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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