Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different probabilistic and bivariate statistical approaches; namely, frequency ratio (FR), weights-of-evidence (WofE), index-of-entropy (IofE), and Dempster–Shafer (DS) models, at Wadi Itwad, Asir region, in the southwestern part of Saudi Arabia. Landslide locations were identified and mapped from interpretation of high-resolution satellite images, historical records, and extensive field surveys. In total, 326 landslide locations were mapped using ArcGIS and divided into two groups; 75 % and 25 % of landslide locations were used for training and validation of models, respectively. Twelve layers of landslide-related factors were prepared, including altitude, slope degree, slope length, topography wetness index, curvature, slope aspect, distance from lineaments, distance from roads, distance from streams, lithology, rainfall, and normalized difference vegetation index. The relationships between the landslide-related factors and the landslide inventory map were calculated using different statistical models (FR, WofE, IofE, and DS). The model results were verified with landslide locations, which were not used during the model training. In addition, receiver operating characteristic curves were applied, and area under the curve (AUC) was calculated for the different susceptibility maps using the success (training data) and prediction (validation data) rate curves. The results showed that the AUC for success rates are 0.813, 0.815, 0.800, and 0.777, while the prediction rates are 0.95, 0.952, 0.946, and 0.934 for FR, WofE, IofE, and DS models, respectively. Subsequently, landslide susceptibility maps were divided into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the percentage of training and validating landslides locations in high and very high landslide susceptibility classes in each map were calculated. The results revealed that the FR, WofE, IofE, and DS models produced reasonable accuracy. The outcomes will be useful for future general planned development activities and environmental protection.     

Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga,Northern Algeria

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

     

Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy)

Landslide susceptibility studies focus on producing susceptibility maps starting from landslide inventories and considering the main conditioning factors. The validity of susceptibility maps must be verified in terms of model accuracy and prediction skills. This paper deals with a GIS-based landslide susceptibility analysis and relative validation in a hilly-coastal test-area in Adriatic Central Italy. The susceptibility analysis was performed via bivariate statistics using the Landslide-Index method and a detailed (field-based) landslide inventory. Selection and mapping of conditioning factors and landslide inventories was derived from detail geomorphological analyses of the study area. The susceptibility map was validated using recent (shallow) landslides in terms of both model accuracy and prediction skills, via Success rate and Prediction rate curves, respectively. In addition, a pre-existing official landslide inventory was applied to the model to test whether it can be used when a detailed (field-based) inventory is not available, thereby extending its usability in similar physiographic regions. The outcome of this study reveals that slope and lithology are the main conditioning factor of landslides, but also highlights the key role of surficial deposits in susceptibility assessment, for both their type and thickness. The validation results show the effectiveness of the susceptibility model in both model accuracy and prediction skills given the good percentage of correctly classified landslides. Moreover, comparison of the susceptibility map with the official Regional landslides inventory proves the possibility of using the developed susceptibility model also in the absence of detailed landslide mapping, by considering inventories that are already available.     

Assessing landslide susceptibility using Bayesian probability-based weight of evidence model

This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W ⁺ and W ^?) was used as weight for each factor’s type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %.     

Evaluating landslide susceptibility based on cluster analysis,probabilistic methods,and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

     

基于GIS的汶川地震滑坡灾害影响因子确定性系数分析

 2008年5月12日14时28分,四川省汶川发生了8.0级大地震,地震诱发了数以万计的滑坡灾害。在大约48 678 km2的区域内,采用震后航空像片与多源卫星影像解译并结合野外调查验证的方法,共圈定出48 007个地震滑坡灾害。在此基础上,选取地层、岩性、断裂、地震烈度、宏观震中、地表破裂调查点、地形坡度、坡向、顺坡向曲率、高程、水系与公路共12个影响因子作为汶川地震诱发滑坡影响因子,利用GIS强大的空间分析能力与确定性系数方法,对这12个影响因子进行敏感性研究。研究结果表明：(1) 寒武与震旦系是地震滑坡易发地层,侵入岩组、灰岩为主的岩组是地震滑坡发育的高敏感性岩组;(2) 地震滑坡受中央断裂影响最大,同时还受控于前山断裂,受后山断裂的影响较小;(3) 地震滑坡易发性分别随着地震烈度、与震中的距离、与地表破裂点距离的增加而减少;(4) 坡度大于40°是地震滑坡的易发坡度,E,ES方向为地震滑坡的易发坡向,高程范围为1 000～2 000 m,尤其是高程1 000～1 500 m范围为地震滑坡易发区;(5) 400 m水系缓冲区和2 000 m公路缓冲区范围内滑坡易发性较高。确定研究区内各地震滑坡影响因子最利于滑坡发生的数值区间,为进一步地震滑坡区域评价及预测奠定基础。     

Identification of landslide hazard and risk ‘hotspots’ in Europe

Landslides are a serious problem for humans and infrastructure in many parts of Europe. Experts know to a certain degree which parts of the continent are most exposed to landslide hazard. Nevertheless, neither the geographical location of previous landslide events nor knowledge of locations with high landslide hazard necessarily point out the areas with highest landslide risk. In addition, landslides often occur unexpectedly and the decisions on where investments should be made to manage and mitigate future events are based on the need to demonstrate action and political will. The goal of this study was to undertake a uniform and objective analysis of landslide hazard and risk for Europe. Two independent models, an expert-based or heuristic and a statistical model (logistic regression), were developed to assess the landslide hazard. Both models are based on applying an appropriate combination of the parameters representing susceptibility factors (slope, lithology, soil moisture, vegetation cover and other- factors if available) and triggering factors (extreme precipitation and seismicity). The weights of different susceptibility and triggering factors are calibrated to the information available in landslide inventories and physical processes. The analysis is based on uniform gridded data for Europe with a pixel resolution of roughly 30 m × 30 m. A validation of the two hazard models by organizations in Scotland, Italy, and Romania showed good agreement for shallow landslides and rockfalls, but the hazard models fail to cover areas with slow moving landslides. In general, the results from the two models agree well pointing out the same countries with the highest total and relative area exposed to landslides. Landslide risk was quantified by counting the number of exposed people and exposed kilometers of roads and railways in each country. This process was repeated for both models. The results show the highest relative exposure to landslides in small alpine countries such as Lichtenstein. In terms of total values on a national level, Italy scores highest in both the extent of exposed area and the number for exposed population. Again, results agree between the two models, but differences between the models are higher for the risk than for the hazard results. The analysis gives a good overview of the landslide hazard and risk hotspots in Europe and allows a simple ranking of areas where mitigation measures might be most effective.     

Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier

Along mountain roads, rainfall-triggered landslides are typical disasters that cause significant human casualties. Thus, to establish effective mitigation measures, it would be very useful were government agencies and practicing land-use planners to have the capability to make an accurate landslide evaluation. Here, we propose a machine learning methodology for the spatial prediction of rainfall-induced landslides along mountain roads which is based on a random forest classifier (RFC) and a GIS-based dataset. The RFC is used as a supervised learning technique to generalize the classification boundary that separates the input information of ten landslide conditioning factors (slope, aspect, relief amplitude, toposhape, topographic wetness index, distance to roads, distance to rivers, lithology, distance to faults, and rainfall) into two distinctive class labels: ‘landslide’ and ‘non-landslide’. Experimental results with a cross validation process and sensitivity analysis on the RFC model parameters reveal that the proposed model achieves a superior prediction accuracy with an area under the curve  of 0.92. The RFC significantly outperforms other benchmarking methods, including discriminant analysis, logistic regression, artificial neural networks, relevance vector machines, and support vector machines. Based on our experimental outcome and comparative analysis, we strongly recommend the RFC as a very capable tool for spatial modeling of rainfall-induced landslides.

     

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibility.      

Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways

The 2015 Gorkha earthquake (Mw?=?7.8) caused significant earthquake triggered landslides (ETL) in a landscape that is heavily intervened by rainfall triggered landslides (RTL). China’s Belt and Road Initiative plan to boost South-Asian regional trade and mobility through two key highway corridors, i.e. 1) Longmu–Rasuwa–Kathmandu (LRK) and 2) Nyalam–Tatopani–Kathmandu (NTK) route, that dissect the Himalayas through this geologically unstable region. To understand the spatial characteristics and susceptibility of these ETL and RTL, we delineate the landslides by means of time variant satellite imageries, assess their spatial distribution and model their susceptibilities along the highway slopes. We use a coupled frequency ratio (FR) – analytical hierarchy process (AHP) model by considering nine landslide determinants, e.g. geomorphic type (slope, aspect, curvature, elevation), hydrologic type (erosive potential of gullies, i.e. stream power index and distance to streams), normalized difference vegetation index, lithology and civil structure type (i.e. distance to roads). The results demonstrate that elevation and slope predominantly control both these landslide occurrences. The model predicts locations of ETL with higher accuracy than RTL. On comparison, NTK was safer with 133.5 km² of high RTL or ETL (or both) landslide susceptible areas, whereas LRK has 216.04 km². For mapping the extent of these landslides, we constricted it to the slope units of highways to reduce the computational effort, but this technique successfully achieved an acceptable threefold average model prediction rate of 82.75% in ETL and 77.9% in RTL. These landslide susceptibility maps and route comparisons would provide guidance towards further planning, monitoring, and implementing landslide risk mitigation measures for the governments.

     

A comparative assessment of information value,frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed,India

This research work presents a comparative performance of geographic information system (GIS)-based statistical models for landslide susceptibility mapping (LSM) of the Himalayan watershed in India. A total of 190 landslide locations covering an area of 14.63 km² were identified in the watershed, using high-resolution linear imaging self-scanning (LISS IV) data. The causative factors used for LSM of the study area are slope, aspect, lithology, curvature, lineament density, land cover and drainage buffer. The spatial database has been prepared using remote sensing data along with ancillary data like geological maps. LSMs were prepared using information value (InV), frequency ratio (FR) and analytical hierarchy process (AHP) models. The validation results using the prediction rate curve technique show 89.61%, 87.12% and 88.26% area under curve values for FR, AHP and InV models, respectively. Therefore, the frequency ratio (FR) model could be used for LSM in other parts of this hilly terrain.

     

Factor analysis for the statistical modeling of earthquake-induced landslides

Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes. In most existing studies, the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records. Unlike rainfall-induced landslides, earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems, and the development of the models for these landslides should instead depend on early earthquake warnings and estimations. Hence, in this study, factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes. Factors such as the slope gradient, lithology (geology), aspect, and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.     

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

     

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

     

滑坡的易滑度分区及其概率预报模式

滑坡的发生主要由地形、地质和降雨三方面因素决定。基于区域地质-气象耦合分析的思路,有可能提高区域性滑坡的预报精度。首先通过将降雨条件和地质环境条件相结合的方法,提出一种新的滑坡概率预报模式,利用数理统计分析,提出利用最大24h雨强和前15d实效降雨量作为滑坡灾害发生的短期预报判据;然后以重庆市区作为研究对象,选择岩性组合、地形坡度、边坡形态、岩体结构和水文地质五大因素及其21种状态为预测变量,利用信息量法进行了易滑度的分区;最后,对概率预报滑坡的可行性进行了实例分析和探讨。     

基于GIS的滑坡预测模型的预测率及其作用

滑坡预测模型的选择直接影响到滑坡预测的准确性,是滑坡预测的关键所在。该研究利用意大利Alpago地区的滑坡数据和其他相关地理空间数据,以模糊伽马模型、模糊代数积模型、模糊代数和模型以及模糊最小模型等4个定量滑坡预测模型为例,探讨滑坡预测模型的预测率在对比、评价和选择不同模型方面的作用。滑坡预测模型的预测率是,模型预测结果图的各个级别类型中,未用于建模的滑坡面积百分比的累积分布函数。在地理信息系统中,利用已知的滑坡分布数据和模型的预测结果图,可以计算滑坡预测模型的预测率。研究结果表明,滑坡模型的预测率是滑坡预测模型自身特性的度量,在输入图层和滑坡类型确定的条件下,滑坡预测模型的预测率可作为对比、评价和选择不同模型的定量指标,可以用来确定最合适的预测模型。     

证据权法在滑坡危险度区划研究中的应用

采用证据权法对长江三峡库区秭归--巴东段进行了滑坡危险度区划研究。数据源主要包括地质图、SPOT5多光谱卫星影像数据及数字高程模型(DEM)等。利用RS和GIS的数据提取和分析功能,分别提取了地质(岩性和构造)、地形(坡度、坡向、水系、高程、沟壑缓冲区、沟壑密度)、水文地质(植被指数)和破坏动力(河流缓冲区)等对研究区滑坡发育影响较大的影响因子的信息。将上述各种影响因子进行分级,建立若干证据层;然后根据已知滑坡在不同证据层中的分布,确定相应证据层的权重值;最后根据不同影响因子权重值的叠加来确定具体某一个单元滑坡发育的概率。分析结果与现有滑坡的分布情况比较吻合。采用证据权法可以客观定量地评价各种影响因子对滑坡发育的影响程度,并据此进行滑坡危险度评价因子(证据因子)的选择及危险度定量评价。     

Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon,Seoul, Korea

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.     

基于GIS技术的区域性滑坡发生概率分析

在MAPGIS平台基础上，针对边坡空间失稳破坏的特征，采用GIS空间数据分析功能，探讨了三峡库区奉节段(三蹬子-王家河)主要滑坡的稳定状况，利用MAPGIS系统对其进行了边坡失稳的确定性系数(CF）计算，并进行滑坡因子敏感性分析，确定了各因子中对应于滑坡发生的最不利数值区间，得出在现有资料条件下，该区段滑坡发生的敏感条件为坡度和边坡所处位置高程的结论。     

GIS支持下基于层次分析法的汶川地震区滑坡易发性评价

 2008年5月12日14时28分,四川汶川发生了Ms8.0级大地震,地震诱发了数以万计的滑坡灾害。在大约48 678 km2的区域内,采用震后遥感影像解译并结合野外调查的方法,共解译出48 007个滑坡。应用GIS技术,建立了汶川地震诱发滑坡灾害及相关地形、地质空间数据库,分析了断层、岩性、高程、坡度、坡向、河流、公路等7个因素与滑坡分布的关系,应用滑坡面积百分比这一标准来分别衡量每个因素中各个级别对滑坡的影响程度;然后使用层次分析法对这7个参数进行权重分析;在GIS平台下对这些参数进行综合分析,通过分析结果将研究区内滑坡按易发程度分为极高易发区、高易发区、中易发区、低易发区与极低易发区5类,极高易发区与高易发区面积约8 211 km2,占研究区总面积的16.9%;最后,使用汶川地震滑坡数据库对研究结果进行检验,检验曲线表明分区效果良好,其中极高易发区与高易发区内实际发生滑坡面积为430 km2,占滑坡总面积的60.5%。