基于专家知识的滑坡危险性模糊评估方法

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。     

GIS and ANN model for landslide susceptibility mapping

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.     

GIS and ANN model for landslide susceptibility mapping

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…     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

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.     

The effects of logging on frequency and distribution of landslides in three watersheds on Vancouver Island, British Columbia

Three hundred and sixty three landslides in three watersheds that totaled 382 km² were identified from air photographs, beginning at a date that preceded logging to the present. The three watersheds all lie on Vancouver Island; however, they have different precipitation regimes, topography, and amounts logged. Landslide areas in the watersheds varied in size from 200 m² to more than 1 ha. Nearly 80% of the landslides were debris slides; 15% were debris flows, and the remainder primarily rock falls. Following logging, the number of landslides increased substantially in all watersheds although the amount of increase was variable: approximately 11, 3, and 16 times in Macktush Creek, Artlish River, and Nahwitti River, respectively. Other analyses of changes in landslide density also produced highly variable results, with the number of landslides increasing between 2.4 and 24 times. Further, 2–12 times more landslides reached streams following logging activities. Densities for landslides impacting streams increased for the period of record from 1.5 to 10 times following logging activities. The densities were substantially greater where only landslides that reached streams since development began in a watershed were considered. Roads had the greatest spatial impact in the watersheds (compared to their total area), with frequencies determined to have increased by 27, 12, and 94 times for Macktush, Artlish, and Nahwitti, respectively. The results highlight the relative impact of roads and their role in slope stability.     

Simulation of event-based landslides and debris flows at watershed level

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.     

基于GIS的澜沧江下游区滑坡灾害危险性分析

澜沧江流域是中国西南地区滑坡灾害较为严重的地区。对澜沧江下游区滑坡灾害及其控制因素分析,建立基于G IS的滑坡灾害危险性评价模型,实现澜沧江下游区滑坡危险性区划,为该区滑坡灾害防治和生态环境保护等提供重要决策依据。     

Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method

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.     

Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina

A qualitative landslide susceptibility zonation map (scale 1:100,000) of a sector of the Río Mendoza Valley was prepared by overlapping thematic maps of conditioning factors. The main parameters affecting the distribution of landslides in the area were ranked according to slope instability. Geomorphological surveys and field observations enabled identification of 300 historical or prehistoric landslides. A landslide inventory was developed classifying the processes involved in slope instability, and analysing the degree of activity. The two most influential factors in decreasing slope stability were lithology and slope angle.     

Comparison of satellite and air photo based landslide susceptibility maps

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.     

怒江干流（云南段）滑坡灾害危险性分析

滑坡是怒江流域主要的地质灾害,对流域内人民生命财产和生态系统安全带来了极大的危害,因此本研究针对研究区内滑坡灾害主要诱发因子进行判识。利用1991～2006年云南省减灾年鉴、长系列流域内相关站点的年平均降雨量、2006年云南省1：5万的TM影像数据等,以GIS技术为平台对其相关因子关联性进行统计与分析。研究发现：沿怒江干流发生的滑坡灾害主要受到坡度、植被盖度、降雨强度及公路建设等因子的影响,分析灾害点的分布与相关因子间的相关性,发现相关性比较密切的是坡度〉25。的地带;植被盖度为30％～70％的地带;年降水量达到1250—1500mm的地带,以及公路沿线的地带,并以相关性作为灾害发生风险度评价的权重,建立了基于GIS的滑坡灾害危险性评价模型,实现了对怒江干流区域滑坡灾害危险性区划。     

Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan

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.     

An integrated model for predicting rainfall-induced landslides

This study proposes a novel method that combines a deterministic slope stability model and a statistical model for predicting rainfall-induced landslides. The method first uses the deterministic model to derive the rainfall rate critical to induce slope failure for each land unit. Then it calculates the difference between the critical rainfall threshold and estimated rainfall intensity. Using the difference and estimated rainfall duration as explanatory variables, the method derives a logit (integrated) model to compute landslide occurrence probabilities. To demonstrate the effectiveness of this method, the study used radar rainfall estimates and landslides associated with a typhoon (tropical cyclone) to develop the integrated model and the same types of data associated with another typhoon to validate the model. The model had a modified success rate of 84.0% for predicting landslides and stable areas, and model validation yielded a modified success rate of 87.4%. Both rates were better than those from the critical rainfall model. The main advantage of the integrated model lies in its use of rainfall variables that are not included in calculating the critical rainfall. Also, as a probabilistic model, the integrated model is better suited for decision-making in watershed management. This study has advanced the method for predicting rainfall-triggered landslides.     

Validation of a region-wide model of landslide susceptibility in the Manawatu–Wanganui region of New Zealand

Since European settlement 160 years ago, much of the indigenous forest in New Zealand hill country has been cleared for pastoral agriculture, resulting in increased erosion and sedimentation. To prioritise soil conservation work in the Manawatu–Wanganui region, we developed a model of landslide susceptibility. It assigns high susceptibility to steep land not protected by woody vegetation and low susceptibility everywhere else, following the commonly used approach for identifying inappropriate land use. A major storm on 15–16 February 2004 that produced many landslides was used to validate the model. The model predicted hills at risk to landsliding with moderate accuracy: 58% of erosion scars in the February storm occurred on hillsides considered to be susceptible. The model concept of slope thresholds, above which the probability of landsliding is high and below which the probability is low, is not adequate because below 30° the probability of landsliding is approximately linearly related to slope. Thus, reforestation of steep slopes will need to be combined with improved vegetation management for soil conservation on moderate slopes to significantly reduce future landsliding.     

基于遥感与地理信息系统的分布式斜坡稳定性定量评估模型

文章介绍一种进行斜坡稳定性定量研究的分布式模型——SINMAP模型。该模型以水文学理论为基础,耦合稳定状态水文模型TOPMODEL与大范围斜坡稳定性模型,在充分考虑各种影响因素的基础上,对研究区域进行斜坡稳定性评价。选取汉江江口流域作为试验研究区,以DEM、遥感影象、各种专题图件及地面考察资料作为信息源,利用SINMAP方法获得可视化的研究区地表稳定性指数专题图。经实际资料检验表明,该模型可获取较高的预测精度,尤其在流域尺度上具有极大的应用价值。     

Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km² was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region.The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

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%。     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

Uncertainty due to DEM error in landslide susceptibility mapping

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km²) and the other for model extrapolation (approximately 4600 km²). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.     

金沙江流域(云南境内)山地灾害危险性评价

云南境内的金沙江流域是斜坡不稳定的敏感区,根据1988-2000年的区域调查和统计,区内发育山地灾害点1697处,其中流域面积大于1km2的泥石流沟808条,体积大于1×104m3的滑坡580处,体积大于1000m3的崩塌309处。用于山地灾害危险性评价的主要敏感因子包括岩土体类型、山坡坡度、降雨、土地利用、地震烈度和人类活动。在对这些因子进行了敏感性评价的基础上,应用GIS对敏感因子集成评价而产生了云南金沙江流域山地灾害危险性评价图。评价结果表明:高危险区面积占全区面积6464km2的8 77%,中危险区占全区总面积的41 51%,低危险区占41 12%,无危险区占8 60%。山地灾害危险性评价图可以帮助规划者或工程师在土地发展规划中选择最佳建设场所,以减轻灾害的影响。