共查询到19条相似文献，搜索用时 171 毫秒
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滑坡灾害预测预报分类 总被引：21，自引：2，他引：19
殷坤龙 《中国地质灾害与防治学报》2003,14(4):12-18
有人主张滑坡灾害的空间和时间预测预报应是并存的,即脱离时间预测预报的空间预测是不可取的。但也有人认为两者可以相互独立而又互为补充。事实上,空间预测是时间预测预报的先决条件,只有在明确了预测的对象之后,方可有目的地开展滑坡灾害的时间预测预报。因而,一般地讲,滑坡灾害空间和时间预测具有先后序次关系。但从减灾的角度考虑,二者又具有相对的独立性。即可以在时间预测之外进行空间预测。目前对滑坡灾害预测预报分类的系统研究不多,所开展的预测预报事例报导基本上属于个例研究。论文将滑坡灾害预测预报分为空间和时间2大类,并进一步将空间预测划分成区域空间预测、地段空间预测和场地空间预测;将时间预测预报划分成长期时间预测、短期时间预测和临滑时间预测预报。文章针对滑坡灾害时间预测预报的特点,对预测预报的信息源进行了分析和分类。并对不同的工程阶段所要预测的滑坡灾害问题进行了分类归纳。 相似文献
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浙江省永嘉县滑坡灾害危险性区划 总被引：7，自引：0，他引：7
永嘉县是浙江省滑坡灾害发生频繁的区县之一,其滑坡受地质、地形和人类工程活动等因素的影响。本文根据永嘉县滑坡灾害分布情况,选择了影响滑坡分布的主要因素,将各种因子归一化处理后转换成相同分辨率的定量数据,选择了逻辑回归分析模型和信息量模型进行滑坡灾害危险性评价。在逻辑回归模型中,利用SPSS软件,通过逐步回归分析筛选出影响滑坡的最直接的因子,计算出各个因子的回归系数,得到逻辑回归方程,据此编制了危险性预测分区图。在信息量模型中,通过MAPGIS软件及其二次开发的信息量模型,对永嘉县滑坡灾害进行了危险性区划,并依信息量法的结果编制了该区的危险性预测分区图。两种方法所编制的危险性分区图中高危险区和中危险区重合率达到了87%,具有很高的一致性,起到了相互验证的作用,为滑坡的有效防治提供了依据。最后根据"云娜"台风期间永嘉县实际灾害发生情况的资料分析,新灾害点绝大部分落在危险性预测区中的高危险区,表明模型的预测准确率很高。 相似文献
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基于精细DEM的崩塌滑坡灾害识别及主控因素分析——以雅鲁藏布江缝合带加查—朗县段为例 总被引：1，自引：0，他引：1
雅鲁藏布江缝合带加查-朗县段位于青藏高原东南部地区,地形起伏度大,地质灾害分布密集。本文主要基于机载雷达获取的10 m精度影像数据,卫星遥感数据,以及高精度无人机航拍数据,对崩塌、滑坡地质灾害进行识别,并研究其主控因素。共计识别41处崩塌与92处滑坡,利用统计方法,分析崩塌、滑坡与各主控因素的相关性。对于识别的崩塌滑坡进行厚度识别,从而建立了灾害面积与体积之间的函数关系,实现了在已知崩塌滑坡灾害面积的情况下,对灾害规模的估算。本文阐明了区内地质灾害的空间分布情况,并研究了区域内崩塌滑坡地质灾害的主控因素。结果表明:滑坡主要发育在雅鲁藏布江南岸以及北岸坡体的中下部,而崩塌主要发生在北岸坡体的中上部。地层岩性、地形地貌、地质构造和岩体结构是崩塌、滑坡的主控因素,崩塌主要集中在砾岩和花岗岩地区,而千枚岩地区多发育有滑坡灾害。研究区内的崩塌由坡度、坡向和高程共同控制,其中坡度为主控因素;滑坡主要受到断层的控制,坡度对滑坡的发育具有一定的影响作用,高程和坡向对滑坡的影响较小。滑坡主要以牵引型为主,且大多数滑坡滑动的方向大致垂直于断裂的走向;崩塌主要以滑移式为主,通过对岩体结构面的提取可以分析其结构面发育情况,从而分析结构面对崩塌的控制作用。 相似文献
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基于GIS的巴东新县城滑坡灾害风险系统 总被引：3，自引：0，他引：3
本文提出了基于GIS的滑坡灾害风险预测系统流程。并将滑坡灾害风险评价模型与GIS技术先进的图形处理和空间分析功能相结合,建立了巴东县新县城区滑坡灾害风险预测系统。系统由信息管理子系统、危险性预测子系统、易损性预测子系统、风险预测子系统四大子系统构成。系统在对相关信息进行采集、存贮、检索和管理的基础上,结合物元模型、BP模型等专业预测模型,实现了滑坡灾害危险性、易损性评价,最终取得了滑坡灾害风险分布图,为三峡库区内各县的滑坡灾害信息管理和风险预测提供了新途径。预测成果可为研究区的国土规划和移民工程的顺利实施提供依据和保障。 相似文献
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滑坡灾害危险性区划研究在城市规划决策方面具有重要的现实意义。聚类分析以统计学的形式将具有相似特征的数据进行归类,能够实现滑坡灾害危险性空间分布情况的定量评价。根据湖北省巴东县滑坡灾害统计资料,选择具有代表性的滑坡灾害影响因素作为危险性区划评价指标,采用熵权法和层次分析法相结合,综合评判各指标权重。并在此基础上,以MapGIS为操作平台,以C#语言编程实现了快速聚类算法,对研究区86216个单元进行了滑坡灾害属性分类及危险性等级自动识别,预测结果较好。本研究将综合权重评判方法与聚类模型结合,同时克服了聚类结果不能自动排序的困难,对处理大批量,多属性数据具有一定的创新性和实用价值。 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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在充分调查万州区地质环境及滑坡灾害基本特征的基础上,根据资料的有效性和可获得性,选取地表高程、坡度、地层岩性、地质构造、土地利用类型、区域交通建设及河流侵蚀冲刷7个影响滑坡发生的因素作为评价指标,采用AHP法确定各个指标权重并建立滑坡灾害危险性指数模型,通过GIS系统的空间分析功能进行栅格运算,得出研究区滑坡灾害危险性分区。采用上述指标和方法将重庆市万州区的滑坡灾害划分为极高危险区、高危险区、中危险区、低危险区和极低危险区,划分结果符合该区滑坡灾害的实际情况。 相似文献
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基于贡献率权重法的区域滑坡影响因子敏感性分析 总被引：2，自引：0，他引：2
滑坡影响因子是区域滑坡危险性评价的基础,而因子的敏感性直接反应评价过程中权重大小,因此因子敏感性的精度将影响着评价结果的精度。本文以平昌县滑坡灾害为例,选取坡度、地层、高差、高程、坡形五个因子作为滑坡灾害的敏感性分析因子,采用贡献率权重法对研究区滑坡灾害影响因子的内部敏感性及因子间敏感性进行分析,该评价方法结构简单,不受地域限制,能客观的反应出各评价因子间以及因子内对地质灾害的敏感性。分析结果显示:选取的5个指标敏感性大小依次为:坡形坡度地层高差高程;在各评价指标内部中,坡度10°~30°、侏罗系下统蓬莱镇组上段地层、高差4.94~12.36 m、高程300~800 m、凸形与直线形坡是滑坡发生的高敏感区间。 相似文献
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基于GIS的重庆市万州区滑坡灾害危险性评价 总被引：1，自引：0，他引：1
在充分调查万州区地质环境及滑坡灾害基本特征的基础上,根据资料的有效性和可获得性,选取地表高程、坡度、地层岩性、地质构造、土地利用类型、区域交通建设和河流侵蚀冲刷7个影响滑坡发生的因素作为评价指标,采用AHP法确定各个指标的权重并建立滑坡灾害危险性指数模型,通过GIS系统的空间分析功能进行栅格运算,得出研究区滑坡灾害危险性分区.采用上述指标和方法将重庆市万州区的滑坡灾害划分为极高危险区、高危险区、中危险区、低危险区和极低危险区,划分结果符合该区滑坡灾害的实际情况. 相似文献
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Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection,Greece 总被引：2，自引：0，他引：2
The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models. 相似文献
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Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method.
相似文献13.
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Determination and application of the weights for landslide susceptibility mapping using an artificial neural network 总被引：38，自引：0，他引：38
The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping. 相似文献
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Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley) 总被引：2，自引：0，他引：2
Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M w values of 5.5 and 7.5, respectively. 相似文献
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Shallow landslides usually occur during hevy rainfall and result in casualties and property losses. Thus, the possible locations where landslides are likely to occur must be identified in advance in order to avoid or reduce the harm they cause. When performing a slope-instability analysis, soil thickness is an important factor; however, soil thickness information from landslide-prone areas is rarely obtained. The objective of this study is to realize the influences of spatial distribution of soil thickness on shallow landslide prediction. Three different spatial soil-thickness distributions were applied to perform a slope-instability analysis, and uniform-distributed soil thicknesses from 0.4 m to 2.0 m were also applied for comparison. Geomorphologic information and hydrological records from a landslide-prone area in southern Taiwan were collected. Results show that the spatial distribution of soil thickness related to wetness index provides a reasonable estimation in order to avoid an over-prediction for landslide-prone areas or an under-prediction for stable areas. The analytical procedure used in this study is a simple way for assessing hillslope instability for shallow landslide prediction. 相似文献
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基于集对分析的滑坡变形动态建模研究 总被引：4，自引：1，他引：3
滑坡是一个确定-不确定的动态系统,其变形表现出复杂的非线性演化特征。为了克服传统分析方法在处理系统不确定性方面的不足,将集对分析法（SPA）引入岩土变形监测分析领域,并结合层次分析法（AHP）提出了滑坡变形动态预测模型,给出了基于概率论的最优预测算法,提出并证明了集对论中最大同一度在等势条件下存在极限解,据此提出位移势的概念。在位移势的基础上,进行SPA二次建模,提出了基于SPA的滑坡变形与水库蓄水过程相关性动态分析模型。运用上述预测模型对新近发生的刘家沱滑坡进行变形监测定量分析,实践证明：最优预测值具有良好的短期预报精度;位移势能够表征系统当前状态下位移所能达到的最大潜力,可作为短期预测的上限值,其变化能够反映系统宏观层面上的演化特征,对滑坡演变加速预警具有指导意义;运用集对分析方法进行滑坡变形响应滞后效应的定量研究是一条可行的新思路,反演结果与实际情况吻合;该方法在岩土监测分析领域有良好的应用前景。 相似文献
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在陕西省宝鸡市附近长寿沟地区滑坡详细调查和遥感解译的基础上,完成了1∶10000滑坡编目图。通过使用GIS的水文分析功能,运用正反DEM技术,将长寿沟地区划分为216个自然斜坡单元,其中包括123个滑坡单元和93个未发生滑坡单元,分析滑坡发生与坡高、坡度、坡向、坡形、人类工程活动和水文地质条件影响因子之间的统计规律。利用经遗传算法优化后的BP神经网络对80个滑坡样本和40个未滑坡样本进行训练学习,然后再利用训练好的网络对预测样本进行评价分析。结果表明:43个已滑坡单元中只有3个被误判为无滑坡,正确率为9302%,53个未滑坡单元中有10个被预测为滑坡,正确率为8113%,总体正确率为8646%。通过对被预测为滑坡的10个斜坡单元进行分析,发现这些单元在坡形、坡高等影响因素的组合上已经具备了发生滑坡的条件,虽然目前没有发生滑坡,但作为潜在的滑坡危险区,可以为滑坡灾害预测预报和防灾减灾工作提供参考。 相似文献
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基于集对分析和模糊马尔可夫链的滑坡变形预测新方法研究 总被引：4，自引：1，他引：3
滑坡体的变形表现出复杂的非线性演化特征,为克服传统分析方法在处理系统不确定性方面的不足,引入集对分析（SPA）理论,并结合模糊马尔可夫（fuzzy-Markov）理论来对滑坡SPA模型中的不确定系数进行二次预测,提出了滑坡变形SPA-fuzzy-Markov预测新模型。针对fuzzy-Markov子系统,分别提出了基于相关系数和特征值方差的多维空间模糊马尔可夫链拟合效果检验的两个新方法,并据此定量考察fuzzy-Markov子系统对参数响应的敏感性,用以确定最佳的模型参数组合。运用上述理论模型对刘家沱滑坡变形监测成果进行分析,综合定量考察了模型对模糊区间重合等级和马尔可夫状态转移步数这两个参数的敏感程度。结果表明：该算例条件下状态转移步数为敏感因子;与单纯SPA模型相比,复合模型能够进一步提高整体预测精度,在岩土监测分析领域中具有良好的实用价值;同时fuzzy-Markov理论的引入也为深化SPA基础理论研究提供了一条途径,不仅在岩土监测领域,在其他相关领域也具有理论和实用价值。 相似文献