Groundwater vulnerability mapping of Witbank coalfield in South Africa using deep learning artificial neural networks

Emmanuel Sakala, Francois Fourie, Modreck Gomo, Henk Coetzee


This study highlights the usage of deep learning artificial neural networks in the assessment of groundwater vulnerability of a coalfield.  The network uses the DRIST model with parameters (depth to water level, recharge, the impact of the vadose zone, soils and topographic slope) as training inputs and borehole sulphate concentration as training output. This technique was applied to Witbank coalfield, where acid mine drainage emanating from coal mining operations is a huge concern for the surrounding environment and groundwater resources. The generated groundwater vulnerability model was validated with another sulphate dataset not used during model training. The deep neural network model with dropout and decaying learning rate regularisers correlated very well with sulphate data from another source as compared to the index and overlay DRIST model. The approach, differentiated areas in terms of vulnerability to acid mine drainage, which can aid policy, and decision makers to make scientifically informed decisions on land use planning. The approach developed in this research can be applied to other coalfields in order to evaluate its robustness to different hydrogeological and geological conditions.

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