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1.
GIS支持下的地震诱发滑坡危险区预测研究 总被引：24，自引：0，他引：24
为了满足对地震诱发滑坡危险区预测的不断增长的迫切要求，灾害评价成为帮助决策过程重要的基础工具之一。即使地震滑坡危险性各组份的评价很困难，但地理信息可辅助提出这种灾害制图的有关方法。描述了用于地理信息系统识别和定量计算不同地震滑坡危险区的技术方法，确定了地震烈度、地形坡度、岩土体类型和现存滑坡密度共4个因子参与的地震诱发滑坡危险性分析。在ARC／INFO DRID支持下，进行叠合分析，由此编制了云南省地震诱发滑坡危险区预测图。由地貌学家提出的地震诱发滑坡预测为规划和工程师提供了对区域规划和建筑工程有价值的技术方法。 相似文献
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The Modelling Of Landslide Hazards Using Gis 总被引：11，自引：0，他引：11
Cees J. Van Westen 《Surveys in Geophysics》2000,21(2-3):241-255
Slope instability hazard assessment is based on theanalysis of the terrain conditions at sites whereslope failures occurred in the past. For the analysisof the causative factors the application of geographicinformation systems (GIS) is an essential tool in thedata analysis and the subsequent hazard assessment.Three scale levels of hazard mapping are defined – adirect experience-driven mapping at reconnaissancelevel, a statistical approach to determine thecausative factors in a quantitative susceptibilitymapping, and a methodology at large scale making use ofdeterministic models. 相似文献
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A one-dimensional deterministic slope stability model (infinite slope model) was used to calculate average safety factors and failure probabilities for the city of Manizales, in central Colombia. An engineering geological data base has been created on the basis of a series of parameter maps (geology, geomorphology, Digital terrain models and slope classes), using logic reasoning in Geographic Information Systems (GIS). A two-dimensional hydrological model was applied to estimate groundwater levels in relation to rainfall events. A simple method for the calculation of horizontal seismic acceleration was used for different earthquake events. To calculate average safety factors a number of scenarios were developed, by combining the effects of groundwater and seismic acceleration with different return periods. A simple method for error propagation was used to calculate the variance of the safety factor, and the probability that it will be less than 1, for each pixel, within a time period of 20 years. The highest probability value of the various scenarios was selected for each pixel, and a final hazard map for translational landslides was constructed. The results presented here are a first approach towards a deterministic landslide hazard analysis using GIS. It has a number of drawbacks, which should be solved in future work. 相似文献
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利用GIS技术对地震危险等级进行评定时,由于其地形控制点选取合理性较差,导致其所采集遥感图像清晰度较低,地震等级评定不够精准。针对此问题提出一种新的地震灾情遥感信息危险等级在线应急评定方法。利用图像几何校正法,对遥感图像进行分幅裁剪,基于裁剪结果选取地面控制点,提取有价值遥感数据信息,建立遥感解译评估指标。将推导出的综合震灾指数引入到指标中,将各个评价单元的信息进行等级排序和划分,完成地震灾情遥感信息危险等级在线应急评定。仿真实验中,对所提方法和GIS地震危险等级评定方法进行有效性对比测试。实验结果表明,地震灾情遥感信息危险等级在线应急评定方法提升了灾情地形控制点选取的合理性,使获取的遥感图像更清晰,灾情等级评定结果更精准。 相似文献
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Rigorous landslide hazard zonation using Newmark's method and stochastic ground motion simulation 总被引：2，自引：0，他引：2
Researchers and practitioners in earthquake engineering have recognized geographic information systems (GIS) to be a significant tool in modeling spatial phenomenon related to hazard and risk. GIS, as an engineering tool, has been primarily used for its spatial data storing and presentation features. Models are often simplified to be more compatible with the light computational capabilities of many GIS. If not simplified, heavy computations are generally performed external to the GIS. A prototype vector-based GIS was developed that employs a rigorous approach to Newmark's displacement method for assessing earthquake triggered landslide hazards. The rigorous Newmark's analysis provides desirable flexibility by allowing input of actual ground motions. The prototype hazard GIS incorporates a popular shot filtered noise technique for generating artificial ground motions. The rigorous approach was compared to a popular simplified approach for computing Newmark displacements. Distribution of regional displacements was found to be similar with the simplified approach giving more and larger extreme displacements. The rigorous approach is suitable for large scales to model various seismic scenarios and their effect on seismically induced landslide potential. 相似文献
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The MS7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km2. The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area. 相似文献
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地震滑坡危险性评估可为震后应急响应等提供科学的决策依据。纽马克位移法可不依赖同震滑坡编目快速评估同震滑坡危险性。工程岩体物理力学参数是该方法的核心参数之一,但其赋值过于单一,难以反映复杂地质背景下岩体强度的空间差异性。针对上述问题,本文在分析地震滑坡影响因子的基础上,选择距断层距离、高程和距水系距离作为影响岩体强度的评价指标并建立岩体强度评价模型,获得区域岩体强度修正系数,进而修正传统方法的临界加速度。结合震后的即时地震动峰值加速度,采用简化纽马克位移法计算边坡累积位移,开展地震滑坡危险性快速评估,并以汶川MW7.9地震的地震滑坡危险性评估为例验证本文方法。结果表明,相对于传统方法,本文方法划分的地震滑坡危险区与同震滑坡分布更加一致。 相似文献
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APPLICATION OF TWO SIMPLIFIED NEWMARK MODELS TO THE ASSESSMENT OF LANDSLIDES TRIGGERED BY THE 2008 WENCHUAN EARTHQUAKE 
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下载免费PDF全文 At present, with the wide application of the Newmark method, various Newmark empirical formulas with different ground motion parameters have been fitted by many researchers based on global strong-motion records. However, the existing study about the Wenchuan earthquake does not quantitatively evaluate the applicability of different Newmark models based on the actual landslides distribution. The aim of this paper is to present a comparison between observed landslides from the 2008 Wenchuan earthquake and predicted landslides using Newmark displacement method based on different ground motion parameters. The factor-of-safety map and critical acceleration(ac)map in the study area are obtained by using the terrain data and geological data. The distribution of Arias intensity(Ia)and PGA in the study area is obtained by using the attenuation formulas of Arias intensity(Ia)and PGA, which is regressed by Wenchuan ground motion records. Based on the distribution of Arias intensity(Ia)and PGA parameters, we obtained the predicted locations of landslide using Newmark regression equations which are generated using global strong-motion records. The results shows that the assessment results can better reflect the macroscopic distribution characteristics of co-seismic landslides, most predicted landslide cells are distributed on the two sides of the Beichuan-Yingxiu Fault, especially the Pengguan complex rock mass in the hanging wall. The abilities to predict landslide occurrence of the two Newmark simplified models are different. On the whole, the evaluated result of simplified model based on parameter Ia is better than that based on PGA parameter. The GFC values obtained by the Newmark model of Ia and PGA parameters are 65.7% and 34.9%respectively. The evaluated result based on Ia can better reflect the macro distribution of coseismic landslides. The Ls_Pred value based on the Newmark model of parameter Ia is 26.5%, and the Ls_Pred value based on the Newmark model of PGA parameter is 10.3%. However the total area of predicted landslides accounts for 2.4% of the study area, which indicates that the predicted landslide cells are greater than the observed landslide cells. This reminds us that depending on the current input of shear strength and ground-motion parameters, we can only conduct landslide hazard assessment in macro areas, the ability to predict landslide can be improved using more accurate topographic data and input parameters. 相似文献
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基于统计学习理论与地理信息系统(GIS)技术的地震滑坡灾害空间预测是一个重要的研究方向,其可以对相似地震条件下地震滑坡的发生区域进行预测.2010年4月14日07时49分(北京时间),青海省玉树县发生了Mw6.9级大地震,作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡,这些滑坡大概分布在一个面积为1455.3 km2的矩形区域内.本文以该矩形区域为研究区,以GIS与支持向量机(SVM)模型为基础,开展基于不同核函数的地震滑坡空间预测模型研究.应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、坡度、坡向、斜坡曲率、坡位、水系、地层岩性、断裂、公路、归一化植被指数(NDVI)、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为地震滑坡预测因子.以SVM模型为基础,基于线性核函数、多项式核函数、径向基核函数、S形核函数等4类核函数开展地震滑坡空间预测研究,分别建立了玉树地震滑坡危险性指数图、危险性分级图、预测结果图.4类核函数对应的模型正确率分别为79.87%,83.45%,84.16%,64.62%.基于不同的训练样本开展模型训练与讨论工作,表明径向基核函数是最适用于该地区的地震滑坡空间预测模型.本文为地震滑坡空间预测模型中核函数的科学选择提供了依据,也为地震区的滑坡防灾减灾工作提供了参考. 相似文献
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Predicting global landslide spatiotemporal distribution： Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates 总被引：10，自引：0，他引：10
Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components： surface landslide susceptibility and a real-time space-based rainfall analysis system （http：//trmm.gsfc.nasa.gov）. First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics （digital elevation topography, slope, soil types, soil texture, land cover classification, etc.） using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world. 相似文献
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玉树地震诱发了2 036处滑坡。应用地理信息系统与遥感技术,选取与地表破裂距离、峰值加速度(PGA)、高程、坡度、坡向、曲率、坡位、与水系距离、岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)等12个因素作为玉树地震滑坡危险性评价因子,采用加法与减法2种证据权方法,开展玉树地震滑坡危险性评价研究工作。结果表明:基于加法证据权方法得到评价结果的正确率为80.32%,基于减法证据权方法得到结果的正确率为80.19%。将滑坡危险性评价结果图分为极高危险区、高危险区、中危险区、低危险区与极低危险区5类。这一成果可划分出滑坡危险区,为灾后滑坡防治、基础设施重建与自然环境保护提供参考。 相似文献
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Traditionally, earth scientists assess landslide occurrence on the basis of geomorphological investigations carried out through aerial photograph interpretation and ?eldwork. Conversely, local administrators primarily evaluate the impact of natural catastrophes, such as landsliding, on the basis of historical records and data. Owing to the substantial difference in the structure and spatial density of these two types of information, it is dif?cult to compare them directly and few investigators have attempted this. We compared landslide information derived from geomorphological mapping and historical data in a pilot area (the Staffora river basin, northern Italy). To do this we generated two multivariate statistical models where the dependent variable was either the mapped landslide deposits (geomorphological model), or the historical sites affected by landslide‐induced damage (historical model). By quantitatively comparing these two model maps, we demonstrate that the geomorphological model performs better in terms of percentage of terrain units correctly predicted as stable or unstable. The historical model underestimates landslide hazard mainly where human structures are lacking. However, it highlights slopes where landslide movements take place with a high frequency at the temporal scale of human life. Hence, the joint use of these two models may facilitate the knowledge of the overall instability conditions of a given region and the identi?cation of the landslides that are most frequently reactivated. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
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Landslides threaten lives and property throughout the United States, causing in excess of $2 billion in damages and 25–50 deaths annually. In regions subjected to urban expansion caused by population growth and/or increased storm intensities caused by changing climate patterns, the economic and society costs of landslides will continue to rise. Using a geographic information system (GIS), this paper develops and implements a multivariate statistical approach for mapping landslide susceptibility. The presented susceptibility maps are intended to help in the design of hazard mitigation and land development policies at regional scales. The paper presents (a) a GIS‐based multivariate statistical approach for mapping landslide susceptibility, (b) several dimensionless landslide susceptibility indexes developed to quantify and weight the influence of individual categories for given potential risk factors on landslides and (c) a case study in southern California, which uses 11 111 seismic landslide scars collected from previous efforts and 5389 landslide scars newly digitized from local geologic maps. In the case study, seven potential risk factors were selected to map landslide susceptibility. Ground slope and event precipitation were the most important factors, followed by land cover, surface curvature, proximity to fault, elevation and proximity to coastline. The developed landslide susceptibility maps show that areas classified as having high or very high susceptibilities contained 71% of the digitized landslide scars and 90% of the seismic landslide scars while only occupying 26% of the total study area. These areas mostly have ground slopes higher than 46% and 2‐year, 6‐hour precipitation greater than 51 mm. Only 12% of digitized landslides and less than 1% of recorded seismic landslides were located in areas classified as low or very low susceptibility, while occupying 42% of the total study region. These areas mostly have slopes less than 27% and 2‐year, 6‐hour precipitation less than 41 mm. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区,根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数（NDVI）、降雨、岩性、距道路距离和距河流距离10种影响因子,利用三种具有代表性的定量方法：信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出,人工神经网络模型得到的分区图较为合理,既表现出沿河谷地区集中分布的趋势,也呈现出对滑坡历史数据较为独立的特征,这一研究结果与前人研究结果一致。根据受试者工作特征曲线（ROC曲线）对三种模型的精度进行检验,检验得到的AUC值分别为0.818、0.829和0.837,说明三种评价结果均具有较高的可靠性,基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度,能更好地进行滑坡危险性的预测和评价,其中高程、降雨、岩性以及距道路距离对评价结果影响更大,这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。 相似文献
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In Japan, landslides triggered by heavy rainfall tend to occur during the annual rainy season from early June until the middle of July; these landslides constitute a major hazard causing significant property damage and loss of life. This paper proposes the use of back propagation neural networks (BPNN) to predict the probability of landslide occurrence for a scenario of heavy rainfall in the Minamata area of southern Kyushu Island, Japan. All of the landslides were detected from aerial photographs taken in 1999, 2001 and 2003, and a geospatial database of lithology, topography, soil characteristics, land use and precipitation was constructed using geographical information systems (GIS). The training sample consists of 602 cells that include landslide activity and 1600 cells in stable areas. Using the trained BPNN with 49 input nodes, three hidden layers, and one output node, 239 589 cells were processed to produce a map of landslide probability for a maximum daily precipitation of 329 mm and a maximum cumulative precipitation of 581 mm for an incessant, intense rainfall event in the future. The resultant hazard map was classified into four hazard levels; it can be referenced for land‐use planning and decision‐making for community development. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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A. R. Darnell J. Barclay R. A. Herd J. C. Phillips A. A. Lovett P. Cole 《Bulletin of Volcanology》2012,74(6):1337-1353
Many research tools for lahar hazard assessment have proved wholly unsuitable for practical application to an active volcanic system where field measurements are challenging to obtain. Two simple routing models, with minimal data demands and implemented in a geographical information system (GIS), were applied to dilute lahars originating from Soufrière Hills Volcano, Montserrat. Single-direction flow routing by path of steepest descent, commonly used for simulating normal stream-flow, was tested against LAHARZ, an established lahar model calibrated for debris flows, for ability to replicate the main flow routes. Comparing the ways in which these models capture observed changes, and how the different modelled paths deviate can also provide an indication of where dilute lahars, do not follow behaviour expected from single-phase flow models. Data were collected over two field seasons and provide (1) an overview of gross morphological change after one rainy season, (2) details of dominant channels at the time of measurement, and (3) order of magnitude estimates of individual flow volumes. Modelling results suggested both GIS-based predictive tools had associated benefits. Dominant flow routes observed in the field were generally well-predicted using the hydrological approach with a consideration of elevation error, while LAHARZ was comparatively more successful at mapping lahar dispersion and was better suited to long-term hazard assessment. This research suggests that end-member models can have utility for first-order dilute lahar hazard mapping. 相似文献
18.
WEN Boyu XU Chong HE Xiangli MA Siyuan SHAO Xiaoyi LI Kai ZHANG Zhongjian LI Zhengfang 《中国地震研究》2020,34(1):64-80
An MW6.6 earthquake occurred in eastern Hokkaido, Japan on September 6th, 2018. Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution (3 m) planet satellite, we manually interpret 9 293 coseismic landslides and select 7 influencing factors of seismic landslide, such as elevation, slope, slope direction, road distance, flow distance, peak ground acceleration (PGA) and lithology. Then, 9 293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3. In detail, the training sample has 6 505 landslide points and the validation sample has 2 788 landslide points. The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades, including very low risk area, low risk area, moderate risk area high risk area and very high risk area. The results show that there are 7 576 landslides in high risk area and very high risk area, accounting for 81.52% of the total landslide number, and the landslide area is 22.93 km2, accounting for 74.35% of the total area. The hazard zoning is in high accordance with the actual situation. The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number. The results show that the success rate of the information value method is 78.50% and the prediction rate is 78.43%. The evaluation results are satisfactory, indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area. 相似文献
19.
以四川省地理环境条件为研究背景,根据相关单位提供的资料,对四川省滑坡危险性等级进行了区划研究。将地形地貌、地层岩性、断裂构造、河流水系、降雨量、地震烈度等6项作为滑坡的主要影响因素,采用模式识别方法进行滑坡危险性等级区划,对算法中所涉及到的危险系数计算公式、因子权重分析、阈值选取等进行了一系列控制试验,验证了算法的可行性以及降低算法中存在的不确定性。采用“识别率”和“改变率”2个准则来判断“分类结果作为新的训练集”,即RTS试验的收敛性,从而给出识别率高、改变率稳定的分类结果,以及能合理反映识别结果的最佳参数。通过3次逐级识别分类,将四川省滑坡危险度划分为7个等级,区划结果与实际滑坡发生情况吻合。本文方法同样适用于其它地区的滑坡危险性等级区划。 相似文献
20.
The purpose of this study is to develop landslide susceptibility analysis techniques using an arti?cial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identi?ed in the study area from interpretation of aerial photographs, ?eld survey data, and a spatial database of the topography, soil type and timber cover. The landslide‐related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by arti?cial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of arti?cial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veri?ed using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to ef?ciently analyse the vast amount of data, and an arti?cial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献