Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst prediction with the use of machine learning (ML) is currently gaining attention, as its prognosis capability in many cases outperforms widely used empirical approaches. However, the required data for conducting any analysis are limited, while also having imbalances in their recorded instances associated with rockburst intensities. These, combined with the multiparametric nature of the phenomenon, can deteriorate the performance of the ML algorithms. This study focuses on the enhancement of the prediction performance of ML algorithms by utilizing the oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE). Five ML algorithms, namely Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, were used in a series of parametric analyses considering different combinations of input parameters, such as the maximum tangential stress, the uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. All models kept their hyperparameters fixed, and were trained with the initial dataset, in which synthetic instances were added gradually aiming in the attenuation of a balanced dataset and its further expansion, until the number of synthetic instances reached the number of real data. The assessment of the SMOTE technique is given and its performance is evaluated though the different strategies adopted. The results indicate that SMOTE has a considerable positive effect in the accuracy of the overall classification and especially in the improvement of the within-class classification accuracy, even after the balancing of the dataset.