Scholarlink

<p>Slope landslides in open-pit coal mines critically threaten mining safety. Traditional early-warning systems face limitations, including data silos, false alarms, and an overemphasis on data collection over analysis. To overcome these challenges, this study develops a novel cloud-based Early Warning System (EWS) that integrates the Internet of Things (IoT) for multi-source data acquisition, Long Short-Term Memory (LSTM) networks for high-precision time-series prediction, and Dempster–Shafer (D-S) evidence theory for uncertainty-based multi-indicator fusion. Unlike conventional EWSs relying on single-threshold rules, the proposed framework performs real-time, data-driven risk reasoning, significantly reducing false alarms and enhancing system robustness under heterogeneous monitoring conditions. Field deployment at the Zhonglian Runshi open-pit mine demonstrated that the proposed LSTM model achieved a prediction accuracy of R<sup>2</sup> = 0.91 (RMSE &lt; 0.11 mm), while maintaining a tenfold lower computational cost than Transformer-based models. Through multi-source integration of 69 sensors, the system achieved continuous, real-time risk assessment with reliable multi-channel alerts. These results confirm the feasibility of lightweight, cloud-enabled intelligent EWSs for large-scale open-pit mines, providing a scalable paradigm for intelligent geohazard monitoring and early warning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cloud-based slope risk monitoring and early warning system for open-pit coal mines: a case study of Zhonglian Runshi

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月23日

查看全文
  • Xiaoyong Dong,
  • Shuang Li,
  • Ran Ma,
  • Weihui Tian,
  • Keke Zhao,
  • Hui Xiang,
  • Jiongjiong Zhu,
  • Yang Qiu

摘要

Slope landslides in open-pit coal mines critically threaten mining safety. Traditional early-warning systems face limitations, including data silos, false alarms, and an overemphasis on data collection over analysis. To overcome these challenges, this study develops a novel cloud-based Early Warning System (EWS) that integrates the Internet of Things (IoT) for multi-source data acquisition, Long Short-Term Memory (LSTM) networks for high-precision time-series prediction, and Dempster–Shafer (D-S) evidence theory for uncertainty-based multi-indicator fusion. Unlike conventional EWSs relying on single-threshold rules, the proposed framework performs real-time, data-driven risk reasoning, significantly reducing false alarms and enhancing system robustness under heterogeneous monitoring conditions. Field deployment at the Zhonglian Runshi open-pit mine demonstrated that the proposed LSTM model achieved a prediction accuracy of R2 = 0.91 (RMSE < 0.11 mm), while maintaining a tenfold lower computational cost than Transformer-based models. Through multi-source integration of 69 sensors, the system achieved continuous, real-time risk assessment with reliable multi-channel alerts. These results confirm the feasibility of lightweight, cloud-enabled intelligent EWSs for large-scale open-pit mines, providing a scalable paradigm for intelligent geohazard monitoring and early warning.