Abdul Kadir, Muhd K. (2013) Food security modelling using two stage hybrid model and fuzzy logic risk assessment. PhD thesis, University of Warwick.

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Abstract

Food security has become a key issue worldwide in recent years. According to the
Department for Environment Food and Rural Affair (DEFRA) UK, the key
components of food security are food availability, global resource sustainability,
access, food chain resilience, household food security, safety and confidence of public
towards food system. Each of these components has its own indicators which need to
be monitored. Only a few studies had been made towards analysing food security and
most of these studies are based on conventional data analysis methods such as the use
of statistical techniques. In handling food security datasets such as crops yield,
production, economy growth, household behaviour and others, where most of the data
is imprecise, non-linear and uncertain in nature, it is better to handle the data using
intelligent system (IS) techniques such as fuzzy logic, neural networks, genetic
algorithm and hybrid systems, rather than conventional techniques. Therefore this
thesis focuses on the modelling of food security using IS techniques, and a newly
developed hybrid intelligent technique called a 2-stage hybrid (TSH) model, which is
capable of making accurate predictions. This technique is evaluated by considering
three applications of food security research areas which relate to each of the indicators
in the DEFRA key food security components. In addition, another food security
model was developed, called a food security risk assessment model. This can be used
in assessing the level of risk for food security.
The TSH model is constructed by using two key techniques; the Genetic Algorithm
(GA) module and the Artificial Neural Network (ANN) module, where these modules
combine the global and local search, by optimizing the inputs of ANN in the first
stage process and optimizing of weight and threshold of ANN, which is then used to
remodel the ANN resulting in better prediction. In evaluating the performance of the
TSH prediction model, a total of three datasets have been used, which relate to the
food security area studied. These datasets involve the prediction of farm household
output, prediction of cereal growth per capita as the food availability main indicators
in food security component, and grain security assessment prediction. The TSH
prediction model is benchmarked against five others techniques. Each of these five
techniques uses an ANN as the prediction model. The models used are: Principal
Component Analysis (PCA), Multi-layered Perceptron-Artificial Neural Network
(MLP-ANN), feature selection (FS) of GA-ANN, Optimized Weight and Threshold
(OWTNN) and Sensitive Genetic Neural Optimization (SGNO). Each of the
application datasets considered is used to show the capability of the TSH model in
making effective predictions, and shows that the general performance of the model is
better than the other benchmarked techniques. The research in this thesis can be
considered as a stepping-stone towards developing other tools in food security
modelling, in order to aid the safety of food security.

Item Type:	Thesis (PhD)
Subjects:	H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Food security -- Mathematical models, Fuzzy logic, Risk assessment
Official Date:	March 2013
Dates:	Date Event March 2013 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hines, Evor, 1957-
Sponsors:	Universiti Kuala Lumpur (UniKL); Mara (Organization : Malaysia)
Extent:	234 leaves : illustrations.
Language:	eng

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