Browsing by Author "Gaffoor, Zaheed"

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    Applicability of big data analytics to support groundwater management in Southern Africa
    (University of the Western Cape, 2022) Gaffoor, Zaheed; Kanyerere, Thokozani
    Groundwater is a vital resources for member states in the Southern African Development Community (SADC). However, sustainable managing groundwater resources in SADC is a challenge. Amongst the many issues facing groundwater managers in SADC, limited access to high fidelity data, as well as a poor understanding of the techniques needed to transform the data into information, has hampered the decision making process. Big data relates to large, voluminous and heterogenous datasets which are being generated through numerous activities, such as computer simulations, remote sensing, commercial transactions, internet activity, monitoring networks, IoT sensors, historical documents, social media and many others.
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    Big data analytics and its role to support groundwater management in the Southern African development community
    (MDPI, 2020) Gaffoor, Zaheed; Pietersen, Kevin; Jovanović, Nebojša Z.
    Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.
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    A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa
    (MDPI, 2022) Gaffoor, Zaheed; Pietersen, Kevin; Jovanovic, Nebo
    Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer.
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    Using geostatistical-hydrogeological approach to develop groundwater monitoring system in South Western Karoo, South Africa
    (University of the Western Cape, 2017) Gaffoor, Zaheed; Kanyerere, Thokozani
    Groundwater in the South Western Karoo plays a vital role in the overall water supply in the region. However, this resource is vulnerable to impacts from anthropogenic and natural activities. Mitigating the impacts on groundwater quality and quantity depends on the information provided by groundwater monitoring networks. The information provided by groundwater monitoring networks allow for timely and effective intervention to take place before widespread degradation occurs. In recent times, there has been interest in exploiting potentially vast natural resources of shale gas in the South Western Karoo. However, studies have highlighted links between shale gas development and groundwater contamination. There are concerns that these issues of groundwater contamination and overexploitation can occur in the South Western Karoo during shale gas development. One of the key features that need addressing is the lack of a statistically sound baseline that can inform on the natural conditions of the groundwater system, before development of shale gas exploitation.