三峡水库区万州独生基滑坡稳定性分析

独生基滑坡位于重庆市万州区长江右岸。为百安坝斜坡松散堆积层沿下伏软弱层面滑动的滑坡体。属于降雨及地表生活排水诱发、人类工程活动加剧变形而形成的新滑坡。论文在分析和研究滑坡区地质环境背景、平剖面形态、特征及诱发因素的基础上,采用传递系数法计算了滑坡体各工况及荷载组合条件下的稳定系数。通过计算,滑坡在天然状态、加载及暴雨条件下均处于不稳定状态。最后根据滑坡特征、主要诱发因素及其稳定性,针对滑坡体失稳特征,通过防治方案对比,初步拟定以抗滑桩工程进行治理,以保证滑坡体的稳定。     

基于机器学习的滑坡灾害易发性评价——以三峡库区为例

三峡库区滑坡灾害分布广、数量多、规模大、危害严重,因此开展滑坡灾害易发性评价对该地的地灾防治与处理具有重要参考意义。本文提取了地层岩性、地质构造、坡度、坡向、曲率、斜坡形态、植被指数、水系等17 个因子,选用逻辑回归模型、支持向量机模型、集成学习的梯度提升迭代决策树模型和深度学习中的长短期记忆神经网络与卷积神经网络耦合模型四个机器学习模型进行滑坡灾害易发性评价,选取最优评价模型,完成三峡库区的易发性分区评价,总结研究区易发性空间区划特性。对比四种模型的AUC（Area Under Curve）精度可以得出结论：GBDT模型（Gradient Boosting Decision Tree Model）的AUC精度相对较高,优于其他三个模型,更适合三峡库区的滑坡易发性研究。GBDT的易发性评价结果显示：研究区内极高易发性区域和高易发性区域主要集中于渝东、鄂西一带以及长江沿岸和支流沿岸。研究结果是对整个库区的易发性进行评价,可为后续库区的防灾减灾提供参考。     

A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.     

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

Mechanism of the Anlesi landslide in the Three Gorges Reservoir, China

Many gentle dip translational rock slides have taken place in the Three Gorges Reservoir, China. In order to study the mechanism of these translational rock slides, the authors use the Anlesi landslide as a typical case study to investigate in detail. Field investigations show that the slip zones of the Anlesi landslide were formed from a white mudstone in Jurassic red strata. X-ray diffraction and infrared ray analysis showed that the main mineral components of the slip zones are montmorillonite, illite, feldspar and quartz. Laboratory tests indicate that the slip zone soils are silty clay, of medium-swelling potential, the shear strength decreasing significantly as the slip zone attracts water and saturates.The main factors contributing to the Anlesi landslide are recent tectonic activity, incompetent beds, and intensive rainfall. Recent tectonic activity had caused shear failure along the incompetent beds, and joints within the sandstone. With the effect of intensive rainfall, water percolates to the incompetent beds along tectonic fissures, resulting in swelling of the soil material and high groundwater pressures within fissures in the strata. As a consequence, the Anlesi slope is prone to slide along these incompetent beds.Flac3D software was used to simulate the mechanism of the Anlesi landslide considering the rheological properties of soil and rock. The simulation results demonstrate that the stress, displacement and failure area changes with simulated creep time. The maximum displacement in the X direction reaches 7.59 m after 200-year simulated creep. Therefore, the mechanism of the Anlesi landslide can be illustrated considering the rheological properties of Jurassic red strata.     

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.     

GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)

Devrek town with increasing population is located in a hillslope area where some landslides exist. Therefore, landslide susceptibility map of the area is required. The purpose of this study was to generate a landslide susceptibility map using a bivariate statistical index and evaluate and compare the results of the statistical analysis conducted with three different approaches in seed cell concept resulting in different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of (a) crowns and flanks, (b) only crowns, and (c) only flanks of the landslides by using ten different causative parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each corresponding seed cell data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce fairly acceptable results. In each data set analysis, elevation, lithology, slope, aspect, and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared using Seed Cell Area Indexes (SCAI). This comparison shows that the crown data set produces the most accurate and successful landslide susceptibility map of the study area.     

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).     

The July 14, 2003 Qianjiangping landslide,Three Gorges Reservoir,China

The Qianjiangping landslide occurred after the first impoundment of the Three Gorges Reservoir in July 2003. Field investigation revealed that failure occurred when the reservoir reached 135 m, but the stability of the affected slope was already reduced by pre-existing bedding-plane shears, quarrying of mudstone from the landslide toe, and previous heavy rain. A possible explanation of the rapid and long runout mechanism of the landslide is that movement on a bedding-plane shear ruptured the calcite cement and rapidly reduced the sandstone strength to residual shear strength.     

Mechanism and failure process of Qianjiangping landslide in the Three Gorges Reservoir,China

The Qianjiangping landslide is a large planar rock slide which occurred in July 14, 2003 shortly after the water level reached 135 m in the Three Gorges Reservoir, China. The landslide destroyed 4 factories and 129 houses, took 24 lives, and made 1,200 people homeless. Field investigation shows that the contributing factors for the landslide are the geological structure of the slope, the previous surface of rupture, the water level rise, and continuous rainfall. In order to reveal the mechanism and failure process of the landslide, numerical simulation was conducted on Qianjiangping slope before sliding. Based on the characteristics and the engineering conditions of the landslide, the topography and the geological profiles of Qianjiangping slope before sliding is reconstructed. The seepage field of Qianjiangping slope before sliding was simulated with the Geostudio software. The results show that ground water table rises and bends to the slope during the rise of water level, and the slope surface becomes partially saturated within the period of continuous rainfall. Using the ground water table obtained above, the failure process of Qianjiangping slope is simulated with the Flac3D software. The results demonstrate that the shear strain increment, displacement, and shear failure area of the slope increased greatly after the water level rose and continuous rained, and the landslide was triggered by the combined effect both of water level rise and continuous rainfall. The development of shear strain increment, displacement, and shear failure area of the slope shows that the landslide was retrogressive in the lower part of the slope and progressive in the upper part of the slope.     

三峡库区云阳五峰山滑坡防治工程方案研究

本文将三峡工程重庆库区云阳老县城五峰山滑坡划分为顺层滑坡、堆积层滑坡以及2001年1月17日发生的新滑坡("1·17"滑坡)等3部分,结合老县城的安全提出了综合治理方案,即:采用两排预应力锚索进行顺层滑坡的加固,采用钻孔灌注桩进行堆积层滑坡阻滑,在五峰山滑坡下部与西城老滑坡过渡地带植树造林,防止上部崩塌体直接危害县城,并完善后部拦山堰等地面排水系统。本文还介绍了五峰山"1·17"新滑坡应急防护工程设计方案和工程实施情况。     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

三峡库区四方碑滑坡稳定性与变形趋势预测

三峡水库建成后,库水位周期性涨落和暴雨产生的渗流作用导致大量古滑坡的复活或新滑坡的发生。以库区近水平层状结构的四方碑滑坡为例,依据库水位实际调动,将水位从175 m至145 m不同降速与50年一遇暴雨进行工况组合,计算4种工况下滑坡的稳定性及破坏概率。然后采用Geo-studio软件的Sigma模块对滑坡进行变形模拟,运用R/S分析方法判断滑坡的变形持续性,并结合野外调查情况,综合评价分析四方碑滑坡的稳定性。结果表明:滑坡在各工况下整体均处于基本稳定状态,具有低危险性;变形模拟结果显示滑坡前缘位移最大,与野外调查情况一致;各监测点Hurst指数均介于0.5~1,表明时间序列具有正持续性,在研究的时间限度内滑坡的局部破坏增强,应在汛期加强对滑坡前缘的巡查和预警。     

三峡库区安渡滑坡成因机制分析与稳定性预测

奉节县安渡滑坡正处于蠕滑变形-加速变形阶段,其破坏力极大、影响甚广.本文在深入分析滑坡地质特征、结构特征和变形特征的基础上,对滑坡的成因机制和影响因素进行了深入研究.采用传递系数法计算并评价其稳定性.研究表明,安渡滑坡体目前处于不稳定状态,三峡水库蓄水到175m或遭遇长时间高强度的暴雨时,可能整体失稳.     

考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律

对于滑坡易发性预测中的水系、公路和断层等线状环境因子,现有研究大多采用缓冲分析提取距离线状因子的距离.但缓冲分析得到的线距离属于离散型变量,带有大小不等的随机波动性且对点或线要素的误差较为敏感,导致滑坡易发性建模精度下降.提出了使用水系和公路的空间密度等连续型变量改进线状环境因子的适宜性.以江西省安远县为例,选取高程、...     

Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County,Three Gorges Reservoir

Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ～40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.