The application of machine learning for groundwater level prediction in the Steenkoppies compartment of the Gauteng and North West dolomite aquifer, South Africa
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Date
2020-11
Authors
Gibson, Kirsty
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Publisher
University of the Free State
Abstract
Groundwater in the Steenkoppies compartment of the Gauteng and North West dolomite aquifer is extensively used for agriculture practices that can potentially lead to groundwater storage depletion, threatening groundwater sustainability in the compartment. Groundwater models are needed to describe the complex, interdependent relationships occurring in a groundwater system. Groundwater levels represent the response of an aquifer to changes in storage, recharge, discharge and hydrological stresses. Groundwater levels in an aquifer are, therefore, useful to identify limits and unacceptable impacts on an aquifer and to use this information to implement sustainable groundwater management decisions. Conventionally, numerical techniques are used for groundwater modelling. The use of machine learning techniques for groundwater modelling is relatively new in South Africa. Unlike numerical models, machine learning models are data-driven and learn the behaviour of the aquifer system from measured values without needing an understanding of the internal structure and physical processes of an aquifer. In this study, Neural Network Autoregression (NNAR) was applied to obtain groundwater level predictions in the Steenkoppies compartment of the Gauteng and North West Dolomite Aquifer in South Africa. Multiple variables (rainfall, temperature, groundwater usage and spring discharge from the Maloney’s Eye spring) were chosen as input parameters to facilitate groundwater level predictions. The importance of each of these inputs to aid the prediction of groundwater levels was assessed using the mutual information index. The NNAR model was also used to predict groundwater levels under scenarios of change (change in recharge and abstraction). The coefficient of determination, mean squared error, root mean squared error and mean absolute error was used to evaluate the predictions made by the NNAR. The results showed that the NNAR could be used to make groundwater level predictions in 18 boreholes across the Steenkoppies aquifer and that the model can be used to make predictions for scenarios of change. Overall, the NNAR performed well in predicting and simulating groundwater levels in the Steenkoppies aquifer. The transferability of the NNAR to model groundwater levels in different aquifer systems or groundwater levels at different temporal resolutions should be tested to confirm the robustness of the NNAR to predict groundwater levels.
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Keywords
Dissertation (M.Sc. (Geohydrology))--University of the Free State, 2020, Machine learning -- Groundwater, Groundwater utilization, Groundwater preservation