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.     

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%。     

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.     

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.     

High-magnitude landslide events on a limestone-scarp in central Germany: morphometric characteristics and climatic controls

There are both internal and external controlling factors leading to slope instability and susceptibility to mass movement processes. This paper explores which external climatic variables of different temporal scales influence the occurrence of high-magnitude landslide events. The investigations were focused on the Wellenkalk-cuesta scarp in the Thuringia Basin (Thüringer Becken) in central Germany. The cuesta scarp is composed of a densely jointed limestone caprock (Wellenkalk), and impermeable mudstones and marls of the Upper Bunter Sandstone (Röt) in the lower part of the slope. Mass movements are a typical feature of the Wellenkalk-scarp. The entire scarp slope length (1000 km) has been systematically mapped and investigated in a comprehensive research project in order to provide reliable information on the spatial distribution and control of the mass movements. More than 20% of the scarp slope has been influenced by various types of Holocene mass movements with strong differences in spatial frequencies. Sixteen high-magnitude landslides (Sturzfließungen) were identified by field inspection and mapping of slope geomorphology. Information on locations, morphometric characteristics, stratigraphic positions and rainfall-related attributes of the scarp slopes affected by the large landslides is presented.Mean annual rainfall amounts decrease from more than 800 mm in the western part of the basin to less than 550 mm in the east. Meteorological statistics on the spatial distribution of heavy rainfall intensities of different durations and return periods show that greatest precipitation intensities in short events (1 h) are reached at the western margin. Differences in rainfall intensities between the western and eastern parts decrease with increasing duration of the events. Where available, event-related meteorological information was collected. The spatial distribution of the landslides is most closely mirrored in the pattern of mean annual precipitation, though this variable is generally not thought to be a satisfactory or reliable climatic controlling factor. No landslides occur below a threshold value of 700 mm. The empirical data show that some general level of average perennial water input into the slope system seems to be of decisive importance for the occurrence of high-magnitude mass movements. Only when the precondition of a more general hydrological disposition is fulfilled, a direct triggering of high-magnitude mass movements by a short-time intensive rainfall period or event can become effective.     

On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong

This paper presents a statistical approach to study the spatial relationship between landslides and their causative factors at the regional level. The approach is based on digital databases, and incorporates such methods as statistics, spatial pattern analysis, and interactive mapping. Firstly, the authors propose an object-oriented conceptual model for describing a landslide event, and a combined database of landslides and environmental factors is constructed by integrating the various databases within such a conceptual framework. The statistical histogram, spatial overlay, and dynamic mapping methods are linked together to interactively evaluate the spatial pattern of the relationship between landslides and their causative factors. A case study of an extreme event in 1993 on Lantau Island indicates that rainfall intensity and the migration of the center of the rainstorm greatly influence the occurrence of landslides on Lantau Island. A regional difference in the relationship between landslides and topography is identified. Most of the landslides in the middle and western parts of the island occurred on slopes with slope angles of 25–35°, while in the eastern part, the corresponding range is 30–35°. Overlaying landslide data with land cover reveals that a large number of landslides occurred in the bareland and shrub-covered area, and in the transition zones between different vegetation types. The proposed approach can be used not only to analyze the general characteristics of such a relationship, but also to depict its spatial distribution and variation, thereby providing a sound basis for regional landslide prediction.     

Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): Characteristics and causal factors

Manjiya County on the Ugandan slopes of Mount Elgon is a densely populated mountainous area where landslides have been reported since the beginning of the twentieth century. The numerous fatalities and the damage done during the extreme rainfall events of 1997 to 1999 drew attention to this phenomenon. In order to better understand the causal factors of these landslides, 98 recent landslides in the study area, mostly debris slumps, were mapped and investigated. Together, they displaced 11 millions m³ of slope material. Statistical analysis shows that landslides dominate on steep concave slope segments that are oriented to the dominant rainfall direction (northeast) and at a relatively large distance from the water divide. Based on landslide occurrence and impact, four different zones can be distinguished within the study area. Causal factors as well as landslide characteristics differ greatly between the four zones.Besides the fact that steep slopes, high rainfall and typical soil properties and stratification turn Manjiya into an inherently unstable area, human interference cannot be neglected. Whereas deforestation has reduced the stability of the shallow soils on the eastern slopes of the study area, the excavation of slopes, mainly for house building, is an important destabilizing factor for the western slopes. The growing population density not only increases the risk on damage, but hampers the search for solutions for the landslide problem as well.     

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.     

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…     

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。     

滑坡危险度评价的地形判别法

选取影响滑坡发育的坡度、坡形、坡向、坡体的相对高度和地形与地层产状的组合关系5个主要地形因素，结合三峡库区重点滑坡段(云阳-巫山)205个滑坡统计资料，利用地形判别法，对典型滑坡危险度进行评价。将各地形判别因子在区域滑坡发育上的贡献率作为评价典型滑坡危险度的评价值，利用层次分析法，建立典型滑坡危险度判别矩阵。将判别矩阵的归一化特征向量作为判别因子的权重，得到典型滑坡的危险度。通过建立典型滑坡危险度评价表，对滑坡进行有效的管理。此研究方法有效地避免了对评价因子赋值的主观性，并提出了对不同危险度等级的滑坡管理措施。     

Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence

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.     

一种基于地理环境相似度的滑坡负样本可信度度量方法

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。     

台湾崩塌地的分类与防治工法

台湾本岛南北轴长约385km，东西最这处约143km，中央山脉纵贯本岛南北长约350km，其中至少有25个以上的主峰海拔高度超过3000m，台湾山地面积约占总面积70％，不但地质环境复杂，地势陡峻，土壤浅薄、河流短急，降雨多、强度大且集中，山坡地崩塌常随着台风、豪雨及地震、或伴随着人为的破坏还引起，屡屡造成重大灾害。崩塌常发生于特定地质或地质结构上，尤以坡地或台地的缓坡面为最多，其移动缓慢而有特续性及周期（反复）性。本研究针对台湾坡地崩塌的特性试作型态上的分类，并寻求其有效且可行之防治工法，盼能降低灾害所造成之损失，并确保该区域人民之生命及财产之安全。     

GIS支持下滑坡斜坡类型定量化及制图研究

斜坡类型描述岩层产状与斜坡的角度关系,很大程度上决定了斜坡岩土体变形的方式和强度,对地质灾害分布具有重要作用。斜坡的顺向坡、反向坡与地形的阳坡、阴坡概念相似,可以利用改进的太阳辐射地形因子计算模型(TOBIA指数)对斜坡类型进行定量化表达。计算TOBIA指数需要斜坡坡度、坡向、岩层倾角、倾向4个参数。以三峡库区顺向坡基岩滑坡多发地段青干河流域为例,通过区域地质图上产状点获取离散岩层倾角和倾向数值,经空间插值得到空间连续分布的倾角和倾向参数;通过数字高程模型获取坡度和坡向参数,得到区内TOBIA指数分布。在此基础上进一步研究指数和滑坡发育关系。结果表明,TOBIA指数值与区内斜坡类型密切相关,根据TOBIA指数值能很好地区分斜坡类型。以二分类变量逻辑回归模型对坡度和指数两个变量进行分析,发现引入TOBIA指数后,回归模型对已知滑坡拟合度由55%提高到71.5%,能有效提高区域滑坡灾害危险性区划结果精度。     

Predictive landslide susceptibility assessment using the weight-of-evidence method in north-central Garhwal Himalaya,India

Landslides are frequent natural disasters in mountainous regions, particularly in the Himalayas in India during the southwest monsoon season. Although scientific study of landslides has been in progress for years, no significant achievement has been made to preclude landsliding and allay disasters. This research was undertaken to understand the areal distribution of landslides based on geological formations and geomorphological processes, and to provide more precise information regarding slope instability and mechanisms of failure. After completing a landslide inventory, prepared through field investigation and satellite image analysis, 493 landslides, comprising 131 investigated in the field and 362 identified from satellite imagery, were identified and mapped. The areal distribution of these landslides shows that sites more prone to landsliding have moderate to steep slopes, the lithology of the Lesser Himalayan formations, and excavations for road corridors. Landslide susceptibility zones were delineated for the area using the weight-of-evidence method on the basis of the frequency and distribution of landslides. Weights of selected variables were computed on the basis of severity of triggering factors. The accuracy of landslide susceptibility zones, calculated statistically (R² = .93), suggests high accuracy of the model for predicting landsliding in the area.     

GIS支持下的黄土高原地震滑坡区划研究

分析了影响黄土滑坡的各项影响因子,利用层次分析法(AHP)确定各影响因子的权重。在GIS支持下,建立包括各因子图的空间数据库,对各因子进行分级赋值,然后进行因子加权叠加分析,完成三种超越概率下(50年超越概率2%、10%和63.5%)黄土高原地震滑坡区划图。黄土地震滑坡灾害最严重地区一个是宁夏南部及与其相邻的甘肃白银地区,另一个是甘肃天水地区。     

Earthquake‐induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: A case study at the Chiufenershan area in Central Taiwan

Monitoring and assessment of landslide hazard is an important task for decision making and policy planning in the landslide area. Massive landslides, caused by the catastrophic Chi‐Chi earthquake in 1999, occurred in Central Taiwan, especially at Chiufenershan area in Nantou county. This study proposed two useful indicators coupled with the Self‐organizing map (SOM) neural network and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) technique to quickly extract accurate post‐quake landslides from multi‐temporal Système Probatoire de l'Observation de la Terre (SPOT) images. A GIS‐based system was developed to simplify and integrate the procedures such as image pre‐processing, the SOM training, the PROMETHEE calculation, landslide extraction and accuracy assessment. The evaluated result shows that the landslide area soon after the earthquake is 209.50 ha (Kappa coefficient 96.88%). Over seven years of vegetation recovery, the denudation area has declined to 112.64 ha (Kappa coefficient 90.64%). Most earthquake‐induced landslides could be restored by natural vegetation succession. The developed system is a useful decision‐making tool for landslide area planning.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.