Data Taxonomy Towards the Applicability of the Digital Twin Conceptual Framework in Disaster Management

Abstract

The Digital Twin (DT) offers a novel approach to the management of critical infrastructures, including energy, water, traffic, public health, and communication systems, which are indispensable for the functioning of modern societies. The increasing complexity and interconnectedness of these infrastructures necessitate the development of robust disaster response and management strategies. During crises and disasters, data source availability for critical infrastructure may be severely constrained due to physical damage to communication networks, power outages, overwhelmed systems, sensor failure or intentional disruptions, hampering the ability to effectively monitor, manage, and respond to emergencies. This research introduces a taxonomy and similarity function for comparing data sources based on their features and vulnerability to crisis events. This assessment enables the identification of similar, complementary, and alternative data sources and rapid adaptation when primary sources fail. The paper outlines a data source manager as an additional component for existing DT frameworks, specifically the data ingress and scenario mangement. A case study for traffic data sources in an urban scenario demonstrates the proposed methodology and its effectiveness. This approach enhances the robustness and adaptability of DTs in disaster management applications, contributing to improved decision-making and response capabilities in critical situations.