Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria,Italy) with considerations on the role of causal factors

The “Costa Viola” mountain ridge (southern Calabria), in the sector between Bagnara Calabra and Scilla, is particularly exposed to geo-hydrological risk conditions. The study area has repeatedly been affected by slope instability events in the last decades, mainly related to debris slides, rock falls and debris flows. These types of slope movements are among the most destructive and dangerous for people and infrastructures, and are characterized by abrupt onset and extremely rapid movements. Susceptibility evaluations to shallow landslides have been performed by only focusing on source activation. A logistic regression approach has been applied to estimating the presence/absence of sources in terms of probability, on the basis of linear statistical relationships with a set of territorial variables. An inventory map of 181 sources, obtained from interpretation of air photographs taken in 1954–1955, has been used as training set, and another map of 81 sources, extracted from 1990 to 1991 photographs, has been adopted for validation purposes. An initial set of 12 territorial variables (i.e. lithology, land use, soil sand percentage, elevation, slope angle, aspect, across-slope and down-slope curvatures, topographic wetness index, distance to road, distance to fault and index of daily rainfall) has been considered. The adopted regression procedure consists of the following steps: (1) parameterization of the independent variables, (2) sampling, (3) calibration, (4) application and (5) evaluation of the forecasting capability. The “best set” of variables could be identified by iteratively excluding one variable at a time, and comparing the ROC results. Through a sensitivity analysis, the role of the considered factors in predisposing shallow slope failures in the study area has been evaluated. The results obtained for the Costa Viola mountain ridge can be considered acceptable, as 98.1 % of the cells are correctly classified. According to the susceptibility map, the village of Scilla and its surroundings fall in the highest susceptibility class.     

Landslide susceptibility mapping based on frequency ratio and logistic regression models

The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning.     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.     

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.     

东营凹陷盐22块沙四上亚段砂砾岩粒度概率累积曲线特征

在岩芯观察和沉积相分析基础上,利用粒度分析资料对东营凹陷北部陡坡带盐22块沙四上亚段近岸水下扇砂砾岩体的粒度概率累积曲线进行了研究。研究表明,该地区近岸水下扇砂砾岩中的粒度概率累积曲线主要包括：＂上拱弧形＂、＂简单一段式＂、＂准牵引流型＂等3种基本类型和＂近似上拱弧形＂、＂台阶状多段式＂、＂低斜率两段式＂、＂低斜率三段...     

Landslide susceptibility assessment in the İzmir (West Anatolia,Turkey) city center and its near vicinity by the logistic regression method

A landslide susceptibility assessment for İzmir city (Western Turkey), which is the third biggest city of Turkey, was performed by a logistic regression method. A database of landslide characteristics was prepared using detailed field surveys. The major landslides in the study area are generally observed in the field, dominated by weathered volcanics, and 39.63% of the total landslide area is in this unit. The parameters of lithology, slope gradient, slope aspect, distance to drainage, distance to roads and distance to fault lines were used as variables in the logistic regression analysis. The effect of each parameter on landslide occurrence was assessed from the corresponding coefficients that appear in the logistic regression function. On the basis of the obtained coefficients, lithology plays the most important role in determining landslide occurrence and distribution. Slope gradient has a more significant effect than the other geomorphological parameters, such as slope aspect and distance to drainage. Using a predicted map of probability, the study area was classified into five categories of landslide susceptibility: very low, low, moderate, high and very high. Whereas 49.65% of the total study area has very low susceptibility, very high susceptibility zones make up 11.69% of the area.     

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect.     

基于栅格径流汇流模拟的西藏林芝市泥石流灾害预警模型初探

西藏林芝市泥石流灾害频发,亟需建立泥石流灾害预警模型,预测林芝市泥石流灾害可能发生的区域,减少泥石流灾害导致的损失。文章提出了一种基于栅格径流汇流的林芝市泥石流灾害预警模型,从栅格像元尺度上模拟流域各位置上的水深,以提高泥石流预警的空间针对性。该模型将泥石流致灾因子分为背景因子和激发因子。通过林芝市裸岩率、河床纵比降等因子的逻辑回归,获取林芝市泥石流灾害概率,作为泥石流预警模型的背景因子;引入栅格径流汇流模型,以站点降水和雪水当量为模型的水量输入,模拟预警时段内的流域各位置上的模型水深,作为泥石流预警模型的激发因子。利用二元逻辑回归的方法计算背景因子和激发因子的权重,建立泥石流预警模型。利用2011—2020年18次历史灾害对模型进行验证,落入预警区内的灾害点占比64.4%,预警精度较高,对于林芝市泥石流灾害预警具有一定的指导意义。     

Slope instability in Nicaragua triggered by Hurricane Mitch: distribution of shallow mass movements

The heavy rains associated with Hurricane Mitch triggered off a number of slope instability processes in several Central American countries. Different instability processes have been acknowledged for the various mountainous regions of Nicaragua. An enormous movement of the Casita Volcano slopes resulted in numerous deaths and some deep movements have been reactivated. On the other hand, numerous shallow mass movements and debris flows have given rise to great material loss throughout a large part of Nicaraguan mountains.Mapping the shallow mass movements in an area of Central Nicaragua clearly reveals the close ties between their distribution and some geomorphological factors. A susceptibility model has been constructed for shallow mass movements based on field mapping of the shallow mass movement distribution, the geomorphological map as well as the digital slope and accumulated flow models. A logistical regression analysis was applied. The study area has been categorized into three classes of relative landslide susceptibility. Given that phenomena of this nature occur much more frequently in the high susceptibility class, 94% of the shallow mass movements that have been used to test the model are in the high and medium susceptibility classes . The geological and geomorphological conditions of the study area are representative of a large sector of the central Nicaraguan region. Consequently, the methodology followed in this paper is deemed to constitute a useful tool, both regarding the design of new infrastructures, and as a guide to the urban development of the area.     

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

汶川震区北川9.24暴雨泥石流特征研究

2008年9月24日汶川震区的北川县暴雨导致区域性泥石流发生,这次9.24暴雨泥石流灾害导致了42人死亡,对公路和其他基础设施造成严重损毁。本研究采用地面调查和遥感解译方法分析地震与暴雨共同作用下的泥石流特征,获取的气象数据用于分析泥石流起动的临界雨量条件。本文探讨了研究区泥石流起动和输移过程,并根据野外调查,分析了泥石流形成的降雨、岩石和断层作用,特别是强降雨过程与物源区对泥石流发生的作用。根据应急调查发现北川县境内暴雨诱发的泥石流72处,其分布受岩石类型、发震断层和河流等因素控制。根据对研究区震前和震后泥石流发生的临界雨量和雨强的初步分析,汶川地震后,该区域泥石流起动的前期累积雨量降低了14.8%～22.1%,小时雨强降低25.4 %～31.6%。震区泥石流起动方式主要有二种,一是由于暴雨过程形成的斜坡表层径流导致悬挂于斜坡上的滑坡体表面和前缘松散物质向下输移,进入沟道后转为泥石流过程;二是消防水管效应使沟道水流快速集中,并强烈冲刷沟床中松散固体物质,导致沟床物质起动并形成泥石流过程。调查和分析发现沟内堆积的滑坡坝对泥石流的阻塞明显,溃决后可导致瞬时洪峰流量特别大。研究结果表明了汶川震区已进入一个新的活跃期。因此,应该开展对汶川地震区的泥石流风险评估和监测、早期预警,采取有效的工程措施控制泥石流的发生和危害。     

Debris flow prediction models based on environmental factors and susceptible subarea classification in Sichuan, China

Environmental factors account for the occurrence of debris flow, as well as different weights of subareas with different risk levels. Considering the relationship between debris flow and rainfall (including the intraday rainfall and the effective rainfall of the previous 10 days), seven environmental factors, including elevation, slope, aspect, flow accumulation, vegetation coverage, soil, and land use, were added in this study. The whole area of Sichuan Province was divided into subareas according to different risk levels. Debris flow prediction models were then established by using a logistic regression model. Results showed that the prediction accuracy was decreased approximately by 3 % after the environmental factors were introduced to the entire study area. The prediction accuracy of the prediction models that comprised the introduced environmental factors was increased by 22.2, 9.7, and 14.3 % in different susceptible areas (moderately susceptible, highly susceptible, very highly susceptible), respectively, compared with that of the prediction models in which rainfall was only considered. Therefore, the research method that introduced the environmental factors may be used to improve the accuracy of debris flow prediction models based on susceptible area classification.     

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.     

Modeling debris flow initiation and run-out in recently burned areas using data-driven methods

In the framework of the landslide susceptibility assessment, the maps produced should include not only the landslide initiation areas, but also those areas potentially affected by the traveling mobilized material. To achieve this purpose, the susceptibility analysis must be separated in two distinct components: (1) The first one, which is also the most discussed in the literature, deals with the susceptibility to failure, and (2) the second component refers to the run-out modeling using the initiation areas as an input. Therefore, in this research we present a debris flow susceptibility assessment in a recently burned area in a mountain zone in central Portugal. The modeling of debris flow initiation areas is performed using two statistical methods: a bivariate (information value) and a multivariate (logistic regression). The independent validation of the results generated areas under the receiver operating characteristic curves between 0.91 and 0.98. The slope angle, plan curvature, soil thickness and lithology proved to be the most relevant predisposing factors for the debris flow initiation in recently burned areas. The run-out is simulated by applying two different methods: the empirical model Flow Path Assessment of Gravitational Hazards at a Regional Scale (Flow-R) and the hydrological algorithm D-infinity downslope influence (DI). The run-out modeling of the 36 initiation areas included in the debris flow inventory delivered a true positive rate of 83.5% for Flow-R and 80.5% for DI, reflecting a good performance of both models. Finally, the susceptibility map for the entire basin including both the initiation and the run-out areas in a scenario of a recent wildfire was produced by combining the four models mentioned above.     

喜马拉雅山地区泥石流发育特征研究

喜马拉雅山地区是世界上最高大的也是最年轻的山脉,这里地处两大板块的碰撞带,地质构造复杂,新构造运动强烈,山地灾害发育,泥石流异常活跃,经常给生命线工程和当地人民生命财产造成威胁。但是由于高寒缺氧,加之技术手段所限,研究程度一直很低。本文在对该区大型、巨型泥石流进行了遥感研究和实地调查资料基础上,发现该区泥石流存在16个泥石流较集中分布区,并且北坡比南坡更为发育。在此基础上,对其发育规律进行细致分析,发现:(1)喜马拉雅山地区泥石流的活动目前正处于活跃期; (2)研究区泥石流沟口主要分布在两个高程段,一个是2800~3400m范围,另一个是4200~4900m范围; (3)研究区衰败期泥石流沟道比降大都小于100,而发育期的泥石流沟道比降一般比较大,大都大于300,旺盛期的泥石流沟道比降则介于100~300之间。另一方面,我们发现本区冰雪融水与雨水型泥石流的沟道比降几乎相同,其动力条件相差不大。这对于该区泥石流灾害的防治、判断泥石流的活动性具有重要意义。     

基于逻辑回归模型的泥石流易发性评价与检验：以金沙江上游奔子栏—昌波河段为例

基于地理信息系统(ArcGIS100)平台和小流域单元,采用逻辑回归(LR)模型对金沙江上游(奔子栏—昌波河段)干热河谷区进行泥石流易发性评价,并对预测结果进行总体检验与随机个案检验。评价与检验结果表明,得到的最优指标组合下LR评价模型的AUC值为827%;预测的极高易发区、高易发区面积合占全区面积的3598%,实发泥石流面积占泥石流总面积的6503%;在个案检验中,位于各等级分区的检验组样本实发泥石流比例随着分区易发性等级降低,依次为917%(极高)、750%(高)、364%(中等)、167%(低)、0(极低),表明评价效果良好。研究区泥石流集中发育于金沙江沿岸的东北部、中部和西南部,主导性的评价指标依次为距主干道路距离、岩性、距断裂带距离、雨季月平均降雨量。人类活动与季节性降雨为研究区干热河谷泥石流的主要诱发条件。基于逻辑回归模型的泥石流易发性评价方法提高了泥石流发生可能性的预测精度,可为干热河谷区泥石流预测预警和防治提供参考依据。     

鄂西地区茅口组重力流沉积特征及古地理意义

通过对鄂西地区中二叠统详细的野外剖面测量及沉积微相分析,发现鄂西地区中二叠统茅口组发育重力流沉积,沉积类型多样,发育于碳酸盐岩斜坡沉积环境中,自上而下分为滑塌沉积、碎屑流沉积、颗粒流沉积。滑塌沉积主要发育于湖南慈利江垭剖面,颗粒流与碎屑流发育于湖北长阳资丘剖面。滑塌沉积物主要由杂乱堆积的棱角状—次棱角状砾屑灰岩组成,分...     

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model.     

Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills,Tamil Nadu (India)

Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds > 10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the stage for the event are the availability of materials, type of materials, stream power, slope gradient, aspect and curvature, lithology, land use and land cover, lineament density, and drainage. Rainfall is the most common triggering factor that causes debris flow in the Palar subwatershed and seismicity is not considered as it is a stable continental region and moderate seismic zone. Also, there are no records of major seismic activities in the past. In this study, one of the less explored heuristic methods known as the analytical network process (ANP) is used to map the spatial propensity of debris flow. This method is based on top-down decision model and is a multi-criteria, decision-making tool that translates subjective assessment of relative importance to weights or scores and is implemented in the Palar subwatershed which is part of the Western Ghats in southern India. The results suggest that the factors influencing debris flow susceptibility in this region are the availability of material on the slope, peak flow, gradient of the slope, land use and land cover, and proximity to streams. Among all, peak discharge is identified as the chief factor causing debris flow. The use of micro-scale watersheds demonstrated in this study to develop the susceptibility map can be very effective for local level planning and land management.