Following the Talk by Helen Seyler, hosted by the Western Cape Branch on 28 January 2021, Zaheed further extrapolated on the research into the applicability of machine learning for the forward prediction of groundwater levels and flow regime using results from the dolomite aquifers in South Africa, particularly the Romotswa/North West and Gauteng Dolomites.
This research supports a larger programme researching the use of big data analytics for water secure transboundary systems.

Z Gaffoor as speaker at the SADC-GMI Conference 2019, Johannesburg

About the Speaker:
Zaheed is a Specialist groundwater consultant with expertise in groundwater exploration services, earth sciences related services, borehole drilling services and water quality analysis related services, amongst others. His current employment is with L2K2 Consultants, in Cape Town.
He is also pursuing a PhD degree with The Institute for Water Studies at the University of the Western Cape, focusing Big Data analytics and its application in groundwater sciences.

Zaheed Acknowledge the following partners & sponsors as part of a multi-party programme called the: Big Data and Transboundary Water Collaboration

The Big Data Analytics and Transboundary Water Collaboration is a funding initiative comprising the SADC Groundwater Management Institute, the USAID Global Development Lab and Southern African Regional Mission, the South African Department for Science and Technology, IBM Africa Research Lab and the US Geological Survey, supported by the Sustainable Water Partnership.  The Collaboration includes 4 funded projects seeking to support decision-making on groudwater in transboundary aquifers, with its own Community of Practice.

Talk Resources

Want to learn more about Dolomitic Areas in South Africa?
General references:
1) Visit the DWS Groundwater website: DWS Dolomite Units/ Compartments Maps & Guidelines
2) Council for Geoscience: https://www.geoscience.org.za/images/geohazard/Sinkholes.pdf
3) Engineering, hydrogeological and vadose zone hydrological aspects of Proterozoic dolomites (South Africa) https://doi.org/10.1016/j.jafrearsci.2018.07.024

GWD-Z-Gaffoor-PDF presentation (3,01 MB)Download

This Talk is available via the GWD YouTube Channel. (Please contact Zaheed via zaheed.gaff@nullgmail.com for further discussions relating to this presentation):

FORMAL PUBLICATION: Gaffoor Z, Pietersen K, Jovanovic N, et al (2020) Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community. Water 12:28. https://doi.org/10.3390/w12102796 (The paper is open source. It should be a good entry point for the subject and provide additional references)

Q&A and Comments:

From Melissa Lintnaar-Strauss: Zaheed will this work assist us to better evaluate mining applications e.g. for mine closures or better manage acid mine drainage?

From Michael Maluleke DWS: Other than the two sites mentioned in the presentation dolomite and alluvial aquifer, was the model tested successfully in other non dolomite areas with enough data to run the model. Generally, how was the performance of the model?

From Dr Thokozane Kanyerere: In data scarce aquifer systems, do you discourage or caution the use machine learning models?

From Adolf.October: How important is cloud computing in machine learning?Why are we using the cloud?

About the Talk: Fundamental to the definition of groundwater availability and the management of any aquifer is an understanding of the changes in groundwater levels and storage, recharge, and groundwater discharge to surface water, when the aquifer is pumped. This understanding forms the foundation for the determination of limits of future abstraction and thresholds of unacceptable impact, and provides a tool against which to compare future datasets and make groundwater management decisions.

Given the complex nature of groundwater and the interdependent responses of the system to change, quantifying the relationship between the aquifer flow regime and abstraction, and determining the long-term implications of different thresholds on these systems requires the use of models. Generating accurate simulations for groundwater behaviour with numerical models is however challenging due to the requirement to accurately understand the physical system in order to simulate it and overcome the non-uniqueness of the numerical solutions, which in turn requires detailed datasets. It has therefore become attractive to test the application of machine learning techniques in the simulation of groundwater behaviour.

This talk presents research into the applicability of machine learning for the forward prediction of groundwater levels and flow regime, as an alternative to numerical modelling, using results from the dolomite aquifers in South Africa. The research supports a larger programme researching the use of big data analytics for water secure transboundary systems.

About the Speaker: Helen has thirteen years experience as a hydrogeologist (in South Africa), including experience in various aspects of groundwater resources management, and specializing in numerical modelling for water resource quantification and scenario planning, wellfield operating rules, surface water – groundwater interactions, and the groundwater aspects for mining EIAs.  She has a particular interest in "sustainable" groundwater use, and in social and economic development challenges as they relate to resources management. Her PhD thesis currently underway: Groundwater Decision Support Systems including Sustainability Indicators for Sustainable Groundwater Use.

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National Chairperson: Mr Fanus Fourie
Vice-Chairperson: Ms Nicolette Vermaak
National Treasurer: Mr Yazeed van Wyk
National Secretariat: Dr Jaco Nel
National Coordinator: Ms Elanda Schaffner

Please contact this Committee via [email protected]

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