共查询到18条相似文献，搜索用时 156 毫秒
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滑坡易发性定量评估是预测滑坡发生空间概率的重要手段,基于统计分析原理的评估方法目前在国内外应用最为广泛,且不同评估方法的对比研究逐渐成为热点。以青海沙塘川流域黄土梁峁区为例,剖析了信息量模型和逻辑回归模型在滑坡易发性评估中的优越性和局限性,并探索提出基于二者的耦合模型。考虑坡度、坡向、起伏度、岩性、与干流距离、与支流距离和植被指数等7个影响因素,对比分析了基于信息量、逻辑回归及二者耦合模型的滑坡易发性评估的技术流程及结果。3种模型的成功率分别为:耦合模型成功率(78. 9%)>信息量模型成功率(71. 8%)>逻辑回归模型成功率(70. 8%)。在沙塘川流域黄土滑坡的易发性评估中,信息量和逻辑回归模型的表现基本相当,但信息量-逻辑回归耦合模型的成功率明显提升。该研究结果可为黄土高原区滑坡易发性定量评估提供借鉴。 相似文献
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空间三维滑坡敏感性分区工具及其应用 总被引：1，自引：0，他引：1
对于滑坡敏感性分区目前有三种方法:定性法、统计法和基于岩土定量模型的确定性方法。定性法基于对滑坡敏感性或灾害评估的人为判断;统计法用一个来源于结合了权重因子的预测函数或指标;而确定性法,或者说是物理定量模型法以质量、能量和动量守恒定律为基础。二维确定性模型广泛用于土木工程设计,而无限边坡模型(一维)也用于滑坡灾害分区的确定性模型。文中提出了一个新的基于GIS(地理信息系统)的滑坡敏感性分区系统,这个系统可用于从复杂地形中确认可能的危险三维(3-D)滑坡体。所有与滑坡相关的空间数据(矢量或栅格数据)都被集成到这个系统中。通过把研究区域划分为边坡单元并假定初始滑动面是椭球的下半部分,并使用Monte Carlo随机搜索法,三维滑坡稳定性分析中的三维最危险滑面是三维安全系数最小的地方。使用近似方法假定有效凝聚力、有效摩擦角和三维安全系数服从正态分布,可以计算出滑坡失稳概率。3DSlopeGIS是一个计算机程序,它内嵌了GIS Developer kit(ArcObjects of ESRI)来实现GIS空间分析功能和有效的数据管理。应用此工具可以解决所有的三维边坡空间数据解问题。通过使用空间分析、数据管理和GIS的可视化功能来处理复杂的边坡数据,三维边坡稳定性问题很容易用一个友好的可视化图形界面来解决。将3DSlopeGIS系统应用到3个滑坡敏感性分区的实例中:第一个是一个城市规划项目,第二个是预测以往滑坡灾害对临近区域可能的影响,第三个则是沿着国家主干道的滑坡分区。基于足够次数的Monte Carlo模拟法,可以确认可能的最危险滑坡体。这在以往的传统边坡稳定性分析中是不可能的。 相似文献
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滑坡系统静动态规律及斜坡不稳定性空时定量预测 总被引：21，自引：0，他引：21
文章基于近数年来室内外测绘试验分析研究工作,论述了滑坡静、动态规律。针对其群体“静态” 规律,论证了斜坡不稳定性空间定量预测的某些基本原理方法;针对其群、单体动态规律讨论了滑坡灾害暴发时间的预测预报基础技术。从系统论观点出发,概括了滑坡系统空时定量预测的三类数学模型——确定性、随机性及灰色系统模型的基本分工和联合模式的勾通特点与功能。文章以陕西东南部变质岩滑坡和长江中上游中生界砂泥岩地层滑坡为实例,讨论了斜坡不稳定性空、时定量预测的重点研究成果。文末概括了几点重要结论。 相似文献
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基于Verhulst模型的滑坡位移预测研究及其程序化实现 总被引：1，自引：1，他引：0
以甘肃省黄茨滑坡位移时间预测为例,在滑坡工程地质条件、成因、发生与发展过程分析的基础上,结合地面监测桩以及位移计监测的位移时间数据,运用Verhulst预测模型建立了该滑坡位移预测研究的思路.在此基础上,运用Ex-cel内嵌的VBA语言编写了相应的位移时间预测预报程序,解决了笔算困难问题.通过具体实例分析,将Verhulst模型、灰色GM(1,1)模型预测结果与实际监测结果进行对比分析,验证了该模型在滑坡位移时间预测中的适用性以及程序的可靠性.研究结果表明,Verhulst预测模型适宜于滑坡临滑预报,而灰色GM(1,1)预测模型适宜于滑坡中短期预测预报,通过Ver-hulst模型预测黄茨滑坡的临滑时间在1995-01-26至1995-01-27之间,预测结果与滑坡实际滑动时间较为一致,由此说明运用Verhulst预测模型对滑坡进行临滑预报是可行的. 相似文献
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基于场模型的成矿信息提取方法研究
———以桂西-滇东南锰矿为例 总被引：2，自引：0，他引：2
文章针对矿产资源GIS评价中成矿信息尤其是地质信息提取不充分的问题,提出了一种基于场模型的成矿信息提取方法即成矿信息场分析方法,并以桂西-滇东南地区锰矿为例进行了成矿信息的场分析提取实例研究.通过成矿信息的距离渐变性和影响叠加性的特征分析,引入场论概念,讨论了成矿信息的场模型表达方法,包括成矿影响场模型和成矿距离场模型.利用场论叠加原理和空间距离分析方法,借助参数方程和积分方法,推导出了地质体(点状、线状和面状)的成矿影响场数学模型和成矿距离场数学模型.在成矿背景分析的基础上,利用成矿信息场分析方法,建立了同生沉积断裂和成锰沉积盆地的成矿信息场模型,分析了同生沉积断裂和成锰沉积盆地的成矿信息场与锰矿化分布的空间关联关系.实例分析表明,成矿信息场分析方法可有效地提取研究区同生沉积断裂、成锰沉积盆地的控矿信息,提取结果具有显著的统计效果和明确的地质意义. 相似文献
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《西北地质》2021,(2)
为探索区域滑坡易发性评价模型的适用性和评价结果的合理性,以滑坡灾害高发的白龙江流域为研究区,首先选取坡度、地形起伏度、距断层距离、地层岩性、流域沟壑密度、植被指数等6项影响滑坡发生的孕灾因子作为易发性的评价指标,以研究区2 093处滑坡灾害点为样本数据,依据各指标条件下的信息量值、确定性系数值和证据权重值曲线突变规律,并结合滑坡面积及分级面积频率比曲线作为等级划分的临界值来确定因子分级状态;其次,基于指标因子状态分级和相关性分析结果,采用信息量法、确定性系数法、证据权法分别与逻辑回归组合的3种模型开展区域滑坡灾害易发性评价,并从模型结果、适用性和精度等方面采用多手段对3种组合模型进行比较和讨论。研究结果表明:在区域滑坡易发性评价方面,3组模型均表现较为理想,信息量和逻辑回归组合模型的预测精度为94.6%,其预测精度和准确性优于其他2种组合模型。笔者以白龙江流域中游及其岷江支流段为例,开展滑坡灾害易发性评价模型适用性、评价结果分析以及预测精度评价对比和研究等,成果可为该区地质灾害防灾减灾和国土空间用途管制规划决策提供参考。 相似文献
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针对滑坡空间预测中遇到的问题,简述了滑坡空间预测的主要过程,首先介绍了栅格数据的准备过程,结合栅格数据的存储特点,提出了基于栅格数据的信息量模型预测流程。以三峡库区巴东新城区为例开展了滑坡空间预测,重点阐述了预测过程中关键技术,如基于栅格数据计算信息量的解决方案、预测因素状态的确定方法以及预测结果的分级方法,同时探讨了影响巴东新城区滑坡的重要因素,在此基础上开展了巴东新城区滑坡危险性预测并对预测结果进行了合理性分析。通过解决这些滑坡空间预测中的关键性问题,为滑坡空间预测提供了一个高效合理的解决办法。 相似文献
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数字滑坡技术及其应用 总被引：26，自引：5，他引：21
“数字滑坡”技术,就是以遥感（RS）和空间定位方法为主,结合其他勘探、试验、调查手段获取数字形式的、与地理坐标配准的滑坡基本信息;并利用GIS技术存贮和管理这些数字信息;在此基础上,根据滑坡地学原理进行空间分析,研制各类模型,并服务于滑坡调查、监测、研究、滑坡灾害评价、危险预测、灾情评估、滑坡防治等。通过金龙山三维数字模型,卫星监测易贡滑坡、三峡库区重点城镇滑坡及千将坪滑坡等地的遥感调查说明数字滑坡技术的实际应用。 相似文献
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近年来，国内外滑坡研究日益重视滑坡风险评估和管理技术方法的研究，但滑坡风险评估依然是存在很多问题和难点,尤其是在中等—大比例尺区域滑坡风险定量评估方面，主要表现在滑坡编录数据库建设、滑坡影响因素的识别和建模、滑坡时间、空间预测的不确定性，滑坡诱发因素动态变化的定量刻画，承灾体识别和易损性定量评价等方面。在阐述滑坡风险评估流程的基础上，围绕滑坡风险评估与制图中滑坡编录和基础数据获取与更新，危险性分析中的滑坡空间、时间概率和滑坡特征预测、损失评估中的易损性分析与定量和承灾体定量化制图等技术方法中的难点和存在的问题，概述针对这些问题所取得的研究进展，并指出了滑坡风险研究的技术发展趋势。 相似文献
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Binh Thai Pham Dieu Tien Bui Indra Prakash Long Hoang Nguyen M. B. Dholakia 《Environmental Earth Sciences》2017,76(10):371
Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas. 相似文献
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Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide
occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR)
ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment.
However, to assess the probability of landslide within a specified period of time and within a given area, it is important
to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations,
the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted
regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide
hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented
to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect,
curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments),
land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing
factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain
landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show
that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were
investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison
maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR
models over or under estimate the landslide hazard potential were identified. 相似文献
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Qigen Lin Pedro Lima Stefan Steger Thomas Glade Tong Jiang Jiahui Zhang Tianxue Liu Ying Wang 《地学前缘(英文版)》2021,12(6):262-276
China is one of the countries where landslides caused the most fatalities in the last decades.The threat that landslide disasters pose to people might even be greater in the future,due to climate change and the increasing urbanization of mountainous areas.A reliable national-scale rainfall induced landslide suscep-tibility model is therefore of great relevance in order to identify regions more and less prone to landslid-ing as well as to develop suitable risk mitigating strategies.However,relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area.The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China.In this context,it is aimed to explore the benefit of mixed effects mod-elling to counterbalance associated bias propagations.Six influencing factors including lithology,slope,soil moisture index,mean annual precipitation,land use and geological environment regions were selected based on an initial exploratory data analysis.Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information:Set 1(disregards the presence of incomplete landslide information),Set 2(excludes factors related to the incompleteness of landslide data),Set 3(accounts for factors related to the incompleteness via random effects).The vari-able sets were then introduced in a generalized additive model(GAM:Set 1 and Set 2)and a generalized additive mixed effect model(GAMM:Set 3)to establish three national-scale statistical landslide suscep-tibility models:models 1,2 and 3.The models were evaluated using the area under the receiver operating characteristics curve(AUROC)given by spatially explicit and non-spatial cross-validation.The spatial pre-diction pattern produced by the models were also investigated.The results show that the landslide inven-tory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models.The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However,although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9),it was not associated with the most plausible representation of landslide susceptibility.The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias.The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility.However,a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g.,the Kuenlun Mountains in the northern Tibetan Plateau).The non-linear mixed-effects model(Model 3)reduced the impact of these biases best by introducing bias-describing variables as random effects.Among the three models,Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive perfor-mance(median AUROC of spatial cross validation 0.84)compared to the other two models(median AUROCs of 0.81 and 0.79,respectively).We conclude that ignoring landslide inventory-based incomplete-ness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas. 相似文献
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A review of assessing landslide frequency for hazard zoning purposes 总被引：11，自引：0，他引：11
The probability of occurrence is one of the key components of the risk equation. To assess this probability in landslide risk analysis, two different approaches have been traditionally used. In the first one, the occurrence of landslides is obtained by computing the probability of failure of a slope (or the reactivation of existing landslides). In the second one, which is the objective of this paper, the probability is obtained by means of the statistical analysis of past landslide events, specifically by the assessment of the past landslide frequency. In its turn, the temporal frequency of landslides may be determined based on the occurrence of landslides or from the recurrence of the landslide triggering events over a regional extent. Hazard assessment using frequency of landslides, which may be taken either individually or collectively, requires complete records of landslide events, which is difficult in some areas. Its main advantage is that it may be easily implemented for zoning. Frequency assessed from the recurrence of landslide triggers, does not require landslide series but it is necessary to establish reliable relations between the trigger, its magnitude and the occurrence of the landslides. The frequency of the landslide triggers can be directly used for landslide zoning. However, because it does not provide information on the spatial distribution of the potential landslides, it has to be combined with landslide susceptibility (spatial probability analysis) to perform landslide hazard zoning. Both the scale of work and availability of data affect the results of the landslide frequency and restrict the spatial resolution of frequency zoning as well. Magnitude–frequency relationships are fundamental elements for the quantitative assessment of both hazard and risk. 相似文献
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王涛等基于简化Newmark位移模型的区域地震滑坡危险性快速评估以汶川MS8.0级地震为例一文的地震滑坡危险性快速评价结果与2008年汶川地震触发实际滑坡空间分布的相关性较低。本文试图通过对该文中基础数据、分析处理过程、研究结果的分析与讨论,找出这种相关性较低的原因。结果表明值得针对王涛等文章中的Arias烈度分布数据的准确性、工程地质岩组的划分情况、汶川地震滑坡危险性评价结果的客观性共三个方面开展更深入的分析与研究。本文对探索与发掘更客观的地震滑坡危险性评价模型起到了积极的作用。 相似文献