Stochastic vulnerability on complex networks

• Grant number: 18/06205-7
• Support type: Scholarships abroad - Research
• Effective date (Start): June 27, 2019
• Effective date (End): June 26, 2020
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Leonardo Bacelar Lima Santos
• Grantee: Leonardo Bacelar Lima Santos
• Host: Dr. Igor Michailovitsch Sokolov
• Home Institution: Centro Nacional de Monitoramento e Alertas de Desastres Nacionais (CEMADEN). Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brasil). Cachoeira Paulista , SP, Brazil
• Local de pesquisa : Humboldt University, Germany
• Associated research grant: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications, AP.TEM

Abstract

In a scenario of global change, extreme weather and climatic events are expected to increase in frequency and intensity, and have greater social and economic impacts in several sectors, such as Critical Infrastructure, like Gas, Oil and Water Network, Power Grid, Telecommunications and Transportation system.Brazil and Germany are signatories to the Sendai Framework for Disaster Risk Reduction 2015-2030. Among the seven global targets of this document, there is one related to "Substantially reduce disaster damage to critical infrastructure and disruption of basic services".In the Disaster Risk Reduction (DRR) community, vulnerability is a key concept. According to Cheung (2007), vulnerability is "the characteristics of a person or group and their situation that influence their capacity to anticipate, cope with, resist and recover from the impact of a natural hazard (an extreme natural event or process)" (Cheung, 2007).The measurement and mapping of vulnerability constitutes a subject of global interest. The Complex Networks approach may offer a valuable perspective considering one type of vulnerability specially related to DRR on critical infrastructures: the topological vulnerability.The main questions to be analyzed in this project are:1. Is the traditional formulation of vulnerability on Complex Networks (CN) compatible with the concept of vulnerability in Disaster Risk Reduction (DRR) community?It is expected conceptual contributions for both areas - CN and DRR, on a multidisciplinary approach.2. What is the vulnerability map of some actual critical infrastructures against natural hazards?Will be analyzed some actual data from cities around the world (based on Open Street Maps open data) and hazard maps, mainly for floods, from GloFAS (Global Flood Awareness System - Dottori et al., 2016) and Cemaden (Brazilian National Center of Early Warning on Natural Disasters).3. What are the limits and uncertainties in the determinist approach for vulnerability measuring on complex networks on phenomena on social domains, such as urban mobility?This is the main contribution of this project: a stochastic approach for vulnerability analysis on complex networks. This approach will be applied on actual data for transportation and urban mobility networks. Stochastic approaches can handle with uncertainties - this is so important on research on social domains.The traditional approach for vulnerability on complex networks is based on deterministic paths: the shortest path length dij as the smallest sum of links throughout all the possible paths (without loops) in the graph from i to j. On urban mobility, each person can choose different paths to go from one region to another - not necessarily the path of smallest number of streets, kilometers or minutes. How this behavior plurality impacts on the vulnerability measurement? This project will analyze this question, based on simulations (computational) and, if possible, analytical approximations.Between each pair of nodes i and j in a graph there is a set of paths {pij}. Each path has a length, that can be the number of links between i and j (topological length), or the total kilometers (geographical length) or minutes (time length). The effective path chosen by one person to go from i to j, will be considering based on a probability distribution function associated with each possible path. This function can be, for example, an exponential or algebraic decay over the length (topological, geographical or time length). The output of this approach is the vulnerability index not as a scalar value (a number), but as a distribution of values. The main proprieties of this distribution, in the hypothesis of this project, could be applied on the discussion of vulnerability of the modeled critical infrastructure against natural disasters.