IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v260y2025ics0951832025002030.html
   My bibliography  Save this article

Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning

Author

Listed:
  • Liu, Chengfei
  • Wang, Enyuan
  • Li, Zhonghui
  • Zang, Zesheng
  • Li, Baolin
  • Yin, Shan
  • Zhang, Chaolin
  • Liu, Yubing
  • Wang, Jinxin

Abstract

The reliable early warning of risks associated with gas, fire, dust, and roof hazards is crucial for the safe mining of coal mines. Traditional warning methods suffer from singular disaster risk warnings, low integration of risk information across different indicators, and insufficient perception of multi-hazard coupling relationships. To address these challenges, this paper proposes a method for adaptive integration of risk warnings that quantitatively learns the relationships between various indicators and warning tasks. Anomaly-transformer and E2GAN models are first employed to detect anomalies and impute missing values in time-series data. Subsequently, an improved MMoE model is used for multi-indicator fusion and prediction, allowing the simultaneous forecasting of future trends for all early-warning indicators. Finally, an adaptive multi-hazard risk integration warning model is developed, utilizing original and predicted data to calculate the current and future risk probabilities for various hazards. Comprehensive risk identification and warning are then performed using a multi-hazard grading identification. Experimental results show that the improved MMoE model outperforms LSTNet and TCN in prediction accuracy, and the integration model exceeds CNN and GRU in warning performance. Field validation confirms that this approach effectively identifies risks and enhances the reliability of intelligent early warning systems, ensuring coal mining safety.

Suggested Citation

  • Liu, Chengfei & Wang, Enyuan & Li, Zhonghui & Zang, Zesheng & Li, Baolin & Yin, Shan & Zhang, Chaolin & Liu, Yubing & Wang, Jinxin, 2025. "Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002030
    DOI: 10.1016/j.ress.2025.111002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025002030
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.111002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Mousavi, Milad & Shen, Xuesong & Zhang, Zhigang & Barati, Khalegh & Li, Binghao, 2025. "IoT-Bayes fusion: Advancing real-time environmental safety risk monitoring in underground mining and construction," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    2. Shuang Song & Shugang Li & Tianjun Zhang & Li Ma & Shaobo Pan & Lu Gao, 2021. "Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN," Energies, MDPI, vol. 14(5), pages 1-18, March.
    3. Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2021. "Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining," Energies, MDPI, vol. 14(21), pages 1-19, October.
    4. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Tan, Qiong & Fu, Ming & Wang, Zhengxing & Yuan, Hongyong & Sun, Jinhua, 2024. "A real-time early warning classification method for natural gas leakage based on random forest," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. Zang, Zesheng & Li, Zhonghui & Yin, Shan & Kong, Xiangguo & Niu, Yue & Liu, Binglong & Li, Huanhuan, 2024. "Study on the propagation and multifractal characteristics of stress waves in coal based on electric potential and DIC characterization," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    7. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    8. Jing, Qi & Yu, Lin & Lan, Fengyi & Li, Yuntao, 2024. "Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    9. Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2022. "A Gas Concentration Prediction Method Driven by a Spark Streaming Framework," Energies, MDPI, vol. 15(15), pages 1-13, July.
    10. Nguyen, Hoang & Bui, Xuan-Nam & Topal, Erkan, 2023. "Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Qiao, Wanguan, 2021. "Analysis and measurement of multifactor risk in underground coal mine accidents based on coupling theory," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    12. Wang, Kai & Li, Kangnan & Du, Feng & Zhang, Xiang & Wang, Yanhai & Sun, Jiazhi, 2024. "Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN," Energy, Elsevier, vol. 290(C).
    13. Lin, Haifei & Li, Wenjing & Li, Shugang & Wang, Lin & Ge, Jiaqi & Tian, Yu & Zhou, Jie, 2024. "Coal mine gas emission prediction based on multifactor time series method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    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. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Casualty analysis methodology and taxonomy for FPSO accident analysis," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    2. Moses Tunde Oyerinde & Folake Feyisayo Olowokudejo & Musa Adebayo Obalola, 2024. "Assets and Liabilities Management: A Determinant of Financial Performance of Pension Funds Administrators (PFAs)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 46-54.
    3. Chen, Qiaojun & Qu, Hu & Liu, Chun & Xu, Xingguo & Wang, Yu & Liu, Jianqing, 2025. "Spontaneous coal combustion temperature prediction based on an improved grey wolf optimizer-gated recurrent unit model," Energy, Elsevier, vol. 314(C).
    4. Mousavi, Milad & Shen, Xuesong & Zhang, Zhigang & Barati, Khalegh & Li, Binghao, 2025. "IoT-Bayes fusion: Advancing real-time environmental safety risk monitoring in underground mining and construction," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    5. Yin, Linfei & Ge, Wei, 2024. "Mobileception-ResNet for transient stability prediction of novel power systems," Energy, Elsevier, vol. 309(C).
    6. Zhou, Aitao & Li, Jingwen & Gong, Weili & Wang, Kai & Du, Changang, 2023. "Theoretical and numerical study on the contribution of multi-hole arrangement to coalbed methane extraction," Energy, Elsevier, vol. 284(C).
    7. Padma Charan Mishra & Rashmi Ranjan Panigrahi & Avinash K. Shrivastava, 2024. "Geo-environmental factors’ influence on mining operation: an indirect effect of managerial factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(6), pages 14639-14663, June.
    8. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    9. Jing, Qi & Song, Xingwang & Sun, Bingcai & Li, Yuntao & Zhang, Laibin, 2025. "Efficient estimation of natural gas leakage source terms using physical information and improved particle filtering," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    10. Jiadong, Qiu & Ohl, Joy P. & Tran, Trung-Tin, 2024. "Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Li, Qilin & Wang, Yang & Chen, Wensu & Li, Ling & Hao, Hong, 2024. "Machine learning prediction of BLEVE loading with graph neural networks," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Xiangqian Wang & Ningke Xu & Xiangrui Meng & Haoqian Chang, 2022. "Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    13. Liu, Zengkai & Ma, Qiang & Cai, Baoping & Shi, Xuewei & Zheng, Chao & Liu, Yonghong, 2022. "Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    14. Alshboul, Odey & Shehadeh, Ali & Almasabha, Ghassan, 2025. "Reliability of information-theoretic displacement detection and risk classification for enhanced slope stability and safety at highway construction sites," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    15. Xiuyu Wu & Pengkai Sun, 2024. "Dynamic Analysis and Temporal Governance of Safety Risks: Evidence from Underground Construction Accident Reports," Sustainability, MDPI, vol. 16(19), pages 1-24, September.
    16. Dar, Roouf Un Nabi & Alagappan, P., 2024. "A performance-based ballistic design framework for RC panels and a probabilistic model for crater quantification," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    17. Hai, Nan & Gong, Daqing & Liu, Shifeng & Dai, Zixuan, 2022. "Dynamic coupling risk assessment model of utility tunnels based on multimethod fusion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    18. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    19. Hongxia Li & Yiru Zhang & Wanli Yang, 2023. "Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-19, November.
    20. Xiangrui Meng & Haoqian Chang & Xiangqian Wang, 2022. "Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis," Energies, MDPI, vol. 15(6), pages 1-15, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:reensy:v:260:y:2025:i:c:s0951832025002030. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.