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1.
以阿坝藏族羌族自治州地质灾害频发的理县为研究区,从地形地貌、地质环境、水文条件和人类工程活动等方面选取11个影响因子,通过皮尔森相关系数研究各因子之间的相关性,从而构建滑坡易发性评价指标体系。利用信息量模型计算各影响因子的信息量值,从信息量模型得出的极低和低易发性分区中选取非滑坡样本,在此基础上将样本数据代入随机森林和径向基函数神经网络2种机器学习模型开展滑坡易发性评价,并通过接收灵敏度（Receiver Operating Characteristic,ROC）曲线进行精度验证。结果显示：随机森林模型预测出的高易发区单位面积内分布的滑坡点数量更为集中,在仅占6.666%的区域分布了74.026%的灾害点,评价结果优于径向基函数神经网络模型。ROC曲线中两模型AUC（Area Under Curve）值分别为0.893、0.874,说明随机森林模型具有更高的可靠性,比径向基函数神经网络在该区域地质灾害易发性评价中更具优势。 相似文献
2.
Kelsey A. Krueger 《自然地理学》2016,37(5):344-360
During previous work in the San Juan Mountains of Colorado, we observed that headwater (first-order) streams draining landslides were often characterized by the presence of beaver (Castor canadensis) dams whereas other headwater tributaries typically lacked evidence of beaver. Here, we hypothesize that hummocky landslide topography attracts beaver. To test the hypothesis, we examined 10 landslides and 11 adjacent headwater streams in the area, noting location, vegetation, elevation, and evidence of beaver activity, and then compared the landslide and non-landslide headwater streams using the G-test to determine whether or not variables were independent of one another. We reject the null hypothesis that beaver dam presence is unrelated to landslide deposits (p = 0.003). We further hypothesize that this relationship results from differences in stream gradient and concavity between landslide streams and other streams. We found streams on landslides to have a greater portion of their gradients below what geologic and ecologic literature suggests is a reasonable upper threshold (12%) for beaver dam maintenance. Additionally, streams on landslides are more concave. We conclude that the relationship between beaver presence and landslides results from a higher proportion of reaches below the 12% threshold and increased concavity of headwater streams on landslides. 相似文献
3.
Shou-Hao ChiangKang-Tsung Chang Alessandro C. MondiniBor-Wen Tsai Chen-Yu Chen 《Geomorphology》2012,138(1):306-318
A coupled model has been developed to simulate, at watershed level, landslides and debris flows induced by a severe typhoon (tropical cyclone) in Taiwan. The model comprises a landslide susceptibility model to predict landslide occurrence, an empirical model to select debris-flow initiation points, and a debris flow model to simulate the transport and deposit of failed materials from the identified source areas. In raster format with a 10 m spatial resolution, the model output includes unstable cells, debris-flow initiation cells, debris-flow velocities, runout paths, and deposition zones. The model was first tested and calibrated in a small area, where the damage by landslides had been investigated and recorded. It was then applied to a watershed, and the simulation results were validated by comparing them with a landslide/debris-flow inventory map prepared from satellite images using a multiple change detection technique. Model test and validation results confirm the usefulness of the model in predicting the number and size of affected areas (landslides and runouts combined), runout path, and volume of runout deposits. It is a common practice in Taiwan to separate landslide and debris-flow inventories and to study debris flows only in select drainage basins. This study suggests that landslide and debris flow should be modeled as a sequential process for efficient watershed management. 相似文献
4.
Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands 总被引：1，自引：0，他引：1
M. Van Den Eeckhaut J. Moeyersons J. Nyssen Amanuel Abraha J. Poesen Mitiku Haile J. Deckers 《Geomorphology》2009,105(3-4):239-252
During the last decade, slope failures were reported in a 500 km2 study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis. 相似文献
5.
Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium) 总被引：3，自引：1，他引：3
A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the “proximity to active fault” criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps. 相似文献
6.
GIS and ANN model for landslide susceptibility mapping 总被引：1，自引：0，他引：1
XU Zeng-wang 《地理学报》2001,11(3):374-381
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards. 相似文献
7.
GIS and ANN model for landslide susceptibility mapping 总被引：4，自引：0，他引：4
XU Zeng-wang 《地理学报(英文版)》2001,11(3):374-381
1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a… 相似文献
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9.
Arif Mert Eker Mehmet Dikmen Selim Cambazoğlu Şebnem H.B. Düzgün 《International journal of geographical information science》2013,27(1):132-158
The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time. 相似文献
10.
Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method 总被引：6，自引：0，他引：6
This work provides a landslide susceptibility assessment model for rainfall-induced landslides in Central Taiwan based on the analytical hierarchy process method. The model considers rainfall and six site factors, including slope, geology, vegetation, soil moisture, road development and historical landslides. The rainfall factor consists of 10-day antecedent rainfall and total rainfall during a rainfall event. Landslide susceptibility values are calculated for both before and after the beginning of a rainfall event. The 175 landslide cases with detailed field surveys are used to determine a landslide-susceptibility threshold value of 9.0. When a landslide susceptibility assessment value exceeds the threshold value, slope failure is likely to occur. Three zones with different landslide susceptibility levels (below, slightly above, and far above the threshold) are identified. The 9149 landslides caused by Typhoon Toraji in Central Taiwan are utilized to validate the study's result. Approximately, 0.2%, 0.4% and 15.3% of the typhoon-caused landslides are located in the three landslide susceptibility zones, respectively. Three villages with 6.6%, 0.4% and 4.9% of the landslides respectively are used to validate the accuracy of the landslide susceptibility map and analyze the main causes of landslides. The landslide susceptibility assessment model can be used to evaluate susceptibility relative to accumulated rainfall, and is useful as an early warning and landslide monitoring tool. 相似文献
11.
Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan 总被引：7，自引：0，他引：7
This paper proposes a statistical decision-tree model to analyze landslide susceptibility in a wide area of the Akaishi Mountains, Japan. The objectives of this study were to validate the decision-tree model by comparing landslide susceptibility and actual landslide occurrence, and to reveal the relationships among landslide occurrence, topography, and geology. Landslide susceptibility was examined through ensemble learning with a decision tree. Decision trees are advantageous in that estimation processes and order of important explanatory variables are explicitly represented by the tree structures. Topographic characteristics (elevation, slope angle, profile curvature, plan curvature, and dissection and undissection height) and geological data were used as the explanatory variables. These topographic characteristics were calculated from digital elevation models (DEMs). The objective variables were landslide occurrence and reactivation data between 1992 and 2002 that were depicted by satellite image analysis. Landslide susceptibility was validated by comparing actual data on landslides that occurred and reactivated after the model was constructed (between 2002 and 2004).This study revealed that, from 2002 to 2004, landslides tended to occur and reactivate in catchments with high landslide susceptibility. The landslide susceptibility map thus depicts the actual landslide occurrence and reactivation in the Akaishi Mountains. This result indicates that the decision-tree model has appropriate accuracy for estimating the probabilities of future landslides. The tree structure indicates that landslides occurred and reactivated frequently in the catchments that had an average slope angle exceeding ca. 29° and a mode of slope angle exceeding 33°, which agree well with previous studies. A decision tree also quantitatively expresses important explanatory variables at the higher order of the tree structure. 相似文献
12.
Comparing landslide inventory maps 总被引：10，自引：1，他引：9
Mirco Galli Francesca Ardizzone Mauro Cardinali Fausto Guzzetti Paola Reichenbach 《Geomorphology》2008,94(3-4):268
Landslide inventory maps are effective and easily understandable products for both experts, such as geomorphologists, and for non experts, including decision-makers, planners, and civil defense managers. Landslide inventories are essential to understand the evolution of landscapes, and to ascertain landslide susceptibility and hazard. Despite landslide maps being compiled every year in the word at different scales, limited efforts are made to critically compare landslide maps prepared using different techniques or by different investigators. Based on the experience gained in 20 years of landslide mapping in Italy, and on the limited literature on landslide inventory assessment, we propose a general framework for the quantitative comparison of landslide inventory maps. To test the proposed framework we exploit three inventory maps. The first map is a reconnaissance landslide inventory prepared for the Umbria region, in central Italy. The second map is a detailed geomorphological landslide map, also prepared for the Umbria region. The third map is a multi-temporal landslide inventory compiled for the Collazzone area, in central Umbria. Results of the experiment allow for establishing how well the individual inventories describe the location, type and abundance of landslides, to what extent the landslide maps can be used to determine the frequency-area statistics of the slope failures, and the significance of the inventory maps as predictors of landslide susceptibility. We further use the results obtained in the Collazzone area to estimate the quality and completeness of the two regional landslide inventory maps, and to outline general advantages and limitations of the techniques used to complete the inventories. 相似文献
13.
滑坡负样本在统计型滑坡危险度制图中具有重要作用,能抑制统计模型对滑坡危险度的高估。当前滑坡负样本采样方法采集的负样本可信度未知,在负样本采样过程中,极有可能将那些潜在滑坡点错选为负样本,这些假的负样本会降低负样本集的质量和训练样本集的质量,进而影响统计模型的精度。本文基于“地理环境越相似、地理特征越相似”的地理学常识,认为与正样本有着相似地理环境的点极有可能是未来发生滑坡的点;与正样本的地理环境越不相似的点,则越有可能是负样本。基于此假设提出一种基于地理环境相似度的负样本可信度度量方法,将该方法应用于滑坡灾害频发的陇南山区油房沟流域,对油房沟进行滑坡负样本可信度评价制图;使用油房沟流域的滑坡发生初始面来验证该方法的有效性。结果发现：滑坡发生初始面上所有栅格点的负样本可信度平均值为0.26,超过95%的栅格点的负样本可信度都小于0.5,说明本文提出的负样本可信度度量方法合理。 相似文献
14.
GIS支持下三峡库区秭归县滑坡灾害空间预测 总被引：3，自引：1，他引：2
基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。 相似文献
15.
Landslide susceptibility maps can be prepared in a variety of ways. Many geoscientists favour the use of an overlay model approach in which several map layers are combined by some arithmetic rules to determine the potential for sliding in an area or region. The resulting susceptibility maps, although based on a subjective weighting of relevant factors, can often be of high accuracy and utility. In order to obtain the relevant input data for this type of analysis, remotely sensed data are often used. To date, susceptibility mapping, just as the mapping of historic and individual landslides, has tended to require higher-resolution imagery. This has somewhat limited the application of landslide susceptibility mapping. While high-resolution air photo or satellite imagery is superior to lower resolution imagery for the purpose of mapping of historic and individual landslides, such higher levels of resolution may not be required for the development of landslide susceptibility maps. In order to determine if medium-resolution satellite imagery, such as SPOT or ASTER, could provide the needed data for landslide susceptibility mapping, a comparison was undertaken of landslide susceptibility model output resulting from the use of stereo NAPP aerial photography versus the use of data obtained from stereo SPOT imagery. The test area selected for this study consisted of two watersheds, Pena Canyon and Big Rock Canyon, situated west of Santa Monica, California, USA, along the Pacific Coast Highway. Both watersheds have a long and well-documented history of landslide activity and sufficient geologic variability and complexity to provide a good test site. The specific overlay model used in this evaluation required input data consistent with the needs of many other models of this type. The model output derived from the two different data sources and presented here in the form of susceptibility maps were virtually identical. Statistical and difference analysis confirmed that both methods of obtaining input data provide similar results and successfully identified landslide prone areas. These results suggest that satellite imagery, in this instance, SPOT images, could potentially be used in lieu of conventional air photos, to evaluate landslide susceptibility. In many situations, especially in the case of remote locations and/or developing countries, this capability should result in substantial savings in terms of time, financial resources, and overall viability. 相似文献
16.
基于专家知识的滑坡危险性模糊评估方法 总被引：6，自引：0，他引：6
滑坡发生的影响因素众多, 其危险性与各因素之间的关系多呈非线性关系, 同时各因素之 间也存在或强或弱的相关性, 而目前的危险性评价方法难以体现这些要求。本文提出了一种借助 滑坡专家知识并利用模糊推理理论进行滑坡危险性评价的方法。该方法通过建立了①坡度与岩 层倾角之差和坡向与岩层倾向之差、②坡度和岩性、③临空面和岩性、④坡形和岩性等四种环境 因子组合, 以此将不同环境因子之间的相关性融入各组合模型中, 并将四种组合所得的模糊危险 度进行叠加用于滑坡危险度的模糊评价。环境组合模型中的参数利用专家经验给出。将该方法应 用于三峡库区云阳- 巫山段, 得到了滑坡危险性的分级分布图。从滑坡危险性分布图上可清楚发 现, 本方法所计算出的危险性值在滑坡发生的地区明显高于未发生滑坡的地区, 该结果可以用于 城镇建设和重要基础规划设施的参考。 相似文献
17.
Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba 总被引：1，自引：0，他引：1
Geomorphological information can be combined with decision-support tools to assess landslide hazard and risk. A heuristic model was applied to a rural municipality in eastern Cuba. The study is based on a terrain mapping units (TMU) map, generated at 1:50,000 scale by interpretation of aerial photos, satellite images and field data. Information describing 603 terrain units was collected in a database. Landslide areas were mapped in detail to classify the different failure types and parts. Three major landslide regions are recognized in the study area: coastal hills with rockfalls, shallow debris flows and old rotational rockslides denudational slopes in limestone, with very large deep-seated rockslides related to tectonic activity and the Sierra de Caujerí scarp, with large rockslides. The Caujerí scarp presents the highest hazard, with recent landslides and various signs of active processes. The different landforms and the causative factors for landslides were analyzed and used to develop the heuristic model. The model is based on weights assigned by expert judgment and organized in a number of components such as slope angle, internal relief, slope shape, geological formation, active faults, distance to drainage, distance to springs, geomorphological subunits and existing landslide zones. From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria. The model combines all weights into a single hazard value for each pixel of the landslide hazard map. The hazard map was then divided by two scales, one with three classes for disaster managers and one with 10 detailed hazard classes for technical staff. The range of weight values and the number of existing landslides is registered for each class. The resulting increasing landslide density with higher hazard classes indicates that the output map is reliable. The landslide hazard map was used in combination with existing information on buildings and infrastructure to prepare a qualitative risk map. The complete lack of historical landslide information and geotechnical data precludes the development of quantitative deterministic or probabilistic models. 相似文献
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19.
Pece V. Gorsevski Paul E. Gessler Jan Boll William J. Elliot Randy B. Foltz 《Geomorphology》2006,80(3-4):178-198
Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions. 相似文献
20.
Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence 总被引：12，自引：0，他引：12
Ranjan Kumar Dahal Shuichi Hasegawa Atsuko Nonomura Minoru Yamanaka Santosh Dhakal Pradeep Paudyal 《Geomorphology》2008,102(3-4):496-510
Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with > 88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model's performance is contingent on the availability of data from past landslides. 相似文献