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.
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
Want to learn more about Dolomitic Areas in South Africa?
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
This Talk is available via the GWD YouTube Channel. (Please contact Zaheed via email@example.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)
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?