降雨型浅层滑坡危险性预测模型

通过分析SHALSTAB和TRIGRS等浅层滑坡物理确定性模型存在的问题,提出了基于降雨入渗动态守恒的瞬态降雨入渗模型,该模型考虑了初期降雨过程、降雨历程以及饱和非饱和入渗过程,证明了SHALSTAB模型是该模型的特殊形式,并克服了TRIGRS模型参数繁多及一维入渗路径的问题.将无限边坡模型、瞬态降雨入渗模型和GIS进行耦合,研发了可用于大范围降雨型浅层滑坡危险性预测的集成系统,根据边坡的地质条件、地形参数和降雨特征即可对降雨条件下浅层滑坡的危险性进行评估.     

基于TRIGRS模型的浅层滑坡稳定性分析

采用基于网格的瞬态降雨入渗(TRIGRS)模型,以滑坡灾害频发的陕南安康市东部巴山东段白河县为研究区,探讨模型适用性及不同降雨条件下边坡稳定性空间分布规律。根据中国土壤分布图并结合已有研究,选取模拟所需的水土力学参数。将模拟所得研究区稳定性分布图与实际滑坡目录对比分析进行TRIGRS模型精度评估,分别模拟连阴雨和短时间强降雨两种降雨情景,探讨研究区边坡稳定性空间分布规律,结果表明:1)TRIGRS模型在模拟预测降雨诱发型浅层滑坡时,结合受试者特征ROC曲线进行精度评估,曲线下面积为0.752,说明此模型在白河县进行滑坡模拟时具有一定的合理性与准确性,能反应该地区滑坡灾害的空间分布特征;2)连阴雨情景模拟下,极不稳定区域主要集中在北部低山地貌区,以冷水镇和麻虎镇为主,随降雨历时增加向东部和南部增多,西部仓上镇、西营镇和双丰镇的极不稳定区域面积较少,能承受长时间连续性降雨。短时间强降雨对边坡稳定性的影响更为直接,极不稳定区域随降雨强度增大而增加,以冷水镇和麻虎镇为主要防范区域。结合地形分析,极陡峭区域边坡稳定性最差,无法承受持续性降雨和高强度降雨,较陡峭区域更易受到降雨历时和降雨强度的影响,而平缓区域则能承受长时间及高强度的降雨;3)TRIGRS模型根据不同降雨条件预测易发生滑坡灾害的区域,为滑坡实时预报警系统提供了新的可能方法。     

降雨型浅层黄土滑坡危险性评价与区划

黄土高原是我国地质灾害最为发育的地区之一,其中降雨诱发的浅层黄土滑坡又最为典型。以典型黄土地貌区-柳林县为例,应用SINMAP模型,探讨模型在黄土地区的适用性,分析了随着研究区内降雨量的增加,滑坡变形失稳区域的面积变化、分布位置和扩展趋势。研究表明,随着降雨量的增加,滑坡所处位置逐渐由稳定状态向失稳状态发展,位于失稳分区的滑坡数量逐渐增加,说明降雨对该研究区的斜坡稳定性影响较为明显。通过将模拟结果与实际发生的由降雨触发的滑坡灾害进行对比分析,可以得出SINMAP模型在黄土地区,对区域性降雨诱发浅层黄土滑坡稳定性的模拟预测有效,可以用于黄土地区浅层滑坡的稳定性评价研究。     

典型黄土丘陵区浅层黄土滑坡稳定性评价——以延安市志丹县为例

黄土丘陵地区地质环境脆弱,每到雨季极易诱发浅层黄土滑坡,对居民的生命和财产安全构成威胁也阻碍着当地经济的发展。对浅层滑坡进行稳定性评价,不仅有助于认识浅层滑坡的发生发展过程,而且对防灾减灾和地区规划建设具有十分重要的指导意义。本研究选择SINMAP模型作为评价浅层黄土滑坡的重要工具,评价了陕西省延安市志丹县黄土丘陵区浅层滑坡的稳定性,评价结果表明:1)研究区整体稳定性程度较高,在降雨量为8.6 mm、15 mm、25 mm、50 mm和100 mm时不稳定区域(包括极不稳定、不稳定和潜在不稳定)面积分别占研究区总面积的9.12%、18.93%、23.17%、30.94%和38.67%,不稳定区域的面积不超过整个研究区面积的一半,极不稳定区域的滑坡密度最大,其次为不稳定区域和潜在不稳定区域;2)随着降雨量的增大,潜在不稳定和不稳定区域的面积会逐渐扩大,极不稳定区始终位于坡度大且水流侵蚀强烈的地方,变化幅度小;3)浅层滑坡的稳定性很大程度上依赖于当地的地形条件:坡度分布为20°~51°,高程分布范围为1302~1606 m,在坡向上阴坡的发生数量多于阳坡,西向和西北向浅层滑坡最为发育;4)流域内的滑坡多属降雨诱发的山体滑坡,确定性模型SINMAP为预测这一类滑坡提供了强大的工具,不仅评估了现有的已发生的滑坡的稳定性,也预测了未来在不同降雨条件下可能发生滑坡的地区。分析结果可为预防和减轻滑坡灾害带来的损失,合理的城市规划和道路选址等提供参考。     

基于有效雨量的鹰厦铁路降雨诱发滑坡预警预报概率模型

大气降雨是造成铁路滑坡的重要诱因。文章以浅层滑坡SHALSTAB模型为基础,以鹰厦铁路为研究区域,研究了降雨诱发铁路滑坡的机理,进一步分析滑坡与有效雨量的关系。结合滑坡概率预报模型,计算鹰厦铁路降雨诱发滑坡临界雨量,并对研究区的滑坡灾害危险性进行了预警分级。     

浅层顺层滑坡成灾机制分析及其稳定性评价

为准确掌握浅层顺层滑坡的成因机制,合理评价其稳定性,基于滑坡所处地质条件,先开展其成因机制分析,再利用传递系数法和变形预测模型分别开展滑坡稳定性的现状评价及预测评价。实例分析表明：浅层顺层滑坡的影响因素相对较多,且由于浅层滑坡的滑体厚度较薄,受外界扰动影响易较大,因此,浅层滑坡对各类因素的敏感性均较强。在滑坡稳定性现状评价结果中：天然工况条件下的稳定性系数为1.21,属稳定状态;暴雨工况条件下的稳定性系数为1.02,属欠稳定状态;地震工况条件下的稳定性系数为1.09,属基本稳定状态。在滑坡稳定性预测评价结果中,滑坡各监测点位置处的变形预测速率均一定程度上大于现有变形速率,说明滑坡后续累计变形仍会进一步加速,其说明在本次监测时段后的滑坡稳定性将趋于减弱,需尽快开展此滑坡防治研究。研究成果可为浅层顺层滑坡灾害防治提供一定的理论指导。     

不同降雨条件下黄土高原浅层滑坡危险性预测评价

黄土地区浅层滑坡发育非常广泛,由于其具有分布规律性差、前期变形迹象小、分布范围大、面小点多等特征,目前还无法进行有效预测,因此给黄土地区工程安全带来严重威胁。根据无限边坡模型,结合降雨入渗-土体强度衰减规律和GIS（地理信息系统）技术,构建了不同降雨条件下黄土地区浅层滑坡发育危险性评价模型,并将该评价模型应用到延河一级支流幸福川流域,预测在有效降雨量30、50、100、200 mm条件下,该流域浅层滑坡发育程度,并与当前较为流行的SINMAP模型(地形稳定性模型)进行对比。结果表明：①不稳定和潜在不稳定浅层滑坡主要分布在末级河流的两侧和源头,稳定和较稳定区域主要分布在一级河流河道两侧和塬面上;通过对比分析,SINMAP模型计算的结果与本文建立的模型在降雨强度30 mm时的计算结果较为一致。②在本文建立的模型评价结果中,随着有效降雨量的增加,F_s（稳定性系数）<1.00的不稳定区域所占比例逐渐增加,从30 mm的1.12%到200 mm的4.79%;相反,稳定区域则出现逐渐减少的趋势。③根据已发生灾害点的分布,随着有效降雨量的增加,研究区域已发生的灾害点分布在F_s<1.25的比例明显增加,从30 mm的62%到200 mm的88%,在SINMAP评价模型中,研究区域已发生的灾害点的64%分布在不稳定和潜在不稳定区域内,说明本文所建立的评价模型具有一定的精度。通过与SINMAP评价模型对比,本文建立的模型主要采用基于降雨入渗规律,而SINMAP评价模型主要基于降雨汇流过程,因此在利用过程中应根据区域特征选择利用。     

基于最大熵-无限边坡模型的降雨诱发浅层黄土滑坡稳定性评价方法研究

无限边坡（Sinmap）模型在评价降雨作用下浅层黄土滑坡稳定性时精度较低。针对这一问题,基于最大熵（Maxent）模型对Sinmap模型评价进行改进,构建了最大熵-无限边坡（Maxent-Sinmap）模型,评价降雨作用下区域性浅层降雨型黄土滑坡稳定性。以黄土滑坡高发区的陕西省志丹县为例,利用野外及室内相关工作获取地形、岩土体力学参数及地质灾害等相关数据,通过Maxent模型获取主要环境变量,再根据主要环境变量进行分区,通过Sinmap模型对降雨作用下不同分区的浅层黄土滑坡稳定性进行评价。研究结果表明：基于Maxent模型得到志丹县内滑坡主要受坡度、降雨量、地貌、道路缓冲区及归一化植被覆盖指数等5个指标影响,对历史灾点的贡献率分别为27.1%、20.3%、18.8%、18.7%、6.2%。相较于传统Sinmap模型,该模型不稳定区域灾点密度在小雨、中雨、大雨、暴雨和大暴雨情况下分别提高了17.26%、16.54%、17.39%、14.20%、12.96%。Maxent-Sinmap模型计算结果相较于Sinmap模型计算结果具有更大的稳定区域,且稳定区的扩大区无历史灾点分布。表明该模型具...     

库水位下降及降雨作用下麻柳林滑坡稳定性评价与预测

研究库水位升降与降雨影响下三峡库区堆积层滑坡的稳定性变化特征,并对其进行预测具有重要意义。建立了三峡库区万州区麻柳林滑坡的地质模型,基于P-Ⅲ型分布曲线对降雨重现期进行分析后共设置了12种计算工况;利用Geostudio软件对滑坡稳定性进行了模拟。结果表明:库水位下降和降雨入渗均会使滑坡稳定性系数减小,不仅变化幅度与库水位下降速率和降雨强度均为正相关关系,而且滑坡稳定性对于降雨条件更加敏感。2种最不利工况条件下的滑坡稳定性系数分别为0.95和0.949,此时滑坡失稳破坏。利用灰色模型对最不利工况下的滑坡稳定性系数进行了滚动预测,其MAPE为2.86%,MSE为0.033,预测精度优于多项式模型。     

基于XGBoost模型的三峡库区燕山乡滑坡易发性评价与区划

滑坡易发性评价是精细化滑坡灾害风险评价的基础。为了提升滑坡易发性评价模型的精度和稳健性,以三峡库区万州区燕山乡为例,选取工程地质岩组、堆积层厚度等九个影响因子构建滑坡易发性评价指标体系,应用信息量模型定量分析滑坡发育与指标之间的关系。在此基础上,随机选取70%/30%的滑坡样本作为训练/验证数据集,应用极致梯度提升模型(extreme gradient boosting, XGBoost)开展易发性评价。随后从模型预测精度和模型稳定性两方面将其与决策树模型(decision tree, DT)和梯度提升树模型(gradient boosting decision tree, GBDT)进行对比。结果表明：研究区堆积层滑坡主要受长江水系、堆积层厚度和工程地质岩组影响。XGBoost模型具有最高的准确率(94.3%)和预测精度(97.3%)。在模型稳定性验证中,平均预测精度最高(97.3%),优于DT(91.3%)和GBDT(95.7%),模型标准差和变异系数均为0.01,低于其余两种模型。XGBoost在区域滑坡易发性评价与制图中得到了可靠的结果,为滑坡灾害空间预测提供了新的技术支撑。     

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS

The purpose of this study is to assess the susceptibility of landslides in parts of Western Ghats, Kerala, India, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analysis of the topographical maps. The landslide triggering factors are considered to be slope angle, slope aspect, slope curvature, slope length, distance from drainage, distance from lineaments, lithology, land use and geomorphology. ArcGIS version 8.3 was used to manipulate and analyse all the collected data. Probabilistic-likelihood ratio was used to create a landslide susceptibility map for the study area. The result was validated using the Area under Curve (AUC) method and temporal data of landslide occurrences. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. As the result, the success rate of the model was (84.46%) and the prediction rate of the model was (82.38%) shows high prediction accuracy. In the reclassified final landslide susceptibility zone map, 5.68% of the total area is classified as critical in nature. The landslide susceptibility map thus produced can be used to reduce hazards associated with landslides and to land cover planning.     

Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji,China

The main objective of this study was to apply a statistical (information value) model using geographic information system (GIS) to the Chencang District of Baoji, China. Landslide locations within the study area were identified using reports and aerial photographs, and a field survey. A total of 120 landslides were mapped, of which 84 (70 %) were randomly selected for building the landslide susceptibility model. The remaining 36 (30 %) were used for model validation. We considered a total of 10 potential factors that predispose an area to a landslide for the landslide susceptibility mapping. These included slope degree, altitude, slope aspect, plan curvature, geomorphology, distance from faults, lithology, land use, mean annual rainfall, and peak ground acceleration. Following an analysis of these factors, a landslide susceptibility map was produced using the information value model with GIS. The resulting landslide susceptibility index was divided into five classes (very high, high, moderate, low, and very low) using the natural breaks method. The corresponding distribution area percentages were 29.22, 25.14, 15.66, 15.60, and 14.38 %, respectively. Finally, landslide locations were used to validate the results of the landslide susceptibility map using areas under the curve (AUC). The AUC plot showed that the susceptibility map had a success rate of 81.79 % and a prediction accuracy of 82.95 %. Based on the results of the AUC evaluation, the landslide susceptibility map produced using the information value model exhibited good performance.     

A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.     

A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.     

基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价

准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度,以三峡库区龙驹坝为例,选取坡度等10个因子构建滑坡易发性评价指标体系,应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上,随机选取70%/30%的滑坡数据作为训练/测试样本,应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost),径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示:水系距离、坡度等是滑坡发育的主控因素;RBNN-Adaboost耦合模型的预测精度最高(0.820),优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能,所提出的耦合模型结合了两者的优点,具有更强的预测能力,是一种可靠的滑坡易发性评价模型.      

基于典型剖面分析的区域斜坡稳定性分区方法

为了研究大范围内区域斜坡的稳定性,本文提出了一套基于典型剖面分析的区域斜坡稳定性分区方法。首先,在ArcGIS水文分析的基础上划分斜坡单元;其次,利用MATLAB开发程序,完成斜坡单元的剖分、典型剖面的搜索和提取工作;再次,利用MATLAB动态生成FLAC2D命令流文件,自动计算得到区域斜坡稳定性系数的分布;最后,基于定量计算在ArcGIS中对区域斜坡进行稳定性分区评价。本文选取陕西耀县幅1:5万环境地质调查区内黄土斜坡较发育段作为研究对象,对本方法进行验证。计算结果:（1）研究区域划分为不稳定区、基本稳定区和稳定区3个区;（2）整个方法体系采取智能化的设计理念,各计算步骤之间自动调用执行,程序衔接良好;（3）将GIS分析与外部计算相结合,实现单个斜坡的稳定性计算到区域的推广。     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.