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

•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...

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Published inInternational journal of information management Vol. 56; p. 102049
Main Authors Fan, Chao, Zhang, Cheng, Yahja, Alex, Mostafavi, Ali
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2021
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Abstract •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.
AbstractList •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.
ArticleNumber 102049
Author Yahja, Alex
Mostafavi, Ali
Fan, Chao
Zhang, Cheng
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  givenname: Chao
  surname: Fan
  fullname: Fan, Chao
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  givenname: Cheng
  surname: Zhang
  fullname: Zhang, Cheng
  organization: Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA
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  givenname: Alex
  surname: Yahja
  fullname: Yahja, Alex
  organization: EpiSys Science Inc, San Diego, CA, USA
– sequence: 4
  givenname: Ali
  surname: Mostafavi
  fullname: Mostafavi, Ali
  organization: Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA
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SubjectTerms Digital twin
Disaster management
Information flow
Machine learning
Title Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management
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