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1.
利用GIS技术详细研究汶川地震在甘肃省陇南市武都区和文县触发的滑坡地质灾害的分布规律及其与地震烈度、地形坡度、断层、高程、地层岩性的相关关系，采用基于GIS的加权信息量模型的崩塌滑坡危险性评价方法，对研究区的地震滑坡危险性进行学科分析。结果表明：极高危险区在高程上主要分布在集水高程区，高度危险区主要沿白水江、白龙江等主干河流两侧极高易发区的边界向两侧扩展，轻度和极轻度危险区面积占比较小，主要分布在低烈度、活动断裂不发育、人类活动微弱的高海拔地区，另外国道G215沿极高危险性区域分布明显；利用危险性等级分区结果统计人口公里格网数据，得到武都区和文县潜在影响人口，发现研究区约78万人将受到地震滑坡灾害的潜在影响。 相似文献
2.
丽江—小金河断裂全新世活动强烈、地震频发，沿断裂带的滑坡地质灾害极为发育。以断裂带中南段两侧10 km为研究区，根据地质地理环境和滑坡发育特征，选取高程、坡度、坡向、距活动断裂距离、距河流水系距离、距道路距离、工程地质岩组、降雨量、土地利用类型以及地震动峰值加速度10个影响因子为评价指标，运用加权证据权模型，开展丽江—小金河断裂中南段滑坡易发性评价，基于自然断点法将滑坡易发程度划分为高易发、中等易发、低易发和非易发4个级别，评价结果AUC值为0.81。结果显示：(1)研究区内滑坡受坡度、断裂、水系、岩性因素的影响程度更高；(2)高易发区和中等易发区主要沿断裂带和金沙江等主要河流水系两侧分布，在玉龙县、松坪乡、大东乡等周边区域较集中；(3)西川乡处于高易发区，但目前滑坡灾害点较少，应加强关注。 相似文献
3.
玉树地震诱发了2 036处滑坡。应用地理信息系统与遥感技术,选取与地表破裂距离、峰值加速度(PGA)、高程、坡度、坡向、曲率、坡位、与水系距离、岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)等12个因素作为玉树地震滑坡危险性评价因子,采用加法与减法2种证据权方法,开展玉树地震滑坡危险性评价研究工作。结果表明:基于加法证据权方法得到评价结果的正确率为80.32%,基于减法证据权方法得到结果的正确率为80.19%。将滑坡危险性评价结果图分为极高危险区、高危险区、中危险区、低危险区与极低危险区5类。这一成果可划分出滑坡危险区,为灾后滑坡防治、基础设施重建与自然环境保护提供参考。 相似文献
4.
黄土地震滑坡危险性分析对黄土地区城镇化、工程建设的规划和地震灾害预防具有重要意义。以甘肃省定西市岷县—漳县交界处为研究区域，通过统计分析该区历史地震滑坡灾害数据，归纳并建立包含地震、坡度、坡高、坡向、地层岩性、年平均降雨量、河流流域和地貌类型等8个影响因子的评价指标体系，采用信息量模型、逻辑回归模型和信息量-逻辑回归耦合模型分别分析该区域黄土地震滑坡危险性。结果表明：(1)地震、河流和降雨是诱发黄土滑坡灾害发生的主要因素，其中地震因子贡献率最大；(2)研究区可划分为高、较高、中、低和极低危险区五个等级，其中高危险区主要集中于岷县、漳县与陇西县等地；(3)根据受试者工作特性(ROC)曲线精度检验结果，三种模型的AUC值分别为0.889、0.617和0.898,信息量-逻辑回归耦合模型结果的精确性相比其他两个模型更高。 相似文献
5.
基于逻辑回归模型的九寨沟地震滑坡危险性评估 总被引：1，自引：0，他引：1
发生于2017年8月8日的四川九寨沟M_S7. 0地震触发了大量的同震滑坡。基于Geoeye-1震后0. 5m分辨率的遥感影像开展极震区同震滑坡解译,圈定了4 834处滑坡。选择高程、坡度、坡向、水平断层距离、垂直断层距离、震中距离、河流距离、道路距离、TPI指数以及岩性共10个因子作为地震滑坡的影响因子,应用逻辑回归(Logistic Regression,LR)模型开展九寨沟地震滑坡危险性评价,并对评价结果的合理性进行检验。结果表明,基于LR模型的滑坡危险性评价图与实际滑坡发育情况十分吻合,其中五花海—夏莫段、火花海和九寨天堂洲际大饭店—如意坝段均为滑坡危险性极高的区域。采用ROC曲线对危险性评价结果进行模型成功率与预测率的定量评价,结果显示,LR模型的预测精度较为理想,训练样本集和验证样本集的AUC值分别为0. 91和0. 89。文中结论为震区恢复重建工作中地震滑坡的防灾减灾提供了科学参考。 相似文献
6.
地震滑坡灾害的震前预测与震后快速评估已成为减轻地震次生灾害的重要手段之一。本文使用简化Newmark模型,设定地震震级(MS5.0),利用区域地质图、数字高程模型等基础数据,考虑地形对地震动的放大效应,对文泰震区潜在同震滑坡区域开展评估工作。研究表明,干燥与饱和状态下,设定地震作用下研究区内地震滑坡高危险区均主要分布在距设定震中15km以内的范围内,其分布与区内岩土体处于临界稳定状态的分布趋势相同。区内水库坝址与水库库体未受到潜在同震滑坡的影响,划定的重点关注区内位于潜在滑坡体下方的千秋门村、驮加村、高西村、杜山村、南峤村、包坑村、龙前村、新厂村以及各级公路易受到同震滑坡的影响,应提升重点关注区内承灾体的风险防范能力,尽可能减少潜在同震滑坡对区内生命财产安全造成的威胁。 相似文献
7.
滑坡是一种破坏性非常强的地质灾害,其中地震与降雨均为诱导滑坡发生的关键因素。从降雨期间发生地震的角度考虑,基于Green-Ampt降雨入渗模型对Newmark模型进行改进,推导两因素耦合作用下的边坡安全系数FS。以云南省鲁甸县某一区域为例,分别开展无降雨、降雨无积水与降雨积水三种情况下的地震滑坡危险性预测及坡度与入渗深度因子对位移影响分析。通过比较上述三种情况,得到研究区域内的Newmark累积位移分布及危险性区划。结果表明：与未降雨情况相比,后两种情况下地震滑坡高危险程度区域面积占比计算区域随着降雨时间的增加从1%分别提高至9%、12%,滑坡低危险程度区域面积从51%分别降低至35%、33%;坡度值与入渗深度值越大,滑坡位移越大,危险性越高。Newmark改进模型充分考虑了降雨对地震滑坡产生的促进作用,能更好地反映出研究区每个场点相对的滑坡危险性,对滑坡危险性预测具有一定指导意义。 相似文献
8.
基于CF多元回归模型的地震滑坡危险性研究——以香港屯门为例 总被引：1，自引：0，他引：1
本文根据前人的研究成果以及对自1300年以来地震滑坡震例研究的基础上,选取了岩性、断裂距离、地震烈度、地形坡度、高程、水系等6个因子作为地震诱发滑坡的影响因子,并确定出各个因子在地震滑坡事件中的影响权重.通过GIS技术将滑坡确定性系数CF与回归模型相融合,建立CF值多元回归模型,以解决滑坡评价过程中影响因子的选择及量化的问题.最后,将模型应用于香港屯门地区,进行了该区的地震滑坡空间分布及稳定性初步分析. 相似文献
9.
利用决策树模型,基于五期土地利用评价因子,对甘肃省永靖县进行近40年的长时间尺度下的滑坡易发性评价,五期评价结果均显示研究区内滑坡灾害的极高和高易发性区域主要集中在中部黄河流域(盐锅峡镇至刘家峡水电站段)周边、西南部川城村—红泉镇—王台乡周边区域以及中部偏东的三条岘乡,该区域人口密集,人类活动较多.研究结果与前人研究结果类似,且通过受试者工作特征曲线的精度检验,说明五期评价结果均具有较高的可靠性.另外,研究区内的自然植被和裸土地与滑坡易发性指标之间具有负相关关系,而旱地、水域和城乡建设用地等人类活动频繁的区域则更容易导致滑坡灾害的发生.从时间尺度上来看,极高和高易发性分区面积逐年下降,但自 2000年,极高和高易发性分区面积减少速度出现显著减缓,同期,该区域内的土地利用变化为城乡建设用地面积增加而植被面积减少,这使得区域内边坡稳定性下降,使部分防灾工程措施的减灾能力下降.本研究为该地区的灾害预防、预测和城乡土地规划提供了参考. 相似文献
10.
《地震工程与工程振动》2016,(6)
地震滑坡是大陆内部山区一种最为常见的地震次生灾害类型。本论文基于Arc GIS平台开发了地震滑坡危险性快速评估模块,实现震后1 h内地震滑坡危险性的评估,为震后应急求援提供一定的决策依据。本论文选取地震烈度、坡度、坡向、高程、水系距和断裂距6个参数作为地震滑坡影响因子,通过对历史地震滑坡数据进行统计分析,确定影响因子的分级量化标准,在GIS平台对影响因子数据进行一系列的数据处理,完成影响因子的量化赋值。采用层次分析法,确立各个影响因子的权重,并建立地震滑坡危险性评估数学模型。在此基础上,基于Arc GIS平台开发了地震滑坡危险性快速评估模块,可在震后1 h内获得评价区的地震滑坡危险性分布的公里网格数据。最后,以2013年四川省芦山县7.0级地震对评估模型进行了验证。结果表明,评估结果与实际滑坡点的分布基本符合,基于GIS的地震滑坡易发性快速评估模型是可靠的。 相似文献
11.
Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia 总被引：5，自引：2，他引：3
Ji-dong Wu Ning Li Hui-juan Yang Chun-hua Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(6):719-725
According to disaster and risk evaluation theory, we proposed an indicator system containing environmental possibilities with
hazard, disaster inducing factors and disaster bearing bodies to analyze the risk of heavy snow disaster in Xilingol, Inner
Mongolia, based on the analysis of heavy snow events that have occurred in the last several decades. A risk evaluation model
of heavy snow disaster was established using back-propagation artificial neural network (BP-ANN). Data obtained from a number
of heavy snow events samples were used to train artificial neural network (ANN). The objective of this study is to produce
a new evaluation model using BP-ANN for heavy snow risk analysis. As a result, BP-ANN model showed an advantage in heavy snow
risk evaluation in Xilingol compared to the conventional method of evaluation criteria equation (ECE) introduced by Inner
Mongolia Municipality Animal Husbandry Bureau. Thus, the BP-ANN model provides an alternative method for heavy snow risk analysis
in the area. 相似文献
12.
APPLICATION OF LOGISTIC REGRESSION MODEL FOR HAZARD ASSESSMENT OF EARTHQUAKE-TRIGGERED LANDSLIDES: A CASE STUDY OF 2017 JIUZHAIGOU(CHINA)MS7.0 EVENT 
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下载免费PDF全文 The MS7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km2. The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area. 相似文献
13.
本文从地震灾害、建筑物、人口、经济、抗震救灾等多方面出发，将自然属性与社会属性进行有效结合，对地震危险性、建筑物抗震性能等影响因素进行详细分析，构建城镇地震灾害风险评价指标体系，以张家口地区16个县区为例，采用专家-层次分析法，建立精细化地震灾害风险评估模型。研究结果表明，城镇建筑物抗震性能普遍较差，怀来县地震灾害风险最大，桥东区、蔚县、涿鹿县、桥西区次之，沽源县、康保县地震灾害风险最小，并对各县区地震灾害风险主要影响因素进行讨论，发现地震风险指数与地形结构、建筑物抗震性能具有相关性，评估结果可为城镇制定防震减灾规划提供依据。 相似文献
14.
Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level 总被引：3，自引：1，他引：2
Cheng Lian Zhigang Zeng Wei Yao Huiming Tang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(8):1957-1972
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. 相似文献
15.
WEN Boyu XU Chong HE Xiangli MA Siyuan SHAO Xiaoyi LI Kai ZHANG Zhongjian LI Zhengfang 《中国地震研究》2020,34(1):64-80
An MW6.6 earthquake occurred in eastern Hokkaido, Japan on September 6th, 2018. Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution (3 m) planet satellite, we manually interpret 9 293 coseismic landslides and select 7 influencing factors of seismic landslide, such as elevation, slope, slope direction, road distance, flow distance, peak ground acceleration (PGA) and lithology. Then, 9 293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3. In detail, the training sample has 6 505 landslide points and the validation sample has 2 788 landslide points. The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades, including very low risk area, low risk area, moderate risk area high risk area and very high risk area. The results show that there are 7 576 landslides in high risk area and very high risk area, accounting for 81.52% of the total landslide number, and the landslide area is 22.93 km2, accounting for 74.35% of the total area. The hazard zoning is in high accordance with the actual situation. The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number. The results show that the success rate of the information value method is 78.50% and the prediction rate is 78.43%. The evaluation results are satisfactory, indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area. 相似文献
16.
我国滑坡灾害频发,尤其是西部地区,滑坡的隐蔽性强且危害巨大,对其灾害隐患进行早期识别对防灾减灾意义重大。传统的人工排查、大地测量等手段在山区难以开展且耗时耗力,合成孔径雷达干涉测量技术(InSAR)作为新兴的遥感测量手段,可以更精确、高效地进行大范围的滑坡灾害隐患识别。以黄河流域刘家峡-兰州段为研究区,采用永久散射体InSAR技术对覆盖该区域的111景Sentinel-1 C-SAR数据进行处理,并使用GPS数据进行趋势改正,获取研究区2014年10月—2019年12月间视距向形变场及形变特征,成功识别出1处位于永靖县的滑坡隐患区。该区的规模和量级均大于永靖县黑方台滑坡区,且未被前人提及,具有较大危险性。将InSAR结果与投影到视距向的GPS结果及前人结果进行比较,验证PS-InSAR方法的有效性;并结合历史强震资料、实地考察及时间序列对研究结果进行验证分析,结果表明该滑坡隐患区可能与1125年兰州M7.0地震和2017年九寨沟M_S7.0地震有关。本研究可为当地防灾减灾提供数据和技术支持。 相似文献
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当前震后建筑经济损失评估模型得到的震后建筑经济损失评估精确度、效率低,针对单一神经网络易产生局部极值等问题,对神经网络方法进行了改进,提出LM-BP神经网络在震后建筑损失评估模型中的应用。输入样本要素为影响震后建筑经济损失的5项因素,输出样本是震后建筑经济损失评估结果,在此基础上采用LM-BP神经网络将训练转化成最小二乘问题,结合LM算法重新定义隐含层节点数量,构建基于LM-BP的神经网络震后经济损失评估模型,采用该模型获取最优震后建筑经济损失评估结果。仿真实验结果表明,所设计的评估模型最小评估误差为0.1%,相比同类模型具有高精确度的优势,是一种可靠的震后建筑经济损失评估模型。 相似文献