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Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

Author

Listed:
  • Diyuan Li

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Zida Liu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Danial Jahed Armaghani

    (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia)

  • Peng Xiao

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Jian Zhou

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences.

Suggested Citation

  • Diyuan Li & Zida Liu & Danial Jahed Armaghani & Peng Xiao & Jian Zhou, 2022. "Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:787-:d:761871
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    References listed on IDEAS

    as
    1. Ning Li & R. Jimenez, 2018. "A logistic regression classifier for long-term probabilistic prediction of rock burst hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 197-215, January.
    2. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
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    Cited by:

    1. Keyou Shi & Yong Liu & Weizhang Liang, 2022. "An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    2. Mohammadreza Khanmohammadi & Danial Jahed Armaghani & Mohanad Muayad Sabri Sabri, 2022. "Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    3. Zhe Liu & Jianhong Chen & Yakun Zhao & Shan Yang, 2023. "A Novel Method for Predicting Rockburst Intensity Based on an Improved Unascertained Measurement and an Improved Game Theory," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
    4. Shaofeng Wang & Xin Cai & Jian Zhou & Zhengyang Song & Xiaofeng Li, 2022. "Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics," Mathematics, MDPI, vol. 10(18), pages 1-5, September.
    5. Weijun Liu & Zhixiang Liu & Zida Liu & Shuai Xiong & Shuangxia Zhang, 2023. "Random Forest and Whale Optimization Algorithm to Predict the Invalidation Risk of Backfilling Pipeline," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    6. Qinghe Zhang & Weiguo Li & Liang Yuan & Tianle Zheng & Zhiwei Liang & Xiaorui Wang, 2024. "A review of tunnel rockburst prediction methods based on static and dynamic indicators," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 10465-10512, September.
    7. Yakun Zhao & Jianhong Chen & Shan Yang & Zhe Liu, 2022. "Game Theory and an Improved Maximum Entropy-Attribute Measure Interval Model for Predicting Rockburst Intensity," Mathematics, MDPI, vol. 10(15), pages 1-22, July.

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