Big data analytics and its role to support groundwater management in Southern Africa
Abstract
Southern Africa and its communities are heavily dependent on groundwater resources to meeting fresh water needs for domestic and industrial use. However, the beneficial use of groundwater is hampered by a number of concerns such as over-abstraction, pollution, institutional mismanagement, lack of data, and the lack of understanding in the techniques to leverage available data. Addressing these concerns are paramount to the sustainable use of groundwater and the lives and livelihoods that depend on it. In this regard, this research focused on understanding the role and applicability of Big Data and big data analytics to support sustainable groundwater management in Southern Africa, by filling the data and information gap. Specifically, this research focused on investigating the potential Big Data sources available in Southern Africa, as well as the tools and techniques needed to leverage the data for information discovery, from a groundwater perspective. In addition, we demonstrate the use of data science techniques, such as artificial intelligence and machine learning, across a number of groundwater management scenarios, in various aquifers across Southern Africa. For example, in the Ramotswa/North-West/Gauteng Dolomites we rely on remotely sensed, simulated, and in-situ datasets to develop an ensemble machine learning algorithm to model and forecast chronic lowering of groundwater levels. Finally, in an effort to facilitate the application of big data analytics, a conceptual framework is developed linking the various data sources, algorithms and groundwater management scenarios. Together, this highlights the potential of data driven approaches within the groundwater domain, in the hope that it may shift the paradigm of groundwater sciences in Southern Africa into the 4IR era.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H26A..03G