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

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。     

一种结合SMOTE和卷积神经网络的滑坡易发性评价方法

大规模的人类工程活动诱发和加剧了滑坡灾害的致灾情况,严重威胁工程安全和环境安全。滑坡易发性评价是滑坡监测预警的关键技术。针对传统滑坡监测手段数据源有限、缺乏挖掘滑坡灾害空间分布特征及其诱发因素的有效方法等问题,以位于三峡库区的中国重庆市万州区为研究区,基于地形、地质和遥感影像等多源数据,首先提取了22个滑坡易发性评价因子,并对这些因子进行多重共线性检验;然后采用合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)解决滑坡和非滑坡样本比例不平衡问题,建立输入训练集;最后构建卷积神经网络(convolutional neural networks,CNN)模型,定量预测滑坡易发性,生成滑坡易发性分区图。采用受试者工作特征曲线分析评价结果,测试数据集模型精度达89.50%,说明该模型是一种高性能的滑坡易发性评价方法。     

GIS支持下应用PSO-SVM模型预测滑坡易发性

滑坡灾害易发性预测是滑坡监测、预警与评估的关键技术。如何有效地选取评价因子和构建预测模型是滑坡灾害定量预测研究中的难题。本文以三峡库区长江干流岸坡作为研究区,通过地形、地质和遥感等多源数据融合,提取滑坡孕灾环境和诱发因素的信息作为评价因子。在此基础上,针对滑坡灾害的非线性和不确定性特征,采用粒子群算法对支持向量机模型参数进行全局寻优,构建粒子群算法(particle swarm optimization,PSO)-支持向量机(support vector machine,SVM)模型,定量预测滑坡易发性。最后通过分类精度比较分析基于格网单元和对象单元的滑坡易发性预测精度,结果表明,基于对象单元的PSO-SVM预测精度较高,其曲线下面积为0.841 5,Kappa系数为0.849 0,预测结果与野外实际调查情况较为一致,可为三峡库区滑坡防灾减灾工作提供参考。     

基于不同模型和单元的滑坡易发性评价比较

基于地理信息系统的滑坡灾害空间预测研究发展迅速,出现了多种滑坡空间预测模型。在总结滑坡灾害空间预测研究现状的基础上,简要介绍了决策树和支持向量机2种模型的基本原理。以秭归县一个研究区为例,选取11个滑坡影响因子,采用2种不同的研究单元,分别建立了决策树和支持向量机模型并对滑坡易发性作出了评价。结果表明针对同一模型,面向对象单元的滑坡易发性评价精度优于栅格单元;针对同一数据单元,支持向量机模型的滑坡易发性评价精度优于决策树模型。     

CF耦合LR模型的地质灾害易发性评价

为对自然灾害频发的西北地区进行地质灾害易发性研究,本文考虑了高程、坡向等10类因素,利用确定性系数(CF)模型和确定性系数耦合逻辑回归(CF-LR)模型对城固县地质灾害易发性进行评价。结果表明,CF模型和CF-LR模型的灾害比均呈递增状态,高、极高灾害点密度分别为4.75、5.97个/km²,且两种模型的测试集受试者工作特征(ROC)曲线下面积(AUC)分别为0.812、0.835,验证集ROC和AUC值分别为0.862、0.891。两种模型均能有效评价区域内地质灾害易发性,且CF-LR模型具有更高的评价精度。     

GIS支持下滑坡灾害空间预测方法研究

滑坡预测在防灾减灾工作中具有重要意义,它包括空间、时间预测两个方面。基于统计模型进行区域评价与空间预测是滑坡灾害研究的重要方向,但是预测结果往往依赖样本数量和空间分布等。本文以马来西亚金马伦高原为研究区,选择高程、坡度、坡向、地表曲率、构造、土地覆盖、地貌类型、道路和排水系统作为评价因子,探讨运用地理信息系统(GIS)和遥感(RS)获取与管理滑坡灾害信息,以及热带雨林地区湿热环境下滑坡空间预测的方法。支持向量机(SVM)和逻辑(Logistic)回归模型分别应用于滑坡空间预测,结果显示平均预测精度分别为95.9%和86.2%,SVM法具有较高的描述精度,值得推荐;同时,基于SVM模型的滑坡空间预测受样本影响较小,预测结果相对比较稳定,这对于滑坡灾害区域评价与预测的快速实现具有实际意义。     

层次分析法的黄河上游滑坡易发性评价

针对黄河上游龙羊峡至积石峡段滑坡灾害分布易发性评价与区划成图问题,该文以ArcGIS为平台,联系评价区的实际特点,选取地貌类型、地层岩性、降雨、断层、坡度为评价因子,运用层次分析法(AHP)确定各评价因子的权重,建立研究区滑坡易发性评价模型,结合GIS的空间分析功能实现研究区内滑坡灾害的易发性区划。结果表明,滑坡灾害主要集中在龙羊峡库区右岸和群科-尖扎盆地。区划结果与野外实际调查基本吻合,为今后GIS应用于地质灾害区划提供了思路,同时可为区内地质单位进行灾害监测提供基础数据和依据。     

旋转森林模型在滑坡易发性评价中的应用研究

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。     

新源县汇水域单元滑坡灾害易发性评价

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。     

基于卷积神经网络的区域滑坡易发性评价:以三峡库区万州区为例

开展区域滑坡易发性评价是滑坡气象预警与风险评价的关键。针对目前诸多易发性研究未考虑滑坡发生与邻接环境有关的情况,本文提出了一种基于卷积神经网络(CNN)的区域滑坡易发性建模框架。以三峡库区万州区为例,选取坡度、坡向等12个因子构建评价指标体系,通过信息量法分析因子对滑坡发育的影响程度,采用二维矩阵构建数据集,运用CNN进行易发性建模,得到易发性评价图,同时探究构建样本时二维矩阵的大小对精度的影响。研究结果表明,越靠近水库带越易发生滑坡,水系和人类工程活动对于滑坡发育具有较大影响;CNN模型精度为0.925,相比机器学习模型精度明显提升;增大构建样本时的二维矩阵可提高精度。CNN模型在多维空间数据处理方面具有优势,它考虑了滑坡位置及其邻接环境的影响,是一种准确可靠的区域滑坡易发性评价方法。     

Evaluation and comparison of LogitBoost Ensemble,Fisher’s Linear Discriminant Analysis,logistic regression and support vector machines methods for landslide susceptibility mapping

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.     

迁移学习支持下的高分影像山地滑坡灾害解译模型

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。     

Comparison of landslide susceptibility mapping based on statistical index,certainty factors,weights of evidence and evidential belief function models

The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models.     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

顾及样本敏感性的滑坡易发性评价

滑坡作为一种危害极大的自然地质现象,严重威胁着人民的生命财产安全。因此,科学、准确地评价滑坡体的易发性至关重要。随着机器学习的发展,基于机器学习的滑坡易发性评价逐渐成为研究热点。而在真实情况中,滑坡区域与非滑坡区域面积占比悬殊,这使得机器学习模型的应用存在较严重的样本不均衡问题。本文采用样本敏感性分析方法,综合多个机器学习模型在不同比例的正负滑坡样本集上的表现,以获取最均衡滑坡样本集;并在此样本集基础上采用深度随机森林模型,在示范研究区开展滑坡易发性评价。最终的评价结果接近真实分布,表明本文方法具有较好的有效性。     

进化多核相关向量机的大坝变形预测

针对大坝变形系统的非线性、复杂性以及不确定等特点,提出一种优化多核相关向量机的大坝变形预测模型方法。通过对实验数据进行归一化处理,核函数的加权组合以及遗传算法对模型参数的优化,建立遗传算法优化多核相关向量机的大坝变形预测模型。实验结果表明:数据归一化能归纳统一样本的统计分布性,加快梯度下降求解最优解速度和提高预测精度;优化的加权核函数能有效提高模型预测精度;各项精度指标值均优于BP神经网络方法、多项式核相关向量机方法预测精度,证实优化的多核相关向量机模型是一种精度较高的大坝变形预测方法。     

城市道路的多特征多核SVM提取方法

针对高分辨率遥感影像中城市道路提取的复杂性及SVM的分类性能,提出了一种城市道路的多特征多核SVM提取方法。首先利用FCM算法将原始影像粗分为建成区和非建成区两类,剔除非建成区;然后根据分水岭分割算法分割建成区并提取分割对象的光谱特征与空间特征,以全局核函数和局部核函数加权组合的方式构建多核SVM对建成区进行二次分类,去除建成区中的建筑物等非道路信息;最后利用数学形态学处理,获得最终的道路提取结果。试验结果表明：文中所提方法能够较精确地提取城市道路信息,分类精度高于单核SVM提取及其他对比方法。     

Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

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.     

Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio,certainty factor,and index of entropy

The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning.