Food security risk level assessment: a fuzzy logic-based approach

Abdul Kadir, M.K. et al. (2013) Food security risk level assessment: a fuzzy logic-based approach. Applied Artificial Intelligence, 27(1), pp. 50-61. (doi: 10.1080/08839514.2013.747372)

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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:Articles
Glasgow Author(s) Enlighten ID:Subramanian, Dr Arjunan
Authors: Abdul Kadir, M.K., Hines, E.L., Qaddoum, K., Collier, R., Dowler, E., Grant, W., Leeson, M., Iliescu, D., Subramanian, A., Richards, K., Merali, Y., and Napier, R.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Applied Artificial Intelligence
ISSN (Online):1087-6545

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