Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong

 Steep terrain and the high frequency of tropical rainstorms make landslide occurrence on natural terrain a common phenomenon in Hong Kong. For example, more than 800 slope failures were triggered by a rainstorm in November 1993 on Lantau Island, Hong Kong. Maps of recent landslides interpreted from aerial photographs, in combination with a geographical information system, were used to evaluate the frequency and distribution of landslides, with particular reference to such physical parameters as lithology, slope gradient, slope aspect, elevation, vegetation cover, and proximity to drainage line, all of which are considered to be influential in the occurrence of landslides. A stepwise logistic regression model was obtained between landslide susceptibility and the above mentioned physical parameters. The study area has been classified into five classes of relative landslide susceptibility, namely, very low, low, moderate, high, and very high, based on this methodology. Received: 17 December 1999 · Accepted: 21 March 2000     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

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.     

Landslide hazard mapping using logistic regression model in Mackenzie Valley,Canada

A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley.     

浙西梅雨滑坡易发性评价模型对比

新疆巩留县广泛发育冻融降雨型滑坡地质灾害,对其现有的研究多考虑降水,而缺乏温度影响的研究,为此,本文特增加了温度因子来进行巩留县滑坡灾害危险性评价。基于巩留县已发生的682个滑坡灾害点,选取坡度、起伏度、坡向、曲率、温度、距断层距离、距河流距离、距道路距离、工程地质岩组等9个评价因子。采用信息量模型(I)、确定性系数模型(CF)、信息量模型+逻辑回归模型(I+LR)以及确定性系数模型+逻辑回归模型(CF+LR)等4种模型对巩留县滑坡危险性进行了评价,划分为极高、高、中和低4个危险等级分区并进行了精度检验与现场实际验证。结果表明：(1)温度对滑坡有较大的触发作用;(2)耦合模型极高、高危险性分区面积明显低于单一模型极高、高危险性分区面积,其中CF+LR模型的极高、高危险性分区面积最小,低危险性分区面积最大;(3)4种模型ROC精度检验AUC值分别为0.889、0.893、0.895和0.900,均能较为客观地评价巩留县滑坡危险性。CF+LR模型精度最高,且经局部地区现场检验,CF+LR模型评价结果与实际情况也最为相符,研究成果对新疆地区巩留县滑坡地质灾害的预防和治理具有一定的借鉴意义。     

Applicability of landslide susceptibility and hazard zoning at different scales

Landslide susceptibility and hazard zoning can notably improve land-use planning, and thus can be considered an efficient way to reduce future damage and loss of lives caused by landslides. However, the lack of standard procedures restricts the use of susceptibility and hazard-zoning maps, notwithstanding their extensive development over the last decades.JTC-1, the Joint Technical Committee on Landslides and Engineered Slopes, fills this void by proposing International Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for land-use planning, which provide definitions, terminology and international standards for methods, levels, scales and types of zoning. The Guidelines also promote the use of quantitative risk-management principles, essential to compare risk from landslides with risks related to other hazards and with loss of life tolerance criteria.This paper focuses on the applicability of landslide susceptibility and hazard zoning at different scales. Several zoning examples, referring to active, dormant and occasionally reactivated slides, provide insights into and highlight the relationships among different methods, levels and types of zoning. The examples also stress the importance of a correct characterization of the processes leading to landsliding to produce reliable susceptibility and hazard-zoning maps.     

Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and geotechnical approach

The Suusamyr region is located in the northern part of the Tien Shan Range in Central Asia. In 1992, this region was hit by the Ms = 7.3 Suusamyr earthquake triggering several large landslides along the Suusamyr Valley and on the southern slopes of the adjacent Suusamyr Range. One of these landslides had been investigated by geophysical and geotechnical methods in order to determine local trigger factors. The present paper focuses on the influence of geological and morphological factors upon landslide occurrence on a regional scale. The analysis is based on a digital data set including landslides triggered in 1992 and several older landslides as well as various types of digital elevation models (DEMs), ASTER image data, and geological and active fault maps. These data were combined to compute landslide susceptibility (LS) maps using statistical methods, Landslide Factor and Conditional Analyses (LFA, CA), as well as a geotechnical one, the Newmark's Method (NM). The landslide data set was also analyzed with respect to the size–frequency relationship. An erratum to this article can be found at     

Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach.     

GIS-based landslide susceptibility for Arsin-Yomra (Trabzon,North Turkey) region

The purpose of this study is to assess the susceptibility of landslides around Yomra and Arsin towns near Trabzon, in northeast of Turkey, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analyses of the topographical map. The landslide triggering factors are considered to be slope angle, slope aspect, distance from drainage, distance from roads and the weathered lithological units, which were called as “geotechnical units” in the study. Idrisi and ArcGIS packages manipulated all the collected data. Logistic regression (LR) and weighted linear combination (WLC) statistical methods were used to create a landslide susceptibility map for the study area. The results were assessed within the scope of two different points: (a) effectiveness of the methods used and (b) effectiveness of the environmental casual parameters influencing the landslides. The results showed that the WLC model is more suitable than the LR model. Regarding the casual parameters, geotechnical units and slopes were found to be the most important variables for estimating the landslide susceptibility in the study area.     

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

Analysis of a spatial distribution of landslides triggered by the 2004 Chuetsu earthquakes of Niigata Prefecture, Japan

On October 23, 2004, a series of powerful earthquakes with a maximum M _w = 6.6 located near the western coast of northern Honshu struck parts of northern Japan, particularly Niigata Prefecture; these earthquakes were known as the Chuetsu event. Thousands of landslides, as a secondary geotechnical hazard associated with these earthquakes, were triggered over a broad area; these landslides were of almost all types. The purpose of this study was to detect correlations between landslide occurrence with geologic and geomorphologic conditions, slope geometry, and earthquake parameters using two indexes based on Geographic Information Systems (GIS). In the study area, the landslide–area ratio (LAR), which is defined as the percentage of the area affected by landslides, was 2.9%, and the landslide concentration (LC), the number of landslides per square kilometer, was 4.4 landslides/km², which is much more than other reported cases of seismic activity with the same magnitude. This was possibly due to heavy rainfall just before the Chuetsu earthquakes. Statistical analyses show that LAR has a positive correlation with slope steepness and distance from the epicenter, while LC is inversely correlated with distance from the epicenter. The Wanazu Formation had the most concentrated landslide activity, followed by the Kawaguchi, Ushigakubi, Shiroiwa and Oyama Formations, although the Wanazu Formation occupied only 4.5% of the total area of geological units. With 8.2% of the area affected by seismic landslides, the Kawaguchi Formation had the highest LAR. It was followed by the Shiroiwa, Ushigakubi and Wanazu Formations with LAR ranging from 4.6% to 6.0%. For lots of geological subunits, landslides are more frequent in a range of slope angles between 15° and 40°. The susceptibility to landsliding of each geologic unit was thus evaluated to correlate with slope steepness. It was also noted that the effects of the earthquakes were made far worse by antecedent rainfall conditions induced by a␣typhoon, and further research emphasizing the role of antecedent rainfall was discussed.     

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

The main purpose of this study is to highlight the conceptual differences of produced susceptibility models by applying different sampling strategies: from all landslide area with depletion and accumulation zones and from a zone which almost represents pre-failure conditions. Variations on accuracy and precision values of the models constructed considering different algorithms were also investigated. For this purpose, two most popular techniques, logistic regression analysis and back-propagation artificial neural networks were taken into account. The town Ispir and its close vicinity (Northeastern part of Turkey), suffered from landsliding for many years was selected as the application site of this study. As a result, it is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence (1) data were taken from the landslide masses. When the generalization capacities of the models are taken into consideration, these reactions cause imprecise results, even though the area under curve (AUC) values are very high (0.915 < AUC < 0.949). On the other hand, the susceptibility maps, based on the samplings in which the presence (1) data were taken from a zone which almost represents pre-failure conditions constitute more realistic susceptibility evaluations. However, considering the spatial texture of the final susceptibility values, the maps produced using the outputs of the back-propagation artificial neural networks could be interpreted as highly optimistic, while of those generated using the resultant probabilities of the logistic regression equations might be evaluated as pessimistic. Consequently, it is evident that, there are still some needs for further investigations with more realistic validations and data to find out the appropriate accuracy and precision levels in such kind of landslide susceptibility studies.     

A time-space based approach for mapping rainfall-induced shallow landslide hazard

A rainfall-induced shallow landslide is a major hazard in mountainous terrain, but a time-space based approach is still an unsettled issue for mapping rainfall-induced shallow landslide hazards. Rain induces a rise of the groundwater level and an increase in pore water pressure that results in slope failures. In this study, an integrated infinite slope analysis model has been developed to evaluate the influence of infiltration on surficial stability of slopes by the limit equilibrium method. Based on this new integrated infinite slope analysis model, a time-space based approach has been implemented to map the distributed landslide hazard in a GIS (Geographic Information Systems) and to evaluate the shallow slope failure induced by a particular rainfall event that accounts for the rainfall intensity and duration. The case study results in a comprehensive time-space landslide hazard map that illustrates the change of the safety factor and the depth of the wetting front over time.     

山地环境地质灾害易发性县级区划研究以重庆城口县为例

本文以重庆市城口县作为研究区域, 基于地理空间数据库, 结合逻辑回归模型, 开展山地环境地质灾害易发性县级区划研究。在研究过程中选取坡度、坡向、剖面曲率、微地貌、坡位、距水系距离、岩性、距断层距离、距道路距离、高程、归一化植被指数(NDVI)、顺逆向坡、年平均降雨量等13个因子作为诱发地质灾害的影响因子, 应用GIS技术将各个因子专题图层栅格化, 建立地理空间数据库。采用逻辑回归模型, 在R软件中进行逻辑回归模型训练, 得到各个影响因子的回归系数。建立地灾等级评价图, 并将危险等级划分为极低、低、中、高、极高5种类型。对于研究结果运用受试者工作特征曲线(ROC曲线)进行合理性检验, 位于ROC曲线下的面积AUC值为0.901, 结果表明逻辑回归模型适用于重庆市城口县山地环境地质灾害易发性区划研究。     

Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, many procedures have been used to produce such maps. In this study, a new attempt is tried to produce landslide susceptibility map of a part of West Black Sea Region of Turkey. To obtain the fuzzy relations for producing the susceptibility map, a landslide inventory database is compiled by both field surveys and airphoto studies. A total of 266 landslides are identified in the study area, and dominant mode of failure is rotational slide while the other mode of failures are soil flow and shallow translational slide. The landslide inventory and the parameter maps are analyzed together using a computer program (FULLSA) developed in this study. The computer program utilizes the fuzzy relations and produces the landslide susceptibility map automatically. According to this map, 9.6% of the study area is classified as very high susceptibility, 10.3% as high susceptibility, 8.9% as moderate susceptibility, 27.5% as low susceptibility and 43.8% as very low susceptibility or nonsusceptible areas. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. For this purpose, strength of the relation (r_ij) and the root mean square error (RMSE) values are calculated as 0.867 and 0.284, respectively. These values show that the produced landslide susceptibility map in the present study has a sufficient reliability. It is believed that the approach employed in this study mainly prevents the subjectivity sourced from the parameter selection and provides a support to improve the landslide susceptibility mapping studies.     

Climatic effect of ENSO associated with landslide occurrence in the Central Andes, Mendoza Province, Argentina

Temporal distribution of landslides can be verified by means of climatic anomalies linked to the ENSO phenomenon. An increasing number of landslides triggered by rainfall have been recorded during warm episodes (El Niño) in the Cordillera Frontal, and a decreasing number during cold episodes (La Niña), concluding that this geological province is mainly influenced by the Pacific Anticyclone. However, slope instability in the Precordillera, located east of the Cordillera Frontal, seems to be mainly influenced by the Atlantic Anticyclone. Analysis of variance shows that there is no significant difference between landslide records and cold-warm episodes, and a higher number of landslides were recorded in years linked to wet periods than during dry periods. Furthermore, the precipitation threshold value associated with landslide occurrence and antecedent precipitation are analysed.     

Landslide susceptibility mapping using GIS-based weighted linear combination,the case in Tsugawa area of Agano River,Niigata Prefecture,Japan

A spatial database of 791 landslides is analyzed using GIS to map landslide susceptibility in Tsugawa area of Agano River. Data from six landslide-controlling parameters namely lithology, slope gradient, aspect, elevation, and plan and profile curvatures are coded and inserted into the GIS. Later, an index-based approach is adopted both to put the various classes of the six parameters in order of their significance to the process of landsliding and weigh the impact of one parameter against another. Applying primary and secondary-level weights, a continuous scale of numerical indices is obtained with which the study area is divided into five classes of landslide susceptibility. Slope gradient and elevation are found to be important to delineate flatlands that will in no way be subjected to slope failure. The area which is at high scale of susceptibility lies on mid-slope mountains where relatively weak rocks such as sandstone, mudstone and tuff are outcropping as one unit.     

A preliminary application of weights of evidence in gold exploration in Xiong-er Mountain Region, He-Nan Province

Weights of evidence modeling for combining indicator patterns in mineral resource evaluation is based on an application of Bayes' rule. Two weights are defined for each indicator pattern and Bayes' rule is applied repeatedly to combine indicator patterns. If all patterns are conditionally independent with respect to deposits, the logit of the posterior probability can be calculated as the sum of the logit of the prior probability plus the weights of the overlay patterns. The information to be integrated for gold exploration in Xiong-er Mountain Region comes from a geological map, an interpreted map of a Thematic Mapper (TM) image, and the locations of known gold deposits. Favorable stratigraphic units, structural control factors, and alteration factors are considered. The work was conducted on an S600 I²S image-processing system. FORTRAN programs were developed for creating indicator patterns, statistical calculations, and pattern integration. Six indicator patterns were selected to predict mineral potential. They are conditionally independent according to pairwiseG ² tests, and an overall chi-square test. The potential area predicted using the 32 known deposits generally coincides with the prospect areas determined by geological fieldwork.     

Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.     

国道213汶川—松潘段滑坡隐患遥感识别与易发性评价

国道213汶川—松潘段位于强震山区,滑坡灾害频发,每年都会因滑坡灾害导致交通中断,迫切需要查明沿线滑坡隐患的空间分布,并对其易发性进行评价。利用光学遥感和InSAR综合遥感技术对沿线滑坡隐患进行识别,共识别滑坡隐患288处,其中InSAR探测出有形变的滑坡隐患点27处。以识别出的滑坡隐患为评价样本,选取高程、坡度、坡向、地表曲率、工程地质岩组、归一化植被指数(normalizied difference vegetation index,NDVI)、距道路距离、距河流距离、距断层距离等9个影响因素作为评价因子,采用Logistic回归模型评价该沿线滑坡隐患易发性,评价结果可为国道213汶川—松潘段滑坡灾害防治提供参考。