Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making
Dissertation (MSc)--University of Pretoria, 2010.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Published: |
University of Pretoria
2013
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613564336865280 |
|---|---|
| access_status_str | Open Access |
| author2 | Breetzke, Gregory Dennis |
| author_browse | Breetzke, Gregory Dennis |
| author_facet | Breetzke, Gregory Dennis |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Dissertation (MSc)--University of Pretoria, 2010. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/28874 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:38:08.659Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/28874 Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making Breetzke, Gregory Dennis sanet.eksteen@up.ac.za Van Heerden, P.S. (Pieter Schalk) Eksteen, Sanet Patricia GIS Geographical Information Systems Artificial neural networks UCTD Dissertation (MSc)--University of Pretoria, 2010. 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. Geography, Geoinformatics and Meteorology MSc Unrestricted 2013-09-07T14:24:02Z 2010-10-20 2013-09-07T14:24:02Z 2010-09-02 2010-10-20 2010-10-20 Dissertation Eksteen, SP 2010-10-20, Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/28874> E10/727/gm http://hdl.handle.net/2263/28874 http://upetd.up.ac.za/thesis/available/etd-10202010-172346/ © 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. application/pdf University of Pretoria |
| spellingShingle | GIS Geographical Information Systems Artificial neural networks UCTD Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title | Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title_full | Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title_fullStr | Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title_full_unstemmed | Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title_short | Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making |
| title_sort | integrating geographical information systems and artificial neural networks to improve spatial decision making |
| topic | GIS Geographical Information Systems Artificial neural networks UCTD |
| url | http://hdl.handle.net/2263/28874 http://upetd.up.ac.za/thesis/available/etd-10202010-172346/ |