共查询到17条相似文献，搜索用时 778 毫秒
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GIS支持下三峡库区秭归县滑坡灾害空间预测 总被引：3，自引：1，他引：2
基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。 相似文献
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基于随机森林的山西省柳林县黄土滑坡空间敏感性评价 总被引：1，自引：0，他引：1
基于随机森林模型,以GF-6影像和ALOS DEM数据为基本信息源,结合高程,地形起伏度及地形湿度等11项因子,对山西省柳林县进行滑坡敏感性空间区划。模型精度评价表明：随机森林模型精度为0.75,支持向量机模型精度为0.7,表明随机森林更适合柳林县的滑坡敏感性评价。指标重要性分析结果表明：高程、坡度、距道路距离以及距河流距离,是影响柳林县滑坡发育的主要因素。敏感性空间区划结果表明：高度敏感区约占柳林县总面积的28%,主要分布在三川河流域的南北边界及邻近区域内,其中贾家垣乡分布面积最广。从时间成本、训练难度、稳定度以及精确度考虑,随机森林模型更适合滑坡敏感性评价这类非线性计算问题。 相似文献
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以阿坝藏族羌族自治州地质灾害频发的理县为研究区,从地形地貌、地质环境、水文条件和人类工程活动等方面选取11个影响因子,通过皮尔森相关系数研究各因子之间的相关性,从而构建滑坡易发性评价指标体系。利用信息量模型计算各影响因子的信息量值,从信息量模型得出的极低和低易发性分区中选取非滑坡样本,在此基础上将样本数据代入随机森林和径向基函数神经网络2种机器学习模型开展滑坡易发性评价,并通过接收灵敏度（Receiver Operating Characteristic,ROC）曲线进行精度验证。结果显示：随机森林模型预测出的高易发区单位面积内分布的滑坡点数量更为集中,在仅占6.666%的区域分布了74.026%的灾害点,评价结果优于径向基函数神经网络模型。ROC曲线中两模型AUC（Area Under Curve）值分别为0.893、0.874,说明随机森林模型具有更高的可靠性,比径向基函数神经网络在该区域地质灾害易发性评价中更具优势。 相似文献
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基于GIS的区域群发性降雨型滑坡时空预报研究 总被引：2，自引：0，他引：2
以滑坡灾害突出的雅安市雨城区为例,综合考虑降雨强度、前期降雨量及下垫面(地形、岩性、植被覆盖等)构建了基于GIS分析获取的易发指数+BP型神经网络时空预报模型。首先通过试验确定了模型的网络参数和网络结构,然后通过危险性区划图获取降雨型滑坡易发指数,并利用GIS的空间插值功能和雨量站数据获取相应降雨型滑坡的雨量数据,将量化后下垫面的易发指数和降雨数据作为神经元输入层数据。将模型应用于研究区,其中46个降雨型滑坡数据作为训练样本,10个降雨型滑坡数据作为检验样本,预测精度达到90%,显示该模型对于降雨型滑坡的时空预报精度较高。 相似文献
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"一带一路"倡议是中国参与全球治理的重要切入点,对"一带一路"地区滑坡灾害风险评估与区划,可为沿线国家和地区的防灾减灾提供依据。首先,选取坡度和地形起伏度两个指标,提取研究区滑坡灾害安全区域。其次,采用模糊层次分析法（FAHP）确定滑坡灾害风险评估体系并计算各因子综合权重,基于滑坡灾害风险评估模型定量评估"一带一路"地区滑坡灾害危险性、损失和风险。最后,运用滑坡灾害点和近百年"一带一路"地区滑坡灾害致死人数和经济损失空间分布分别验证评估的滑坡灾害危险性和损失。结果表明：（1）滑坡灾害安全区域主要分布在平原、盆地和沙漠等地区,仅有4.7%（56个）的滑坡灾害点分布在安全区域内,提取结果较为合理。（2）"一带一路"地区容易诱发滑坡灾害的条件为坡度介于25°~45°之间,地形起伏度大于900 m,距河网的距离小于500 m,多年平均降雨量介于400~800 mm,地震密度3×10-4~2×10-3个·km-2之间,工程地质岩组为中等硬质岩体、软质岩和土质岩体。非安全区域中,滑坡灾害以中、低危险性为主,危险性评估结果精度AUC值为0.823。（3）"一带一路"地区容易造成潜在损失的滑坡灾害承灾体条件为：人口密度为80~160人·km-2,公路线密度为0.2~0.9 km·km-2,夜间灯光指数为20~60。非安全区域中,滑坡灾害潜在损失普遍较低,损失区划结果与近百年滑坡灾害致死人数和经济损失空间分布具有很好的一致性。（4）"一带一路"非安全区域,滑坡灾害极低、低、中等、高和极高风险区面积所占比例分别为44.7%、25.5%、15.3%、10.3%、4.2%,以极低和低风险为主。 相似文献
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不完备样本条件下基于支持向量回归模型的滑坡易发性评价 总被引：2，自引：1，他引：1
区域滑坡易发性评价对灾害中长期预测预报具有重要意义,在基于统计模型进行评价过程中,样本选取对评价结果有较大影响,构建较稳健的、受样本数量影响小的分析模型非常重要。本文以马来西亚热带雨林地区为例,选择坡度、坡向、地表曲率、地貌类型、岩性、构造、土地覆盖、道路和排水系统等9大要素作为评价因子,结合支持向量回归(SVR)模型计算研究区滑坡易发性指数,并探讨不完备样本条件下易发性评价方法,分析样本数量和评价精度之间的关系。结果显示,基于SVR模型进行该区滑坡易发性分析评价,其成功率验证法的描述精度约为95.9%;同时,样本数量的增减对分析精度影响较小;SVR方法是一种适于热带雨林地区高植被覆盖条件下的分析模型,可为今后同类地区的滑坡灾害管理工作提供支持。 相似文献
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《云南地理环境研究》2019,(5)
采用芦山地震震后滑坡灾害地面排查及无人机遥感影像解译,利用敏感性分析方法进行"4.20"震区滑坡发育因子敏感性分析,结合地震Arias强度分布开展区域性地震次生滑坡灾害危险性评价,得到结论如下:1.基于因子敏感性分析结合地震Arias强度分布方法可解决大震后大区域尺度滑坡灾害危险性评价问题,结果与震后已发滑坡灾害点吻合,准确度和精度均得到提高;2."4.20"震后滑坡灾害发生随坡度增加呈指数上升,与岩性组合有关,最敏感的组合依次为硬岩夹软岩、硬岩夹硬岩岩层及硬岩夹极软岩层,侏罗纪及白垩纪地层易发,易于在降雨变差率中等的区域发生;3."4.20"震后滑坡高危区表现为与龙门山断裂带平行的5个高危带,在其内的乡镇、县城及与之相交的公路成为重点危害对象;4.宝兴县五龙乡、芦山县鱼泉乡和宝盛乡存在较大滑坡风险,要进一步排查,以防震后雨季发生滑坡。宝兴县城虽然极度危险区呈零星分布,但由于地处县城,灾害一旦发生危害极大,需要进行治理;5.在沿线主要道路设置雨季监测员,对来往车辆进行人工预警;6.加强极度危险区群策群防工作。 相似文献
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基于三种不同模型的区域滑坡灾害敏感性评价及结果检验研究 总被引：1，自引：0，他引：1
选取相对高差、坡度、坡向、水系、距断层距离、植被覆盖、地层岩性和道路等影响因子,采用信息量法、Logistic回归和人工神经网络3种模型进行滑坡灾害的敏感性评价,并对评价结果进行检验。结果表明：① 评价分类结果的准确性会关系到社会经济成本。经过采用Cohen’s Kappa系数法、Sridevi Jadi精度评估方法和ROC曲线3种方法对评价结果进行比较分析,结果显示人工神经网络模型具有更好的评价精度。② 宁强县滑坡地域分布上,呈现一带三区。其中高、中和低敏感区分别占全县总面积的39.96%,37.7%和22.33%。 相似文献
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快速、精确地识别地震后滑坡、泥石流沟的空间分布与覆盖范围,对于认识滑坡、泥石流灾害机理和震后灾区治理至关重要。目前提取滑坡、泥石流沟分布的方法主要是基于光谱信息与纹理信息,人为因素影响大,训练过程繁琐。该文提出一种基于半方差函数(semi-variance)模型与高空间分辨率影像实现少光谱信息、无训练样本条件下自动提取滑坡、泥石流沟的方法。以汶川重灾区四川省平武县洪溪河流域为例进行实验研究,结果表明:在滑坡以裸土、岩石出露为主,且具有数字高程模型(DEM)地形信息的情况下,该方法可以很好地识别典型滑坡与泥石流沟,并能勾画其边界范围;研究区内48.21%的滑坡与泥石流沟覆盖面积得以正确识别,特大型滑坡与大型滑坡识别数量比例分别为100%与80%,泥石流沟识别数量比例为70%。 相似文献
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Kai Xu Qiong Guo Zhengwei Li Jie Xiao Yanshan Qin Dan Chen 《International journal of geographical information science》2013,27(7):1111-1124
A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering. 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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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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Arif Mert Eker Mehmet Dikmen Selim Cambazoğlu Şebnem H.B. Düzgün 《International journal of geographical information science》2013,27(1):132-158
The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time. 相似文献