Title page for ETD etd-10202010-172346

Document Type Master's Dissertation
Author Eksteen, Sanet Patricia
Email sanet.eksteen@up.ac.za
URN etd-10202010-172346
Document Title Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making
Degree MSc
Department Geography, Geo-Informatics and Meteorology
Advisor Name Title
Prof P Van Heerden Co-Supervisor
Dr G D Breetzke Supervisor
  • GIS
  • Geographical Information Systems
  • Artificial Neural Networks
Date 2010-09-02
Availability unrestricted

GIS has been used in Veterinary Science for a couple of year and the application thereof has been growing rapidly. A number of GIS models have been developed to predict the occurrences of certain types of insect species including the Culicoides species (spp), the insect vectors responsible for the transmission of the African horse sickness (AHS) virus. AHS is endemic to sub-Saharan Africa and is carried by two midges called Culicoides Imicola and Culicoides Bolitinos. The disease causes severe illness in horses and has significant economic impact if not dealt with timeously. Although these models had some success in the prediction of possible abundance of the Culicoides spp. the complicated nature and high number of variables influencing the abundance of Culicoides spp. posed some challenges to these GIS models. This informs the need for models that can accurately predict potential abundance of Culicoides spp to prevent unnecessary horse deaths.

This lead the study to the use of a combination of a GIS and an artificial neural networks (ANN) to develop a model that can predict the abundance of C. Imicola and C. Bolitinos. ANNs are models designed to imitate the human brain and have the ability to learn through examples. ANNs can therefore model extremely complex features. In addition, using GIS maps to visualise the predictions will make the models more accessible to a wider range of practitioners.

2010, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.

Please cite as follows:

Eksteen, SP 2010, Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-10202010-172346/ >


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