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Author (up) Kenneth Johnson; Javier Cámara; Roopak Sinha; Samaneh Madanian; Dave Parry pdf  openurl
  Title Towards Self-Adaptive Disaster Management Systems Type Conference Article
  Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021  
  Volume Issue Pages 49-61  
  Keywords disaster management, self-adaptive systems, formal verification, probabilistic model checking, constraint solving  
  Abstract Disasters often occur without warning and despite extensive preparation, disaster managers must take action to respond to changes critical resource allocations to support existing health-care facilities and emergency triages. A key challenge is to devise sound and verifiable resourcing plans within an evolving disaster scenario. Our main contribution is the development of a conceptual self-adaptive system featuring a monitor-analyse-plan-execute (MAPE) feedback loop to continually adapt resourcing within the disaster-affected region in response to changing usage and requirements. We illustrate the system's use on a case study based on Auckland city (New Zealand). Uncertainty arising from partial knowledge of infrastructure conditions and outcomes of human participant's actions are modelled and automatically analysed using formal verification techniques. The analysis inform plans for routing resources to where they are needed in the region. Our approach is shown to readily support multiple model and verification techniques applicable to a range of disaster scenarios.  
  Address Auckland University of Technology; University of York; Auckland University of Technology; AUT university; Auckland University of Technology  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-61-5 ISBN Medium  
  Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2312  
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