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Food security risk level assessment : a fuzzy logic-based approach
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Abdul Kadir, Muhd K., Hines, Evor, Qaddoum, Kefaya, Collier, Rosemary, Dowler, Elizabeth, Grant, Wyn, Leeson, Mark S., Iliescu, Daciana, Subramanian, Arjunan, Richards, Keith, Merali, Yasmin and Napier, R. (2013) Food security risk level assessment : a fuzzy logic-based approach. Applied Artificial Intelligence, Volume 27 (Number 1). pp. 50-61. doi:10.1080/08839514.2013.747372 ISSN 0883-9514.
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WRAP_Napier_0380026-lf-220814-food_security.pdf - Accepted Version - Requires a PDF viewer. Download (539Kb) | Preview |
Official URL: http://dx.doi.org/10.1080/08839514.2013.747372
Abstract
A fuzzy logic (FL)-based food security risk level assessment system is designed and is presented in this article. Three inputs—yield, production, and economic growth—are used to predict the level of risk associated with food supply. A number of previous studies have related food supply with risk assessment for particular types of food, but none of the work was specifically concerned with how the wider food chain might be affected. The system we describe here uses the Mamdani method. The resulting system can assess risk level against three grades: severe, acceptable, and good. The method is tested with UK (United Kingdom) cereal data for the period from 1988 to 2008. The approach is discussed on the basis that it could be used as a starting point in developing tools that may either assess current food security risk or predict periods or regions of impending pressure on food supply.
| Item Type: | Journal Article | ||||
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| Subjects: | H Social Sciences > HD Industries. Land use. Labor | ||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Social Sciences > Warwick Business School > Information Systems & Management Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Social Sciences > Politics and International Studies Faculty of Social Sciences > Sociology |
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| Library of Congress Subject Headings (LCSH): | Food security, Food supply -- Risk assessment, Fuzzy logic | ||||
| Journal or Publication Title: | Applied Artificial Intelligence | ||||
| Publisher: | Taylor & Francis Inc. | ||||
| ISSN: | 0883-9514 | ||||
| Official Date: | 10 January 2013 | ||||
| Dates: |
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| Volume: | Volume 27 | ||||
| Number: | Number 1 | ||||
| Page Range: | pp. 50-61 | ||||
| DOI: | 10.1080/08839514.2013.747372 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Date of first compliant deposit: | 25 December 2015 | ||||
| Date of first compliant Open Access: | 25 December 2015 | ||||
| Funder: | Seventh Framework Programme (European Commission) (FP7), Great Britain. Department for Environment, Food and Rural Affairs (DEFRA), University of Warwick. School of Engineering, Malaysia, University of Malaya in Kuala Lumpur | ||||
| Grant number: | 211457 (FP7) |
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