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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
Chapter

Abstract

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.

Notes

Acknowledgements

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.

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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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