Electronic Theses and Dissertations (Masters)

Permanent URI for this collectionhttps://hdl.handle.net/10539/38009

Browse

Has files: YesSubject: search.filters.subject.SDG-9: Industry, innovation and infrastructure

Search Results

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Item
    Optically stimulated luminescence dating of Kalundu and Urewe tradition ceramics
    (University of the Witwatersrand, Johannesburg, 2024-03) Haupt, Rachel Xenia; Schoeman, Maria; Evans, Mary
    Optically stimulated luminescence (OSL) dating is a method of providing the direct age of artefacts. While radiocarbon and seriation dating provide indispensable insight into archaeological sites, the direct dating of artefacts is beneficial in entangled contexts. The Lydenburg Heads Site is significant to the beginning of the Early Farming Communities (EFCs) sequence within the Mpumalanga province. The site has been occupied multiple times, as can be seen from the presence of the two major ceramic traditions of the age, Urewe and Kalundu. The site was originally excavated and analysed by Evers (1982) in the 1970s, with a reanalysis of the ceramic assemblage by Whitelaw (1996) and organic residue analysis on the ceramics by Becher (2021). The use of OSL dating on twelve ceramic sherds allowed for new insights into the chronological intricacies within the study site. To determine the age of the ceramics, the OSL quartz dating technique was used. The adjustments to the technique involved the use of a less destructive means of sample extraction. A slightly altered version of the standard means of sample extraction was used to create a comparison and allow the dating of the ceramics to be reliable. The minimal destruction technique (MET) combined with the bulk sampling proved useful to the dating of the ceramics. The use of previously excavated ceramics meant that some aspects of age determination required estimation and analysis. The major obstacles from such were the water content, the depth of burial, and the lack of in situ soil samples. In light of the elements of ambiguity for the site, the OSL dating considered these variations and how they affected the age. The Urewe tradition ceramics were determined to be in 6th and 8th century AD. The finding creates the alignment with the range of the radiocarbon ages done within previous work and the assumptions made by Evers (1982) and Whitelaw (1996). The Kalundu tradition ceramics ages were determined to be between the 7th and 10th century AD, conflicting with previous assumptions on the occupation. The result is the possibility the ceramic assemblages could be considered to be contemporaneous. The work in this thesis has, in part, been presented at the Luminescence and Electron Spin Resonance Dating conference in Copenhagen (LED2023) and at the Association of Southern African Professional Archaeologists 2024 Biennial Meeting (ASAPA 2024).
  • Thumbnail Image
    Item
    Assessing the Validity of the Exclusion of Night-time Thermal Comfort in Tourism Climate Indices
    (University of the Witwatersrand, Johannesburg, 2024-09) Mnguni, Zandizoloyiso; Fitchett, Jennifer
    Biometeorological indices are instruments that can be used to streamline complex climatic information for economic and other decision-making. Indices hold inherent assumptions where the use of an index is only reliable and valuable if those assumptions are true. The Holiday Climate Index (HCI) is presented as the improved version of the TCI, with a key difference being the removal of night-time thermal comfort due to the assumption that air conditioning is ubiquitous throughout Europe. This study investigated the validity of this exclusion of night-time thermal comfort in tourism climate indices, particularly for the HCI using the six European cities for which the index was developed – Barcelona, Stockholm, London, Istanbul, Paris and Rome. The assumption of ubiquitous air conditioning was investigated using Booking.com accommodation listings, the night-time economy and prevalence of night-time activities outside of each accommodation establishment, and whether tourists experienced adverse thermal comfort during the night through posted reviews. Without the air conditioning filter applied, the proportion of listings categorized as offering air conditioning ranged from 28.8% for Stockholm to 98.9% for Rome. With the filter applied, the proportions ranged from 96.4% for Stockholm and 99.0% for Paris. A total of 24,252 TripAdvisor reviews were also examined for both accommodation establishments and night-time tourist activities. The reviews were manually examined for the mention of weather, climate, night-time temperature and air conditioning. The findings of this study exhibit a range of night-time activities, many of which are outdoors, where tourists did comment on night-time thermal comfort. The research disproves the claim of the original authors, and it was found that air conditioning is not ubiquitous. Therefore, the assumption that the HCI is based on is problematic, and the index should be used with caution. Moreover, a similar approach in index validity testing should be performed prior to future studies seeking to apply indices.
  • Thumbnail Image
    Item
    Assessing and comparing the performance of different machine learning regression algorithms in predicting Chlorophyll-a concentration in the Vaal Dam, Gauteng
    (University of the Witwatersrand, Johannesburg, 2024-03) Mahamuza, Phemelo Hope; Adam, Elhadi
    The state of Vaal Dam is influenced by various land uses surrounding the Dam, including agricultural activities, mining operations, industrial enterprises, urban settlements, and nature reserves. Mining activities, farming practices, and sewage outflows from nearby villages led to access contamination within the Dam, increasing algal bloom levels. Sentinel-2 MSI data were utilized to forecast and comprehend the spatial pattern of Chlorophyll-a concentration, indicating algal bloom occurrence in the Vaal Dam. Targeting Sentinel-2 Level-1C, the image was preprocessed on the Google Earth Engine (GEE) with acquisition dates from 25 – 26 October 30, 2016, corresponding to the on-site data collection between October 26 and October 28, 2016. Due to limited resources, up-to-date data on the Vaal Dam could not be collected. However, since this study focuses on applying various machine learning regression models to predict chlorophyll-a levels in waterbodies, the dataset is used to test the models rather than reflect the current state of the Vaal Dam. The dataset, comprising 23 samples, was divided into 70% training and 30% test sets, allowing for comprehensive model evaluation. Band ratio reflectance values were extracted from the satellite image and correlated with in-field Chlorophyll-a values. The highest correlation coefficient values were utilized to train five machine-learning models employed in this study: Random Forest (RF), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression, and Multilinear Regression (MLR). Each model underwent training with ten iterations each; the best learning iteration was then used to generate the final Chlorophyll-a predictive model. The predictive models were validated using the Sentinel-2 MSI satellite data and in-situ measurements using R2, RMSE, and MAPE. Among the five machine learning algorithms trained, RF performed the best, with an R2 of 0.86 and 0.95, an RMSE of 1.38 and 0.8, and MAPE of 15.09% and 10.92% for the training and testing sets, respectively, indicating its ability to handle small, non-linear datasets. SVR also demonstrated a fair performance, particularly in handling multicollinearity in the data points with an R2 of 0.68 and 0.87, an RMSE of 2.37 and 1.56, and MAPE of 18.13% and 19.28% for the training and testing sets, respectively. The spatial pattern of Chlorophyll-a concentrations, mapped from the RF model, indicated that high concentrations of Chlorophyll-a are along the Dam shorelines, suggesting a significant impact of land use activities on pollution levels. This study emphasizes the importance of selecting suitable machine learning algorithms tailored to the dataset's characteristics. RF and SVR demonstrated proficiency in handling nonlinearity, with RF displaying enhanced generalization and resistance to overfitting. Limited field data evenly distributed across the Dam and satellite overpass dates may affect result accuracy. Future research should align satellite pass dates with fieldwork dates and ensure an even distribution of in-field samples across the Dam to represent all land uses and concentration levels.
  • Thumbnail Image
    Item
    GIS-Based Location-Allocation Modelling of School Accessibility in the Bojanala Platinum District Municipality, South Africa
    (University of the Witwatersrand, Johannesburg, 2024-09) Molefe, Kebarileng Christinah; Atif, Iqra
    School accessibility modelling performs a crucial part in guaranteeing that educational institutions are physically and practically reachable by every student, irrespective of their abilities, disabilities, or socioeconomic status. Neglecting school accessibility perpetuates inequality, reinforces negative stereotypes, and isolates affected students. Therefore, the principal goal of this research was to evaluate the distribution of schools across the Bojanala Platinum District Municipality, focusing on their accessibility to local communities. The study employed an integrated approach, combining geostatistical techniques, location-allocation modelling, and multicriteria decision analysis. By considering both quantitative data and spatial relationships, these methodologies contributed to robust decision-making and informed policy recommendations. The study utilized population data and school-related information sourced from the Department of Education and the HUMDATA websites, both dated to the year 2020. The study examined the distribution of schools in the Bojanala Platinum District Municipality. It was discovered that the schools were clustered, with a concentration in the Rustenburg local municipality, followed by Madibeng. However, a significant inequality in school access was evident. Secondary school students faced the greatest vulnerability, as most accessible schools primarily served primary students. To address this, potential school sites were proposed across the district. The study emphasizes the need for effective interventions by educational administrators and policymakers to eliminate this inequality. This study recommends the establishment of new schools in underserved regions, strategically enhance existing schools, and maximize school accessibility for all residents. Adequate school provision promotes equity, reduces travel burdens, and strengthens community bonds.
  • Thumbnail Image
    Item
    An integrated approach for detecting and monitoring the Campuloclinium macrocephalum (Less) DC using the MaxEnt and machine learning models in the Cradle Nature Reserve, South Africa
    (University of the Witwatersrand, Johannesburg, 2024) Makobe, Benjamin; Mhangara, Paidamoyo
    The invasion of ecosystems by invasive plants is considered as one of the major human- induced global environmental change. The uncontrolled expansion of invasive alien plants is gaining international attention, and remote sensing technology is adopted to accurately detect and monitor the spread of invasive plants locally and globally. The Greater Cradle nature reserve is a world heritage site and intense research site for archaeology and paleontology.It was accorded the world status by the United Nations Educational, Scientific and Cultural Organizations (UNESCO) in 1991 due to its variety of biodiversity present and carries information of significance about the evolution of mankind. The invasion of Campuloclinium macrocephalum (pompom weed) at the Cradle nature reserve is downgrading the world status accorded to the site, lowers the grazing capacity for game animals and replaces the native vegetation. This research study explored the capability of Sentinel-2A multispectral imagery in mapping the spatial distribution of pompom weed at the nature reserve between 2019 and 2024. The non-parametric classification models, support vector machine (SVM) and random forests (RF) were evaluated to accurately detect, and discriminate pompom weed against the co-existing land cover types. Additionally, the species distribution modelling MaxEnt Entropy was incorporated to model spatial distribution and pompom weed habitat suitability. The findings indicates that SVM yielded 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively. Whereas, the RF model indicates that the spatial coverage of pompom weed was 31.1% and 39.3% in 2019 and 2024, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weed at nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. SVM and RF models had classification accuracy above 75%, indicating that they could distinguish pompom weeds from existing land cover types. The preliminary results of this study call for attention in using predictive models in predicting current and future spatial distribution of invasive weeds, for effective eradication control and environmental management.
  • Thumbnail Image
    Item
    SBLS Lithic Technology and its Behavioural Implications
    (University of the Witwatersrand, Johannesburg, 2024) Bielderman, Sebastian Christopher; Wurz, Sarah
    In the Middle Stone Age (MSA) of the southern Cape in South Africa significant research has been undertaken to understand the behaviours linked to coastal adaptation as well as the exploitation of terrestrial resources, however but relatively little is understood on how lithic technology relates to human behaviour during certain MSA periods in this region. The Silty Black Soils (SBLS) layer at Klasies River main site (KRM), which is older than 110 000 years ago, falls within one of these lesser understood periods and has yielded lithic material in association with both faunal and shellfish remains and other important features such as hearths. Understanding the behaviours of the SBLS is significant in broadening our understanding of the MSA I/earlier MSA technologies. Through the analysis of the Chaî ne Opé ratoire (or production sequence), macro-fractures, and the Tip Cross-Sectional Area of the SBLS lithics, significant information on the manufacturing and utilisation behaviours has been inferred. The data gained from these analyses allow for widespread behavioural comparison between the SBLS, overlaying KRM layers, and other sites. Broadly speaking, the assemblage shares several technological signatures with the MSA I/Klasies River technology previously identified at KRM and on a technological attribute level widespread similarities are shared with several MIS 5 assemblages in South Africa; an example of this is the widespread use of locally available raw materials. There is, however, a key behavioural inference which clearly indicates that the SBLS is different to other assemblages both at KRM and in the broader MIS 5. The SBLS points and their TCSA values point towards significantly smaller points. This supports a different and varied hunting approach which is unique to KRM during this period at KRM