ABSTRACT
In this thesis, a methodology for integrated catchment water resources assessment using
Bayesian Networks was developed. A custom made software application that combines
Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling.
Dynamic Bayesian Networks were implemented in the software for time-series modelling.
The structures of three Bayesian Network models were created automatically using a Hybrid
Genetic Algorithm (HGA) which was implemented in a custom developed software product.
The creation of the networks was done in a one step process with the discretisation of the
continuous datasets. The discretisation was done using an equal binning method and the three
networks resulted from variations in the number of intervals defined for the bins. The three
networks were scored using the error rate, the logarithmic metric, the Brier score and the
spherical score. From this evaluation, the states of the continuous variables were finalised and
the optimum Bayesian Network model (the one with the most favourable scores) emerged. The
model was then populated with the data collated for the Great Kei catchment in the Eastern
Cape Province in South Africa. The results were used to explore scenarios on the likely impacts of variations of some query
variables over other variables in the network. This was performed through sensitivity analyses,
scenario analyses and ―what if‖ analyses. The findings from the model conform to existing
knowledge on the study area which illustrated that Bayesian Networks can be successfully
applied in integrated catchment assessment. The use of Bayesian Networks for spatial
prediction was successfully proven with an example on the effects of surface water EC in one
catchment on other neighbouring catchments. This information can be used in assessing the
likely impacts of changes in surface water quality on connected catchments.
Lastly, the capability of Dynamic Bayesian Networks for temporal prediction was
demonstrated. Dynamic Bayesian Networks were tested for predicting monthly rainfall and
temperature and the results compared to that obtained from the static Bayesian Network. The
results showed that Dynamic Bayesian Networks provided better predictions mainly because of
the ability to incorporate evidence from the preceding months.
The major finding is that there is need for adequate data at the required scale. This was evident
from the fact that some well-known relationships from theory could not be established using
the automatic structure mining method used. The importance of selecting the appropriate
discretisation technique was also highlighted by the different patterns obtained with variations
in the discretisation levels. In the absence of the required data, expert knowledge should be
collected and used to inform the modification of the relationships obtained using automatic
methods and for the infilling of gaps in data.
Dondo, C (2021). Bayesian Networks for spatio-temporal integrated catchment assessment. Afribary. Retrieved from https://tracking.afribary.com/works/bayesian-networks-for-spatio-temporal-integrated-catchment-assessment
Dondo, Chiedza "Bayesian Networks for spatio-temporal integrated catchment assessment" Afribary. Afribary, 25 Apr. 2021, https://tracking.afribary.com/works/bayesian-networks-for-spatio-temporal-integrated-catchment-assessment. Accessed 24 Nov. 2024.
Dondo, Chiedza . "Bayesian Networks for spatio-temporal integrated catchment assessment". Afribary, Afribary, 25 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/bayesian-networks-for-spatio-temporal-integrated-catchment-assessment >.
Dondo, Chiedza . "Bayesian Networks for spatio-temporal integrated catchment assessment" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/bayesian-networks-for-spatio-temporal-integrated-catchment-assessment