Browsing by Author "Pietersen, Kevin"

Now showing 1 - 4 of 4
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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 water law enforcement to demonstrate the effectiveness of regulations for the protection of water resources
    (University of the Western Cape, 2021) Smith, Farrel; Kanyerere, Thokozani; Pietersen, Kevin
    The South African National Water Act attracted attention of the international water community as one of the most reformist pieces of water legislation in the world, and a major step forward in the transformation of the concept of integrated water resources management (IWRM) into legislation. However, 20 years later after the National Water Act was promulgated, implementation of the same act has been partially successful. In many aspects, the, implementation has been weak. The argument is that the water law enforcement is not been implemented to demonstrate the effectiveness of regulations for the protection of water resources.
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    Why do we need to care about transboundary aquifers and how do we solve their issues?
    (SpringerLink, 2023) Rivera, Alfonso; Pietersen, Kevin; Pétré, Marie‑Amélie
    As the reliance on transboundary groundwater is increasing globally, it is important to understand and address the specifc issues raised by the assessment and management of transboundary aquifers (TBAs). Building on 20 years of TBA experience and through a three-pillar framework (assessment, cooperation-collaboration, shared management), the key elements to addressing TBA issues are described, including a multidisciplinary approach, identifcation of hotspot zones, local vs border-wide approaches, appropriate funding models, and an increased recognition of the role and value of each TBA.