Coates, G, Li, C, Ahilan, S orcid.org/0000-0003-3887-331X et al. (2 more authors) (2019) Agent-based modeling and simulation to assess flood preparedness and recovery of manufacturing small and medium-sized enterprises. Engineering Applications of Artificial Intelligence, 78. pp. 195-217. ISSN 0952-1976
Abstract
Severe flooding has caused major damage and disruption to households, communities, businesses, and organizations
in many parts of the world. In the United Kingdom (UK), flooding has been responsible for significant
losses to the economy due to its impact on businesses, 99.9% of which are Small and Medium-sized Enterprises
(SMEs). This paper reports on how agent-based modeling and simulation has been developed and used to assess
the effectiveness of a range of physical/structural and social preparedness adaptation measures that can be
implemented by manufacturing SMEs to reduce the impact of and expedite recovery from a major flood event.
Results indicate the effectiveness of combinations of these adaptation measures in relation to a one in 1000 year
flood event that has been modeled and simulated in a key industrial area of the UK which, in addition to having
experienced severe flooding, has a high concentration of SMEs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Ltd. This is an author produced version of a paper published in Engineering Applications of Artificial Intelligence . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Agent-based modeling/simulation; Flood preparedness/recovery; SMEs |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) > Inst for Pathogen Control Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Dec 2018 11:57 |
Last Modified: | 10 Dec 2019 01:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.engappai.2018.11.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139943 |