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1.
This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km2), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.  相似文献   

2.
For the assessment of shallow landslides triggered by rainfall, the physically based model coupling the infinite slope stability analysis with the hydrological modeling in nearly saturated soil has commonly been used due to its simplicity. However, in that model the rainfall infiltration in unsaturated soil could not be reliably simulated because a linear diffusion-type Richards’ equation rather than the complete Richards’ equation was used. In addition, the effect of matric suction on the shear strength of soil was not actually considered. Therefore, except the shallow landslide in saturated soil due to groundwater table rise, the shallow landslide induced by the loss in unsaturated shear strength due to the dissipation of matric suction could not be reliably assessed. In this study, a physically based model capable of assessing shallow landslides in variably saturated soils is developed by adopting the complete Richards’ equation with the effect of slope angle in the rainfall infiltration modeling and using the extended Mohr–Coulomb failure criterion to describe the unsaturated shear strength in the soil failure modeling. The influence of rainfall intensity and duration on shallow landslide is investigated using the developed model. The result shows that the rainfall intensity and duration seem to have similar influence on shallow landslides respectively triggered by the increase of positive pore water pressure in saturated soil and induced by the dissipation of matric suction in unsaturated soil. The rainfall duration threshold decreases with the increase in rainfall intensity, but remains constant for large rainfall intensity.  相似文献   

3.
Shallow landslides are a prevalent concern in mountainous or hilly regions that can result in severe societal, economic, and environmental impacts. The challenge is further compounded as the size and location of a potential slide is often unknown. This study presents a generalized approach for the evaluation of regional shallow landslide susceptibility using an existing shallow landslide inventory, remote sensing data, and various geotechnical scenarios. The three-dimensional limit equilibrium model derived in this study uses a raster-based approach that uniquely integrates tree root reinforcement, earth pressure boundary forces, and pseudo-static seismic accelerations. Contributions of this methodology include the back-calculation of soil strength from a landslide inventory, sensitivity analyses regarding landslide size-pixel size relationships, and the determination of shallow landslide susceptibility for a landscape or infrastructure considering various root, water, and seismic conditions using lidar bare-earth DEMs as a topographic input. Using a distribution of inventoried landslide points and random points in non-landslide locales, the proposed methodology demonstrated reasonable correlation between regions of high landslide susceptibility and observed shallow landslides within a 150-km2 region of the Oregon Coast Range when the water-height ratio was 0.5. The method may be improved by considering spatial hydrologic conditions and geology more explicitly.  相似文献   

4.
Shallow landslides usually occur during hevy rainfall and result in casualties and property losses. Thus, the possible locations where landslides are likely to occur must be identified in advance in order to avoid or reduce the harm they cause. When performing a slope-instability analysis, soil thickness is an important factor; however, soil thickness information from landslide-prone areas is rarely obtained. The objective of this study is to realize the influences of spatial distribution of soil thickness on shallow landslide prediction. Three different spatial soil-thickness distributions were applied to perform a slope-instability analysis, and uniform-distributed soil thicknesses from 0.4 m to 2.0 m were also applied for comparison. Geomorphologic information and hydrological records from a landslide-prone area in southern Taiwan were collected. Results show that the spatial distribution of soil thickness related to wetness index provides a reasonable estimation in order to avoid an over-prediction for landslide-prone areas or an under-prediction for stable areas. The analytical procedure used in this study is a simple way for assessing hillslope instability for shallow landslide prediction.  相似文献   

5.
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.  相似文献   

6.
 Hydrological landslide-triggering thresholds separate combinations of daily and antecedent rainfall or of rainfall intensity and duration that triggered landslides from those that failed to trigger landslides. They are required for the development of landslide early warning systems. When a large data set on rainfall and landslide occurrence is available, hydrological triggering thresholds are determined in a statistical way. When the data on landslide occurrence is limited, deterministic models have to be used. For shallow landslides directly triggered by percolating rainfall, triggering thresholds can be established by means of one-dimensional hydrological models linked to the infinite slope model. In the case of relatively deep landslides located in topographic hollows and triggered by a slow accumulation of water at the soil-bedrock contact, simple correlations between landslide occurrence and rainfall can no longer be established. Therefore real-time failure probabilities have to be determined using hydrological catchment models in combination with the infinite slope model. Received: 15 October 1997 · Accepted: 25 June 1997  相似文献   

7.
For landslide susceptibility mapping, this study applied, verified and compared the Bayesian probability model, the weights-of-evidence to Panaon Island, Philippines, using a geographic information system. Landslide locations were identified in the study area from the interpretation of aerial photographs and field surveys, and a spatial database was extracted from SRTM (Shuttle Radar Topographic Mission) DEM (Digital Elevation Model) imagery, aerial photograph, topographic map, and geological map. The factors that influence landslide occurrence, such as slope, aspect, curvature, topographic wetness index and stream power index of topography, were calculated from SRTM imagery. Distance from drainage was extracted from topographic database. Lithology and distance from fault were extracted and calculated from geological database. Terrain mapping unit was classified from aerial photographs. The spatial association between the factors and the landslides was calculated as the contrast values, W + and W − using the weights-of-evidence model. Tests of conditional independence were performed for the selection of the factors, allowing the large number of combinations of factors to be analyzed. For each factor rating, the contrast values, W + and W − were overlaid for landslide susceptibility mapping. The results of the analysis showed that contrast rating (78.60%) for each factor’s multiclass had better accuracy of 5.90% than combinations of factor assigned to binary class with W + and W − (72.70%).  相似文献   

8.
The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.  相似文献   

9.
降雨诱发浅层滑坡渐进破坏分析研究   总被引：1，自引：0，他引：1  
王正宇  樊辉 《贵州地质》2021,38(4):443-448
为分析降雨诱发浅层滑坡的演变过程。本文以湖南湘西古丈滑坡为例,基于Green-Ampt入渗模型,进行了降雨诱发浅层滑坡渐进破坏分析。研究结果表明:在强降雨作用下,滑坡的失稳破坏主要是由于前缘土体以及中前部土体的局部破坏,而逐渐发展为整体破坏。并且,受滑坡地形影响,地形平缓的区域虽然湿润锋下渗较快,土体抗剪强度较低,但由于土体饱和带的渗流作用较小,而重力提供垂直于滑面的分力较大,该部分稳定性较为良好,故湿润锋对于滑坡稳定性的影响还应该根据不同地形条件加以分析。渐进式滑坡破坏分析方法对滑坡的监测和防治具有重要的指导意义。  相似文献   

10.
The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.  相似文献   

11.
Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters. The applied physical model SLope-Infiltration-Distributed Equilibrium (SLIDE) takes into account some simplified hypotheses on water infiltration and defines a direct relation between factor of safety and the rainfall depth on an infinite slope. This prototype model is applied to a case study in Honduras during Hurricane Mitch in 1998. Two study areas were selected where a high density of shallow landslides occurred, covering approximately 1,200 km2. The results were quantitatively evaluated using landslide inventory data compiled by the United States Geological Survey (USGS) following Hurricane Mitch’s landfall. The agreement between the SLIDE modeling results and landslide observations demonstrates good predictive skill and suggests that this framework could serve as a potential tool for the future early landslide warning systems. Results show that within the two study areas, the values of rates of successful estimation of slope failure locations reached as high as 78 and 75%, while the error indices were 35 and 49%. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geologic information. Advantages and limitations of this physically based model are discussed with respect to future applications of landslide assessment and prediction over large scales.  相似文献   

12.
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。  相似文献   

13.
Field variability of landslide model parameters   总被引：5，自引：1，他引：4  
 A data set of parameters (slope, soil depth and soil shear strength) relevant to spatially distributed modelling of shallow landslides triggered by rain and snowmelt events was determined from field measurements in 250 grid elements of dimensions 25 m (downslope)×10 m (across slope) in an area of 250 m×250 m on a hillslope in Scotland. These data provide an unusually detailed basis for the evaluation of spatial variability and uncertainty in model parameterisation. The variations in slope and soil strength are represented adequately by normal distributions; a Weibull distribution is suggested for the soil depth data. The factor of safety calculated at each point in the grid was shown partially to identify observed landslides, with a number of false predictions of occurrence. Trend analysis and semivariogram analysis of the data set suggest that the use of kriging could improve upon this approach to landslide prediction by providing areal estimates of parameters at the grid element scale with associated error bounds. Received: 30 October 1996 · Accepted: 25 June 1997  相似文献   

14.
It has been known that ground motion amplitude will be amplified at mountaintops; however, such topographic effects are not included in conventional landslide hazard models. In this study, a modified procedure that considers the topographic effects is proposed to analyze the seismic landslide hazard. The topographic effect is estimated by back analysis. First, a 3D dynamic numerical model with irregular topography is constructed. The theoretical topographic amplification factors are derived from the dynamic numerical model. The ground motion record is regarded as the reference motion in the plane area. By combining the topographic amplification factors with the reference motions, the amplified acceleration time history and amplified seismic intensity parameters are obtained. Newmark’s displacement model is chosen to perform the seismic landslide hazard analysis. By combining the regression equation and the seismic parameter of peak ground acceleration and Arias intensity, the Newmark’s displacement distribution is generated. Subsequently, the calculated Newmark’s displacement maps are transformed to the hazard maps. The landslide hazard maps of the 99 Peaks region, Central Taiwan are evaluated. The actual landslide inventory maps triggered by the 21 September 1999, Chi-Chi earthquake are compared with the calculated hazard maps. Relative to the conventional procedure, the results show that the proposed procedures, which include the topographic effect can obtain a better result for seismic landslide hazard analysis. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.  相似文献   

15.
The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W + and W ? of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis.  相似文献   

16.
This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.  相似文献   

17.
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.  相似文献   

18.
Strong earthquakes are among the prime triggering factors of landslides. The 2008 Wenchuan earthquake (M w = 7.9) triggered tens of thousands of landslides. Among them, the Daguangbao landslide is the largest one, which covered an area of 7.8 km2 with a maximum width of 2.2 km and an estimated volume of 7.5 × 108 m3. The landslide is located on the hanging wall of the seismogenic fault, the Yingxiu–Beichuan fault in Anxian town, Sichuan Province. The sliding mass travelled about 4.5 km and blocked the Huangdongzi valley, forming a landslide dam nearly 600 m high. Compared to other coseismic landslides in the study area, the Daguangbao landslide attained phenomenal kinetic energy, intense cracking, and deformation, exposing a 1-km long head scarp in the rear of the landslide. Based on the field investigation, we conclude that the occurrence of the landslide is controlled mainly by the seismic, terrain, and geological factors. The special location of the landslide and the possible topographic amplification of ground motions due to the terrain features governed the landslide failure. The effects of earthquakes on the stability of slopes were considered in two aspects: First, the ground shaking may reduce the frictional strength of the substrate by shattering of rock mass. Second, the seismic acceleration may result in short-lived and episodic changes of the normal (tensile) and shear stresses in the hillshopes during earthquakes. According to the failure mechanism, the dynamic process of the landslide might contain four stages: (a) the cracking of rock mass in the rear of the slope mainly due to the tensile stress generated by the ground shaking; (b) the shattering of the substrate due to the ground shaking, which reduced the frictional strength of the substrate; (c) the shearing failure of the toe of the landslide due to the large shear stress caused by the landslide gravity; and (d) the deposition stage.  相似文献   

19.
We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.  相似文献   

20.
In the framework of a regional landslide susceptibility study in southern Sicily, a test has been carried out in the Tumarrano river basin (about 80 km2) aimed at characterizing its landslide susceptibility conditions by exporting a “source model”, defined and trained inside a limited (about 20 km2) representative sector (the “source area”). Also, the possibility of exploiting Google Earth™ software and photo-images databank to produce the landslide archives has been checked. The susceptibility model was defined, according to a multivariate geostatistic approach based on the conditional analysis, using unique condition units (UCUs), which were obtained by combining four selected controlling factors: outcropping lithology, steepness, plan curvature and topographic wetness index. The prediction skill of the exported model, trained with 206 landslides, is compared with the one estimated for the whole studied area, by using a complete landslide archive (703 landslides), to see to what extent the largest time/money costs needed are accounted for. The investigated area stretches in the fore-deep sector of southern Sicily, where clayey rocks, mainly referring to the Numidian Flysch and the Terravecchia Formations, largely crop out. The results of the study confirm both the exploitability of Google Earth™ to produce landslide archive and possibility to adopt in assessing the landslide susceptibility for large basin, a strategy based on the exportation of models trained in limited representative sectors.  相似文献   

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