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基于WebGIS云南滑坡灾害气象预警分析模型 总被引：1，自引：0，他引：1
针对云南主要由降雨引起的滑坡地质灾害现象．对云南近8年来的滑坡灾害数据和降雨量数据进行了分析,研究并设计了一种针对滑坡地质灾害发生的时空耦合预警分析模型。并在此基础上,通过与WebGIS技术的耦合。运用新一代的RIA技术Silvedight．基于MapGISIGServer平台,在异步响应的网络中使用流程化的操作方式构建出云南省地质灾害气象预警系统。通过实例检验表明该滑坡地质灾害气象预警分析模型的应用．能够为相关部门提供更精确、及时的减灾防灾对策。 相似文献
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滑坡的孕育和发生是受不同内外因素的影响而发生的灾害现象。滑坡灾害危险性区划在滑坡编目和灾害敏感性分析结果的基础上,应用定性分析和定量分析、确定性模型和随机性模型相结合对滑坡灾害易发程度进行分区表示。随着地理信息系统在滑坡灾害区划中的广泛应用,灾害危险性的定量研究得到进一步的深化和发展。本文全面介绍了滑坡灾害危险性区划的主要定量模型,分析了未来滑坡灾害区划的发展趋势,并提出了基于空间数据挖掘的滑坡灾害危险性分析框架。 相似文献
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我国部分地区地质灾害爆发频率高,多年来崩滑坡、泥石流等高危险性地质灾害严重威胁我国人民财产与生命安全。因此,地质灾害及时预警一直是我国在应急方面亟待解决的难题。随着北斗、GPS等全球卫星导航系统的建设组网,GNSS技术逐渐成为当前提供时空位置信息的重要手段,具有数据实时性、设备低功耗、搭建便捷化等特性,成为解决地质灾害监测与预警的理想技术手段。本文利用BDS+GPS+GLONASS时空信息数据,构建了面向地质灾害监测的中长基线毫米级精度的崩滑坡监测解算方法,确定了双差观测的载波相位解算模型。最后在重庆新浦区域展开试验应用,实现了基于高精度时空信息的毫米级滑坡灾害的监测与预警。为预计该区域的地质灾害提供了数据支持,为人民的生产生活提供了技术保障。 相似文献
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滑坡地质灾害的危险性评价对区域内地质灾害的防治规划有着重要意义,本文探讨了基于GIS的滑坡地质灾害危险性评价体系的构建,概述了滑坡危险性评价的主要流程以及步骤。根据影响区域滑坡灾害形成的各种因素,分析了滑坡地质灾害评价因子的选择方法。 相似文献
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邹聪聪 《测绘与空间地理信息》2023,(4):69-73
滑坡灾害作为一种与人们生命紧密相连的常见自然灾害,对其进行危险性评价研究极为重要。以江西省九江市修水县作为研究区域,以修水县243个滑坡地质灾害点作为研究对象,根据对修水县滑坡灾害的发育特征和关联因素的分析,选取了九大评价因子,利用信息量模型和AHP对修水县进行滑坡地质灾害危险性分区。其按危险程度分为极低、低、中等、高和极高5个危险区,分别占总面积的4.25%、14.97%、32.14%、35.17%和13.58%。综合研究区滑坡概况对各危险区进行分析,为研究区的地质灾害预防提供建议。 相似文献
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针对传统知识驱动型滑坡灾害研究多依赖专业人员经验,具有主观性和不确定性的问题,该文提出了基于数据驱动滑坡致灾因子评价及危险性区划的方法。采用证据权模型,较好地平衡了滑坡危险性区划中准确性与高效性之间的矛盾,实现了较为精确的滑坡易发性及危险性区划;利用感知层、网络层、应用层的物联网技术,实现了高危险区滑坡点在线预警监测。3S技术支持下的滑坡危险性区划及监测实验结果表明:所用模型及监测技术不仅可以准确评价滑坡致灾因子权重及危险性区划,还能够精准、高效实现滑坡点实时监控预警。 相似文献
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GIS技术应用于地质灾害研究,已有三十余年历史,而且GIS发挥的作用变得日趋重要。由于MORPAS系统是基于GIS的金属矿产资源评价预测系统,因此将MORPAS应用于地质灾害的研究极少,作者尝试将MORPAS证据权法应用于吕梁地区滑坡泥石流灾害预测与评估研究并取得了良好的效果。本文在分析吕梁地区地质构造背景,提取大小型线型构造、植被信息,综合降水、坡度数据的基础上,运用MORPAS证据权法对吕梁地区重大突发性地质灾害—滑坡、泥石流进行了预测与灾害等级分区,并建立了灾害危害性评估模型。从预测图来看,研究区共分为三个灾害等级,从南向北共划分五个灾害预测区,从预测结果的准确性角度来看,已发生在中阳县境内的滑坡验证了预测Ⅰ区的准确性,同时证明了本文所提出方法的可行性。 相似文献
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滑坡位移预测是滑坡灾害实时监测预警的重要组成部分,良好的滑坡位移预测模型有助于预测地质灾害发生。滑坡变形受多种外界因素影响呈现出随机性和非线性的特点,在现有的滑坡位移预测方法中,机器学习方法在滑坡位移预测中得到了广泛的应用。针对滑坡位移预测是趋势项位移和周期项叠加的特点,本文研究采用基于集成经验模态分解(EEMD)的滑坡趋势项和周期项位移提取方法,结合支持向量回归(SVR)模型实现对滑坡的位移预测。首先,详细介绍了该模型的构建过程和预测性能,并以均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和决定系数(R2)作为评估模型的预测性能指标。然后,分别利用EEMD-SVR、SVR、Elman模型对贵州省岩溶山区的一处滑坡进行位移预测,结果表明,EEMD-SVR模型连续1 d预测的RMSE值、MAPE值和R2值分别为0.648 mm、0.518%和0.996 8,可以提供更高可靠的滑坡位移预测精度,对同类滑坡的位移预测具有一定的参考价值。 相似文献
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近年来,降雨诱发的滑坡灾害日益频繁,给人民生命财产安全造成了严重的威胁。因此,深入开展滑坡灾害气象预警研究具有重要的理论意义和实用价值。为了解决传统滑坡灾害气象预警方法在计算性能和预警精度等方面的不足,本文立足于滑坡灾害气象预警工作,选取汶川M s 8.0级强烈地震重灾区的62县市为研究区,深入分析研究区滑坡灾害与地质环境、降雨之间的关联关系,构建适用于研究区的滑坡因子指标体系,运用机器学习理论和方法,建立了基于机器学习的滑坡灾害气象预警模型,并利用研究区历史监测数据进行试验,验证了该方法的准确性和可靠性。 相似文献
11.
以滑坡蠕变阶段坡体的蠕变会引起环境条件的改变，进而影响植被生长状况的野外考察客观现实为依据，提出一种间接监测滑坡变化的新方法。利用高分辨率光学遥感技术，对滑坡蠕变阶段遥感影像上坡体上覆植被的异常特征进行判识，建立遥感影像上植被异常与滑坡蠕变的关系，反映滑坡的演化过程，弥补GPS技术、InSAR技术及部分地面监测手段在地势高、地形陡峭、植被茂盛等条件下监测工作的不足，为后续的滑坡预测研究提供帮助。以植被覆盖度较高的新磨村山体高位滑坡为例，首先，对研究区域进行分区；其次，计算各分区的植被覆盖度；最后，利用植被覆盖度分析遥感影像上的植被异常与滑坡蠕变的关系，并根据滑后遥感影像和实地考察情况进行验证。结果显示，2014年—2016年，滑坡的主要物源区、变形体上方细长局部崩滑区和泉眼及冲沟周边的植被覆盖度出现明显的下降，即随着滑坡发生时间的临近，植被受滑坡蠕变的影响变大，植被生长状况变差；而且随着距裸地等滑坡风险较大区域的距离增大，植被受滑坡蠕变的影响变小，植被生长状况变好。这表明，植被异常与滑坡蠕变存在明显的时空相关性，体现了滑坡蠕变阶段遥感影像上植被异常与滑坡蠕变的内在联系，反映了滑坡逐步失稳的演化过程，为进一步预测滑坡的发生提供依据。 相似文献
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金沙江流域因两岸地势陡峭、软弱岩层发育、降雨集中等,使得流域内滑坡灾害分布密集。高分辨率遥感是滑坡识别的重要手段,但通过目视解译法开展的大范围滑坡灾害识别,具有工作量大、效率低的特点。针对此问题,本文采用基于面向对象的分类方法,提出了利用滑坡灾害的光谱、形状、空间等特征进行区域内滑坡灾害的快速识别。同时,选取金沙江流域巴塘县王大龙村区段进行了滑坡识别提取试验,区域内利用面向对象分类方法识别出滑坡18处,其中12处与目视解译结果相同,一致性为75%;发现3处目视解译未识别出的隐蔽性滑坡。结果表明,该方法识别效果较好,可为后续的金沙江流域乃至川藏铁路沿线的大范围滑坡识别提取及滑坡编目工作提供参考。 相似文献
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黄河上游干流地区由于特殊的地形地貌和地质构造使得滑坡灾害频发,对其开展滑坡灾害监测、分析研究,具有十分重要的意义。本文利用2015年间Google Earth遥感数据,提取并分析了该地区的滑坡灾害分布信息,取得了如下成果及认识:1)研究区的空间展布形态主要有7种,滑体性质类型有6种,岩质滑坡数量最多。2)从空间分布特征看,共发现研究区有各类滑坡162处,滑坡主要集中分布在群科-尖扎盆地;从滑坡类型看,研究区滑坡主要为大型滑坡和巨型滑坡。3)滑坡体长、宽主要集中在0~1 500 m和500~1 500 m之间,且长、宽呈两极化方向延伸,滑坡体面积分布不均,滑坡数量随着方量的增大呈现减少的趋势,发生的滑坡主要是滑坡体厚度在25~50 m的深层滑坡。4)滑坡数量在0°~90°之间有峰值出现,然后向两端逐渐减少。 相似文献
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Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high. 相似文献
15.
Binh Thai Pham Dieu Tien Bui Ha Viet Pham Hung Quoc Le Indra Prakash M. B. Dholakia 《Journal of the Indian Society of Remote Sensing》2017,45(4):673-683
Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management. 相似文献
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M. A. A. Patwary P. K. Champati ray Irshad Parvaiz 《Journal of the Indian Society of Remote Sensing》2009,37(3):493-509
Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides
are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain
conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic
Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation
of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites
have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic
approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide
susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya
around Rishikesh. 相似文献
18.
时序InSAR技术探测芒康地区滑坡灾害隐患 总被引：3，自引：2，他引：1
位于中国西藏自治区东南部的芒康地区受自然条件制约和人类活动影响，近年来滑坡等地质灾害频发，对电网建设运行、交通干线通行和人民生命财产安全构成严重威胁，亟需有效技术手段对该地区分布的滑坡灾害隐患进行探测识别，从而为防灾减灾提供决策信息支持。采用小基线集（SBAS）时间序列雷达干涉测量技术，对覆盖芒康地区的历史存档ALOS PALSAR和ENVISAT ASAR数据集进行处理分析，探测发现了分布在318国道沿线和金沙江河谷的多处疑似滑坡灾害隐患点，获得了潜在滑坡形变的空间分布图和时间演化特征，证明了时序InSAR技术应用于藏东区域地质灾害调查的可行性和有效性。 相似文献
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The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. 相似文献
20.
The aims of this study were to apply, verify and compare a frequency ratio model for landslide hazards, considering future climate change and using a geographic information system in Inje, Korea. Data for the future climate change scenario (A1B), topography, soil, forest, land cover and geology were collected, processed and compiled in a spatial database. The probability of landslides in the study area in target years in the future was then calculated assuming that landslides are triggered by a daily rainfall threshold. Landslide hazard maps were developed for the two study areas, and the frequency ratio for one area was applied to the other area as a cross-check of methodological validity. Verification results for the target years in the future were 82.32–84.69%. The study results, showing landslide hazards in future years, can be used to help develop landslide management plans. 相似文献