Browsing Faculty of Natural and Agricultural Sciences by Subject "Machine learning"
Now showing items 1-4 of 4
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Biotransport of metallic trace elements from marine to terrestrial ecosystems by seabirds
(Wiley, 2019)Physical systems, such as currents and winds, have traditionally been considered responsible for transporting contaminants. Although evidence is mounting that animals play a role in this process through their movements, ... -
Combining historical remote sensing, digital soil mapping and hydrological modelling to produce solutions for infrastructure damage in Cosmo City, South Africa
(MDPI, 2020)Urbanization and hydrology have an interactive relationship, as urbanization changing the hydrology of a system and the hydrology commonly causing structural damage to the infrastructure. Hydrological modelling has been ... -
Comparing algorithms to disaggregate complex soil polygons in contrasting environments
(Elsevier, 2019)In South Africa, the only soil resource available with full spatial coverage is the national resource inventory. Disaggregating this polygon-based inventory, is thus a logical step to create more detailed soil maps covering ... -
Review of innovations in the South African collection industry
(ASSAf, 2018)The objective of this review was to provide an overview of new developments and innovations within the collections industry that could possible enhance the performance of collection agencies, specifically in South Africa. ...