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Stope Stability Prediction Model Using Ensemble Learning by Daniel Moravanský is a document available to read on EtoBox.
This study presents the development of a stope stability prediction model using ensemble learning techniques, specifically focusing on Random Forest, Gradient Boosting, Bootstrap Aggregating, and Adaptive Boosting algorithms, based on 472 case histories from AngloGold Ashanti Ghana. The Bootstrap Aggregating model achieved the highest performance metrics, including a Matthews Correlation Coefficient of 96.84%, making it the recommended tool for predicting stope stability. The research highlights the necessi
- Author
- Daniel Moravanský
- Language
- EN