Moving Towards Accurate Monitoring and Prediction of Gold Mine Underground Dam Levels

In this paper a comparison between an ensembles (multi-classifier) constructed of several machine learning methods (support vector machine, artificial neural network, naive Bayesian classifier, decision trees, radial basis function and k nearest neighbors) versus each single classifiers of these methods in term of gold mine underground dam levels prediction is presented. The ensembles as well as the single classifiers are used to classify, thus monitoring and predicting the underground water dam levels on a single-pump station deep gold in South Africa. In order to improve the classification accuracy an ensemble was constructed based on each single classifier performance, therefore, five ensembles were built and tested. In terms of misclassification error, the results show the ensemble to be more efficient for classification of underground water dam levels compared to each of the single classifiers.