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Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

1
Department of Earth Science, University of the Western Cape, Cape Town 7535, South Africa
2
IBM Research, Africa Labs, Johannesburg 2000, South Africa
3
Institute for Water Studies, University of the Western Cape, Cape Town 7535, South Africa
4
Council for Scientific and Industrial Research, Stellenbosch 7600, South Africa
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Department of Computer Science, University of the Western Cape, Cape Town 7535, South Africa
*
Author to whom correspondence should be addressed.
Water 2020, 12(10), 2796; https://doi.org/10.3390/w12102796
Received: 13 July 2020 / Revised: 22 September 2020 / Accepted: 25 September 2020 / Published: 9 October 2020
(This article belongs to the Special Issue The Application of Artificial Intelligent in Hydrology)
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. View Full-Text
Keywords: transboundary aquifers; data-mining; Internet of things; machine learning; remote sensing transboundary aquifers; data-mining; Internet of things; machine learning; remote sensing
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Gaffoor, Z.; Pietersen, K.; Jovanovic, N.; Bagula, A.; Kanyerere, T. Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community. Water 2020, 12, 2796.

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