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1.
2010年4月14日07时49分(北京时间),青海省玉树县发生了Ms7.1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。这些滑坡受地震地表破裂控制强烈,规模相对较小,常常密集成片分布。滑坡类型多样,以崩塌型滑坡为主,还包括滑动型、流滑型、碎屑流型、复合型等类型的滑坡。本文基于地理信息系统(GIS)与遥感(RS)技术,应用逻辑回归模型开展玉树地震滑坡危险性评价,并对结果合理性进行检验。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、斜坡坡度、斜坡坡向、斜坡曲率、与水系距离、坡位、断裂、地层岩性、归一化植被指数(NDVI)、公路、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为玉树地震滑坡影响因子,在GIS平台下将这些因子专题图层栅格化。应用逻辑回归模型得到每个因子分级的回归系数,然后建立滑坡危险性指数分布图。利用玉树地震滑坡空间分布图对滑坡危险性指数图进行检验,正确率达到83.21%。滑坡危险性分级结果表明,在占研究区总面积4.97%的"很高危险度"的较小范围内,实际发育滑坡数量为766个,占总滑坡面积的比例高达37.62%,表明地震滑坡危险性评价结果良好。不同危险性级别的滑坡点密度统计结果表明,滑坡点密度随着危险性级别的升高而非常迅速的升高。 相似文献
2.
提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D（early effective rainfall-rainfall duration）阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量（early effective rainfall,EE）和降雨历时（D）为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示:（1）宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e4.062+0.747 4×D-0.079 44×EE,其拟合优度为0.983;（2）2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;（3）C5.0决策树预测滑坡易发性的精度显著高于多层感知器;（4）近5年的4次降雨型滑坡的连续概率危险性值都在0.8以上,且高和极高预警区的面积较传统滑坡危险性分区更小.可见连续概率滑坡危险性预警法相较于传统危险性分区法具有更高的预警精度和空间辨识度,且通过叠加滑坡易发性图及其临界降雨阈值可开展实时滑坡危险性预警制图. 相似文献
3.
秦岭中部太白县地质灾害发育特征及危险性评估 总被引：2，自引：0，他引：2
以陕西省太白县为例,分析了秦岭中部山区地质灾害形成的地质环境条件,重点指出在植被茂密山区,异常强降雨及农耕、建房、修路和矿山开采4种人类工程活动对地质灾害的关键诱发作用。对崩塌、滑坡、泥石流和不稳定斜坡4类典型地质灾害进行了亚类细分和发育特征分析,并总结指出了地质灾害区域宏观分布特征。筛选了9种关键的地质灾害影响和诱发因素,基于将集中调查区指示的地质灾害发育规律,外推应用于全区地质灾害评估的思路,利用信息量模型对太白县全区进行了地质灾害危险性定量评估,结果显示高危险区主要集中分布在县域北部人口聚居的盆地区,以及南部河流与公路沿线地段。定量检验显示,危险性评估结果与地质灾害的实际分布十分吻合,表明基于信息量模型的地质灾害危险性评估方法能够很好地适用于秦岭腹地山区。 相似文献
4.
Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia 总被引：20，自引：5，他引：15
Biswajeet Pradhan 《Environmental Earth Sciences》2011,63(2):329-349
Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps
is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides,
producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including
direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover,
these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies.
Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic
information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the
techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal
combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross
application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and
the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the
conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the
study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies.
Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic
approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide
susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified
landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics
(ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated
for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated
for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively,
the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results
indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found
to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic
relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones
of relative susceptibility. 相似文献
5.
High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides. 相似文献
6.
Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H L/W R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key. 相似文献
7.
Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand 总被引：2，自引：0，他引：2
Hyun-Joo Oh Saro Lee Wisut Chotikasathien Chang Hwan Kim Ju Hyoung Kwon 《Environmental Geology》2009,57(3):641-651
For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and
statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing.
Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps
of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence,
such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic
database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified
from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility
mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide
location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42%
in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover. 相似文献
8.
Hazard Zoning for Landslides Connected to Torrential Floods in the Jerte Valley (Spain) by using GIS Techniques 总被引：2，自引：0，他引：2
Carrasco R.M. Pedraza J. Martin-Duque J.F. Mattera M. Sanz M.A. Bodoque J.M. 《Natural Hazards》2003,30(3):361-381
The Jerte Valley is anortheast-southwest tending graben located in the mountainous region of west central Spain (Spanish Central System). Mass movements have been a predominant shaping process on the Valley slopes during the Quaternary. Present day activity is characterized as either `first-time failure' (shallow debris slides and debris flows) or `reactivations' of pre-existing landslides deposits.A delineation of landslide hazard zoningwithin the Valley has been carried out by using the detailed documentation of a particular event (a debris slide and a sequel torrential flood, which occurred on the Jubaguerra stream gorge), and GIS techniques. The procedure has had four stages, which are: (1) the elaboration of a susceptibility map (spatial prediction) of landslides; (2) the elaboration of a map of `restricted susceptibility' in the particular case of slopes that are connected to streams and torrents (gorges); (3) the elaboration of a digital model which relates the altitude to the occurrence probability of those particular precipitation conditions which characterized the Jubaguerra event and (4) the combination of the probability model with the `restricted susceptibility map', to establish `critical zones' or areas which are more prone to the occurrence of phenomena that have same typology as this one. 相似文献
9.
Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran 总被引：1，自引：0，他引：1
Reza Talaei 《Journal of the Geological Society of India》2014,84(1):68-86
Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%. 相似文献
10.
Statistical modelling of Europe-wide landslide
susceptibility using limited landslide inventory data 总被引：2，自引：0，他引：2
M. Van Den Eeckhaut J. Hervás C. Jaedicke J.-P. Malet L. Montanarella F. Nadim 《Landslides》2012,9(3):357-369
In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available. 相似文献
11.
Mowen Xie Tetsuro Esaki Cheng Qiu Chunxiang Wang Zengfu Wang 《Geotechnical and Geological Engineering》2009,27(3):355-364
The slope failure and landslide hazard will possess the same properties within a range including the same engineering geological
conditions. To assess the landslide risk of a mountainous area, the study of landslides previously having occurred is very
important to evaluate the landslide risk around the area in which they took place. Based on the study of the mechanism of
a previous landslide recorded in Kumamoto, Japan, this study initially proposes mechanical parameters for evaluating the landslide
hazard using a 3D slope stability method. For each slope unit in the study area, the critical slip surface, which reveals
the minimum safety factor of a slope, can be obtained. The affected streams and range of possible landslide masses are analyzed
based on the debris flow simulation. This is initially applied to simulate the past landslide event and the result shows the
landslide-deduced debris flow effectively re-displayed. Overlayered with layers of infrastructure in Geographic Information
Systems (GIS), this risk map indicates which houses and road sections remain in dangerous areas. 相似文献
12.
Shallow landslides are common in mountainous areas after intense rainfall. Of all landslide hazard assessment methods, deterministic methods provide the best quantitative information on landslide hazard. However, they require a large amount of detailed in situ data, derived from laboratory tests and field measurements, and therefore it is difficult to apply them over large areas. One of the most important input parameters is soil depth. For large areas, it is impossible to obtain soil depth through field measurements. To overcome this difficulty, a statistics-based regression analysis is used to evaluate soil depths. All the terrain attributes that control soil depths are selected as influential factors. By using multi-linear regression, the soil depths at each location can be predicted. Slope stability analysis can then be performed using deterministic methods with the evaluated soil depths. The study area is divided into slope units. For each slope unit, Monte-Carlo simulation and a GIS-based 3D limit equilibrium model are used to locate the critical slip surface and calculate the corresponding safety factor. The effectiveness of the proposed method has been tested by applying it to a mountainous area in Japan. 相似文献
13.
Landslides are unpredictable; however, the susceptibility of landslide occurrence can be assessed using qualitative and quantitative methods based on the technology of the Geographic Information Systems (GIS). A map of landslide inventory was obtained from the previous work in the Minamata area, the interpretation from aerial photographs taken in 1999 and 2002. A total of 160 landslides was identified in four periods. Following the construction of geospatial databases, including lithology, topography, soil deposits, land use, etc., the study documents the relationship between landslide hazard and the factors that affect the occurrence of landslides. Different methods, namely the logistic regression analysis and the information value model, were then adopted to produce susceptibility maps of landslide occurrence. After the application of each method, two resultant maps categorize the four classes of susceptibility as high, medium, low and very low. Both of them generated acceptable results as both classify the majority of the cells with landslide occurrence in high or medium susceptibility classes, which could be believed to be a success. By combining the hazard maps generated from both methods, the susceptibility was classified as high–medium and low–very low levels, in which the classification of high susceptibility level covers 6.5% of the area, while the areas predicted to be unstable, which are 50.5% of the total area, are classified as the low susceptibility level. However, comparing the results from both the approaches, 43% of the areas were misclassified, either from high–medium to low–very low or low–very low to high–medium classes. Due to the misclassification, 8% and 3.28% of all the areas, which should be stable or free of landsliding, were evaluated as high–medium susceptibility using the logistic regression analysis and the information value model, respectively. Moreover, in the case of the class rank change from high–medium susceptibility to low–very low, 35% and 39.72% of all mapping areas were predicted as stable using both the approaches, respectively, but in these areas landslides were likely to occur or were actually recognized. 相似文献
14.
This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system
(GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides
in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology,
rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances
from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified
form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote
sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence
were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river
and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard
zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation
map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes
were suggested by using the GIS technology. 相似文献
15.
Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA 总被引：60，自引：0，他引：60
Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. 相似文献
16.
滑坡灾害空间区划及GIS应用研究 总被引：76，自引：3，他引：76
滑坡灾害空间区划研究是当前国内外滑坡领域的重要研究方向之一。虽然滑坡灾害的发生具有随机性的特点 ,但其发生的区域性和重复性特点则是区域滑坡分布与发生的总体规律。从减灾与土地规划的角度 ,开展滑坡灾害空间区划研究具有十分重要的理论和实际意义。文中重点探讨了滑坡灾害空间区划的理论体系、灾害风险评估的基本术语定义及GIS制图的基本原理 ,采用MAPGIS软件为平台及其二次开发的滑坡灾害信息分析系统 ,在中国滑坡重灾害的汉江流域开展了灾害危险性空间区划应用研究。 相似文献
17.
For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression,
and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations
were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial
database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope
gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database.
Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were
calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility
maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher
prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used
to reduce hazards associated with landslides and to land-use planning. 相似文献
18.
Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment 总被引：2，自引：0，他引：2
María José Domínguez-Cuesta Montserrat Jiménez-Sánchez Ana Colubi Gil González-Rodríguez 《International Journal of Earth Sciences》2010,99(3):661-674
A new method for estimating shallow landslide susceptibility by combining Geographical Information System (GIS), nonparametric
kernel density estimation and logistic regression is described. Specifically, a logistic regression is applied to predict
the spatial distribution by estimating the probability of occurrence of a landslide in a 16 km2 area. For this purpose, a GIS is employed to gather the relevant sample information connected with the landslides. The advantages
of pre-processing the explanatory variables by nonparametric density estimation (for continuous variables) and a reclassification
(for categorical/discrete ones) are discussed. The pre-processing leads to new explanatory variables, namely, some functions
which measure the favourability of occurrence of a landslide. The resulting model correctly classifies 98.55% of the inventaried
landslides and 89.80% of the landscape surface without instabilities. New data about recent shallow landslides were collected
in order to validate the model, and 92.20% of them are also correctly classified. The results support the methodology and
the extrapolation of the model to the whole study area (278 km2) in order to obtain susceptibility maps. 相似文献
19.
20.
证据权法在区域滑坡危险性评价中的应用以贵州省为例 总被引：3，自引：0，他引：3
以GIS为技术平台,采用证据权法对研究区进行了滑坡地质灾害危险性分析。综合分析历史滑坡数据及其环境因素和触发因素,数据源主要有地形图、DEM、地质图,选取地层岩性、构造、高程、坡度、坡向、地形起伏度、道路、水系作为危险性评价因子。首先应用ArcGIS软件对数据源进行处理,提取各个评价因子图层,并对每个图层进行分级、缓冲区分析等处理,建立若干证据层。然后将历史灾害点与评价因子进行空间关联分析,计算每个评价因子等级的权重,最后计算出评价单元的危险性指数,并将危险性分为极高危险区、高危险区、中等危险区、低危险区。采用成功率曲线法对证据权法评价精度进行验证,结果表明本次评价的精度为71%。利用历史滑坡数据对评价结果进行验证,结果显示评价结果与实际情况较为吻合,说明证据权可以客观定量地评价各影响因子对滑坡的影响程度,该方法应用于区域地质灾害危险性评价比较有效。 相似文献