共查询到19条相似文献，搜索用时 142 毫秒
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区域滑坡危险性评价方法还存在许多需要完善和改进的地方。以工程地质类比法为基础,用滑坡的面密度表示滑坡发生的危险性大小,基于线性代数中QR分解理论,提出了一种用高次多项式拟合致险因子与滑坡危险性间关系的算法,并把该算法与层次分析法模型、条件概率模型相融合,建立了一种改进的区域滑坡危险性评价模型。然后,通过在Visual Studio.Net C#环境下借助ArcEngine组件的二次开发实现了该模型。最后选取陕西省麟游县为实验区域,利用上述模型进行了滑坡危险性评价。经实际资料检验表明,该模型具有较高的可信度,可应用于今后的滑坡危险性区域评价工作中。 相似文献
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基于GIS技术,利用信息量模型开展区域滑坡灾害危险性预测研究,编制滑坡灾害易发分区图,为滑坡灾害的风险预测及实时预警预报提供基础资料。以浙江省永嘉县为例,利用MAPGIS二次开发得到的信息量专业模块,结合永嘉县历史滑坡灾害和2004年以来新发生的灾害点,分别评价了研究区的历史滑坡灾害危险性和现状滑坡灾害危险性;提出用历史滑坡灾害危险性图件结合新发生的灾害点来验证评价模型;将历史灾害点和新灾害点结合生成滑坡灾害危险性预测图件的预测过程;研究成果经在永嘉县的实际验证分析,2004年后3次台风期间（2004年的“云娜” 台风,2005年的“海棠”和“麦莎”台风）发生的有准确地点的滑坡灾害点全部位于滑坡灾害易发区内,表明采用的模型具有较好的实用性和可靠性;采用历史统计和快速聚类相结合的方法进行危险性等级的划分,克服了前人研究工作中人为划分易发区的缺陷,更科学、客观。 相似文献
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滑坡灾害危险性区划研究在城市规划决策方面具有重要的现实意义。聚类分析以统计学的形式将具有相似特征的数据进行归类,能够实现滑坡灾害危险性空间分布情况的定量评价。根据湖北省巴东县滑坡灾害统计资料,选择具有代表性的滑坡灾害影响因素作为危险性区划评价指标,采用熵权法和层次分析法相结合,综合评判各指标权重。并在此基础上,以MapGIS为操作平台,以C#语言编程实现了快速聚类算法,对研究区86216个单元进行了滑坡灾害属性分类及危险性等级自动识别,预测结果较好。本研究将综合权重评判方法与聚类模型结合,同时克服了聚类结果不能自动排序的困难,对处理大批量,多属性数据具有一定的创新性和实用价值。 相似文献
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层次分析法在滇西怒江河谷潞江盆地段崩塌与滑坡
地质灾害危险性评价中的应用 总被引：1，自引：0，他引：1
以外动力地质灾害相对多发的滇西怒江河谷潞江盆地段为研究对象,基于该区的1∶5万地质灾害调查结果,在全面掌握该区崩塌与滑坡分布状况的基础上,利用层次分析法对该区地质灾害危险性程度进行综合分析和评价,获得了该区的崩塌和滑坡危险性评价图。可将该区崩塌和滑坡的危险性划分为稳定、基本稳定、不稳定3个等级的区域,稳定区主要是高黎贡山自然保护区,部分基本稳定和不稳定区域主要分布于人类活动相对频繁的地区。 相似文献
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准确的滑坡易发性评价结果是山区滑坡灾害防治的关键,可有效规避潜在滑坡带来的风险。为获得准确、可靠的滑坡预防参考,笔者以云南芒市为研究对象,选取高程、地层岩性、年均降雨量等9项评价因子,通过多重共线性分析,构建研究区滑坡易发性评价指标体系。分别基于支持向量机(SVM)、BP神经网络和随机森林(RF)3种典型机器学习算法进行滑坡易发性评价。利用准确性(ACC)、ROC曲线下面积(AUC)、滑坡比(Sei)及野外实地考察对模型评价结果精度进行对比验证分析。结果显示RF模型的ACC、AUC和极高易发区的SeV值最高,分别为0.867、0.94、9.21;BP神经网络模型次之,其SeV值分别为0.829、0.90、9.14;SVM最低,其SeV值分别为0.794、0.88、6.85。此外,RF算法所得结果还与实地考察情况保持了较高的一致性。实验结果表明与其他两种算法相比,RF算法在芒市区域具有更高的准确性和可靠性,更适合用于该区域的滑坡易发性建模,且利用该模型获得的评价结果,能够为芒市区域的滑坡防治提供理论依... 相似文献
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浙江省永嘉县滑坡灾害危险性区划 总被引：7，自引：0，他引：7
永嘉县是浙江省滑坡灾害发生频繁的区县之一,其滑坡受地质、地形和人类工程活动等因素的影响。本文根据永嘉县滑坡灾害分布情况,选择了影响滑坡分布的主要因素,将各种因子归一化处理后转换成相同分辨率的定量数据,选择了逻辑回归分析模型和信息量模型进行滑坡灾害危险性评价。在逻辑回归模型中,利用SPSS软件,通过逐步回归分析筛选出影响滑坡的最直接的因子,计算出各个因子的回归系数,得到逻辑回归方程,据此编制了危险性预测分区图。在信息量模型中,通过MAPGIS软件及其二次开发的信息量模型,对永嘉县滑坡灾害进行了危险性区划,并依信息量法的结果编制了该区的危险性预测分区图。两种方法所编制的危险性分区图中高危险区和中危险区重合率达到了87%,具有很高的一致性,起到了相互验证的作用,为滑坡的有效防治提供了依据。最后根据"云娜"台风期间永嘉县实际灾害发生情况的资料分析,新灾害点绝大部分落在危险性预测区中的高危险区,表明模型的预测准确率很高。 相似文献
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准确圈定煤矿工作面底板突水预警重点监测区域,实现监测位置和潜在突水点位置在空间上的匹配,是突水灾害预警急需解决的问题之一。为研究煤矿工作面底板突水灾害预警重点监测区域评价技术,采用水文地质分析、GIS空间分析及ANN预测等技术手段,建立了底板突水灾害预警重点监测区域评价指标体系,提出了将不连续指标转化为连续指标的方法,建立了评价模型,研发了重点监测区域评价GIS系统,实现了煤矿底板突水灾害预警重点监测区域GIS与ANN耦合评价技术,最后以赵庄煤矿5303回采工作面底板突水监测预警为例,利用研发的系统圈定了该工作面重点监测区域。研究表明,确定预警重点监测区域的影响因素主要有含水层水压、含水层富水性、含水层防（隔）水煤岩柱厚度、老空区危险性指数、断层危险性指数、陷落柱危险性指数和封闭不良钻孔危险性指数,利用分段函数可以有效将不连续指标转化为连续指标,研发的评价系统可以实现煤矿突水灾害预警监测位置自动评价,评价结果与现场揭露及水害预警系统监测结果一致。 相似文献
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泥石流危险性评价:模糊c均值聚类-支持向量机法 总被引：2，自引：0，他引：2
泥石流是一种能够造成灾难性后果的严重自然灾害,准确可靠的泥石流危险性评价对于其预警及防治工作来说至关重要。泥石流的危险性评价方法有很多,模糊c均值聚类（FCM）方法是其中一种应用广泛的分类方法;相比其他方法而言,其无需主观确定边界,并且能以各级隶属度矩阵为输出结果,方便应用。支持向量机（SVM）是基于结构风险最小化为目标的机器学习理论,以支持向量为算法支撑,具有一定的鲁棒性,并且适合在小样本条件下进行分类。本文选用FCM和SVM联合的方法,开展泥石流危险性的评价;对北京房山区南窖沟泥石流危险性进行分析,并对比其他评价方法所得结果,证明本文提出的评价方法具有较好的效果。 相似文献
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滑坡灾害空间区划及GIS应用研究 总被引：76，自引：3，他引：76
滑坡灾害空间区划研究是当前国内外滑坡领域的重要研究方向之一。虽然滑坡灾害的发生具有随机性的特点 ,但其发生的区域性和重复性特点则是区域滑坡分布与发生的总体规律。从减灾与土地规划的角度 ,开展滑坡灾害空间区划研究具有十分重要的理论和实际意义。文中重点探讨了滑坡灾害空间区划的理论体系、灾害风险评估的基本术语定义及GIS制图的基本原理 ,采用MAPGIS软件为平台及其二次开发的滑坡灾害信息分析系统 ,在中国滑坡重灾害的汉江流域开展了灾害危险性空间区划应用研究。 相似文献
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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
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For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area. 相似文献
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Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management 总被引：1，自引：0，他引：1
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
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This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system
(GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides
in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology,
rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances
from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified
form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote
sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence
were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river
and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard
zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation
map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes
were suggested by using the GIS technology. 相似文献
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Netra Prakash Bhandary Ranjan Kumar Dahal Manita Timilsina Ryuichi Yatabe 《Natural Hazards》2013,69(1):365-388
Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps. 相似文献
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基于逻辑回归模型和确定性系数的崩滑流危险性区划 总被引：1，自引：0，他引：1
崩滑流是崩塌、滑坡和泥石流地质灾害的总称。本文根据逻辑回归模型和贵州省崩滑流地质灾害发生的确定性系数CF,统计贵州省内崩滑流发生概率与其影响因子之间的函数关系; 并利用GIS技术编制贵州省崩滑流地质灾害危险性区划图。首先根据影响因子子集中已发崩滑流灾害面积和影响因子子集面积来计算崩滑流地质灾害发生的确定性系数CF; 其次将灾害是否发生作为因变量,影响因子子集发生崩滑流地质灾害的确定性系数CF作为自变量,应用逻辑回归模型统计分析它们之间的函数关系; 然后利用GIS技术计算研究区内各独立属性单元发生崩滑流地质灾害的概率p,按p值10等分标准将研究区划分为10个危险性等级区,并绘制贵州省崩滑流地质灾害危险性区划图; 最后用已发崩滑流地质灾害的分布数据来检验危险性区划的效果。研究结果表明:本文根据逻辑回归模型和崩滑流地质灾害发生的确定性系数CF,将贵州省分为Ⅰ～Ⅹ的10个崩滑流地质灾害危险性等级区与实际情况基本符合,能够良好地反映贵州省境内发生崩滑流地质灾害的难易程度。 相似文献
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地震滑坡是在地震瞬间诱发的滑坡灾害。本文讨论了汶川地震灾区滑坡风险区划与常规滑坡风险区划的区别,认为地震滑坡风险区划应该在危险度区划中增加与地震相关的指标因子,如滑坡震中距和滑坡断层距。从而反映地震动能量对地震滑坡发育的贡献作用。而易损度区划中是难以体现地震因素作用的,这里采用滑坡密度、人口密度、道路密度、建筑物密度、耕地密度这5个指标进行易损度评价。最后采用权重叠加法进行了汶川地震极震区10个县市(面积26175.77km2)的滑坡风险区划,其中高、较高风险区分别占全区面积的9.03%和14.61%。说明震后灾区依然存在一定的滑坡风险。汶川地震极震区中,北川、青川、都江堰、彭州4地应该成为滑坡风险防御的主要地区。对滑坡风险区划结果进行了实地抽样检验,证明区划结果基本符合汶川极震灾区的情况。由此可见,本文介绍的地震滑坡风险区划方法是可靠的。 相似文献
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殷坤龙 《水文地质工程地质》1993,20(5):21-23
本文在滑坡灾害预测分区的信息模型基础上,重点讨论了灾害预测的计算机制图化的主要过程:因素的数值化,单元边界的确定和彩色图件的绘制。运用中国地质大学计算机系开发的Mapcad系统,在Mv/10000计算机上较好地处理了不规则图幅边界的自然裁剪,不规则单元的输入,以及彩色图件的绘制等问题。 相似文献