>Magister Scientiae - MSc / This research objectively investigates the e ectiveness of machine learning (ML) tools
towards predicting several geo-physical parameters. This is based on a large number
of studies that have reported high levels of prediction success using ML in the eld.
Therefore, several widely used ML tools coupled with a number of di erent feature sets
are used to predict six geophysical parameters namely rainfall, groundwater, evapora-
tion, humidity, temperature, and wind. The results of the research indicate that: a)
a large number of related studies in the eld are prone to speci c pitfalls that lead to
over-estimated results in favour of ML tools; b) the use of gaussian mixture models as
global features can provide a higher accuracy compared to other local feature sets; c)
ML never outperform simple statistically-based estimators on highly-seasonal parame-
ters, and providing error bars is key to objectively evaluating the relative performance
of the ML tools used; and d) ML tools can be e ective for parameters that are slow-
changing such as groundwater.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/8976 |
| Date | January 2021 |
| Creators | Hussein, Eslam |
| Contributors | Ghaziasgar, Mehrdad |
| Publisher | University of Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Format | application/pdf |
| Rights | University of Western Cape |
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