, H.: Applied regression analysis. No. v. 1. In: Wiley Series in Probability and Statistics: Texts and References Section. Wiley, Hoboken (1998)Google Scholar
  27. 27.
    Eleuterio, J., Hattemer, C., Rozan, A.: A systemic method for evaluating the potential impacts of floods on network infrastructures. Nat. Hazards Earth Syst. Sci. 13, 983–998 (2013)CrossRefGoogle Scholar
  28. 28.
    Fenton, N., Neil, M.: Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, Boca Raton (2012)CrossRefGoogle Scholar
  29. 29.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, Chichester (1966)zbMATHGoogle Scholar
  30. 30.
    Folga, S., Talaber, L., McLamore, M., Kraucunas, I., McPherson, T., Parrott, L., Manzanares, T.: Literature review and synthesis for the natural gas infrastructure. Technical Report: ANL/GSS-15/5119262 Argonne National Lab. (ANL), Argonne, IL (United States) (2015).
  31. 31.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel/Hierarchical Models. Volume of Analytical Methods for Social Research. Cambridge University Press, New York (2007)Google Scholar
  33. 33.
    Gleyze, J.F., Rousseaux, F.: Impact of relief accuracy on flood simulations and road network vulnerability analysis. In: ECQTG (2003)Google Scholar
  34. 34.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  35. 35.
    Goldshtein, V., Koganov, G.A., Surdutovich, G.I.: Vulnerability and hierarchy of complex networks (2004). arXiv:cond-mat/0409298v1Google Scholar
  36. 36.
    Gribaudo, M., Remke, A.: Hybrid Petri nets with general one-shot transitions. Perform. Eval. 105, 22–50 (2016)CrossRefGoogle Scholar
  37. 37.
    Gunes, E.F.: Optimal design of a gas transmission network: a case study of the Turkish natural gas pipeline network system. Graduate Theses and Dissertations, p. 13294 (2013)Google Scholar
  38. 38.
    Hearst, M.A.: Support vector machines. IEEE Intell. Syst. 13(4), 18–28 (1998)CrossRefGoogle Scholar
  39. 39.
    Holme, P., Kim, B.L., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Phys. Rev. E 65, 056, 109 (2002)Google Scholar
  40. 40.
    Hsu, W., Lee, M.L., Zhang, J.: Image mining: trends and developments. J. Intell. Inf. Syst. 19(1), 7–23 (2002)CrossRefGoogle Scholar
  41. 41.
    Huning, A.: Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. 170 S. mit 36 Abb. Frommann Holzboog Verlag. Stuttgart 1973. Broschiert (1976)Google Scholar
  42. 42.
    Jenelius, E.: Large-scale road network vulnerability analysis. Doctoral thesis. KTH, School of Architecture and the Built Environment (ABE), Transport and Economics, Transport and Location Analaysis. ISBN: 978-91-85539-63-5 (2010)Google Scholar
  43. 43.
    Judi, D.R., Mcpherson, T.N.: Development of extended period pressure-dependent demand water distribution models. Technical Report: LA-UR-15–22068 Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2015).
  44. 44.
    Khademi, N., Balaei, B., Shahri, M., Mirzaei, M., Sarrafi, B., Zahabiun, M., Mohaymany, A.S.: Transportation network vulnerability analysis for the case of a catastrophic earthquake. Int. J. Disaster Risk Reduct. 12, 234–254 (2015)CrossRefGoogle Scholar
  45. 45.
    Kim, J., Hastak, M.: Social network analysis: characteristics of online social networks after a disaster. Int. J. Inf. Manag. 38(1), 86–96 (2018). CrossRefGoogle Scholar
  46. 46.
    Krause, P., Boyle, D.P., Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci. 5, 89–97 (2005)CrossRefGoogle Scholar
  47. 47.
    Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198, 701 (2001)Google Scholar
  48. 48.
    Latora, V., Marchiori, M.: Vulnerability and protection of critical infrastructures (2004). arXiv:cond-mat/0407491Google Scholar
  49. 49.
    Latora, V., Marchiori, M.: Vulnerability and protection of critical infrastructures. Phys. Rev. E 71, 015, 103R (2005)Google Scholar
  50. 50.
    Lü, L., Chen, D., Ren, X.L., Zhang, Q.M., Zhang, Y.C., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Matisziw, T.C., Murray, A.T.: Modeling s-t path availability to support disaster vulnerability assessment of network infrastructure. Comput. Oper. Res. 36, 16–26 (2009)CrossRefGoogle Scholar
  52. 52.
    Mattsson, G., Jenelius, E.: Vulnerability and resilience of transport systems - a discussion of recent research. Transp. Res. A 81, 16–34 (2015)Google Scholar
  53. 53.
    Mazzoni, D., Tong, L., Diner, D., Li, Q., Logan, J.: Using misr and modis data for detection and analysis of smoke plume injection heights over north American during summer 2004, pp. B853+ (2005)Google Scholar
  54. 54.
    Mitchell, T.M.: Machine Learning. WCB. McGraw-Hill, Boston (1997)zbMATHGoogle Scholar
  55. 55.
    Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., Bin Ghazali, A.H.: Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and gis. Geomat. Nat. Haz. Risk 8(2), 1080–1102 (2017)CrossRefGoogle Scholar
  56. 56.
    Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)zbMATHGoogle Scholar
  57. 57.
    Nash, J., Sutcliffe, J.: River flow forecasting through conceptual models: part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970)CrossRefGoogle Scholar
  58. 58.
    Pregnolato, M., Ford, A., Robson, C., Glenis, V., Barr, S., Dawson, R.: Assessing urban strategies for reducing the impacts of extreme weather on infrastructure networks. R. Soc. Open. Sci. 3, 160, 023 (2016)Google Scholar
  59. 59.
    Purdy, G.: Iso 31000: 2009—setting a new standard for risk management. Risk Anal. 30(6), 881–886 (2010)CrossRefGoogle Scholar
  60. 60.
    Ramaswami, R., Sivarajan, K.N.: Optical Networks: A Practical Perspective. Morgan Kaufmann, Burlington (2010)Google Scholar
  61. 61.
    Rebelo, F.: Geografia física e riscos naturais. Imprensa da Universidade de Coimbra/Coimbra University Press, Coimbra (2010)CrossRefGoogle Scholar
  62. 62.
    Saito, S.: Estudo analítico da suscetibilidade a escorregamentos e quedas de blocos no maciço central de florianópolis - sc. PhD thesis, Dissertação (Mestrado de Geografia). Departamento de Geociências da Universidade Federal de Santa Catarina, Florianópolis-SC (2004)Google Scholar
  63. 63.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 851–860. ACM, New York (2010)Google Scholar
  64. 64.
    Santos, L.B.L., Londe, L.R., Soriano, E., Souza, A., Coelho, A.F.: Potential flood-related daily urban mobility problems in Rio de Janeiro (Brazil). Revista do Departamento de Geografia 29, 175–190 (2015)Google Scholar
  65. 65.
    Santos, L.B.L., Carvalho, T., Anderson, L., Rudordd, C.M., Marchezini, V., Londe, L.R., Saito, S.M.: A rs-gis-based comprehensive impact assessment of floods - a case study in madeira river, western Brazilian Amazon. IEEE Geosci. Remote Sens. Lett. 14, 1614–1617 (2017)CrossRefGoogle Scholar
  66. 66.
    Santos, L.B.L., Jorge, A.A.S., Rossato, M., Santos, J.D., Candido, O.A., Seron, W., de Santana, C.N.: (geo)graphs - complex networks as a shapefile of nodes and a shapefile of edges for different applications. CoRR abs/1711.05879 (2017).
  67. 67.
    Schwefel, H.P.: Evolutionsstrategie und numerische optimierung. Ph.D. thesis, Technische Universität Berlin (1975)Google Scholar
  68. 68.
    Setola, R., De Porcellinis, S.: Complex networks and critical infrastructures. In: Chiuso, A., Fortuna, L., Frasca, M., Rizzo, A., Schenato, L., Zampieri, S. (eds.) Modelling, Estimation and Control of Networked Complex Systems. Understanding Complex Systems, pp. 91–106. Springer, Berlin (2009)Google Scholar
  69. 69.
    Sheikholeslami, R., Kaveh, A.: Vulnerability assessment of water distribution networks: Graph theory method. Int. J. Optim. Civil. Eng. 5(3), 283–299 (2015)Google Scholar
  70. 70.
    Shi, P., Kasperson, R.: World Atlas of Natural Disaster Risk, vol. 366, pp. 1893–1906. Springer, Berlin (2015)Google Scholar
  71. 71.
    Shortridge, J.E., Guikema, S.D., Zaitchik, B.F.: Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrol. Earth Syst. Sci. 20(7), 2611–2628 (2016). CrossRefGoogle Scholar
  72. 72.
    Stojanova, D., Panov, P., Kobler, A., Džeroski, S., Taškova, K.: Learning to predict forest fires with different data mining techniques. In: Conference on Data Mining and Data Warehouses (SiKDD 2006), Ljubljana, Slovenia, pp. 255–258 (2006)Google Scholar
  73. 73.
    Sun, D., Zhao, Y., Lu, Q.: Vulnerability analysis of urban rail transit networks: a case study of Shanghai, China. Sustainability 7, 6919–6936 (2015)CrossRefGoogle Scholar
  74. 74.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  75. 75.
    UNISDR: Terminology on disaster risk reduction. United Nations Office for Disaster Risk Reduction (UNISDR), p. 24 (2009).
  76. 76.
    Vega-Garcia, C., Lee, B., Woodard, P., Titus, S.: Applying neural network technology to human-caused wildfire occurrence prediction. AI Appl. 10(3), 9–18 (1996)Google Scholar
  77. 77.
    Vega-Oliveros, D., Berton, L., Lopes, A., Rodrigues, F.: Influence maximization based on the least influential spreaders. In: SocInf 2015, co-located with IJCAI 2015, vol. 1398, pp. 3–8 (2015)Google Scholar
  78. 78.
    Vega-Oliveros, D.A., Berton, L., Vazquez, F., Rodrigues, F.A.: The impact of social curiosity on information spreading on networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, pp. 459–466. ACM, New York (2017).
  79. 79.
    Villain-Gandossi, C.: Origines du concept de risque en occident. les risques martimes ou fortune de mer et leur compensation: les débuts de l’assurance martime. Annexe: Attestation d’emplois au Moyen Age du terme Risque Malta: Foundation for International Studies (1990)Google Scholar
  80. 80.
    Wang, Q., Taylor, J.E.: Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One 11, e0147,299 (2016)CrossRefGoogle Scholar
  81. 81.
    Wang, Z., Chan, A., Li, Q.: A critical review of vulnerability of transport networks: From the perspective of complex network. In: Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate Chapter, vol. 92, pp. 897–905 (2014)Google Scholar
  82. 82.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440 (1998)CrossRefGoogle Scholar
  83. 83.
    Wisner, B., Gaillard, J., Kelman, I.: Framing disaster: theories and stories seeking to understand hazards, vulnerability and risk. In: Handbook of Hazards and Disaster Risk Reduction. Routledge, London (2011)Google Scholar
  84. 84.
    Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: When to warp? In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–6 (2016)Google Scholar
  85. 85.
    Wu, J.: Advances in K-Means Clustering: A Data Mining Thinking, Springer Theses. Springer, Berlin (2012)Google Scholar
  86. 86.
    Yazdani, A., Jeffrey, P.: Complex network analysis of water distribution systems (2011). arXiv:11040121 [physicssoc-ph]Google Scholar
  87. 87.
    Zhou, C., Yin, K., Cao, Y., Ahmed, B., Li, Y., Catani, F., Pourghasemi, H.R.: Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the three Gorges Reservoir area, China. Comput. Geosci. 112, 23–37 (2018). CrossRefGoogle Scholar

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Authors and Affiliations

  • Leonardo Bacelar Lima Santos
    • 1
    Email author
  • Luciana R. Londe
    • 2
  • Tiago José de Carvalho
    • 3
  • Daniel S. Menasché
    • 4
  • Didier A. Vega-Oliveros
    • 5
  1. 1.National Centre for Monitoring and Early Warnings of Natural Disasters (CEMADEN)São José dos CamposBrazil
  2. 2.CemadenSão José dos CamposBrazil
  3. 3.Department of InformaticsFederal Institute of São Paulo (IFSP)CampinasBrazil
  4. 4.UFRJRio de JaneiroBrazil
  5. 5.DCM-FFCLRP-USPRibeirão PretoBrazil

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