Dai Yongxin, Li Zijian, Lu Jingbiao
Sinosteel Maanshan Mining Research Institute Co., Ltd., Maanshan, China.
National Key Laboratory of Metal Mine Safety and Disaster Prevention and Control, Maanshan, Anhui, China.
PLoS One. 2025 May 23;20(5):e0323650. doi: 10.1371/journal.pone.0323650. eCollection 2025.
Rock slope landslides are characterized by their sudden onset and significant destructive power, posing a major threat to human life as well as the safety of equipment and infrastructure.Currently, research on landslide early hazard warning has largely focused on individual components, such as monitoring data analysis or studies on influencing mechanisms. However, landslide early hazard warning is a complex, multi-stage technical system where each stage is closely interlinked, and focusing solely on a single component cannot fulfill the objectives of effective monitoring and warning. This paper proposes a comprehensive technical system for landslide early hazard warning in open-pit mine slopes, encompassing the full process of monitoring data acquisition and processing, analysis of influencing mechanisms, intelligent algorithm-based prediction, and the construction of early hazard warning indicators. Each stage of the early hazard warning process is systematically researched and summarized.First, the combination of sliding average and wavelet noise reduction is utilized to perform global denoising and local focus noise reduction on the original monitoring data, and the signal-to-noise ratios after two rounds of noise reduction are 36 and 44, respectively, which indicates a good noise reduction effect. The Hodrick-Prescott (HP) filter is used to split the slope displacements into components, the Long Short-Term Memory (LSTM)-Seasonal Autoregressive Integrated Moving Average (SARIMA) hybrid model is proposed to predict the slope of the trend term of displacements and period term of displacements, and the prediction accuracy of the LSTM-SARIMA hybrid model reaches 96%. The excellence of the hybrid-driven model was determined by introducing five data-driven models, a Support Vector Machine (SVM), a Random Forest (RF),eXtreme Gradient Boosting (XGBoost),Recurrent Neural Network(RNN) and Light Gradient Boosting Machine(LightGBM), for comparison.Finally, the improved tangent angle of the T-t curve is employed as the landslide warning criterion, enabling accurate prediction of landslide events in an open-pit mine in East China. The successful application of this system demonstrates that the comprehensive warning framework proposed in this study can accurately predict the occurrence of rock slope landslides.
岩质边坡滑坡具有突发性和巨大破坏力的特点，对人类生命以及设备和基础设施安全构成重大威胁。目前，滑坡早期灾害预警研究主要集中在单个组件上，如监测数据分析或影响机制研究。然而，滑坡早期灾害预警是一个复杂的多阶段技术系统，各阶段紧密相连，仅关注单个组件无法实现有效监测和预警的目标。本文提出了一种露天矿边坡滑坡早期灾害预警的综合技术系统，涵盖监测数据采集与处理、影响机制分析、基于智能算法的预测以及早期灾害预警指标构建的全过程。对早期灾害预警过程的每个阶段进行了系统研究和总结。首先利用滑动平均和小波降噪相结合的方法对原始监测数据进行全局去噪和局部聚焦降噪，两轮降噪后的信噪比分别为36和44，降噪效果良好。使用Hodrick-Prescott（HP）滤波器对边坡位移进行分解，提出长短期记忆（LSTM）-季节性自回归积分移动平均（SARIMA）混合模型来预测边坡位移的趋势项和周期项，LSTM-SARIMA混合模型的预测准确率达到96%。通过引入支持向量机（SVM）、随机森林（RF）、极端梯度提升（XGBoost）、循环神经网络（RNN）和轻量级梯度提升机（LightGBM）这五个数据驱动模型进行比较，确定了混合驱动模型的优越性。最后，采用改进的T-t曲线切线角作为滑坡预警判据，实现了对华东某露天矿滑坡事件的准确预测。该系统的成功应用表明，本研究提出的综合预警框架能够准确预测岩质边坡滑坡的发生。
