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针对现有方法广域范围复杂地貌类型斜坡单元精细划分效率低的问题，该文提出一种顾及地貌形态特征的精细斜坡单元分区划分方法。首先通过典型地貌形态特征对广域范围进行分区，然后基于水文原理和AT最低成本搜索算法精准提取各分区内的斜坡单元，最后根据坡向圆方差对分区内的斜坡单元进行优化。采用四川省全域30 m分辨率的数字高程模型(DEM)数据进行实验，根据地形起伏度和海拔高度将整个区域按6类地貌形态进行分区优化，在保持全域范围斜坡单元内部坡向均质性的同时，相较于常用的正反向DEM水文法划分效率提升了70.31%。与基于格网单元的滑坡易发性评价进行对比，基于该文所提取斜坡单元的滑坡易发性评价在精度上有4%以上的提升。 相似文献
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以三峡库区万州段为研究区,从多源空间数据中提取29个致灾因子作为区域滑坡易发性分析的评价指标,在数字高程模型基础上采用集水区重叠法划分斜坡单元,构建旋转森林集成学习模型,定量预测滑坡空间易发性,并生成滑坡易发性分区图。在易发性分区图中,高易发区占11.6%,主要分布在万州主城区和长江及支流两岸;不易发区占45.6%,主要分布在人类工程活动低、植被覆盖度高的区域。采用受访者工作特征曲线和曲线下面积对旋转森林模型的滑坡易发性进行评价,结果显示该模型的预测精度为90.7%,其预测能力优于C4.5决策树。研究表明,应用旋转森林进行滑坡易发性评价具有预测能力强、精度高等优点。 相似文献
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针对滑坡灾害易发性难以定量评价的问题,提出了以汇水域为基本统计单元,层次分析法与信息量法相结合的滑坡易发性评价模型。该模型是在综合分析已有监测数据的基础上,建立了坡度、黄土分布、土地表覆被、水系、断层、高程、地表粗糙度、坡向等8类要素与滑坡稳定性的相关性,根据评价结果将滑坡易发性划分为5个不同的等级。基于新疆新源县滑坡易发性评价的实验结果表明,该模型评价结果与滑坡实际分布情况相符,能够准确对不同汇水域灾害易发性进行分级评价,可以为相关部门进行防灾、预警提供一定的数据支持。提出了一种主观判断与客观分析相结合的方法,回答了"什么地方最容易发生地质灾害"的问题。 相似文献
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地质灾害的发生受到地形地貌、地质构造等因素的影响,在进行易发性评价时通常选择坡向、道路、地层岩性和斜坡结构作为评价因子。以自然斜坡单元为评价单元,借助于ArcGIS平台的模型构建器和Python脚本语言,结合平台自带的多种分析、统计工具,以输出为输入方式构建评价因子模型。从简单模型和模型构建器+Python 2个角度,分析栅格、线型和面状类评价因子的生成与赋值,探索模型构建器在地质灾害易发性评价中的应用,为评价提供一种流程化、自动化的数据处理方式。 相似文献
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以福建省宁德福安市为研究区,通过综合分析研究区地质灾害调查数据,选取了坡度、坡向、曲率、高程、降水、植被、道路、河流、地质、断裂带及人类工程活动等10个因子作为评价指标,构建区域滑坡地质灾害危险性评价层次结构模型,利用层次分析法(AHP)确定各因子权重,基于GIS地理空间分析评价了研究区地质灾害危险性,划分了5类区域:极高危险区、高危险区、中危险区、低危险区、较低危险区.危险性评价结果可为宁德市减灾防灾以及灾害治理提供依据,也为区域滑坡地质灾害危险性评价提供可行的方法参考. 相似文献
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层次分析-熵值定权法应用于山区城镇地质灾害易发性评价 总被引：2，自引：0，他引：2
我国西南山区很多城镇面临着崩塌、滑坡等地质灾害的威胁,开展山区城镇地质灾害易发性评价研究具有重要意义。本文对重庆市武隆区羊角场镇进行了地质灾害易发性评价研究,在确定区域易发性评价因子的基础上,综合层次分析法和熵权法两种方法确定评价因子的权重,最后采用易发性指数并基于ArcGIS对研究区进行易发性评价和区划。易发性分区结果与已有的地质灾害分布有较好的对应关系。采用的组合赋权法先用层次分析法确定主观权重,然后又结合熵值法对权重进行修正,在一定程度上减少了主观影响,对地质灾害易发性的量化更加准确与合理。本文的研究思路可为山区城镇地质灾害的风险评估和防治提供参考。 相似文献
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综合不同的泥石流评价因子对泥石流危险性进行了研究,利用地理信息系统（ GIS）来计算潜在的泥石流危险区域。选取虹口乡高原村与庙坝村共处坡面为研究区域,在ArcGIS中生成研究区域的坡面立体图。选取适当的评级指标,确定了流域内主要的泥石流影响因子。采用层次分析法得出各主要因子的权重值,将各因子划分为五个不同等级,并分别对各个因子进行数值化和归一化处理。利用ArcGIS形成各评价因子专题层,再分别给不同评价栅格赋权重值,最终运用叠置分析与插值分析得出研究坡面的不同色带所表示的危险等级区划图,从而实现单坡面泥石流危险性的评价。 相似文献
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熵指数融入支持向量机的滑坡灾害易发性评价方法——以陕西省为例 总被引：1，自引：0，他引：1
《武汉大学学报(信息科学版)》2020,(8)
滑坡灾害易发性评价可为滑坡灾害风险管理、国土空间规划及滑坡监测提供科学依据。针对现有滑坡灾害易发性评价模型无法消除易发性评价指标因子在量纲、性质等方面的差异,尚未考虑易发性评价指标因子与滑坡灾害相关性,以及精度较高的经典机器学习模型训练效率较低、参数选取困难等问题,引入熵指数(index of entropy,IOE)和粒子群优化(particle swarm optimization,PSO)算法,提出IOE融入支持向量机(support vector machine, SVM)的滑坡灾害易发性评价方法。首先,基于滑坡灾害易发性评价指标因子,利用IOE模型计算SVM的调节因子;然后,采用PSO算法迭代求解SVM最优解,根据SVM二分类得到的隶属度来区分滑坡灾害易发性;最后,以陕西省作为实验区,从滑坡灾害易发性分区图、分区统计及评价模型精度3个方面将所提方法与SVM方法进行了对比,实验结果表明所提方法的准确性、可靠性优于SVM方法。 相似文献
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小流域已经成为水土流失治理、生态系统修复等研究工作的重要地理单元。本文以贵州省赤水河流域为研究对象,探索我国西南典型喀斯特与非喀斯特地貌混合分布区小流域划分方法及小流域分布特征,基于DEM提取小流域边界,利用1∶5万地形图进行修正,并结合野外调查和地质、水文数据验证,最终将贵州省赤水河流域划定了753个小流域。其中,喀斯特地貌区小流域个数358个,喀斯特+非喀斯特地貌区小流域个数182个,非喀斯特地貌区213个;就不同流域类型小流域划分结果看,完整型小流域530个,区间型小流域180个,坡面型小流域43个;这些小流域中面积小于3km2的有9个,面积在3～10km2的有310个,面积在10～30km2的有348个,面积在30～50km2的有86个。贵州省赤水河流域小流域划分研究可为小流域治理工作提供依据,为区域制定水土保持方案和生态建设措施提供辅助决策,促进喀斯特地区水土保持与生态环境建设。 相似文献
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恢复岩溶古地貌对于分析岩溶储层发育特征具有重要作用。目前关于岩溶古地貌的恢复多为定性方法,定量刻画古地貌的水平有待提高,而GIS在定量分析处理地貌方面独具优势。以高石梯—磨溪区块灯四段岩溶储层为例,在经典古地貌恢复方法的基础上,从GIS的地貌因子出发,提取了包括高程、坡度、地表切割深度、地表粗糙度、地形起伏度在内的地形因子以及包括水流长度、河流等级、流域分析在内的水文因子;再选取核心地形因子,并对地貌因子进行敏感性评价,结果显示高程、坡度和地形起伏度对地形的表征贡献较大。以地貌成因—形态分类原则为地貌分类方案,对敏感地形因子进行重分类操作后作叠加分析处理,地貌分区成图显示研究区由岩溶高地、岩溶缓坡、岩溶缓坡过渡带和岩溶洼地4类地貌单元组成。对岩溶古地貌进行分析评价发现,岩溶缓坡为油气良好集聚地,地貌划分结果与储层分布具有相关性。 相似文献
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基于 DEM 对喀斯特地貌山地及水系信息的提取--以桂林阳朔县为例 总被引：1，自引：0，他引：1
以30 m分辨率的SRTM－DEM数据为基础,通过GIS的空间分析功能,首先对数据进行坡度分析,通过坡度分级法将山地与平原划分,自动提取出山地的信息,再运用碎斑处理结合TM影像对结果进行修正;其次,在ArcGIS的水文分析（ Hydrology）模块下对 DEM进行数字地形分析,提取流域特征。经分析得到了研究区域的山地及河网特征信息,并与实际地形及河流水系大致吻合,改进了传统手工和野外调查为主的提取方法,初步实现了山地、水系信息的自动提取,为研究区域社会经济更好更快地发展具有一定的参考意义。 相似文献
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基于多层次GIS空间分析模型技术,系统地建立了从流域雨洪过程相关影响因子栅格化处理到产汇流过程模型的集成化模拟方法,首次实现了对流域内任意栅格单元内河道断面的径流过程线的可视化测算。同时还对于模型中单元水流长度与河道单元流速率定方法进行了优化改进。实例分析结果表明:模型结构更为严谨、参数设置更为科学,改进的技术方法对于福建汀江十年一遇洪水场次的模拟结果精度有明显提高,能够满足相关海绵城市规划应用需求。 相似文献
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A GIS-based statistical methodology for landslide susceptibility zonation is described and its application to a study area
in the Western Ghats of Kerala (India) is presented. The study area was approximately 218.44 km2 and 129 landslides were identified in this area. The environmental attributes used for the landslide susceptibility analysis
include geomorphology, slope, aspect, slope length, plan curvature, profile curvature, elevation, drainage density, distance
from drainages, lineament density, distance from lineaments and land use. The quantitative relationship between landslides
and factors affecting landslides are established by the data driven-Information Value (InfoVal) — method. By applying and
integrating the InfoVal weights using ArcGIS software, a continuous scale of numerical indices (susceptibility index) is obtained
with which the study area is divided into five classes of landslide susceptibility. In order to validate the results of the
susceptibility analysis, a success rate curve was prepared. The map obtained shows that a great majority of the landslides
(74.42%) identified in the field were located in susceptible and highly susceptible zones (27.29%). The area ratio calculated
by the area under curve (AUC) method shows a prediction accuracy of 80.45%. The area having a high scale of susceptibility
lies on side slope plateaus and denudational hills with high slopes where drainage density is relatively low and terrain modification
is relatively intense. 相似文献
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Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP) 总被引：1，自引：0，他引：1
In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ??M??) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919. 相似文献
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基于DEM和水箱理论识别流域河网方法研究 总被引：2，自引：1，他引：1
本文应用水箱原理处理流域DEM数据中存在的洼地问题,提出流域河网识别及编码的新方法。首先,确定洼地网格,搜索洼地区域,计算洼地容量,根据水箱原理确定洼地出口,判断洼地的流向;然后,逐层合并消减洼地,修正洼地水流方向;最后,计算网格集水面积,进行河网识别和编码。本文结合云南东川蒋家沟流域的DEM资料进行河网识别,得到了完整的流域河网,结果与实际勘测的河网完全吻合,这表明应用该方法提取流域河网是可行的,能够为进一步的分布式水文、泥石流、环境模型提供条件。 相似文献
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This study was undertaken to prepare an inventory on soil erosion of a hilly river watershed — the Aglar watershed, part of Tehri Garhwal and Dehradun districts (U.P.), using terrain physiography and soil survey data obtained from interpretation and analysis of Landsat TM FCC (1:62,500 scale) and limited ground investigations. The watershed is divided into four broad physiographic units viz. higher Himalayas (> 2000m elevation); lower Himalayas (< 2000m elevation); river terraces and flood plains. Each physiographic unit has been further divided into subunits on the basis of aspects and landuse. Three major orders of soils viz. Inceptisols, Mollisols and Entisols were found in different physiographic units. Soil, and land properties of soilscape units viz. soil depth, texture, structure, slope, landuse and soil temperature regime were evaluated for soil-erosion hazard. The results indicate that in the whole watershed 19.13%, 45.68%, 26.51% and 7.92% areas have been found to be under none to slight, moderate, severe and very severe soil erosion hazard categories, respectively. 相似文献
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Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping 总被引：2，自引：1，他引：1
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献