基于网络分析法的滑坡灾害风险区划研究及工程应用

本文介绍了网络分析法基本原理及计算方法,并采用其模型原理,对构成滑坡灾害的地层岩性、坡度、坡向、植被覆盖、河网缓冲、高程等多个评价因子进行了分析和叠合评价。通过分析专家评定所需滑坡评价因子间复杂的关系,得到了庐山风景区滑坡灾害风险等级图,且评价结果与该区调查滑坡资料的分布情况吻合,为滑坡预警和滑坡治理提供了科学依据。     

滑坡灾害危险性研究现状与展望

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。     

“6·28”关岭滑坡特大地质灾害应急遥感调查研究

2010年6月28日14时30分,贵州关岭县岗乌镇大寨村因连续强降雨引发山体滑坡,中国国土资源航空物探遥感中心(简称航遥中心)立即收集了滑坡前卫星遥感数据,并于6月30日成功获取了滑坡区0.08 m分辨率的航摄数据。作者利用滑坡前后的高分辨率影像数据和数字高程模型,采用数字滑坡技术分别对该滑坡的影响范围、滑动方向、规模及灾情进行了定量解译,得出滑坡灾害影响区面积、滑塌体规模、碎屑堆积规模、损毁耕地面积及掩埋房屋数量等,并在第一时间为前线救灾治理工作提供了丰富而准确的调查数据,确定关岭滑坡为一起罕见的滑坡碎屑流复合型特大灾害,这一灾害在贵州历史上未有过记载。     

夏汾高速公路K27滑坡治理点滴

主要阐述了夏汾高速公路K27段滑坡形成的原因以及治理的方法和建议，希望能够对于今后护坡建设具有一定的指导意义。     

重庆市滑坡灾害时空特征分析

重庆市是滑坡灾害高发的城市,滑坡灾害每年都会造成大量的人员伤亡和经济损失。本文结合历年滑坡统计资料,基于GIS平台,分析重庆市滑坡灾害的时空分布特征。自1980年起,重庆发生滑坡的次数明显增加,且集中发生在5~9月,尤其是7月。80%以上的滑坡为降雨引发,其次是自然灾害和人类活动,人类活动诱发滑坡灾害增长速度最快。重庆滑坡密度大的地区为市区以及万州区、忠县等三峡库区核心地带,滑坡高发地区有东北向西南方向变化的趋势。     

滑坡补救措施报告的提议方案

文中列出了一份有关滑坡治理措施的简表,并提出了用于报道滑坡治理的格式。它们对由国际地科联滑坡工作组(原名为联合国教科文组织国际岩土学会世界滑坡名录工作组)提出的滑坡报告作了有益的补充。     

一种面向公众教育的滑坡灾害可视化视觉表征方法

对公众展开滑坡灾害教育,可以提升公众的灾害风险意识。目前的灾害教育主要针对学生与救护人员,其教育方式集中于课本教学及专业培训,内容专业性较强,且教育者与公众之间缺乏有效的沟通渠道,公众难以对滑坡灾害有直观认知。针对上述问题,提出一种面向公众教育的滑坡灾害可视化视觉表征方法,重点研究动静结合的视觉表征框架以及滑坡灾害知识图解与概念图实例化等关键技术,以实现对滑坡过程的整体描述与外在因果的表现。选择2017年6月24日发生的6·24茂县叠溪特大山体滑坡开展案例实验分析,实验结果表明,该方法能够有效地构建滑坡灾害知识的视觉内容,快速地帮助公众了解滑坡灾害知识。     

基于机器学习的汶川震区滑坡灾害气象预警模型研究

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。     

WebGIS支持下的浙江省高速路沿线滑坡灾害预测系统

随着WebGIS技术和计算机技术的发展,它们的应用领域也进一步得到拓展。本文结合浙江省高速路沿线滑坡灾害预测系统的开发实例,提出了将万维网地理信息系统(WebGIS)技术应用到滑坡灾害预测系统中,实现其预测数据的实时更新和可视化管理的解决方案。文章首先指出开发滑坡灾害预测系统的实际意义以及传统预测系统的不足,然后,通过阐述WebGIS技术的优势,提出其解决方案。最后,通过系统结构设计、功能设计、开发环境以及设施步骤等几部分的叙述,探讨了构建浙江省高速路沿线滑坡灾害预测系统的具体过程。     

无人机遥感数据支持下滑坡VR场景探索分析方法

滑坡灾害是最常见的地质灾害之一，无人机遥感和虚拟现实（virtual reality，VR）技术的快速发展为滑坡灾害沉浸式模拟与可视化分析提供了重要的数据资源和技术支持。拟重点开展滑坡灾害VR场景动态构建与探索分析研究，探讨了滑坡灾害数据多样化组织、VR场景动态融合表达等关键技术，提出了基于手柄射线的VR场景交互方法，在此基础上进行了原型系统研发与案例试验分析。试验结果表明，所提方法在无人机遥感数据支持下能够动态构建滑坡灾害VR场景，并且能够支持用户沉浸式交互与滑坡灾情信息分析。     

Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis

An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

3S支持下的滑坡地质灾害监测、评估与建模

在综合论述滑坡灾害系统、成因及过程的基础上,探讨了遥感、地理信息技术、全球定位技术为代表的对地观测技术和空间信息技术在滑坡信息提取、监测与建模中的应用,并以万县地区为例介绍了基于3S技术的滑坡风险评价与分析.     

Assessment of Seismically Induced Landslide Hazard for the State of Karnataka Using GIS Technique

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.     

Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision‐making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k‐means classification were used to assign digital terrain attributes to continuous landform classes whereas the Dempster‐Shafer theory of evidence was used to represent and manage imprecise information and to deal with uncertainties. The paper introduces the integration of the fuzzy k‐means classification method and the Dempster‐Shafer theory of evidence to model landslide hazard in roaded and roadless areas illustrated through a case study in the Clearwater National Forest in central Idaho, USA. Sample probabilistic maps of landslide hazard potential and uncertainties are presented. The probabilistic maps are intended to help decision‐making in effective forest management and planning.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

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.     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

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.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

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.     

Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier

In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management.     

基于MapXtreme2004的WebGIS矢量图形查询系统

实现互联网上的空间信息的管理和发布是当前地理信息科学领域的研究热点。本文分析了具有代表性的WebGIS平台Mapxtreme2004组件的核心和工作原理,并以某省高速路沿线滑坡灾害管理系统为例,介绍了在Net下利用MapXtreme2004组件开发WebGIS矢量图形查询系统的相关技术和方法。     

高分一号汶川极震区滑坡提取研究

选择汶川地震极震区的高分一号卫星影像,通过面向对象的分析技术提取滑坡信息;采用多尺度分割算法,结合高分影像和滑坡特点将以往经验式参数选取方法进行优化,分析极震区滑坡的特征,选择合适的特征参数,构建分类规则,实现滑坡的识别与提取。滑坡灾害信息的提取结果采用野外实际调查的滑坡点进行精度评价,滑坡提取总精度为84%,表明利用高分一号高分辨率卫星数据可以较好地提取滑坡灾害信息,基本满足滑坡灾害识别的要求。