Abstract
Statistical modeling is a useful methodology to support the research on disaster risk reduction (DRR) and the development of strategies for sustainability. It has been established in at least two of the major United Nations Conferences and Summits which have laid the solid foundation for sustainable development: The 2030 Agenda and the Sendai Framework. International standards also indicate various statistical techniques and tools to support risk management, especially at the risk assessment process. The main goal of this chapter is to present the Bayesian inference framework as a promising tool for risk identification process in the context of DRR. For such, it was used the data collected from the S2ID Brazilian database, period 2003–2016. Considering the geographical complexity, this is only a diagnostic research, but it is a first step and a very interesting beginning because Brazilian complexity is a prominent condition to understand risks and disasters deep causes in this country, and an opportunity to strengthen mathematical and statistical solutions to sustainable development challenges.
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Acknowledgements
The authors would like to thank to Federal University of São Paulo—UNIFESP, to CNPq—Brazil, to National Early Warning and Monitoring Center of Natural Disasters (CEMADEN) and to the Thematic Committee Mathematics & Disasters of the Brazilian Society of Applied (SBMAC), by its research support.
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Martins, C.B., Muñoz, V.A., Gomes, A.Y., Savii, R.M., Colla, C.L. (2019). Bayesian Analysis of the Disaster Damage in Brazil. In: Bacelar Lima Santos, L., Galante Negri, R., de Carvalho, T. (eds) Towards Mathematics, Computers and Environment: A Disasters Perspective. Springer, Cham. https://doi.org/10.1007/978-3-030-21205-6_9
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