Disaster Knowledge Management Analysis Framework Utilizing Agent-Based Models: Design Science Research Approach

Disaster Management (DM) knowledge has long been acknowledged as playing a significant role in reducing the impact caused by disasters. It helps people at the decision-making level to produce contextual decisions, as they are produced from the interaction of the involved social entities and their ex...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.124, p.116-124
Main Authors: Inan, Dedi Iskandar, Beydoun, Ghassan
Format: Article
Language:English
Subjects:
Agent-Based Model
Design Science Research
Disaster Management
Knowledge Management
Metamodeling
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Summary:Disaster Management (DM) knowledge has long been acknowledged as playing a significant role in reducing the impact caused by disasters. It helps people at the decision-making level to produce contextual decisions, as they are produced from the interaction of the involved social entities and their experiences and those who are on the ground to appropriately react towards the disaster. While it is seen as critical the DM activities, its adoption is still challenging due to its complex structure and availability. This paper employs the Design Science Research (DSR) methodology in Information System (IS) research to develop and validate a knowledge transfer analysis framework to unify access to semi-structured DISPLANs (Disaster Management Plans) through a unified repository. In the development, Agent-Based Models (ABMs) are used to code the DISPLANs to enable their transfer into a repository. The Meta Object Facility (MOF) Metamodeling Framework is then used to create a repository that is ready for storing the content of ABMs. This developed framework is then validated using a real case study of the flood DISPLAN of the State Emergency Service (SES) the State of Victoria, Australia. At the end, this paper contributes to: (1) a validated knowledge transfer analysis framework supporting DM resilience endeavors; (2) demonstrate the DSR methodology as a frame for IS research.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.12.137