Research Progress on Multi-Technology Integration Early Warning and Prevention of Mine Geological Disasters
Keywords:
Mine geological disasters, Multi-technology integration, Intelligent early warning, Collaborative prevention, Ecological restorationAbstract
Mineral resource development is prone to引发 mine geological disasters, and with the increase in development intensity, they show a high-frequency and frequent occurrence trend. This paper systematically reviews relevant research in the field of mine geological disaster prevention, and discusses the composition method of the multi-technology integration early warning system and the progress of prevention strategies. By integrating geological monitoring technologies in the hydrogeology, engineering geology, and environmental geology fields, intelligent equipment, and ecological restoration technologies, an integrated "monitoring - early warning - prevention - restoration" technical system is proposed. The intelligent early warning system based on multi-source data fusion and engineering-ecological collaborative prevention can effectively improve the prevention effect of mine geological disasters, aiming to provide a new technical path for safe and green mine development.
References
Lin, Z., Liu, X., Xiang, Y., & Hong, Y. (2025). Modeling multivariate degradation data with dynamic covariates under a Bayesian framework. Reliability Engineering & System Safety, 111115.
Lin, Z., Wang, Y., & Hong, Y. (2023). The computing of the Poisson multinomial distribution and applications in ecological inference and machine learning. Computational Statistics, 38(4), 1851-1877.
Zhou, J., & Cen, W. (2024). Investigating the Effect of ChatGPT-like New Generation AI Technology on User Entrepreneurial Activities. Innovation & Technology Advances, 2(2), 1–20. https://doi.org/10.61187/ita.v2i2.124
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Guo, Y. (2025, May). IMUs Based Real-Time Data Completion for Motion Recognition With LSTM. In Forum on Research and Innovation Management (Vol. 3, No. 6).
Yang, J., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
Jin, Y., Zhang, Y., Xu, Z., Zhang, W., & Xu, J. (2024, November). Advanced object detection and pose estimation with hybrid task cascade and high-resolution networks. In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1293-1297). IEEE.
Mehta, R., Patwar, N., Wei, X., Saunders, E., Zhu, X., & Liu, J. (2026). Towards a National AI Security Framework for Financial Infrastructure Protection. International Journal of Advance in Applied Science Research, 5(2), 39–50. Retrieved from https://h-tsp.com/index.php/ijaasr/article/view/251
Zhou, Z. (2025, November). Digital precision distribution strategy for social media content on private domain platforms in the automotive industry: a collaborative filtering model based on user behavior. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 516-521).
Li, W. (2026). Optimizing AI-Driven Bid Pricing Models for Non-Standard Automation Projects: Leveraging Historical Financial Data and Machine Learning Algorithms.
Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).
Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing legal citation text classification A Conv1D-based approach for multi-class classification. Journal of Theory and Practice of Engineering Science, 4(02), 15-22.
Deng, X. (2025). Homomorphic Encryption-Based Data Integrity Verification and Anti-Tampering Mechanism in Cloud Storage Environment.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yan Cheng, Fanchao Zhou
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
