Towards an Info-Symbiotic Decision Support System for Disaster Risk Management

Abstract

Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-This paper outlines a framework for an infosymbiotic modelling system using cyber-physical sensors to assist in decision-making. Using a dynamic data-driven simulation approach, this system can help with the identification of target areas and resource allocation in emergency situations. Using different natural disasters as exemplars, we will show how cyberphysical sensors can enhance ground level intelligence and aid in the creation of dynamic models to capture the state of human casualties. Using a virtual command & control centre communicating with sensors in the field, up-to-date information of the ground realities can be incorporated in a dynamic feedback loop. Using other information (e.g. weather models) a complex and rich model can be created. The framework adaptively manages the heterogeneous collection of data resources and uses agent-based models to create what-if scenarios in order to determine the best course of action.

Big Data in Natural Disaster Management: A Review

“…Mas et al [58] reviewed the agent-based models for tsunami mitigation and evacuation planning through case studies in Indonesia, Thailand, Japan, and Peru. Kureshi et al [59] utilized advanced by combining agent-based models with physical sensors, and adaptively managed the heterogeneous collection of data resources and agent-based models to create what-if scenarios in order to deter-mine the best course of action.…”

A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection

“…We focus on reviewing DSSes that have a data analysis step in their processes. First, there are disaster support system papers that determine important quantities and parameters via simulation and/or physical models of the situation [Alvear, Abreu, Cuesta, and Alonso (2013), Cuesta, Alvear, Abreu, and Silió (2014), Sahebjamnia, Torabi, and Mansouri (2017), Eguchi et al (1997), Fikar, Gronalt, andHirsch (2016), Todini (1999), Yilmaz, Aydemir-Karadag, and Erol (2019), Kureshi, Theodoropoulos, Mangina, O'Hare, and Roche (2015), Yang, Guo, and Yang (2019), van Zuilekom, van Maarseveen, and van der Doef (2005)]. Second, DSS papers can also determine parameters via data processing as in Fertier, Barthe-Delanoë, Montarnal, Truptil, andBénaben (2020), Horita, de Albuquerque, Degrossi, Mendiondo, andUeyama (2015), and H. Zhang and Ritchie (1994), or they can utilize machine learning to determine modeling structures as in Abpeykar and Ghatee (2014).…”

Big Data for National Security in the Era of COVID-19

“…Policy and decision-makers need to have at their disposal technological tools, acting as force multipliers and enabling insights about disasters and unfolding situations, so that an assessment of the scale of the threat to national and international security can be made [4,5]. Big Data technologies can provide a powerful means in this endeavour [6,7].…”