Real-time monitoring and early warning of landslides at relocated Wushan Town,the Three Gorges Reservoir,China

A special monitoring and warning system has been established and improved in the Three Gorges Reservoir area since 1999. It is necessary to develop a real-time monitoring system on landslides because there are dense populations centered in the reservoir area and geo-hazards may be triggered by a 30-m water level fluctuation between 145 and 175 m in elevation during reservoir operation; the regular monitoring could not be suitable to the early warning on landslides. Since 2003, the authors have carried out a real-time monitoring and early warning project on landslides at the relocated Wushan town in the Three Gorges Reservoir area. The monitoring station includes Global Positioning System with high-accuracy double frequency to monitor ground displacement, time domain reflection technology, and immobile borehole, inclinometer to monitor deep displacement, piezometer to monitor pore water pressure, and precipitation and reservoir water level monitoring. Compared with traditional methods, the real-time monitoring is continuous and traceable in the acquisition process, and the cycle of data acquisition is very short, usually within hours, minutes, or even shorter. Based on the landslide monitoring experience at the Three Gorges Reservoir area, the early warning criteria on landslide are established in which the critical situation is classified into four levels: blue, yellow, orange, and red, respectively, expressed by no, slight, moderate, and high risk situation. Comprehensive judgment from multimonitoring data of Yuhuangge landslide in this area since 2004 suggested that the new Wushan town will be at the blue early warning level, although some monitoring data of individual displacement at deep borehole showed that the displacement was increased by 5 mm in 5 months with an average velocity of 1.0 mm/month, and the data of BOTDR also showed an obvious dislocation along a stairway on the landslide.     

基于位移比模型的三峡库区云阳县域内蠕变型滑坡降雨预警

建立高效合理的区域滑坡灾害降雨预警模型对滑坡防治具有重要意义.然而以往的研究多侧重于临滑预警,对蠕变型滑坡在强降雨工况下的短暂加速变形的预警研究还有待深入.以三峡库区云阳县域内滑坡为例,首先根据滑坡地表位移监测数据的特点对统计样本进行合理筛选.再通过降雨因子与滑坡发生的相关性分析以及对滑坡在降雨条件下位移变化情况的数值模拟,确定了适用于不同时间阶段的降雨统计变量.然后将考虑了滑坡规模特征的滑坡位移比（累计位移与滑坡纵长之比）作为变形指标,分时段统计滑坡地表位移监测数据与历史降雨信息,建立了日降雨数据与月位移数据的对应关系,得到了可用于确定降雨量阈值的位移比模型,并获得了云阳县蠕变型滑坡的五级预警分区.最后分别选用研究区滑坡险情实例、长年位移监测数据及极端降雨事件对模型预警效果进行检验.结果显示基于专业监测数据的位移比模型的滑坡降雨预警结果与实际情况相符,可为蠕变型滑坡的预警预报提供依据.      

贵州兴义滑坡特征及过程预警研究

由于滑坡岩土体结构的复杂性和破坏机制的多样性,滑坡预警一直以来都是全球性难题,极具挑战性。本文论述了贵州省兴义滑坡特征及其成功预警,并分析了滑坡成功预警的关键因素。在对滑坡现场进行地质调查的基础上,综合应用卫星遥感、无人机航拍、LiDAR、地表位移监测等技术手段,初步分析结果认为,兴义滑坡属于典型的含软弱夹层的顺层岩质滑坡,滑源区坡体为2014年首次滑动后形成的不稳定斜坡,在不利的坡体结构加之与软弱夹层组合的地质条件下,受到长期重力及地下水作用,最终演变成滑坡地质灾害。兴义滑坡至2014年第一次滑动后,后缘山体对前缘公路和居民就产生了威胁,2019年2月17日凌晨5时53分,贵州省兴义市马岭镇龙井村兴-马大道旁约96×104 m3的山体再次发生顺层滑动。在滑坡发生前,研究人员就在滑坡体上安装了全球导航卫星系统(GNSS)和自适应性裂缝计两种位移监测传感器,对滑坡变形进行持续监控。现场监测数据实时传输到研究人员自主研发的“地质灾害监测预警系统”中,系统通过多种阈值综合预警模型自动计算监测数据并发布预警结果,在滑坡进入临滑阶段后,系统提前53 min发出了红色预警,完全避免了人员和经济损失。该滑坡的成功预警体现了自主研发的地质灾害监测预警系统、预警模型、监测仪器三者的适用性,可为今后类似滑坡的监测预警研究及应用提供借鉴。     

基于主控因子分析与GM-IAGA-WNN联合模型的平推式滑坡位移预测研究——以垮梁子滑坡为例

滑坡位移预测模型是滑坡预警系统建立的核心,而模型可靠性与精确性关键在于主控因子的选取与基础理论模型的构建。学者们通过大量滑坡实例研究,已取得了诸多成果,但是由于滑坡位移变化具有强烈的个性特征及趋势发展的不确定性问题,在多因子联合作用下的位移预测模型尚有不足之处。本文以西南地区普遍存在的平推式滑坡——垮梁子滑坡为研究对象,结合前人已有的研究成果,综合考虑坡体内外各项影响因子,利用灰色关联度与相关性分析对坡体变形主控因子进行优化筛选。以此为基础,提出一种基于GM（1,1）灰色模型与改进型自适应遗传算法（IAGA）进行优化的小波神经网络（WNN）联合预测模型构建方案。通过对垮梁子滑坡历时5年的监测数据挖掘分析,得知滑坡变形受累计降雨、渗压、地下水位及土体含水率影响显著,预测结果与实际监测比较吻合。相较于传统BP神经网络模型、小波神经网络模型和未优化遗传算法-小波神经网络联合模型,该联合模型具有更好的稳定性与精度优势,在滑坡预警预报研究中具有良好的应用前景。     

基于光纤监测和PSO-SVM模型的马家沟滑坡深部位移预测研究

滑坡位移预测效果一方面取决于预测模型的优劣,另一方面取决于野外监测数据的质量。针对目前滑坡常规监测技术与评价方法的不足,本文采用光纤监测技术、监测数据与PSO-SVM预测模型相结合的评价方法,对三峡马家沟Ⅰ号滑坡的深部位移进行了预测;通过对320个滑坡深部位移光纤监测数据分析,基于时间序列法,将滑坡位移分为趋势性位移和波动性位移;趋势性位移采用拟合法进行预测,波动性位移采用PSO-SVM模型进行预测;最后将趋势项和波动项位移预测值叠加得到累积位移的预测值。研究结果表明,PSO-SVM模型对波动性位移预测的均方根误差0.51 mm,平均绝对百分误差0.37 mm,能准确预测滑坡波动项位移;累积位移预测值与实测值的相关系数为0.98,均方根误差为0.54 mm,预测效果较好,可以用来对滑坡深部位移进行短期预测。     

降雨诱发滑坡阶跃型变形的预测分析及应用

滑坡进入蠕动变形阶段之后,往往难以及时开展勘察治理工作,合理的临灾预警是有效减少滑坡灾害损失的重要手段。首先确定降雨诱发“阶跃型”滑坡的预警关键判据为前期降雨、当次降雨、位移速率,并引入“一个降雨过程”定义滑坡监测的降雨区间,将预警过程分为当次降雨和前期降雨+当次降雨两种模式。然后运用最小二乘法确定滑坡“阶跃”变形曲线上的“破坏拐点”和“稳定拐点”用以确定变形加速区间,以此求解前期降雨、当次降雨以及移速率阈值。最后以王家坡滑坡为例,设计了两种模式下的5级递进式分级预警模型。研究表明：（1）前期降雨与当次降雨组成“一个降雨过程”的时间间隔为7 d;（2）王家坡滑坡的位移速率阈值为20 mm/d;（3）前期降雨+当次降雨模式下王家坡滑坡的前期降雨、当次降雨阈值分别为10、15 mm,当次降雨模式下王家坡滑坡的降雨阈值为25 mm。     

突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例

灌溉诱发的黄土滑坡大多数具有明显的突发性特征;斜坡破坏过程变形量小,历时短,具有较大的危险性。由于此类黄土滑坡加速变形阶段经历时间较短,GNSS系统和裂缝计等传统监测手段难以获取加速变形阶段系统完整的监测数据,更难以提前预警。针对这一难题,自主研发了自适应智能变频裂缝仪,它能够根据滑坡变形快慢自动调整采样频率。基于获取的黑方台多个突发型黄土滑坡的全过程变形-时间曲线,对这些变形曲线特征和规律进行分析研究,建立了针对性的黄土滑坡综合预警模型。将变形速率阈值和改进切线角作为滑坡预警的重要指标,建立了4级预警判据,通过自主研发的"地质灾害实时监测预警系统"实现滑坡的实时自动预警,并将预警信息与当地的群防群测信息平台对接,为防灾应急避让提供直接依据。2017年以来已先后6次对黑方台黄土滑坡实施成功预警,避免了重大人员伤亡,取得显著的防灾减灾效果。     

Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data structure. Then, these sub-series except the high frequency are forecasted, respectively, by establishing appropriate ELM models. At last, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original landslide displacement series. A case study of Baishuihe landslide in the Three Gorges reservoir area of China is presented to illustrate the capability and merit of our model. Empirical results reveal that the prediction using M–EEMD–ELM model is consistently better than basic artificial neural networks (ANNs) and unmodified EEMD–ELM in terms of the same measurements.     

滑坡对降雨响应的多指标监测及综合预警探析:以赣南罗坳滑坡为例

降雨量和位移是当前降雨型滑坡监测预警最常用的指标。然而,降雨量和位移监测结果只能反映降雨作用下滑坡的变形情况,不能揭示滑坡内在物理力学性状对降雨的响应。因此,除降雨量和位移监测之外,建立包括体积含水率、基质吸力等反映滑坡动态演化过程的关键指标监测体系必将成为今后更真实地把握滑坡内在演化趋势、更准确地建立滑坡综合预警判据的最有效手段。笔者对赣南地区典型降雨型滑坡进行了多指标监测及综合预警示范研究。结果表明:(1)在降雨条件下滑坡土体内部体积含水率、基质吸力和温度等多指标均产生有规律的动态响应;(2)随着降雨的持续,滑体体积含水率与基质吸力的变化均具有显著的滞后现象;(3)体积含水率和基质吸力变化速率与滑体位移具有显著的正相关性;(4)滑体温度分布变化规律受大气温度和体积含水率的共同影响。以实测数据的滑坡稳定性分析为基准,在考虑实际降雨入渗深度与滑坡稳定性的关联度上,建立了包括日降雨量、体积含水率增加速率、基质吸力减小速率以及位移速度多元指标预警方法体系,提出了基于关键指标综合预警体系及确定方法,旨在为降雨滑坡准确预警提供新模式。     

Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.     

基于表面位移的公路滑坡监测预警研究

公路滑坡是常见的地质灾害,但对运营期公路边坡进行长期监测并成功预警的实例却很少。主要原因是公路边坡点多、线长、规模小、缺乏详细的地质勘探资料、监测费用高、预警难度大。利用拉索触发式位移计对滑坡表面位移进行监测,精度可达1 mm,通过电信的GPRS公网实时传送到远程监控中心,全程自动化,且费用低。同时,利用有限元商用软件 PLAXIS的强度折减模块和塑性分析模块对不同参数组合进行计算,在缺乏滑坡岩土体强度参数、渗透系数、土-水特征曲线等资料的情况下,建立滑坡安全系数与表面监测位移的关系, 从而通过表面位移量的变化进行阶段式预警,并制定相应的预防措施。提出的方法已在湘西某高速公路滑坡获得应用。     

Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1 month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1 year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir.     

基于一元线性回归模型八字门滑坡累积位移预测

我国滑坡灾害发生频繁,但滑坡的变形预测预报一直是难题,因此每年都因滑坡的变形破坏导致重大的人员伤亡和财产损失。以三峡库区八字门滑坡为研究对象,基于十多年的监测数据分析,研究分析了该滑坡的变形特征:八字门滑坡变形的主要影响因素为降雨和库水位下降,并且累积位移曲线具有"阶跃型"的变形特征。当外界因素去除或者减小的情况下,累积位移-时间曲线将变得平稳。根据此特性,选取每年变形曲线"阶跃段"（6~8月份）的监测数据,以累积位移为目标函数,基于一元线性回归模型,对八字门滑坡2004年到2017年同期的滑坡监测数据进行分析。结果表明:一元线性回归模型能够很好地模拟八字门滑坡"阶跃段"的变形过程,此变形阶段累积位移与时间呈线性关系,直线斜率基本相同。根据此线性关系,对滑坡的累积位移进行了预测,结果表明与实际监测数据相比较,预测误差在±5 mm以内,相对误差在1%以下,精度可以满足滑坡监测预警要求,可以为八字门滑坡的防治工作提供参考。     

国产GB-InSAR在特大型水库滑坡变形监测中的应用

水电站库区特大型滑坡的稳定性对于水电站坝工结构及周边人民生命财产安全具有重要影响,对该类滑坡稳定性及变形趋势进行大范围实时精确观测可为滑坡提供可靠的预警和治理信息,具有十分重要的意义。水电站库区滑坡传统监测方法主要以GNSS监测,全站仪监测等为主,本研究将国产先进的地基干涉合成孔径雷达系统LKR-05-KU-S100,应用于澜沧江大华桥水电站沧江桥—营盘滑坡和大华滑坡的监测。现场监测试验表明,该系统精度较高,可进行远距离、全天时、全天候、大范围监测,对于大型及特大型滑坡的监测具有独特的优势和广阔的应用前景。     

Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China

Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models.

     

基于长短期记忆网络的甘肃舟曲立节北山滑坡变形预测

立节镇北山滑坡长期处于蠕动变形状态,已多次发生滑坡、泥石流灾害。监测地表形变,以掌握灾害体地表形变规律,是实现地质灾害预警预报的可靠依据。文章引入一种机器学习模型——长短期记忆网络,通过立节北山监测点位移数据,运用该方法对立节北山滑坡变形进行预测,并且将预测结果与实际数据进行比对和分析。文章预测结果评价指标选用均方根误差、平均绝对误差、决定系数以及可解释方差,其中决定系数和可解释方差均达到0.99,预测值和真实值的拟合均方根误差和平均绝对误差也表现较低,说明长短期记忆网络在立节北山滑坡变形的预测中达到了良好的预测性能。     

基于普适型仪器的滑坡监测预警初探——以甘肃兰州岷县三处滑坡为例

在甘肃省滑坡灾害较为严重的兰州市和岷县,选择了三处滑坡,建设了基于普适型仪器的专群结合监测预警系统。按照《地质灾害专群结合监测预警技术指南(试行)》的要求,选用裂缝计、GNSS、土体含水量仪、雨量计、声光报警器等,对地质灾害体变形破坏、相关因素、宏观前兆等指标开展专业化立体综合监测。监测设备采用蓄电池加太阳能的方案来供电,保证24小时不间断工作;通讯系统采用现场LoRa组网配合2/3/4 G移动通信的方案,能够保证数据传输的效率,同时降低通信成本;监测数据同时发送到国家和省级地质灾害监测数据平台,能够高效支撑地灾预警工作;监测平台能够对实时采集的监测数据自动进行分析,支持用多种预警模型进行判别;监测数据发生变化触发预设条件时,能够自动发送预警信息。通过近四个月的系统试运行,捕捉到了滑坡mm级的蠕变变形,数据可靠,能够满足监测预警的需求。通过三处滑坡监测预警工程的实施,一方面对滑坡变形和环境因素实现了实时监控,同时也为类似问题提供了一个可供参考的解决方案。     

Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area,China

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area.     

A simple definition of a landslide

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program, an unprecedented disaster mitigation program in China, where lots of newly established monitoring slopes lack sufficient historical deformation data, making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards. A slope displacement prediction method based on transfer learning is therefore proposed. Initially, the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data, thus enabling rapid and efficient predictions for these slopes. Subsequently, as time goes on and monitoring data accumulates, fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy, enabling continuous optimization of prediction results. A case study indicates that, after being trained on a multi-slope integrated dataset, the TCN-Transformer model can efficiently serve as a pre-trained model for displacement prediction at newly established monitoring slopes. The three-day average RMSE is significantly reduced by 34.6% compared to models trained only on individual slope data, and it also successfully predicts the majority of deformation peaks. The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%, demonstrating a considerable predictive accuracy. In conclusion, taking advantage of transfer learning, the proposed slope displacement prediction method effectively utilizes the available data, which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.     

考虑监测离群值朱家店滑坡位移预测研究

滑坡位移预测预报是滑坡防灾减灾的重要组成部分,提高滑坡位移预测的准确性与精确度是该项研究的重点与难点。本文在滑坡位移预测中考虑了监测样本的离群值,通过忽略、指定与修正离群值3种方式,研究滑坡位移预测样本离群值的最优处理方式。以三峡库区朱家店滑坡为例,基于ARIMA(p,d,q)模型,分别对累积位移与位移速率时间序列开展了预测研究。研究结果表明:修正离群值的预测结果介于忽略和指定离群值两者之间,更适用于存在监测离群值的滑坡位移预测;对于ARIMA模型,更适合采用位移速率进行预测预报;使用位移速率时间序列ARIMA(1,0,1)并修正离群值的预测结果为:2016年和2017年6月份滑坡前缘GP3"阶跃"位移分别为79. 0 mm和70. 2 mm,截止2017年8月,GP3累积位移将达1647. 7 mm。