Faculty of Engineering and the Built Environment (ETDs)

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Browsing Faculty of Engineering and the Built Environment (ETDs) by SDG "SDG-8: Decent work and economic growth"

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    A Data Science Framework for Mineral Resource Exploration and Estimation Using Remote Sensing and Machine Learning
    (University of the Witwatersrand, Johannesburg, 2023-08) Muhammad Ahsan, Mahboob; Celik, Turgay; Genc, Bekir
    Exploring mineral resources and transforming them into ore reserves is imperative for sustainable economic growth, particularly in low income developing economy countries. Limited exploration budgets, inaccessible areas, and long data processing times necessitate the use of advanced multidisciplinary technologies for minerals exploration and resource estimation. The conventional methods used for mineral resources exploration require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering and clean validated data to build accurate estimations. In the past few years, data science has become increasingly important in the field of minerals exploration and estimation. This study is a step forward in this field of data science and its integration with minerals exploration and estimation. The research has been conducted to develop a state-of-the-art data science framework that can effectively use limited field data with remotely sensed satellite data for efficient mineral exploration and estimation, which was validated through case studies. Satellite remote sensing has emerged as a powerful modern technology for mineral resources exploration and estimation. This technology has been used to map and identify minerals, geological features, and lithology. Using digital image processing techniques (band ratios, spectral band combinations, spectral angle mapper and principal component analysis), the hydrothermal alteration of potential mineralization was mapped and analysed. Advanced machine learning and geostatistical models have been used to evaluate and predict the mineralization using field based geochemical samples, drillholes samples, and multispectral satellite remote sensing based hydrothermal alteration information. Several machine learning models were applied including the Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Support Vector Regression (SVR), Generalized Linear Model (GLM), and Decision Tree (DT). The geostatistical models used include the Inverse Distance Weighting (IDW) and Kriging with different semivariogram models. IDW was used to interpolate data points to make a prediction on mineralization, while Kriging used the spatial autocorrelation to make predictions. In order to assess the performance of machine learning and geostatistical models, a variety of predictive accuracy metrics such as confusion matrix, a receiver operating characteristic (ROC) curve, and a success-rate curve were used. In addition, Mean Absolute Error, Mean Square Error, and root mean square prediction error were also used. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values. The experimental results also showed that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised to generate more accurate predictions. For the case study of copper prediction, the results showed that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. The evaluation results have showed that the data science framework is able to deliver highly accurate results in minerals exploration and estimation. The results of the research were published through several peer reviewed journal and conference articles. The innovative aspect of the research is the use of machine learning models to both satellite remote sensing and field data, which allows for the identification of highly prospective mineral deposits. The framework developed in this study is cost-effective and time-saving and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up- to-date mineral information.
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    An investigation on the impact of landownership dynamics in rural development: A case of Nooitgedacht area in Muldersdrift, Mogale City Local Municipality
    (University of the Witwatersrand, Johannesburg, 2024-02) Masonganye, Thoriso Obakeng; Sithagu, Taki
    South Africa's history of land ownership is deeply intertwined with the legacy of colonialism and segregation. In the contemporary context, land ownership has emerged as a pivotal catalyst for rural development, aimed at addressing historical injustices and fostering sustainable socio-economic growth. Despite numerous reforms and initiatives introduced since the dawn of democracy, challenges persist, particularly in rural regions like Nooitgedacht in the West of Gauteng. In Nooitgedacht, landlessness, underdevelopment, poverty, and minimal economic activity continue to plague the community. This study delves into the intricate relationship between land ownership dynamics and rural development, with a specific focus on Nooitgedacht within the Mogale City Local Municipality. To comprehensively assess this issue, a multi-faceted research approach was employed, incorporating primary and secondary data collection methods. The research methods encompassed a thorough desktop survey, the distribution of questionnaires to community members, and in-depth interviews with municipal officials and community stakeholders. The desktop survey harnessed municipal valuation data to discern the existing patterns of land ownership in Nooitgedacht, revealing a marked imbalance, with most of the land controlled by private individuals and businesses, while national, provincial, and local governments held relatively merger land ownership within the study area. This skewed ownership pattern underscores the profound challenge of land access in Nooitgedacht. Consequently, the area grapples with persistent land inequalities encompassing issues of access, ownership, distribution, and administration, which further manifest through ongoing threats of land invasions, evictions, limited development, and a dearth of economic activities. Despite the presence of municipal policies aimed at addressing these disparities, implementation remains sporadic, fragmented, and inadequately coordinated within the municipality, resulting in a sluggish delivery of rural development. In conclusion, the study underscores the pivotal role of land ownership in shaping the trajectory of rural development in Nooitgedacht. It is evident that without equitable and well-coordinated land ownership policies and practices, the goal of achieving sustainable rural development in this region remains an elusive aspiration.