Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis
Rockburst is a major disaster in deep underground engineering, and its prediction is crucial for engineering safety. This study proposes an optimization method based on multidimensional feature selection and integrated learning that systematically evaluates the impact of different indicator dimensio...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6466 |
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| author | Jiayang Chen Xuebin Xie |
| author_facet | Jiayang Chen Xuebin Xie |
| author_sort | Jiayang Chen |
| collection | DOAJ |
| description | Rockburst is a major disaster in deep underground engineering, and its prediction is crucial for engineering safety. This study proposes an optimization method based on multidimensional feature selection and integrated learning that systematically evaluates the impact of different indicator dimensions by constructing an indicator–indicator system and an indicator–rockburst hierarchy using a combination of seven-, six-, five-, four-, and three-dimensional indicators in conjunction with six machine-learning models, such as XGBoost, LightGBM, and CatBoost. The results show that tree models (e.g., CatBoost, LightGBM, etc.) are naturally resistant to multicollinearity, and PCA preprocessing destroys their nonlinear feature relationships, leading to performance degradation. CatBoost has the best performance and strong overfitting resistance; LightGBM is the second most efficient and suitable for real-time applications. The indicator–indicator system has better overall performance but less stability, and the indicator–rockburst system has slightly lower performance but a more stable downward trend. The six-dimensional system in both types of systems can balance the performance and complexity and is the optimal choice for engineering applications. This study provides theoretical support and practical reference for the selection of rockburst prediction and an evaluation index system. |
| format | Article |
| id | doaj-art-5f0aeb15c0d74e39bb56bb2b29f2cc9e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5f0aeb15c0d74e39bb56bb2b29f2cc9e2025-08-20T03:32:28ZengMDPI AGApplied Sciences2076-34172025-06-011512646610.3390/app15126466Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation AnalysisJiayang Chen0Xuebin Xie1School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaRockburst is a major disaster in deep underground engineering, and its prediction is crucial for engineering safety. This study proposes an optimization method based on multidimensional feature selection and integrated learning that systematically evaluates the impact of different indicator dimensions by constructing an indicator–indicator system and an indicator–rockburst hierarchy using a combination of seven-, six-, five-, four-, and three-dimensional indicators in conjunction with six machine-learning models, such as XGBoost, LightGBM, and CatBoost. The results show that tree models (e.g., CatBoost, LightGBM, etc.) are naturally resistant to multicollinearity, and PCA preprocessing destroys their nonlinear feature relationships, leading to performance degradation. CatBoost has the best performance and strong overfitting resistance; LightGBM is the second most efficient and suitable for real-time applications. The indicator–indicator system has better overall performance but less stability, and the indicator–rockburst system has slightly lower performance but a more stable downward trend. The six-dimensional system in both types of systems can balance the performance and complexity and is the optimal choice for engineering applications. This study provides theoretical support and practical reference for the selection of rockburst prediction and an evaluation index system.https://www.mdpi.com/2076-3417/15/12/6466rockburst predictionfeature selectionmachine learningindicator systemOptuna optimization |
| spellingShingle | Jiayang Chen Xuebin Xie Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis Applied Sciences rockburst prediction feature selection machine learning indicator system Optuna optimization |
| title | Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis |
| title_full | Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis |
| title_fullStr | Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis |
| title_full_unstemmed | Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis |
| title_short | Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis |
| title_sort | optimization of rockburst grade prediction model based on multidimensional feature selection integrated learning and index system correlation analysis |
| topic | rockburst prediction feature selection machine learning indicator system Optuna optimization |
| url | https://www.mdpi.com/2076-3417/15/12/6466 |
| work_keys_str_mv | AT jiayangchen optimizationofrockburstgradepredictionmodelbasedonmultidimensionalfeatureselectionintegratedlearningandindexsystemcorrelationanalysis AT xuebinxie optimizationofrockburstgradepredictionmodelbasedonmultidimensionalfeatureselectionintegratedlearningandindexsystemcorrelationanalysis |