共查询到19条相似文献，搜索用时 406 毫秒
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采用本底因子贡献率法的三峡库区滑坡危险度区划 总被引：9，自引：0，他引：9
在不考虑触发因素的情况下对滑坡本底因子定量化处理,分析与滑坡发育的关系,进行滑坡本底因子危险度区划。采集参加区划的本底因子包括地层岩性(U_1)、坡度(U_2)、坡形(U_3)、高差(U_4)、坡向(U_5)共五种。通过危险度区划,研究区约4650 km~2中,极高危险度区57．44 km~2,高危险度区2 305．15 km~2,中危险度区1241．6 km~2,低危险度区1045．31 km~2。采用的方法：①数据库反演统计；②贡献率与权重分析；③危险度模糊判别。 相似文献
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在GIS技术的支持下,以三峡库区忠县-石柱河段为研究区域(面积260.9km2,滑坡分布面积5.3km2),建立了地质、地形数据库等滑坡因子空间数据库和滑坡空间分布数据库(数据比例尺均为1∶10万);在进行滑坡影响因子敏感性分析的基础上;对双变量分析模型进行了改进应用,对滑坡影响定量因子采用滑坡种子网格数据驱动的分级新方法。在GIS系统中进行了滑坡危险度评价成果图制图,将评价结果分为很低、低、中等、高、很高5个等级,依次占研究区域19.9%、31.69%、27.95%、17.1%和3.6%。评价结果显示危险性高和很高的区域主要分布在长江两岸,这与实际的滑坡分布吻合。研究结果对在三峡库区推广应用、防灾减灾具有实际指导意义。 相似文献
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基于SINMAP模型的区域滑坡危险性定量评估及模型验证 总被引：1，自引：0，他引：1
武利 《地理与地理信息科学》2012,28(2):35-39,113
利用稳定态水文学理论与无限斜坡稳定性模型,构建分布式斜坡稳定性定量评估模型SINMAP,以坡体滑塌十分发育的陕西省略阳县为试验区,利用Grid DEM提取坡度、流向、地形湿度指数和有效汇水面积等流域地形水文数据,将GIS专题图、遥感数据等作为模型输入数据,获得地表斜坡稳定性分级专题图,实现滑坡危险性定量评估;将模型模拟结果与目前国内最具有权威性的中国县(市)地质灾害调查结果进行对比分析,发现两者在稳定性分级标准划分、滑坡点定性评价、滑坡危险性分区等方面都具有很好的相似性和可比性,说明模型的模拟结果能够客观反映研究区地表滑坡危险性,对可能出现的滑坡具有一定的预测精度。因此,该模型的研究有望为定量分析区域滑坡与环境因子的关系、区域滑坡预测等工作奠定基础。 相似文献
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泥石流沟判别与危险度评价研究 总被引：20，自引：0，他引：20
以云南泥石流形环境的区域调查为基础，提出了以１１项因素作为泥石流沟判别与危险度评价预测的背景参数，依据关联性序列分析确定了因素的权重分析，应用数量化理论建立了泥石流沟判别模式和危险度评价预测的计算方法，实际应用结果表明该法可靠、简便和实用，适用蚶类泥石流沟判定与危险度的评价预测。 相似文献
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基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例 总被引：4，自引：2，他引：2
次生滑坡灾害的影响是震后较长时间里人们持续关注的焦点,对其开展敏感性评价具有重要意义。选取5.12地震的重灾区汶川县北部作为研究区,利用遥感与地理信息技术提取地震滑坡信息,在全面分析滑坡与高程、坡度、坡向、岩性、断裂带、地震烈度以及水系等7个影响因子相关特性的基础上,采用信息量法与逻辑回归模型进行灾害敏感性评价,将研究区划分为极轻度、轻度、中度、高度和极高危险5个级别,并对不同模型的适用性开展分析和对比。结果表明,逻辑回归模型在描述区域滑坡灾害危险度总体特征方面稍具优势。 相似文献
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基于专家知识的滑坡危险性模糊评估方法 总被引：6，自引：0，他引：6
滑坡发生的影响因素众多, 其危险性与各因素之间的关系多呈非线性关系, 同时各因素之 间也存在或强或弱的相关性, 而目前的危险性评价方法难以体现这些要求。本文提出了一种借助 滑坡专家知识并利用模糊推理理论进行滑坡危险性评价的方法。该方法通过建立了①坡度与岩 层倾角之差和坡向与岩层倾向之差、②坡度和岩性、③临空面和岩性、④坡形和岩性等四种环境 因子组合, 以此将不同环境因子之间的相关性融入各组合模型中, 并将四种组合所得的模糊危险 度进行叠加用于滑坡危险度的模糊评价。环境组合模型中的参数利用专家经验给出。将该方法应 用于三峡库区云阳- 巫山段, 得到了滑坡危险性的分级分布图。从滑坡危险性分布图上可清楚发 现, 本方法所计算出的危险性值在滑坡发生的地区明显高于未发生滑坡的地区, 该结果可以用于 城镇建设和重要基础规划设施的参考。 相似文献
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正反负矩阵权重分析法在西藏泥石流危险性分区中的应用 总被引：2，自引：0，他引：2
介绍了正反负矩阵权重分析法的基本原理和分析过程。结合西藏地区泥石流发育分布规律,建立了泥石流危险性区划判别模型。模型中选取地质构造、地貌和气候条件(包括降水强度、年均地温和冰川水文)为影响因子,同时考虑到研究区内基础资料少的限制,再选取既能表征影响因子强度又能直接获取得因子参数进行研究。通过对各影响因子的分区赋值,利用G IS空间分析技术,对西藏地区泥石流危险性分区进行了研究。结果表明,西藏东南部泥石流危险度最强,从整个西藏地区来看危险等级由东南向西北递减。 相似文献
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基于GIS的长江三峡库区滑坡影响因子分析 总被引：11，自引：1，他引：10
利用GIS技术和统计方法,对三峡库区选定的研究区域(面积4539km2)滑坡空间分布和地形、地质等滑坡内部因子之间相关性进行统计计算。在建立地质、地形数据库等滑坡因子空间数据库和滑坡空间分布数据库(数据比例尺均为1∶10000)基础上,从地形数据库提取25m分辨率DEM,再派生出高程、高差、坡度、坡向、平面曲率、剖面曲率等地形影响因子;从地质数据库提取地层和岩性组合影响因子。将各个定性的因子按一定规则进行重分类、转换为25m分辨率的栅格数据格式,在GIS中进行地图代数运算、统计计算滑坡和各影响因子相关性。结果表明,滑坡分布和Q4、J1x,J1z、S岩性岩组;90m以下、90～135m和135～175m三个高程带;15～20m局部高差;10°～25°坡度;北、南和西北方向及-1～1曲率范围等影响因子相关性等级都大于1,为滑坡发生的主要影响因子类属。研究的结果是进行滑坡易发性评价的基础,可以指导库区滑坡灾害管理、土地利用等。 相似文献
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探讨了云南昭通的滑坡、泥石流危险度模糊综合评判法，介绍了以概率代替隶属频率，并构成单因素模糊矩阵Ｒ．以此为基础，完成了昭通滑坡、泥石流危险度分区． 相似文献
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斜坡类型描述岩层产状与斜坡的角度关系,很大程度上决定了斜坡岩土体变形的方式和强度,对地质灾害分布具有重要作用。斜坡的顺向坡、反向坡与地形的阳坡、阴坡概念相似,可以利用改进的太阳辐射地形因子计算模型(TOBIA指数)对斜坡类型进行定量化表达。计算TOBIA指数需要斜坡坡度、坡向、岩层倾角、倾向4个参数。以三峡库区顺向坡基岩滑坡多发地段青干河流域为例,通过区域地质图上产状点获取离散岩层倾角和倾向数值,经空间插值得到空间连续分布的倾角和倾向参数;通过数字高程模型获取坡度和坡向参数,得到区内TOBIA指数分布。在此基础上进一步研究指数和滑坡发育关系。结果表明,TOBIA指数值与区内斜坡类型密切相关,根据TOBIA指数值能很好地区分斜坡类型。以二分类变量逻辑回归模型对坡度和指数两个变量进行分析,发现引入TOBIA指数后,回归模型对已知滑坡拟合度由55%提高到71.5%,能有效提高区域滑坡灾害危险性区划结果精度。 相似文献
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在对金沙江流域内的部分大型水电站工程区内的滑坡分析基础上,以两个滑坡为例,针对水电站工程区讨论了单体滑坡的风险评价方法。选取滑坡稳定性,规模和可能造成的涌浪高度3个指标进行危险性评价;并且定性地将大坝的易损性确定为高、中、低三个等级。在此基础上,对研究区的牛滚函滑坡和东岳庙滑坡进行了危险性分析和易损性评价,得出这两个单体滑坡的风险分析结果：牛滚函滑坡为低度风险,东岳庙滑坡为中度风险。研究成果为水电站工程区滑坡减灾防灾与风险管理提供了科学依据。 相似文献
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GIS and ANN model for landslide susceptibility mapping 总被引：1，自引：0，他引：1
XU Zeng-wang 《地理学报》2001,11(3):374-381
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards. 相似文献
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GIS and ANN model for landslide susceptibility mapping 总被引：4，自引：0，他引：4
XU Zeng-wang 《地理学报(英文版)》2001,11(3):374-381
1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a… 相似文献
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Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence 总被引：12，自引：0，他引：12
Ranjan Kumar Dahal Shuichi Hasegawa Atsuko Nonomura Minoru Yamanaka Santosh Dhakal Pradeep Paudyal 《Geomorphology》2008,102(3-4):496-510
Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with > 88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model's performance is contingent on the availability of data from past landslides. 相似文献
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Vijendra Kumar Pandey 《自然地理学》2013,34(6):510-536
Landslides are frequent natural disasters in mountainous regions, particularly in the Himalayas in India during the southwest monsoon season. Although scientific study of landslides has been in progress for years, no significant achievement has been made to preclude landsliding and allay disasters. This research was undertaken to understand the areal distribution of landslides based on geological formations and geomorphological processes, and to provide more precise information regarding slope instability and mechanisms of failure. After completing a landslide inventory, prepared through field investigation and satellite image analysis, 493 landslides, comprising 131 investigated in the field and 362 identified from satellite imagery, were identified and mapped. The areal distribution of these landslides shows that sites more prone to landsliding have moderate to steep slopes, the lithology of the Lesser Himalayan formations, and excavations for road corridors. Landslide susceptibility zones were delineated for the area using the weight-of-evidence method on the basis of the frequency and distribution of landslides. Weights of selected variables were computed on the basis of severity of triggering factors. The accuracy of landslide susceptibility zones, calculated statistically (R2 = .93), suggests high accuracy of the model for predicting landsliding in the area. 相似文献