IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p838-d1060171.html
   My bibliography  Save this article

Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms

Author

Listed:
  • Jiachuang Wang

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

  • Haoji Ma

    (Sifang Gold Mine Co., Baoji 721000, China)

  • Xianhang Yan

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

Abstract

Rockburst is a common and huge hazard in underground engineering, and the scientific prediction of rockburst disasters can reduce the risks caused by rockburst. At present, developing an accurate and reliable rockburst risk prediction model remains a great challenge due to the difficulty of integrating fusion algorithms to complement each other’s strengths. In order to effectively predict rockburst risk, firstly, 410 sets of valid rockburst data were collected as the original data set in this paper, which was used to process these rockburst cases by the SMOTE oversampling method. Then, four integrated algorithms and eight basic algorithms were selected, which were optimized by hyperparameters and five-fold cross-validation and combined with the random search grid method, thus improving the classification performance of these algorithms. Third, the stacking integration algorithm, which was combined with the principles of various machine learning algorithms and the characteristics of the rockburst cases, integrated the optimization of rockburst algorithms with reference to four combinatorial strategies. Further, we adopted the voting integration algorithm, chose multiple combination schemes, and referred to the weighted fusion of accuracy, F1 score, recall, precision, and cv-mean as the weight values, and the optimal model for rockburst risk prediction was obtained. Finally, using the 35 generated stacking integration algorithms and 18 voting integration algorithms, the optimal model in the fusion strategy was selected and the traditional integration algorithm model was analyzed on the basis of different sample combinations of the models. The results showed that the prediction performance of stacking and voting integration algorithms was mostly better than the ordinary machine-learning performance, and the selection of appropriate fusion strategies could effectively improve the performance of rockburst prediction for ensemble learning algorithms.

Suggested Citation

  • Jiachuang Wang & Haoji Ma & Xianhang Yan, 2023. "Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms," Mathematics, MDPI, vol. 11(4), pages 1-29, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:838-:d:1060171
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/838/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/838/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
    2. Guangliang Feng & Guoqing Xia & Bingrui Chen & Yaxun Xiao & Ruichen Zhou, 2019. "A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    3. Ciobanu Dumitru & Vasilescu Maria, 2013. "Advantages and Disadvantages of Using Neural Networks for Predictions," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 444-449, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    3. Mahdi Sedighkia & Asghar Abdoli, 2023. "Design of optimal environmental flow regime at downstream of multireservoir systems by a coupled SWAT-reservoir operation optimization method," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 834-854, January.
    4. Lei Li & Yujiang Xie & Jingqiang Tan, 2020. "Application of Waveform Stacking Methods for Seismic Location at Multiple Scales," Energies, MDPI, vol. 13(18), pages 1-15, September.
    5. Nicola Baldo & Matteo Miani & Fabio Rondinella & Clara Celauro, 2021. "A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    6. Fuping Liu & Ying Liu & Chen Yang & Ruixun Lai, 2022. "A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4785-4797, September.
    7. Shaghayegh Rahnama & Adriana Cortez & Andres Monzon, 2024. "Navigating Passenger Satisfaction: A Structural Equation Modeling–Artificial Neural Network Approach to Intercity Bus Services," Sustainability, MDPI, vol. 16(11), pages 1-33, May.
    8. Ram, Pappu Kalyan & Pandey, Neeraj & Persis, Jinil, 2024. "Modeling social coupon redemption decisions of consumers in food industry: A machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    9. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
    10. Ravita D. Prasad, 2024. "School Electricity Consumption in a Small Island Country: The Case of Fiji," Energies, MDPI, vol. 17(7), pages 1-25, April.
    11. Xingyu Li & Long Li & Longgao Chen & Ting Zhang & Jianying Xiao & Longqian Chen, 2022. "Random Forest Estimation and Trend Analysis of PM 2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    12. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    13. Liu, Jin & Smith, Stephen R., 2020. "A multi-level biogas model to optimise the energy balance of full-scale sewage sludge conventional and THP anaerobic digestion," Renewable Energy, Elsevier, vol. 159(C), pages 756-766.
    14. Hülya Yürekli & Öyküm Esra Yiğit & Okan Bulut & Min Lu & Ersoy Öz, 2022. "Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    15. Thao Nguyen-Da & Yi-Min Li & Chi-Lu Peng & Ming-Yuan Cho & Phuong Nguyen-Thanh, 2023. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    16. Rui-Si Ma & Si-Ioi Ng & Tan Lee & Yi-Jian Yang & Raymond Kim-Wai Sum, 2022. "Validation of a Speech Database for Assessing College Students’ Physical Competence under the Concept of Physical Literacy," IJERPH, MDPI, vol. 19(12), pages 1-11, June.
    17. Jianguo Zhang & Peitao Li & Xin Yin & Sheng Wang & Yuanguang Zhu, 2022. "Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm," Mathematics, MDPI, vol. 10(10), pages 1-16, May.
    18. Jorge Antunes & Rangan Gupta & Zinnia Mukherjee & Peter Wanke, 2022. "Information entropy, continuous improvement, and US energy performance: a novel stochastic-entropic analysis for ideal solutions (SEA-IS)," Annals of Operations Research, Springer, vol. 313(1), pages 289-318, June.
    19. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    20. Alex Jose & Angus S. Macdonald & George Tzougas & George Streftaris, 2022. "A Combined Neural Network Approach for the Prediction of Admission Rates Related to Respiratory Diseases," Risks, MDPI, vol. 10(11), pages 1-35, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:838-:d:1060171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.