Approximately three rhinos are poached daily in South Africa. Rhino poaching is a serious problem
that a ects not only the rhino population of South Africa, but also the rhino population of the world.
South Africa has the largest rhino population and of those rhinos the largest number can be found in
the Kruger National Park (KNP). The KNP has been hit the hardest by the poaching epidemic, losing
1,175 rhinos in 2015 alone. Two big challenges are the size of the park and the unknown locations
of both the poachers and new poaching events. The KNP is the size of a small country and there are
simply not enough rangers to patrol this area e ectively. A costly solution would be to employ more
rangers, but a proposed alternative is to reduce the search space and thus ensure that the rangers are
allocated to the high risk areas first. A mathematical model was developed in the form of a Bayesian
network (BN) to infer the most important factors contributing to poaching events and to model the
rhino poaching problem. This model can be used to predict the area in which a future poaching attack
could take place and thereby reduce the search area of rangers. The model also serves as a vehicle to
enhance the understanding of the problem and encourage reasoning and discussion amongst decision
makers. The map of the KNP is divided into cells and each cell is given a poaching probability, based
on the outcome of the BN. This probability map forms a heat map that can be shown to the control
centre and rangers can then be sent to the respective hotspots on the map. This is a proactive approach,
which is in stark contrast to the numerous reactive approaches attempted thus far. This is the first BN
modelling approach to the rhino poaching problem, and it is also the first BN application to wildlife
crime. Previous applications of BN have only gone so far as environmental modelling, but not wildlife
crimes. In this study the rhino poaching problem was shifted from a complex, ill-structured space to a
position where researchers can begin to address the underlying problems by using a causal model as the
vehicle for understanding the complex interplay between the factors a ecting poaching events. / Ongeveer drie renosters word daagliks in Suid-Afrika gestroop. Renosterstroping is 'n ernstige
probleem wat nie net die renosterbevolking van Suid-Afrika raak nie, maar ook die res van die wêreld.
Suid-Afrika het die grootste renoster bevolking in die wêreld, en die grootste getal van dié renosters
word in die Kruger Nasionale Park (KNP) aangetref. Die KNP word die ergste geraak deur die
stropings epidemie en 1,175 renosters is in 2015 gestroop. Twee groot uitdagings is die grootte
van die park, asook die onbekende posisies van beide die stropers en die nuwe stropingsaanvalle.
Die KNP is die grootte van 'n klein land en daar is eenvoudig nie genoeg veldwagters om hierdie
area e ektief te patrolleer nie. 'n Duur oplossing sou wees om meer veldwagters aan te stel, maar
'n alternatief is om die soekarea van die veldwagters te verklein en sodoende te verseker dat die
veldwagters die hoë-risiko areas eerste, en meer gereeld, patrolleer. 'n Wiskundige model in die
vorm van 'n Bayesiese netwerk (BN) is ontwikkel om die belangrikste faktore te bepaal wat bydra
tot stropingsaanvalle en uiteindelik die probleem te modelleer. Hierdie model kan gebruik word
om die area te voorspel waar 'n stropingsaanval moontlik kan plaasvind en die soekarea van die
veldwagters te verminder. Dit dien ook as 'n kanaal om die begrip van die probleem te verbeter en
redenasie en bespreking onder besluitnemers aan te moedig. Die kaart van die KNP word in selle
verdeel en aan elke sel word 'n stropingswaarskynlikheid toegeken gebaseer op die uitkoms van die BN. Hierdie waarskynlikheidskaart vorm 'n "hittekaart" wat aan die kontrolesentrum gewys kan
word, en veldwagters kan dan na die onderskeie responskolle op die kaart gestuur word. Hierdie
pro-aktiewe benadering is in teenstelling met huidige reaktiewe benaderings. Hierdie is die eerste BN
modellering benadering tot die renosterstropingsprobleem, en dit is ook die eerste BN toepassing tot
natuurlewe-misdaad. Vorige toepassings van BNs het omgewingsmodellering aangespreek, maar nie
natuurlewe-misdade nie. In hierdie studie word aangetoon hoe die renosterstropings probleem geskuif
is vanaf 'n komplekse, swak gestruktureerde probleemruimte na 'n omgewing waar navorsers kan
begin om die onderliggende probleme aan te spreek deur gebruik te maak van 'n kausale model as die
voertuig van begrip om die komplekse wisselwerking tussen faktore wat 'n stropingsaanval beïnvloed,
te verstaan. / Thesis (PhD)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/61301 |
| Date | January 2017 |
| Creators | Koen, Hildegarde Suzanne |
| Contributors | De Villiers, Johan Pieter, hildegarde.koen@gmail.com, Roodt, Henk |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | Unknown |
| Type | Thesis |
| Rights | © 2017 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. |
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