Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great pot...

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Bibliographic Details
Published in:Mathematics (Basel) 2020-05, Vol.8 (5), p.765
Main Authors: Liang, Weizhang, Luo, Suizhi, Zhao, Guoyan, Wu, Hao
Format: Article
Language:English
Subjects:
Algorithms
Decision trees
Discrete element method
extreme gradient boosting (XGBoost)
gradient boosting decision tree (GBDT)
hard rock
Indicators
light gradient boosting machine (LightGBM)
Machine learning
Mathematical models
Mathematics
Methods
Mine safety
Mines
Neural networks
Performance evaluation
Performance indices
Performance prediction
pillar stability
prediction
Prediction models
Risk management
Safety factors
Sensitivity analysis
Simulation
Stability
Support vector machines
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