Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

• Published in: International journal of information management Vol. 56; p. 102049
• Main Authors: Fan, Chao, Zhang, Cheng, Yahja, Alex, Mostafavi, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2021
• Subjects: Digital twin; Disaster management; Information flow; Machine learning; Digital twin; Information flow; Machine learning; Disaster management
• ISSN: 0268-4012; 1873-4707
• DOI: 10.1016/j.ijinfomgt.2019.102049

•A paradigm, Disaster City Digital Twin, integrating AI for disaster management.•Multimodal data collection, integration, and analytics for situational awareness.•Gaming theoretical decision-making approach for learning behaviors of multi-actors.•Dynamic network analysis to assess network resilience and relief performance. This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.