区域滑坡空间预测方法研究及结果分析

区域滑坡空间预测是通过分析滑坡在区域空间分布的丛集性及规律性,圈定出滑坡相对危险性区域。通过MAPGIS软件平台及其二次开发的滑坡灾害分析系统,采用半定量和定量两种方法对浙江省永嘉县区域滑坡进行了预测。半定量方法采用反映历史滑坡强度的袭扰系数和滑坡易发程度指数来评价,编制了危险性预测分区图;定量化方法采用信息量模型来评价,采用规则网格作为预测单元,运用该模型对永嘉县区域滑坡进行了空间定量预测,并依信息量法的结果编制了该区的危险性划决预测分区图,为政府部门进行土地规策、避免在滑坡易发区进行大规模土地开发和工程建设提供了科学依据。同时通过两种方法的预测结果,对比分析了滑坡的形成和各影响因素的关系,为滑坡的有效防治提供了参考。     

证据权法在滑坡危险度区划研究中的应用

采用证据权法对长江三峡库区秭归--巴东段进行了滑坡危险度区划研究。数据源主要包括地质图、SPOT5多光谱卫星影像数据及数字高程模型(DEM)等。利用RS和GIS的数据提取和分析功能,分别提取了地质(岩性和构造)、地形(坡度、坡向、水系、高程、沟壑缓冲区、沟壑密度)、水文地质(植被指数)和破坏动力(河流缓冲区)等对研究区滑坡发育影响较大的影响因子的信息。将上述各种影响因子进行分级,建立若干证据层;然后根据已知滑坡在不同证据层中的分布,确定相应证据层的权重值;最后根据不同影响因子权重值的叠加来确定具体某一个单元滑坡发育的概率。分析结果与现有滑坡的分布情况比较吻合。采用证据权法可以客观定量地评价各种影响因子对滑坡发育的影响程度,并据此进行滑坡危险度评价因子(证据因子)的选择及危险度定量评价。     

基于滑坡分类和加权频率比模型的滑坡易发性评价

根据区域滑坡特点,针对不同类型滑坡的自身特征分别建立指标评价体系,能够使滑坡易发性评价的过程更加科学准确。以三峡库区万州区内滑坡为例,首先,基于对地质环境、滑坡空间分布及自身特征的分析,将全区滑坡分为陡倾角地层滑坡和缓倾角地层滑坡。其次,获取12种指标因子(高差、坡度、坡向、平面曲率、剖面曲率、地层岩性、水系、地质构造、公路、地层倾角、降雨、含蒙脱石软弱夹层厚度)构成基本评价体系。然后提出基于逻辑回归(logistic regression,LR)–模糊层次分析(fuzzy analytical hierarchy process,FAHP)方法(LR-FAHP)的加权频率比模型(weighted frequency ratio model,WFR),通过对指标因子的重要性进行排序,实现各指标因子权重的定量计算,从而建立不同类型滑坡的评价指标体系,再基于GIS平台实现全区滑坡灾害的易发性等级预测。结果表明:与单一的LR,FAHP和FR三种模型相比,WFR模型能将滑坡易发性评价精度提升4%～9%,表明LR-FAHP是一种定量计算指标因子权重的有效方法;同时,基于滑坡分类的WFR模型的易发性评价成功率为79.2%,预测率为79.6%,均优于未进行滑坡分类的WFR模型,为建立评价指标体系和区域滑坡易发性评价提供了可靠途径。     

基于GIS和数量化理论Ⅱ的滑坡危险性预测

使用传统的数量化理论可以把定量和定性的指标结合起来综合判断滑坡发生的危险性,而这些方法在实际应用时面临的问题是如何高效率和高精度地定量把握各种滑坡影响因素的空间分布（如地质、倾角、土地利用、地形起伏、汇水面积等）。提出基于GIS的空间数据输入方法,通过矢量数据和栅格数据变换来快速高效地解决数据的准备问题,同时制作接口将这些数据输出,提供给数量化理论进行计算,并把结果反馈给GIS进行区域滑坡灾害预测图的制作。通过上述方法可以大大提高效率以及灾害预测图的准确度。该方法实际应用于日本熊本县水俣市地区得出的结果,证实提出的方法只需要传统方法所需时间的1／10就可以高精度地完成区域滑坡灾害预测图的分析计算和制作工作。     

基于GIS和数量化理论II的滑坡危险性预测

 使用传统的数量化理论可以把定量和定性的指标结合起来综合判断滑坡发生的危险性,而这些方法在实际应用时面临的问题是如何高效率和高精度地定量把握各种滑坡影响因素的空间分布(如地质、倾角、土地利用、地形起伏、汇水面积等)。提出基于GIS的空间数据输入方法,通过矢量数据和栅格数据变换来快速高效地解决数据的准备问题,同时制作接口将这些数据输出,提供给数量化理论进行计算,并把结果反馈给GIS进行区域滑坡灾害预测图的制作。通过上述方法可以大大提高效率以及灾害预测图的准确度。该方法实际应用于日本熊本县水俣市地区得出的结果,证实提出的方法只需要传统方法所需时间的1/10就可以高精度地完成区域滑坡灾害预测图的分析计算和制作工作。     

基于模糊推理的公共交通分担率预测研究

研究基于模糊推理建立公共交通分担率预测模型的方法。通过分析公共交通出行的影响因素,选择线网密度、国内生产总值、平均车速等十项指标作为影响因素,建立模糊层次结构模型。确定各因素的模糊规则,采用模糊推理预测公交分担率。并以黑龙江省某市交通数据进行预测,最后分析了影响因素与分担率水平的影响关系,应用得到了预期的效果。     

基于不同Bootstrap方法和KELM-BPNN模型的滑坡位移区间预测

为解决传统滑坡位移点预测模型无法对自身预测结果的可靠程度进行有效描述这一问题,引入区间预测方法,提出一种基于不同Bootstrap方法和KELM-BPNN模型的滑坡位移区间预测模型。该模型以4种常用的Bootstrap方法为基础,首先对由各种外界触发因素与滑坡地表位移的监测信息组成的原始数据集,分别进行B次有放回的等概率随机抽样;然后基于不同Bootstrap方法得到的B个伪数据集,分别训练B个KELM模型对系统误差的方差进行估计,并根据估计结果,训练一个BPNN模型对随机误差的方差进行回归逼近;最终将采用相同Bootstrap方法得到系统误差方差和随机误差方差相结合,构造出在不同置信水平下的滑坡位移预测区间,并通过综合对比分析,提出与实际滑坡变形特征相适宜的位移区间预测模型。以三峡库区内具有阶跃式变形特征的典型堆积层滑坡——白水河滑坡为例,选取ZG93和ZG118两个监测点在2004年7月～2013年12月期间的数据进行研究。结果表明,与传统点预测模型相比,该模型不仅能够提供具有一定精度的点预测结果,还能构造出较为清晰、可靠的位移预测区间将真实的位移曲线完全包裹在内。此外,通过预测区间宽度的实时变化,该模型能够较好地量化与解释滑坡演化过程中外界触发因素的动态变化对滑坡变形造成的不确定性影响,为滑坡灾害的预报预警提供了一种新的思路和方法。     

基于ANN和LR的远安县滑坡易发性评价

在人工智能算法快速发展的背景下,选取人工神经网络模型(ANN)和逻辑回归模型(LR)对湖北省远安县进行滑坡易发性评价,得到滑坡易发性区划图,并对结果进行对比分析。该区共发育滑坡177处,提取出了与滑坡发生相关的9类指标因子。利用相关性分析,剔除高程因子,选择其余8类因子用于滑坡易发性评价,利用Arc GIS和SPSS Modeler软件得到研究区滑坡易发性区划图。最后,利用ROC曲线图对两个模型的成功率进行分析,得到人工神经网络模型和逻辑回归模型的AUC值分别为0.864和0.809,说明人工神经网络模型在该研究区的预测能力较好。     

基于权重反分析和标准化模糊综合评价的岩爆预测模型

针对岩爆预测问题,选取应力系数σθ/σc脆性系数σc/σt、弹性能量指数Wet作为评价指标,选择2种分级标准对岩爆指标进行标准化处理,选用正态分布函数构建隶属度函数,以Matlab编程反分析指标权重,建立基于权重反分析的标准化模糊综合评价模型.该模型解决了传统模型存在的权重主观性过大,隶属度函数不符合实际的问题,理论上更完备.选择国内外46组岩爆数据作为样本反分析权重,以3个工程实例对比不同分级标准和权重的应用效果,结果显示基于反分析权重的预测模型比基于主观权重的预测模型效果更优,不同分级标准所得结果并不一致,还需进一步对比分析.     

基于集对分析的巴东新城区滑坡灾害空间预测

集对分析方法的研究和应用对区域性滑坡预测具有较强的理论和实用意义。滑坡空间预测系统是一个受不确定因素影响较多的系统,而集对分析法能够很好地解决确定–不确定问题。将集对分析法引用到滑坡空间预测领域,以MAPGIS为操作平台,在对巴东县新城区滑坡灾害发育的地质背景、分布规律和发育特征进行统计分析的基础上,根据统计结果确定预测危险性等级评价标准,然后根据各预测单元的属性信息判别各预测单元指标等级,最后利用集对分析理论对各预测单元进行危险性等级预测。将所得空间预测图与已知滑坡分布图进行比较,认为结果可靠、方法可行,并得出联系度和集对势理论结合能够提高预测精度的结论。     

Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different probabilistic and bivariate statistical approaches; namely, frequency ratio (FR), weights-of-evidence (WofE), index-of-entropy (IofE), and Dempster–Shafer (DS) models, at Wadi Itwad, Asir region, in the southwestern part of Saudi Arabia. Landslide locations were identified and mapped from interpretation of high-resolution satellite images, historical records, and extensive field surveys. In total, 326 landslide locations were mapped using ArcGIS and divided into two groups; 75 % and 25 % of landslide locations were used for training and validation of models, respectively. Twelve layers of landslide-related factors were prepared, including altitude, slope degree, slope length, topography wetness index, curvature, slope aspect, distance from lineaments, distance from roads, distance from streams, lithology, rainfall, and normalized difference vegetation index. The relationships between the landslide-related factors and the landslide inventory map were calculated using different statistical models (FR, WofE, IofE, and DS). The model results were verified with landslide locations, which were not used during the model training. In addition, receiver operating characteristic curves were applied, and area under the curve (AUC) was calculated for the different susceptibility maps using the success (training data) and prediction (validation data) rate curves. The results showed that the AUC for success rates are 0.813, 0.815, 0.800, and 0.777, while the prediction rates are 0.95, 0.952, 0.946, and 0.934 for FR, WofE, IofE, and DS models, respectively. Subsequently, landslide susceptibility maps were divided into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the percentage of training and validating landslides locations in high and very high landslide susceptibility classes in each map were calculated. The results revealed that the FR, WofE, IofE, and DS models produced reasonable accuracy. The outcomes will be useful for future general planned development activities and environmental protection.     

Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga,Northern Algeria

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

     

Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy)

Landslide susceptibility studies focus on producing susceptibility maps starting from landslide inventories and considering the main conditioning factors. The validity of susceptibility maps must be verified in terms of model accuracy and prediction skills. This paper deals with a GIS-based landslide susceptibility analysis and relative validation in a hilly-coastal test-area in Adriatic Central Italy. The susceptibility analysis was performed via bivariate statistics using the Landslide-Index method and a detailed (field-based) landslide inventory. Selection and mapping of conditioning factors and landslide inventories was derived from detail geomorphological analyses of the study area. The susceptibility map was validated using recent (shallow) landslides in terms of both model accuracy and prediction skills, via Success rate and Prediction rate curves, respectively. In addition, a pre-existing official landslide inventory was applied to the model to test whether it can be used when a detailed (field-based) inventory is not available, thereby extending its usability in similar physiographic regions. The outcome of this study reveals that slope and lithology are the main conditioning factor of landslides, but also highlights the key role of surficial deposits in susceptibility assessment, for both their type and thickness. The validation results show the effectiveness of the susceptibility model in both model accuracy and prediction skills given the good percentage of correctly classified landslides. Moreover, comparison of the susceptibility map with the official Regional landslides inventory proves the possibility of using the developed susceptibility model also in the absence of detailed landslide mapping, by considering inventories that are already available.     

降雨型滑坡预报新方法

详细研究了三峡地区部分县市的滑坡和降雨历史资料，从滑坡与降雨量、暴雨以及降雨时间3个不同角度的关系分析了降雨与降雨型滑坡的关系。在此基础上，提出了降雨因子的概念。同时，还提出了一种预报降雨型滑坡的新方法，定量化地描述了降雨型滑坡的易发程度。按照一定的标准，对每种降雨分因子进行分级，通过多因子叠合分析来研究降雨因子与降雨型滑坡之间的关系，并据此准确地预报滑坡的易发程度。通过将这种滑坡预报新方法应用于三峡的万县地区，证明了它可以比较准确地确定滑坡发生的时间。这种滑坡预报方法将为根据历史降雨和滑坡资料来预测降雨型滑坡奠定良好基础。     

基于GIS的区域滑坡危险性预测方法与初步应用

在GIS平台上，将多元空间信息统计分析方法与非线性统计预测方法相结合，在充分考虑滑坡与各环境因子之间的统计相关性和位置相关性的基础上，研究了滑坡与环境因子之间定量关系的表示方法，建立了单因子分析，多因子分析、整组性分析和多元空间信息的非线性预测模型，在理论基础上，选取香港大屿山岛中部作为试验研究区，利用环境因子进行区域滑坡危险性预测，经实际资料检验表明，该模型可获取较高的预测精度，具有极大的应用潜力。     

Rainfall-induced unstable slope monitoring and early warning through tilt sensors

Real-time unstable slope monitoring is essential for recognition of landslide occurrence, as well as for early warning to reduce landslide-induced damages. This study investigated an unstable slope monitoring system that consists of tilt sensors aiming to establish an advanced time prediction model (TPM) for landslide early warning. The monitoring process utilized additional support devices (e.g. pipe strain gauges, water level gauges and a rain gauge) installed on a natural cut slope site. The tilt sensors could detect movements (in tilt angle) within the slope generated by heavy rains. Analysis of the recorded data revealed that the rate of movement, or tilt, was influenced by groundwater table fluctuations and antecedent rainfalls. A relationship between the tilt rate and the horizontal displacement calculated from pipe strain value has been established. Subsequently, a new classification of slope movement was proposed according to the tilt rates obtained from the deformation process of the slope. Considering the movement characteristics, two warning levels were identified such as warning and evacuation. Further, an advanced TPM was proposed in relation to realtime slope surface tilt rates. The TPM could provide efficient results at the continuous acceleration phase of the landslide occurrence. Therefore, we suggest this technique can be applied for monitoring and early warning of rainfall-induced shallow landslides.     

Landslide susceptibility of the Xiangjiaba Reservoir area associated with the Yaziba Fault

Landslides along active fault zones are important hazards during and after earthquakes. They can also cause secondary disasters such as surges, creation of landslide dams, and flooding, especially in reservoir areas. This study analyzed landslide susceptibility of the Xiangjiaba Reservoir area associated with the Yaziba Fault. Analysis of permanent displacement and failure probabilities were used for the regional assessment. Ground motion attenuation relations were selected and compared to produce a peak acceleration map using the Yaziba Fault as the seismic source. Geotechnical parameters were determined by classification of rock groups and geomorphic data were calculated using GIS tools. A distribution of the permanent displacements and a failure probability map was generated. According to the peak ground acceleration (PGA) map, the C-B model was adopted to present the actual conditions of PGA in greater detail while local specific models are more appropriate if there is little measured data. Results indicated larger displacement values and failures are distributed on both sides of the fault, especially in the hanging wall. The feasibility of the research approach was verified using historic earthquake-induced landslides.     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

斜坡失稳过程的非线性演化机制与物理预报

对应变软化介质材料组成的平面滑动型斜坡,采用Weibull分布描述它的剪应力与应变关系。建立了斜坡系统的尖点突变模型,给出了失稳的充要力学判据,发现斜坡失稳与刚度比和材料的均匀性指标有极大关联性,水的一种重要新作用是增加材料的均匀性(脆性)和降低刚度比。考虑应变软化介质的粘性或蠕变性,建立了斜坡演化的非线性动力学模型--物理预报模型,并给出了根据滑坡位移观测数据反演非线性动力学模型的方法和稳定性判别准则。对鸡鸣寺滑坡根据观测序列进行了动力学模型的反演分析,一个重要发现是:|D|值在加速蠕变阶段陡增出现峰值,而后在接近失稳点时趋于零。根据斜坡在等速蠕变阶段的特点,提出了一个简化的非线性动力学模型和物理预报模型,研究了其分叉和混沌特征,给出了判别混沌的力学准则,发现混沌能否产生取决于滑面介质的力学参数。