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1.
基于信息量模型和数据标准化的滑坡易发性评价 总被引：1，自引：0，他引：1
本文以北川曲山-擂鼓片区为研究区,将坡度、坡向、高程、地层、距断层的距离、距水系的距离和距道路的距离作为该区域滑坡易发性评价因子。采用信息量模型计算了各项评价因子的信息量值,并运用4种标准化模型对信息量值进行标准化处理。各评价因子的权重由层次分析法（AHP）确定。在GIS中将权重值和各评价因子的标准化信息量值,进行叠加计算得到区域滑坡总信息量值,并基于自然断点法对其进行重分类,将研究区划分为极高易发区、高易发区、中易发区、低易发区和极低易发区5级易发区。将基于4种标准化模型和信息量模型得到的滑坡易发性评价结果进行了对比分析,结果表明：基于最值标准化信息量模型的滑坡易发性评价结果的ROC曲线下面积AUC值为0.807,高于其余模型的AUC值,说明最值标准化信息量模型的滑坡易发性评价效果最好。极高易发区面积占研究区面积的20.03%,离断层和水系较近,主要分布地层为寒武系、志留系和三迭系。研究结果可为区内滑坡风险评价和灾害防治提供参考。 相似文献
2.
四川省地形高低悬殊, 岩性构造发育, 各类地质灾害频发, 开展地质灾害易发性评价具有重要意义。崩塌、泥石流属于广义上的滑坡, 以四川省丹巴县为例, 从考虑不同滑坡类别的区域性地质灾害易发性出发综合考虑崩塌、滑坡、泥石流的空间概率分布。基于ArcGIS通过高精度数字高程模型共选取高程、坡度等10个地质灾害关键控制因素, 采用信息量模型对综合地质灾害进行了易发性评价。最终通过ArcGIS的单元统计(Cell Statistics)功能实现多个栅格图层最大值法合成综合易发性, 进一步利用受试者工作特征曲线(ROC)验证单种滑坡类别易发性模型的精度。按照自然断点法将研究区划分为极低、低、中、高、极高易发区, 高易发区和极高易发区主要集中分布在章谷镇、太平桥乡以及甲居镇等地。研究结果证明信息量模型能对单类地质灾害进行评价, 栅格最大值法是获取综合易发性的一种有效评价方法。 相似文献
3.
以乡镇为评价单元开展区域滑坡易发性评价对用地规划、防灾减灾等方面具有重要意义.以万州区临江 段的２３个乡镇单元作为研究对象,首先选取地表高程、坡度、坡向、岩性、构造、土地利用类型、地形湿度指数、水 系、道·９个指标因子,通过 C５．０决策树算法计算该区域发生滑坡的概率,再利用快速聚类算法进行易发性结果 分级;基于 ArcGIS平台得到各乡镇单元的滑坡易发性分区,结果表明:C５．０决策树－快速聚类模型的易发性评价 精度最高,AUC值达到０．９５０,优于人工神经网络－快速聚类模型的０．８２６和贝叶斯－快速聚类模型的０．７７２.利 用 C５．０决策树－快速聚类模型的计算结果,综合考虑极高(高)易发区面积大小及其占乡镇面积比大小,完成各 乡镇单元的滑坡易发性区划.在所有２３个乡镇中,滑坡易发性等级高的包括大周镇、万州城区、溪口乡、新田镇 等乡镇.通过对比各乡镇滑坡面积占研究区滑坡总面积的比重,发现两者结论基本一致,预测结果可为全区滑坡 防灾减灾提供科学依据. 相似文献
4.
以三峡库区万州区为例，选择具有代表性的地质环境指标，分析各指标等级，利用逻辑回归、支持向量机和决策树3种数理统计模型，计算全区滑坡灾害易发性程度，分析3种日降雨工况下滑坡的发生概率，得到各日降雨工况下万州区滑坡灾害危险性分布图。确定了支持向量机模型为万州区滑坡灾害易发性分析的最优模型；万州区滑坡灾害高易发区和高危险区主要表现出沿河道水系呈带状分布、沿高程垂直分布、在城镇区集中分布的特点；特定工况下，万州区滑坡灾害危险性随着日降雨量增大而增大。 相似文献
5.
地质灾害威胁着山区人民生命财产安全, 进行地质灾害易发性评价有助于山区城镇进行规划与建设时规避灾害风险。以川东南古蔺县为例, 基于ArcGIS空间分析获取了研究区高程、坡度、岩性、斜坡结构、植被指数、距断层距离和距道路距离7个评价因子, 采用信息量模型分别对滑坡和崩塌灾害进行易发性评价后, 进一步利用ArcGIS单元统计功能对比了滑坡和崩塌易发性的信息量值, 选取相对更大的信息量值作为该栅格的最终信息量值, 绘制了研究区综合地质灾害易发性图, 利用自然断点法将古蔺县按信息量值的大小划分为极低、低、中、高和极高易发区。结果表明: 地质灾害主要分布在断层和道路附近, 断层和人类工程活动是造成研究区地质灾害频发的主要原因; 高易发区与极高易发区面积之和为1 315.62 km2, 占全区总面积的41.32%;预测模型性能经ROC曲线检验, AUC值为0.812 5, 说明栅格最大值法预测的古蔺县综合地灾易发性效果良好。 相似文献
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Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。 相似文献
7.
蒙阴县位于山东省东南部，岱崮地貌分布范围较广。近年来，随着人类工程活动的增强，崩塌滑坡泥石流灾害进一步加剧。本文在山东省蒙阴县1∶5万地质灾害风险普查的基础上，结合最新的遥感信息，选取坡度、起伏度、工程地质岩组、地质构造、地貌类型5个影响因子作为研究区地质灾害易发性的评价指标，采用信息量模型法对各评价因子进行信息量计算，通过GIS空间分析平台，建立了蒙阴县地质灾害易发性评价体系，为蒙阴县有效开展防灾减灾救灾工作，切实保障经济社会可持续发展提供有效的科学决策依据。研究区划分为地质灾害高易发区、中易发区、低易发区、非易发区４个等级，特征曲线（ROC）的线下面积（AUC）精度检验值为0.833，表明评价精度较高。 相似文献
8.
以湖南省澧源镇为例,利用证据权模型和灰色关联度模型分别计算了坡度、地层岩性、斜坡形态、土地利用类型、人类工程活动5个因子二级状态证据权值和一级因子权重;综合2种模型确定全区滑坡易发性指数后,完成基于斜坡单元的全区滑坡易发性区划;根据研究区岩土体类型（碎屑岩类、碳酸盐岩夹碎屑岩类、碳酸盐岩类和松散岩土体类）分组研究不同滑坡发生概率下的有效降雨阈值曲线（I-D曲线）。研究降雨时间为3日、有效强度为22.4 mm/d的降雨工况下各岩土体类型滑坡发生的时间概率。综合时间概率和易发性结果得到澧源镇基于有效降雨阈值的滑坡灾害危险性区划图。研究结果表明:澧源镇滑坡灾害高和极高易发区占研究区总面积的25%,主要沿澧河分布;极高危险区和高危险区占研究区总面积的14%,主要分布在澧河北侧。 相似文献
9.
巴东县城由于其特殊的地理位置和特有的地质条件，使之成为滑坡灾害多发地带，严重威胁着巴东县城的发展，因此，有必要对巴东县城进行滑坡易发性评价研究。首先，基于GIS平台分别提取影响滑坡发生发育的各指标因子（地层岩性、地形地貌、地质构造、水文地质条件等），并划分证据层；其次，采用证据权法分别计算各证据层的权重及后验概率；然后将单元各证据层后验概率进行叠加，生成滑坡易发性分区图；最后，使用自然断点法将研究区按滑坡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之和约占研究区总面积的33%，其中86%的已有滑坡发生在极高易发区和高易发区，利用成功率曲线检验表明区划效果较好。 相似文献
10.
对于滑坡易发性预测中的水系、公路和断层等线状环境因子, 现有研究大多采用缓冲分析提取距离线状因子的距离。但缓冲分析得到的线距离属于离散型变量, 带有大小不等的随机波动性且对点或线要素的误差较为敏感, 导致滑坡易发性建模精度下降。提出了使用水系和公路的空间密度等连续型变量改进线状环境因子的适宜性。以江西省安远县为例, 选取高程、地形起伏度、距水系和公路距离等14个环境因子(原始因子), 再将距水系和公路距离2个线状因子改进为水系密度和公路密度(改进因子); 之后采用逻辑回归、多层感知器、支持向量机和C5.0决策树等机器学习模型, 分别构建了基于原始因子和改进因子的机器学习模型以预测滑坡易发性; 最后利用ROC曲线和易发性指数分布特征等来研究建模规律。结果表明: ①改进因子机器学习预测精度均高于原始因子机器学习模型, 表明空间密度对于易发性预测的适宜性更好; ②在4类机器学习模型中C5.0模型对于滑坡易发性预测性能最好, 其次是SVM、MLP和LR; ③水系和公路两类环境因子的重要性较高且使用改进因子机器学习后这两类环境因子重要性排名依然非常靠前。 相似文献
11.
《山地科学学报》2015,(6)
Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model. 相似文献
12.
斜坡变形受众多因子综合控制, 不同因子的敏感性与作用规律在变形过程中差异明显。以湖北省阳新县顺层基岩滑坡为研究对象, 通过正交试验结合离散元数值模拟的方法, 研究多个影响因子对应顺层滑坡变形的敏感性并确立主导因素, 随后基于响应面拟合主导因素与滑坡不同部位变形程度间的量化关系, 揭示主导因素交互作用对滑坡变形破坏模式的影响规律。结果表明, 在研究区内坡度与岩层倾角分别为影响顺层滑坡变形的主导与次主导因素, 滑坡的变形破坏模式受控于二者的交互作用。在中-陡倾顺层滑坡中, 当坡度小于岩层倾角时, 滑坡变形主要集中在坡顶, 且变形程度随岩层倾角的增加而增大, 表现出滑移-弯曲的变形破坏模式; 在缓倾顺层滑坡中, 当坡度大于岩层倾角时, 滑坡坡脚位移较坡顶显著, 其坡脚变形程度随坡度的增加而增大, 以滑移-拉裂变形为主。研究成果可为该类滑坡的防治工作提供参考。 相似文献
13.
《山地科学学报》2020,17(7):1596-1612
Landslides are prevalent, regular, and expensive hazards in the Karakoram Highway(KKH) region. The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor(CPEC) context. This region has not only immense economic importance but also ecological significance. The purpose of the study was to map the landslide-prone areas along KKH using two different techniquesAnalytical Hierarchy Process(AHP) and Scoops 3 D model. The causative parameters for running AHP include the lithology, presence of thrust, land use land cover, precipitation, and Digital Elevation Model(DEM) derived variables(slope, curvature, aspect, and elevation). The AHP derived final landslide susceptibility map was classified into four zones, i.e., low, moderate, high, and extremely high. Over 80% of the study area falls under the moderate(43%) and high(40%) landslide susceptible zones. To assess the slope stability of the study area, the Scoops 3 D model was used by integrating with the earthquake loading data. The results of the limit equilibrium analysis categorized the area into four groups(low, moderate, high, and extremely high mass) of slope failure. The areas around Main Mantle Thrust(MMT) including Dubair, Jijal, and Kohistan regions, had high volumes of potential slope failures. The results from AHP and Scoops 3 D techniques were validated with the landslides inventory record of the Geological Survey of Pakistan and Google Earth. The results from both the techniques showed similar output that coincides with the known landslides areas. However, Scoops 3 D provides not only susceptible zones but also the range of volume of the potential slope failures. Further, these techniques could be used in other mountainous areas, which could help in the landslide mitigation measures. 相似文献
14.
Co-seismic landslide inventory and susceptibility mapping in the 2008 Wenchuan earthquake disaster area, China 总被引：6，自引：5，他引：1
The Ms 8.0 May 12,2008 Wenchuan earthquake triggered tens of thousands of landslides.The widespread landslides have caused serious casualties and property losses,and posed a great threat to post-earthquake reconstruction.A spatial database,inventoried 43,842 landslides with a total area of 632 km 2,was developed by interpretation of multi-resolution remote sensing images.The landslides can be classified into three categories:swallow,disrupted slides and falls;deep-seated slides and falls,and rock avalanches.The correlation between landslides distribution and the influencing parameters including distance from co-seismic fault,lithology,slope gradient,elevation,peak ground acceleration(PGA) and distance from drainage were analyzed.The distance from co-seismic fault was the most significant parameter followed by slope gradient and PGA was the least significant one.A logistic regression model combined with bivariate statistical analysis(BSA) was adopted for landslide susceptibility mapping.The study area was classified into five categories of landslide susceptibility:very low,low,medium,high and very high.92.0% of the study area belongs to low and very low categories with corresponding 9.0% of the total inventoried landslides.Medium susceptible zones make up 4.2% of the area with 17.7% of the total landslides.The rest of the area was classified into high and very high categories,which makes up 3.9% of the area with corresponding 73.3% of the total landslides.Although the susceptibility map can reveal the likelihood of future landslides and debris flows,and it is helpful for the rebuilding process and future zoning issues. 相似文献
15.
The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making. 相似文献
16.
《山地科学学报》2015,(4)
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. 相似文献
17.
Earthquake-induced potential landslides are commonly estimated using landslide susceptibility maps. Nevertheless, the fault location is not identified and the ground motion caused by it is unavailable in the map. Thus, potential coseismic landslides for a specific fault motion-induced earthquake could not be predicted using the map. It is meaningful to incorporate the fault location and ground motion characteristics into the landslide predication model. A new method for a specific fault motion-induced coseismic landslide prediction model using GIS (Geographic Information System) is proposed herein. Location of mountain ridges, slope gradients over 45 o , PVGA (Peak Vertical Ground Accelerations) exceeded 0.15 g, and PHGA (Peak Horizontal Ground Accelerations) exceeded 0.25 g of slope units were representing locations that initiated landslides during the 1999 Chi-Chi earthquake in Taiwan. These coseismic landslide characteristics were used to identify areas where landslides occurred during Meishan fault motion-induced strong ground motions in Chiayi County in Taiwan. The strong ground motion (over 8 Gal in the database, 1 Gal = 0.01 m/s 2 , and 1 g = 981 Gal) characteristics were evaluated by the fault length, site distance to the fault, and topography, and their attenuation relations are presented in GIS. The results of the analysis show that coseismic landslide areas could be identified promptly using GIS. The earthquake intensity and focus depth have visible effects on ground motion. The shallower the focus depth, the larger the magnitude increase of the landslides. The GIS-based landslide predication method is valuable combining the geomorphic characteristics and ground motion attenuation relationships for a potential region landslide hazard assessment and in disaster mitigation planning. 相似文献