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1.
Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.  相似文献   

2.
Shiuan Wan   《Engineering Geology》2009,108(3-4):237-251
Spatial decision support system (SDSS) is an interactive, computer-based system designed to support a user in achieving a higher effectiveness of decision-making while solving a semi-structured spatial data. Satellite Remote Sensing and Digital Elevation Modeling are providing a systematic, rational framework for advancing scientific knowledge of our SDSS of geophysical phenomena that, often lead to observe the natural hazards or resources. Taking the advantage of these, more specifically, our study focused on using these to collect and measure the landslide data on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Our source data includes (1) Digital Elevation Modeling is also used to investigate the landform, and (2) remote sensing image data are also employed to analyze the vegetation conditions. In addition, the process of generating landslide susceptibility maps involved on how to effectively extract the site-condition dominant attributes and thresholds for displaying the landslide occurrence accurately. Thus, the information from landslide must be categorized and thoroughly evaluated by an Advanced Data Mining Technique — Entropy-based classification method to construct the landslide knowledge rules. The knowledge scope with regards to core factors and thresholds are solved. Then, the susceptibility hazard maps are drawn and verifications are made. On the other hand, the conventional statistical method of Logistic Regression is used for comparison.  相似文献   

3.
This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.  相似文献   

4.
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献   

5.
区域滑坡易发性评价是国土规划和滑坡中长期防治的重要依据。为进一步提高滑坡易发性评价的准确性,以恩施市龙凤镇为研究区,运用地理信息系统GIS技术,获取了包括工程岩组、坡度、地质构造等在内的13个初始评价因子,利用基于遗传约简算法的粗糙集理论对初始评价因子进行属性约简,去掉冗余属性后获得最小约简,即8个核评价因子:工程岩组、高程、地形曲率、道路、水系、坡度、坡向、径流强度指数,并以此作为BP神经网络的输入层,构建RS-BPNN预测模型,获得滑坡易发性指数LSI及滑坡易发性等级分区图。其中高易发区面积占总面积的12.82%,该区包含的滑坡面积占总滑坡面积的78.11%,通过ROC曲线测试,模型预测精度为90.9%。结果表明,RS-BPNN模型预测性能良好,进一步提高了滑坡易发性评价的精度和准确性,有较高的工程实用价值。   相似文献   

6.
Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.  相似文献   

7.
Mapping landslide susceptibility in Travis County, Texas, USA   总被引：4，自引：0，他引：4  
A geographic information system (GIS) was used to construct a landslide hazard map for Travis County, Texas. The County is experiencing rapid growth, and development has encroached into unstable terrain that is vulnerable to landslides. Four layers of data were superimposed to create the landslide hazard map. Slope was given the most emphasis, followed by geology, vegetation, and proximity to faults. The final map shows areas of low, medium, and high landslide susceptibility. Areas of high susceptibility occupy stream and reservoir banks, rock escarpments, and agricultural land. The landslide hazard map can be a useful geologic criterion for land use planning. Planners can use the map to allocate appropriate land uses to unstable terrain, and to identify existing structures at risk from landslide activity. The methods presented in this paper can be adapted to other counties in the U.S. and elsewhere. Results of this study suggest that geographic information systems can effectively compile and overlay several data layers relevant to landslide hazards.  相似文献   

8.
Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.  相似文献   

9.
区域滑坡易发性的研究是滑坡空间预测的核心内容之一。从影像多尺度分割和面向对象的分类理论出发,以研究区遥感影像的熵、能量、相关性、对比度共4个参数作为影像纹理因子提取易发性特征,利用滑坡所处区域的库水影响等级、坡度、斜坡结构、工程岩组4类地质因子分析地质背景,搭建C5.0决策树的易发性分类模型,实现了对研究区内4类滑坡易发性单元的预测。结果表明:高易发性单元的工程岩组通常发育为软岩岩组和软硬相间岩组,且坡度在15°～30°之间;模型显示该区域训练样本和测试样本平均正确率达91.64%,Kappa系数分别为0.84,0.51,因此这种基于影像多尺度分割与地质因子分级的滑坡易发性分类研究具有一定的适用性。  相似文献   

10.
In Taiwan many reservoirs are constructed in mountain areas. Unfortunately, several earthquakes shook the soil, and typhoons brought a huge amount of water to the reservoir zone. In the past studies, remote-sensing image data were used to effectively monitor the landslide near reservoirs. In recent years, linear discriminant analysis (LDA) has become a well-known method for image classification. However, there are few studies to optimize the linear classification function. While the ancillary information has been adopted easily by new methodologies, the ancillary information must be examined by a landslide image classification system. To explore the effects of optimization on the LDA equations, three approaches were compared: (a) conventional LDA; (b) combined discrete rough sets and LDA (DRS + LDA), which identify the core factors and the corresponding thresholds of landslide occurrence; and (c) combined particle swam optimization algorithm and LDA (PSO + LDA), which optimizes the parameters of LDA equation to attain the best classification outcomes. The above methods were applied to a reservoir region in Taiwan, and the following classification results were obtained. The application of DRS + LDA in our case study reduced the number of ancillary attributes from 14 to 5, and resulted in an accuracy rate of 0.83. On the other hand, the application of PSO + LDA resulted in the same accuracy rate as that of DRS + LDA, whereas the accuracy rate of conventional LDA was found to be 0.78.  相似文献   

11.
危险性评价是滑坡灾害预防与减灾工作首要解决的重要内容.在地理信息系统技术支持下, 以山地灾害频发区——小江流域作为研究对象, 选取坡度、土体粘聚力和内摩擦角这3个评价指标构建滑坡危险性分级评价指标体系, 将投影寻踪技术运用到滑坡危险性等级评价中, 对评价样本的各指标因素进行线性投影, 以最优投影方向所对应的投影特征值作为评价依据, 建立了滑坡危险性等级综合评价模型, 绘制了滑坡危险性等级分布图.结果表明: 研究区极高危险区、高危险区、中等危险区、低危险区和极低危险区的面积比例为14.28∶9.41∶69.12∶7.00∶0.19;根据所建立的5级评价指标体系对研究区60个土质滑坡点资料进行了验证, 在占研究区总面积23.69%的高、极高危险区的小范围内, 实际发生土质滑坡数量45个, 占总土质滑坡数量的75.00%;中等危险性级别以上区域拥有的土质滑坡数量占全部土质滑坡的96.67%;不同危险性级别的滑坡体积方量统计结果表明, 滑坡体积方量密度随危险性级别的提高而迅速增加.对比评价结果及实测结果可知, 投影寻踪分级结果符合实际情况, 证实了该方法的正确性, 为滑坡危险性评价提供了一条新思路.   相似文献   

12.
周超  殷坤龙  曹颖  李远耀 《地球科学》2020,45(6):1865-1876
准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度,以三峡库区龙驹坝为例,选取坡度等10个因子构建滑坡易发性评价指标体系,应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上,随机选取70%/30%的滑坡数据作为训练/测试样本,应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost),径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示:水系距离、坡度等是滑坡发育的主控因素;RBNN-Adaboost耦合模型的预测精度最高(0.820),优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能,所提出的耦合模型结合了两者的优点,具有更强的预测能力,是一种可靠的滑坡易发性评价模型.   相似文献   

13.
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.  相似文献   

14.
The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models.  相似文献   

15.
从实测数据中分析滑坡灾害的成因机理,对于准确识别潜在危险区与及时制定防治措施十分重要。由于现场监测数据的数量庞大、来源多样,常规的数据处理方法难以从海量监测数据中提取出有用的信息,进而对滑坡变形演化趋势作出正确评价和预测。本文基于经典数据挖掘方法中的两步聚类法与关联规则分析,提出了滑坡变形行为的关联分析挖掘技术,并以长江三峡库区新铺滑坡为例,对库水位波动及降雨影响下的特大滑坡位移速率进行了关联分析。结果表明:该滑坡的变形受库水位高程水平、库水位波动速率与降雨强度等因素的多重影响,水位下降、强降雨与滑坡变形密切相关;滑坡不同空间位置处的变形影响因素存在差异,由坡脚至坡顶,库水位波动的影响水平依次降低,降雨强度的影响水平逐渐增强。本文提出的数据挖掘方法可定量分析滑坡变形的控制因素,并通过与实测数据的对比验证了相关规则的可靠性,这对于海量监测数据条件下滑坡灾害的成因分析有重要意义。  相似文献   

16.
A heuristic approach to global landslide susceptibility mapping   总被引：1，自引：0，他引：1  
Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.  相似文献   

17.
基于滑坡分类的西宁市滑坡易发性评价   总被引：1，自引：0，他引：1       下载免费PDF全文
以往的滑坡易发性评价多以全体滑坡为研究对象,忽视了滑坡类型的区别。各评价指标对不同类型滑坡的影响程度不同,也导致指标权重无法精确地反映其对滑坡的影响。为更准确地对滑坡灾害进行空间预测,针对西宁市滑坡特征及发育机理,将全区滑坡分为土质滑坡和岩质滑坡;在野外实际调查的基础上,结合相关性分析,选取坡度、坡向、剖面曲率、平面曲率、工程地质岩组,以及滑坡点距断层、水系、道路的距离远近等8项因素作为滑坡易发性评价指标,并通过滑坡点分布密度和滑坡点相对分布密度,分析各评价指标分别对土质滑坡和岩质滑坡的影响;利用信息量模型,计算各评价指标对两类滑坡的信息量值,利用人工神经网络模型,赋予各评价指标对两类滑坡的权重;最后基于GIS平台利用加权信息量模型对研究区进行易发性评价。通过统计方法和ROC曲线法分别计算滑坡易发性评价成功率,结果表明:评价成功率可达到82.61%和82.30%,与未经滑坡分类的成功率比较,分别提高了10.9%和5.2%;同时,经过滑坡分类后,湟水河两岸地质条件较差的地区转变为滑坡高易发区。  相似文献   

18.
Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.  相似文献   

19.
The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.  相似文献   

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