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。     

基于优化随机森林模型的滑坡易发性评价

以三峡库区沙镇溪镇-泄滩乡为研究区,探索基于最短描述长度原则的信息增益法对滑坡连续型因子进行离散的效果,计算皮尔森系数去除高相关因子。利用信息量法预测的极低、低易发区随机抽取非滑坡样本点。通过迭代计算袋外误差估计确定较优的随机特征及其数目,将优化后的随机森林对研究区滑坡进行易发性评价,并与逻辑回归等方法进行比较。绘制各算法预测结果的接收灵敏度曲线,其中优化后的随机森林预测结果的曲线下面积较高,达91.8%,表明优化随机森林模型在滑坡易发性评价中具有较高的预测能力。     

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。     

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。     

改进的学习向量量化滑坡易发性评价模型研究

针对传统的学习向量量化模型只能进行欧式空间的度量问题,该文将在学习向量量化(LVQ)模型的基础上引入径向基核函数(RBF)建立径向基函数的学习向量量化(RBF-LVQ)评价模型。以文成县为研究区,结合GIS技术选取坡度、坡向、坡形、断层距离、地质岩组、极端小时降雨量、地形湿度指数、地表覆盖、风化层厚度、黏聚力10个评价因子构建滑坡易发性评价体系,随机选取70%数据作为训练样本,分别采用RBF神经网络、LVQ神经网络和RBF-LVQ模型进行滑坡灾害易发性评价,并将剩余的30%数据利用ROC曲线进行精度检验。结果显示,训练后的RBF-LVQ模型AUC值为0.88,优于RBF神经网络的0.85和LVQ的0.86。RBF-LVQ模型拥有更好的预测能力,可为研究区域提供模型和决策支持。     

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。     

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。     

斜坡单元支持下的滑坡易发性评价支持向量机模型

针对传统滑坡预测手段数据源有限、数据更新周期长、难以发现隐藏在复杂滑坡系统中的规律等问题,本文以三峡库区为研究对象,从多源空间数据中提取滑坡孕灾环境和影响因素等信息,采用数字地形水文分析方法划分斜坡单元,对评价因子进行重采样,进而构建两类支持向量机模型。分析了多源影响因素与滑坡易发性的定量关系,并生成滑坡易发性分区图。采用成功率曲线和误差率评价预测结果,模型预测精度达到98.21%,与野外调查实际情况吻合较好。     

顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价

滑坡灾害易发性分析评价对地质灾害的防治与管理具有重要意义。针对滑坡灾害样本选择策略,单核支持向量机多特征映射不合理的问题,本文提出顾及样本优化选择的多核支持向量机(multiple kernel support vector machine,MKSVM)滑坡灾害易发性分析评价方法。为了保证样本平衡性并提高负样本的合理性,采用相对频率比(relative frequency,RF)综合评价各状态对于滑坡灾害易发性影响的重要程度,实现各评价因子状态的合理划分;利用确定性系数法(certainty factor,CF)计算各评价因子各状态分级影响滑坡灾害的敏感性,并在此基础上进行加权求和得到各栅格单元的滑坡灾害易发性指数,在滑坡灾害易发性指数极低和低易发区内随机选择与滑坡灾害点数目一致的非滑坡灾害点作为负样本数据。利用MKSVM对各特征空间最优核函数进行线性组合,解决了单一核函数映射不合理的问题,提高了模型的分类准确率和预测精度。以湖南省湘西土家族苗族自治州为研究区,从滑坡灾害易发性分区图、分区统计及评价模型精度3个方面对CF样本策略的MKSVM模型、CF样本策略的单核SVM模型、随机样本策略的MKSVM模型、随机样本策略的单核SVM模型进行了对比分析。结果表明,4种模型的受试者工作特征曲线(receiver operating characteristic,ROC)下的面积(area under curve,AUC)分别为0.859、0.809、0.798、0.766,验证了CF样本策略的合理性、有效性及MKSVM模型的可靠性。     

利用国产遥感卫星进行金沙江高位滑坡灾害灾情应急监测

高位地质灾害具有强隐蔽性、强破坏性、难排查性等特征,近年来在我国西南山区频频发生,给山区居民造成了严重的人员伤亡和财产损失。以国产高分二号与北京二号等国产遥感卫星影像为数据源,对"10·11"金沙江高位滑坡开展灾情应急监测,分析了滑坡致灾情况、致灾演变及灾前蠕变特征,对灾后堰塞湖周边隐患灾后开展二次排查,查明了堰塞湖全域存在疑似裂缝隐患2处、滑坡隐患16处及5淹没区受损情况。结果表明国产遥感卫星对国家重特大地质灾害应急监测发挥了重大作用。     

GIS-based landslide susceptibility model considering effective contributing area for drainage time

This study employed GIS modelling to ascertain landslide susceptibility on Mt. Umyeon, south of Seoul, South Korea. In this study, an effective contributing area (ECA) for certain drainage time was purposed as a temporal causative factor and then used for modelling in combination with spatial causative factors such as elevation, slope, plan curvature, drainage proximity, forest type, soil type and geology. Landslide inventory map of 163 landslide locations was prepared using aerial photographic interpretation and field verifications after that digitized using GIS environment in 1:5000 scale. A presence-only-based maximum entropy model was used to establish and analyse the relationship between landslides and causative factors. Before final modelling, a jackknife test was performed to measure the variable contributions, which showed that the slope was the most significant spatial causative factor, and ECA with a drainage time of 12 h was the most significant temporal causative factor. The performances of the final models, with and without significant ECA, were assessed by plotting a receiver operating characteristic curve to be 75.5 and 81.2%, respectively.     

基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例

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。     

基于熵权法的组合模型在滑坡变形预测中的应用

以趋势曲线预测模型中的多项式模型和指数模型为基础,基于熵权法建立组合预测模型,就其建模理论和计算流程进行系统分析,并对某滑坡体进行预测。结果表明,文中组合方法相对于其它组合方法而言,理论简单,能够有效地反映滑坡体的变化规律,且提高了拟合预测精度,在预测工作中具有一定的参考价值。     

金沙江下游卵石推移质输移分析

金沙江下游的推移质输移规律及其泥沙来量和级配对梯级水库的淤积分布以及河势将产生长远影响,需要开展深入研究。在金沙江下游入库控制站三堆子水文站水尺上游100 m布设推移质测验断面,对该站2007年、2008年的推移质进行了测验;分析了卵石推移质的年内分布、横向分布、级配以及推移质输移与水位、流量、流速的关系。研究表明:三堆子河段卵石推移质具有数量大、粒径大、时间集中、强推带集中、推移带随水位左右摆动的特征;全年输沙量接近500000 t;最大粒径卵石一般在140～170 m之间,最大粒径230 mm以上;推移量主要集中在汛期,尤以7～9月为甚,占全年的90%以上;强推带为140～180 m 4条垂线,其输沙量占整个断面输沙量的90%以上;洪水期推移带向右岸偏移,低水期则向左岸偏移。     

Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping

Abstract

The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database. In order to run a series of logistic regression models, different random landslide-free sample sets were produced and combined with seed cells. Different susceptibility maps were created with an average success rate of nearly 80%. The coherence among the models showed spatial correlations greater than 90%. Models converged in the parameter selection peculiarly, in that the same nine of 20 were chosen by different logistic regression models. Among these nine parameters, lithology, geological structure (distance/density), landcover-landuse, and slope angle were common parameters selected by both the regression models and literature. Accuracy assessment of the logistic models was assessed by absolute methods. All models were field checked with the landslides resulting from the 12 November 1999, Kayna?li Earthquake (Ms = 7.2).     

Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin,Korea

Rainfall-triggered shallow landslide is very common in Korean mountains and the socioeconomic impact is much higher than in the past due to population pressure in hazardous zones. Present study is an attempt toward the development of a methodology for the integration of shallow landslide susceptibility zones and runout zones that could be reached by mobilized mass. Landslide occurrence areas in Yongin were determined based on the interpretation of aerial photographs and extensive field surveys. Nineteen landslide-related factors maps were collected and analysed in geographic information system environment. Among 109 identified landslides, about 85% randomly selected training landslide data from inventory map was used to generate an evidential belief function model and remaining 15% landslides were used to validate the shallow landslide susceptibility map. The resulting susceptibility map had a success rate of 89.2% and a predictive accuracy of 92.1%. A runout propagation from high susceptible area was obtained from the modified multiple-flow direction algorithm. A matrix was used to integrate the shallow landslide susceptibility classes and the runout probable zone. Thus, each pixel had a susceptibility class in relation to its failure probability and runout susceptibility class. The study of landslide potential and its propagation can be used to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.     

Optimizing landslide susceptibility mapping in the Kongtong District,NW China: comparing the subdivision criteria of factors

Abstract

The standards applied to reclassify landslide-conditioning factors differ among studies and may change the accuracy of identifying landslide-prone areas. Therefore, we identified two standards per factor (elevation, aspect, slope, proximity to roads and proximity to streams) from the existing literature and set them as predisposing criteria in this paper. In addition to the five factors, lithology represented by types and a landslide inventory map produced from field surveys were also used in mapping. Thirty-two landslide susceptibility maps were generated based on weights-of-evidence and evaluated using the relative operative characteristic method. The results show that the subdivision criteria of factors change the accuracy, with the success rate varying from 84.34% to 87.51%. The map with the highest value captures more landslides in relatively higher susceptibility classes and is therefore considered the optimal one. Ultimately, a simplified mode of combining subdivision criteria is proposed to simplify comparison.     

Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang,Korea

Landslides susceptibility maps were constructed in the Pyeong-Chang area, Korea, using the Random Forest and Boosted Tree models. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models. Seventeen landslide-related factors were extracted and constructed in a spatial database. The relationships between the observed landslide locations and these factors were identified by using the two models. The models were used to generate a landslide susceptibility map and the importance of the factors was calculated. Finally, the landslide susceptibility maps were validated. Finally, landslide susceptibility maps were generated. For the Random Forest model, the validation accuracy in regression and classification algorithms showed 79.34 and 79.18%, respectively, and for the Boosted Tree model, these were 84.87 and 85.98%, respectively. The two models showed satisfactory accuracies, and the Boosted Tree model showed better results than the Random Forest model.