Development of a Stope Stability Prediction Model Using Ensemble Learning Techniques - A Case Study

Abstract

The consequences of collapsed stopes can be dire in the mining industry. This can lead to the revocation of a mining license in most jurisdictions, especially when the harm costs lives. Therefore, as a mine planning and technical services engineer, it is imperative to estimate the stability status of stopes. This study has attempted to produce a stope stability prediction model adopted from stability graph using ensemble learning techniques. This study was conducted using 472 case histories from 120 stopes of AngloGold Ashanti Ghana, Obuasi Mine. Random Forest, Gradient Boosting, Bootstrap Aggregating and Adaptive Boosting classification algorithms were used to produce the models. A comparative analysis was done using six classification performance metrics namely Accuracy, Precision, Sensitivity, F1-score, Specificity and Mathews Correlation Coefficient (MCC) to determine which ensemble learning technique performed best in predicting the stability of a stope. The Bootstrap Aggregating model obtained the highest MCC score of 96.84% while the Adaptive Boosting model obtained the lowest score. The Specificity scores in decreasing order of performance were 98.95%, 97.89%, 96.32% and 95.26% for Bootstrap Aggregating, Gradient Boosting, Random Forest and Adaptive Boosting respectively. The results showed equal Accuracy, Precision, F1-score and Sensitivity score of 97.89% for the Bootstrap Aggregating model while the same observation was made for Adaptive Boosting, Gradient Boosting and Random Forest with 90.53%, 92.63% and 95.79% scores respectively. At a 95% confidence interval using Wilson Score Interval, the results showed that the Bootstrap Aggregating model produced the minimal error and hence was selected as the alternative stope design tool for predicting the stability status of stopes.

Keywords: Stope Stability, Ensemble Learning Techniques, Stability Graph, Machine Learning