GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

GIS COMPONENT BASED 3D LANDSLIDE HAZARD ASSESSMENT SYSTEM: 3DSLOPEGIS

In this paper, based on a new Geographic Information System (GIS) grid-based three-dimensional (3D) deterministic model and taken the slope unit as the study object, the landslide hazard is mapped by the index of the 3D safety factor. Compared with the one-dimensional (1D) model of infinite slope, which is now widely used for deterministic model based landslide hazard assessment in GIS, the GIS grid-based 3D model is more acceptable and is more adaptable for three-dimensional landslide. Assuming the initial slip as the lower part of an ellipsoid, the 3D critical slip surface in the 3D slope stability analysis is obtained by means of a minimization of the 3D safety factor using the Monte Carlo random simulation. Using a hydraulic model tool for the watershed analysis in GIS, an automatic process has been developed for identifying the slope unit from digital elevation model (DEM) data. Compared with the grid-based landslide hazard mapping method, the slope unit possesses clear topographical meaning, so its results are more credible. All the calculations are implemented by a computational program, 3DSlopeGIS, in which a GIS component is used for fulfilling the GIS spatial analysis function, and all the data for the 3D slope safety factor calculation are in the form of GIS data (the vector and the grid layers). Because of all these merits of the GIS-based 3D landslide hazard mapping method, the complex algorithms and iteration procedures of the 3D problem can also be perfectly implemented.

黄河流域近40年土地利用/覆被时空变化特征

[目的]研究土地利用/覆被时空变化特征对于黄河流域土地资源保护与合理开发具有重要意义,可为黄河流域生态保护与高质量发展战略实施提供重要参考。[方法]基于黄河流域1980-2021逐年土地利用/覆被数据,采用Mann-Kendall和滑动t检验、地学信息图谱、标准差椭圆和重心迁移模型等方法,探究近40年黄河流域土地利用/覆被变化特征。[结果](1)1980-2021年林地、灌丛、水域、建设用地显著增加,耕地、草地、湿地显著减少。(2)流域土地利用/覆被变化时段可划分为1980-1992年,1993-2001年,2002-2012年,2013-2021年,数量结构逐渐呈现快速变化特点,建设用地、湿地和水域变化敏感。土地利用结构均衡度逐渐增强,呈现的阶段性变化与实施的重大战略措施关联性高,并与划分的阶段较符合。(3)流域中上游以2012-2021年和间歇变化型、耕地和草地的相互转换为主,下游以2001-2012年和2012-2021年变化型、耕地转为建设用地和草地为主。流域内宁夏北部、内蒙古东部及部分沿黄河水系分布的地区变化频繁。(4)湿地、水域和建设用地的重心向西迁移,分别呈现向西南集中分布、向东分散、向西北集中分布态势。[结论]土地利用结构在政策导向下总体趋势变好,未来应重点关注湿地以及土地利用变化频繁的地区。

基于GIS的空间TOPSIS模型的安徽省休宁县滑坡易发性评价

文章在常规TOPSIS(technique for order preference by similarity to ideal solution)模型的基础上提出空间TOPSIS模型,以安徽省休宁县为例,采用地理信息系统(geographic information system,GIS)技术对安徽省休宁县的滑坡易发性进行评价。研究表明:基于GIS的空间TOPSIS模型的滑坡易发性评价,能够处理和分析滑坡评价因子以及滑坡易发性的空间变化特征;评价结果与休宁县境内已经发生滑坡的空间分布较为吻合,评价结果较好。基于GIS的空间TOPSIS模型在滑坡易发性评价中具有较好的适用性。

Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India

Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics(ROC) curve, spatially agreed area approach and seed cell area index(SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

Landslide Susceptibility Assessment of the Youfang Catchment using Logistic Regression

A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.

GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan

District Ghizer is a rugged mountainous territory which experiences several landslides each year. There are 16 major landslide areas and 53 villages that are at high risk to hazards. Keeping in view the severity of natural hazards, the present study was designed to generate landslide susceptibility map based on twelve causative factors viz., slope, aspect, elevation, drainage network, Stream Power Index (SPI), Topographic Wetness Index (TWI), lithological units, fault lines, rainfall, road network, land cover and soil texture. Soil texture was determined by particle size analysis and data for other factors were acquired from freely available sources. Analytical Hierarchy Process (AHP) was employed to identify major landslide causative factors in the district Ghizer. Further, a temporal assessment from 1999 till 2015 was generated to assess the impact of land cover change on landslides. It indicated that the barren soil/ exposed rocks and glaciers have reduced while the vegetation and water classes have shown increment. The total area that lies in moderate to very high landslide susceptible zones was 74.38%, while slope is the main landslide causative factor in the district Ghizer. Validation of the susceptibility map showed 88.1% of the landslides in the study area had occurred in the moderate to very high susceptible zones.

Landslide Susceptibility Mapping and Evaluation along a River Valley in China

Landslide susceptibility evaluation at regional scale is commonly performed based dominantly on the analysis of geological and geomorphological conditions of historical landslide cases. The main content of this type of evaluation covers identifying key casual factors, their critical groupings and relative importance. The present study demonstrates an application of the above concept to a 90 km long segment of Jinsbajiang River valley in China. Correlations of landslide occurrence with potential causative factors are derived according to interpretation of field investigation. Lithology, orientation of bedding planes, slope angle, stream action, rainfall and earthquake intensity are selectively recognized as identifiable/measurable causative factors to establish a factor domain. The membership grades, for field values of quantitative factors, to the susceptibility classes are determined based on the construction of fuzzy sets, while those for descriptive factors are assigned from a fuzzy score table. The analytic hierarchy process （AHP） is adopted for assigning weights to each individual factor. Subsequently, the evaluation is implemented in a GIS program IDRISI, where four classes of landslide susceptibility are identified and delineated in the subject area. The approach described in the present paper showed consistence with the nature and availability of data for evaluating landslide susceptibility at regional scale. The methodology presented can be effectively employed by relevant authorities to identify risky areas for dislocating major infrastructural project, and develop management strategies for land use.

Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt

Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.

An easy approach to assess the susceptibility of a landslide by utilizing simple raster overlay methods: A case study on Huangtupo landslide(P.R. China)

Semi qualitative index based methods using rankings and ratings are commonly used in susceptibility estimations over a wide area. However, generalized ranking and ratings are not applicable for one single landslide. This paper gives an easy and transferable approach to a susceptibility assessment of Huangtupo landslide(P.R. China), using raster addition without taking account for ranking and ratings. Slope, aspect, curvature, location and drainage buffer distance raster data sets have been obtained out of open source digital elevation models using ESRI's Arc GIS. These conditioning factor raster data sets have been translated into raster data sets including simple yes or no criteria, referring to triggering or not. Subsequently they have been added by raster math to acquire a simple raster overlay map.After that this map is compared to initial displacement measurements, obtained by using a ground based synthetic aperture radar device. Acquired data is recalculated to a raster data set using the same spatial extent, to provide the possibility of comparison of the two raster data sets. The results reveal, that 76.35% of all measured movements occur in areas where raster cells include three or more conditioning factors, indicating that easy raster math operations can lead to satisfying results in local scale.

Vulnerability to Landslides in the City of Sao Paulo

The City of Silo Paulo is the largest urban occupation in Brazil, covering 1,500 km^2. Its population is approximately 12 million inhabitants, 12% of them living in subnormal agglomerates, the IBGE （Brazilian Agency for Statistics and Geography） denomination for informal settlements, mostly located in fiver floodplain areas or top of hills. The extreme high level of soil sealing and high slopes have a fundamental role to explain the increase the risks of mass movements or landslides, related to precipitation magnitude and duration. This article presents the construction of curves coupling landslides occurrences and precipitation based on the data analysis of different basins in the city of Sao Panlo, considering also the probability and duration of the rainfall event in order to establish a vulnerability index to estimate the vulnerability category of an specific area. Based on the data analysis of instantaneous radar rain records for seven extreme rainfall events where landslides occurrences records were available, it was possible to plot sigmoid curves linking the number of occurrences with the event probability in terms of return period.

抚顺西露天矿南帮滑坡体空间模型构建及应用

以地质调查资料和不同时期数字正射影像图(DOM)、数字线划图(DLG)等空间信息为数据源,应用GIS技术构建抚顺西露天矿南帮滑坡体的三维空间模型,计算出了滑坡体的体积,并利用滑坡体空间模型成果,对滑坡体的空间分布、运移形态及压脚土方工程量计算进行了空间分析,这对滑坡的成因分析及治理措施研究具有一定的参考意义。

基于信息量模型的滑坡灾害易发性评价——以重庆市为例

以滑坡灾害发育严重的重庆市为研究区,基于相关分析结果选取坡度、坡向、地层岩性、距构造距离、距水系距离、降雨量、土壤类型、土地利用类型、距道路距离、归一化植被指数(NDVI)10个指标构成易发区评价指标体系。以2000—2020年发生的8756个历史滑坡灾害点为样本数据,在遥感和地理信息系统的支持下,采用信息量模型开展研究区滑坡易发性评价。结果表明:影响研究区滑坡发育的主要指标为水系、NDVI、坡度,其中信息量值排名前5位的分别为:距水系距离[200 m,500 m)(0.809)、NDVI中的[8%,38%)(0.563)、坡度[13°,20°)(0.500)、距水系距离[0,200 m)(0.429)、距水系距离[500 m,1000 m)(0.428);极高易发区和高易发区分别占研究区面积的17.26%和31.82%,极高易发区的空间分布与水系分布高度吻合,信息量模型的评价精度为78.20%。研究结果可为重庆市滑坡灾害预测预报和做好防灾减灾工作提供技术参考。

3S技术支持下吕梁地区崩塌滑坡地质灾害调查及特征分析

为有效开展崩塌滑坡等灾害的防控工作,将灾害损失降到最小,以3S技术为基础,针对吕梁地区崩塌滑坡地质灾害调查及特征展开分析。探究吕梁地区的地形地貌、气象水文和人类活动等背景信息;利用遥感、地理信息系统和全球定位系统技术,建立地质灾害数据采集与分析系统,针对吕梁地区崩塌和滑坡的演化模式进行分析研究,确定灾害危险区域;计算灾害参数间的拟合程度,分析幂律特征,明确崩塌滑坡和体积之间存在较强的相关性;将灾害点空间分布密度作为指标,划分不同区域的危险程度;分析灾害的时间分布特征,发现该地区的灾害频发时段为汛期。所提方法能够为地质灾害预防和控制工作提供依据,对于减轻灾害损失具有重要意义。

遥感和GIS技术在滑坡地质灾害风险评估中的应用

四川处在青藏高原和四川盆地过渡带,受地形、地貌、地质构造条件和暴雨、地震等诱发因素频发影响,成为全国地质灾害最重的省份之一。本文以其研究区为例,对该区域的地质灾害进行综合评估和分析,采用AHP对研究区进行风险等级评定,并基于GIS技术绘制出风险等级格子图。

基于GIS技术的巴东新城区滑坡灾害危险性区划

基于ArcGIS8软件平台开发了三峡库区巴东县新城区滑坡灾害信息系统,通过全面分析巴东新城区滑坡灾害的地形条件,工程地质岩组、构造与斜坡结构类型、人类工程活动、水的作用等影响因素,建立了相应的滑坡灾害危险性评价指标体系。采用基于GIS技术的信息量模型和敏感性评价方法,实现了巴东县新城区滑坡灾害危险性区划,其中,高危险区面积3.30km2,占7.1%;中危险区面积5.77km2,占12.4%;低危险区面积16.40km2,占35.3%;基本安全面积21.05km2,占45.2%,可以作为巴东新城区城镇建设规划和减灾防灾的参考依据。

基于三维地理信息的滑坡监测及变形预测智能分析

将滑坡监测与当前先进的三维可视化及地理信息技术相结合,建立了三维滑坡的监测信息系统,实现了滑坡监测信息与监测场址三维地理信息的综合表达,为滑坡监测方案设计和监测成果的综合分析提供了一个可视化的信息平台。针对滑坡位移复杂的非线性演化问题,结合时间序列分析的基本思想,采用遗传算法确定时间序列模型的结构和参数,从而获得滑坡变形的预测模型。以福宁高速公路八尺门滑坡为研究实例,介绍了三维滑坡监测信息系统的结构、功能及算法,并对滑坡变形的非线性位移进行了智能预测分析。

近30年来喜马拉雅山脉西段纳木那尼峰地区冰川变化的遥感监测研究

本文从1976,1990,1999的Landsat及2003年ASTER系列数字遥感影像上提取了喜马拉雅山脉西段纳木那尼峰地区的4期冰川空间分布数据,在Arc/Info中综合各期数据,建立研究区1976—2003年冰川变化图谱,定量分析了纳木那尼峰地区冰川的空间变化。图谱这种数据集成的方法明显优于直接将各期影像分类结果进行比较的传统研究方法。研究结果表明,纳木那尼峰地区的冰川以广泛退缩为主,但北部也有少量前进冰川存在。自1976年到2003年,冰川面积从84.41km2减少到77.29km2,各时段分别以平均0.17km/a,0.19km/a和0.77km/a的速度在退缩,冰川退缩明显加速。冰川退缩面积占研究区总面积的8.4%,明显比高亚洲冰川平均退缩比例(7%)大,表明西喜马拉雅山脉的冰川退缩比较严重。