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1.
GIS-based spatial data integration tasks for predictive geological applications, such as landslide susceptibility analysis, have been regarded as one of the primary geological application issues of GIS. An efficient framework for proper representation and integration is required for this kind of application. This paper presents a data integration framework based on the Dempster-Shafer theory of evidence for landslide susceptibility mapping with multiple geospatial data. A data-driven information representation approach based on spatial association between known landslide occurrences and input geospatial data layers is used to assign mass functions. After defining mass functions for multiple geospatial data layers, Dempster’s rule of combination is applied to obtain a series of combined mass functions. Landslide susceptibility mapping using multiple geospatial data sets from Jangheung in Korea was conducted to illustrate the application of this methodology. The results of the case study indicated that the proposed methodology efficiently represented and integrated multiple data sets and showed better prediction capability than that of a traditional logistic regression model.  相似文献   

2.
An earthquake is a natural phenomenon which is very frequent in Himalayan region in India. In southern peninsula India, the spatial occurrence of earthquake is irregular, whereas the northeastern, the north and the northwestern Himalayan parts of India are subjected to regular occurrences of earthquakes as they mark the boundary of the Eurasian and the Indian Plate. Hence, it is important to study and develop spatial model and information tool to understand the seismic phenomenon. The geoinformatic technique plays a significant role in the analysis of geodatabase to study the natural disaster and hazard assessment. The main aim of the present study is to develop geospatial model based on earthquake hazard assessment tool (EaHaAsTo) through integrated geological and geoinformatic techniques to better understand the earthquake occurrences zones. The spatial and non-spatial data were collected and integrated in a GIS to prepare geospatial databases. The thematic and quantitative databases were generated, and analysis was carried out to understand the seismic characteristics of the study area. The geospatial model was developed by integrating thematic databases and geospatial analyzed using weighted linear combination method. Finally, the GIS based on customized EaHaAsTo was developed to visualize the output of the model in qualitative and quantitative forms.  相似文献   

3.
Wildcat modelling of mineral prospectivity has been proposed for greenfields geologically-permissive terranes where mineral targets have not yet been discovered but a geological map is available as a source of spatial data of predictors of mineral prospectivity. This paper (i) revisits the initial way of assigning wildcat scores (Sc) to predictors of mineral prospectivity and (ii) proposes an improvement by transforming Sc into improved wildcat scores (ISc) by using a logistic function. This was shown in wildcat modelling of prospectivity for low-sulphidation epithermal-Au (LSEG) deposits in Aroroy district (Philippines). Based on knowledge of characteristics of and controls on LSEG mineralization in the Philippines, the spatial predictors of LSEG prospectivity used in the study are proximity to porphyry plutonic stocks, faults/fractures and fault/fracture intersections. The Sc and ISc of spatial predictors are input separately to principal components analysis to extract a favourability function that can be interpreted as a wildcat model of LSEG prospectivity. The predictive capacity of the wildcat model of LSEG prospectivity based on the ISc of geological predictors is roughly 70% higher than that of the wildcat model of LSEG prospectivity based on the Sc of geological predictors. A slight increase of predictive capacity of wildcat modelling of LSEG prospectivity is also achieved when the ISc of geological predictors are integrated with the ISc of geochemical anomalies, but not with the Sc of geochemical anomalies. The proposed improvement is significant because if the study district were a greenfields exploration area, then a wildcat model of LSEG prospectivity based on the old wildcat methodology would have caused several LSEG targets to be missed.  相似文献   

4.
Assessment and inventory of landslide susceptibility are essential for the formulation of successful disaster mitigation plans. The objective of this study was to assess landslide susceptibility in relation to geo-diversity and its hydrological response in the Lesser Himalaya with a case study using Geographic Information System (GIS) technology. The Dabka watershed, which constitutes a part of the Kosi Basin in the Lesser Himalaya, India, in the district of Nainital, has been selected for the case illustration. The study constitutes three GIS modules: geo-diversity informatics, hydro informatics and landslide informatics. Through the integration and superimposing of spatial data and attribute data of all three GIS modules, Landslide Susceptibility Index (LSI) has been prepared to identify the level of susceptibility for landslide hazards. This resonance study, carried out over a period of five years (2007–2011), found that areas of most stressed geo-diversity (comprising very steep slopes above 30°, geology of Lower Krol and Lariakanta formation, geomorphology of moist areas and debris sites, land use of barren land with a very high drainage frequency and spring density) have a high landslide susceptibility because of high rate of average runoff (33 l/s/km2), flood magnitude (307.28 l/s/km2), erosion (398 tons/km2) and landslide density (5–10 landslides/km2). The areas of least stressed geo-diversity (comprising gentle slopes below 10°, geology of Kailakhan and Siwalik formation, geomorphology of depositional terraces, land use of dense forest with low drainage frequency and spring density) have the lowest landslide susceptibility because of the low rate of average runoff (6.27 l/s/km2), flood magnitude (20.49 l/s/km2), erosion (65.80 tons/km2) and landslide density (1–2 landslides/km2).  相似文献   

5.
This study is aimed at conducting a hazard-based sustainability gap analysis considering spatial threats driven by floods and landslides, that is, a multi-hazard-based prioritization of the most important cities in Gorganrood Basin, Iran. Two data-mining models were used to assess the spatial probability of flood inundation and landslide occurrence, namely, support vector machine with the radial basis function kernel (SVM-RBF) and maximum entropy (ME). As inputs, a total of 124 flooded locations and 346 landslides with ten flood/landslide predisposing factors were mapped using geoinformatics and organizational data. The random selection method was used to split the flood and landslide inventories into two sets of train and test data. Tolerance index was used to test the multicollinearity among predictors. Validation of the models was carried out using the area under the receiver operating characteristic (ROC) curve (AUC). Finally, TOPSIS was used, as a multi-criteria decision-making model, to make an internal sustainability gap analysis to prioritize the threatened and safe cities. For flood inundation, the AUC values obtained from the test set revealed that the SVM-RBF outperformed ME in terms of predictive power and generalization capacity with the respective areas of 0.831 and 0.796 under the curve. For landslide susceptibility assessment, SVM-RBF again excelled ME in predictive power with the respective values of 0.887 and 0.84. Therefore, the susceptibility maps derived from SVM-RBF, as the premier model, were used for the next stage. Extracting the flood and landslide spatial probability values to 14 city points, the TOPSIS-Solver software made a prioritization using the similarity function to the ideal solution. Accordingly, Aliabad, Minoodasht, and Azadshahr cities, with having the smallest similarity coefficients, were found to be the top three spatially threatened cities in Gorganrood Basin, while Aq Qala, Gomishan, and Gonbad-e Kavus cities were placed at the bottom as the safest cities. This study can be a pivotal point in regional risk-based planning, implementation of further pragmatic measures, and allocation of resources for improving sustainable development most wisely.  相似文献   

6.
The necessity of estimating the degree and spatial extent of positive impacts with regard to protecting communities and properties through potential flood control projects can be considered one of the main reasons for performing flood modeling. This paper presents an overall systematic approach based on the simulation of some extreme event conditions, using a hydrological model to generate the resulting river flows and then using a hydraulic modeling exercise to decide upon floodplain evolution in the case-study area, Bostanli river basin, which has been under the threat of flooding for many years. The potential serviceability of the planned Bostanli Dam in the study area was examined by using the HEC-HMS and HEC-RAS modeling tools, both integrated with GIS functions for spatial operations. The results indicate that the dam construction as planned would have a somewhat positive impact as a potential flood control measure, since it seems to decrease the flood peaks of 68.9 and 158.7 m3/s (that would potentially be generated by 100- and 500-year storm events under current conditions) to 65.5 and 150.7 m3/s (when the dam is in operation), respectively. However, this seems to contribute little to the overall flood mitigation performance in the basin.  相似文献   

7.
The coastal regions, deltas, and estuaries are severely affected by the sea level rise and cyclonic activities and climate changes. Sundarban delta is one of the most mysterious landscapes in the world, which has successively evolved due to sediment accumulation by the great Ganga and Brahmaputra river system. The area is characterized by low-lying islands and a flat topography coupled with macro-tidal activities, powerful surges, and seasonal cyclonic events. All these conditions put together this landscape defenseless to frequent flood and erosion. Since the last hundred years, the face of Sundarban has been changed remarkably from wildest to human-occupied territory by protecting this low-lying flat plain from tidal inundation through artificial embankment. In this background, the current study attempts to highlight the spatial extent and magnitudes of internal risk factors of the region using the composite vulnerability index. Coastal vulnerability defines a system’s openness to flood and erosion risk due to hydrogeomorphic exposures and socio-economic susceptibility in conjunction with its capacity/incapacity to be resilient and to cope, recover, or adapt to an extent. Coastal vulnerability assesses the potential risk from erosion and flooding of any low-lying coastal region due to its physiographical and hydrological exposures, socio-economic and political susceptibility, and resilience capacity. A natural system affects the socio-economic scenario of any region. Hence, multidimensional databases can be more effective to understand the extent of exposure, susceptibility, and resilience of any system. To throw some light on the situation of vulnerability of western estuarine Sundarban, between Muriganga and Saptamukhi interfluve, the composite vulnerability index has been carried out to delineate the magnitude and spatial extent of vulnerability with the help of quantitative techniques and geospatial tools. The estuarine tracts and coastal parts of the Ganga delta are two of the most densely populated areas in the world. The study highlights the critical situation of the population under different potential risk classes residing in the study area with the intention of suggesting some proper course of action of planning and management to conserve coastal communities in their original habitat.  相似文献   

8.
This study examined the efficacy of three machine ensemble classifiers, namely, random forest, rotation forest and AdaBoost, in assessing flood susceptibility in an arid region of southern Iraq. A dataset was created from flooded and non-flooded areas to train and validate the ensemble classifiers using a binary classification scheme (1—flood, 0—non-flood). The prepared dataset was then partitioned into two sets with a 70/30 ratio: 70% (2478 pixels) for training and 30% (1062 pixels) for testing. A total of 10 influential flood factors were selected and prepared based on data availability and a literature review. The selected factors were surface elevation, slope, plain curvature, topographic wetness index, stream power index, distance to rivers, drainage density, lithology, soil and land use/land cover. The information gain ratio was first utilised to explore the predictive abilities of the factors. The predictive performances of the three ensemble models were compared using six statistical measures: sensitivity, specificity, accuracy, kappa, root mean square error and area under the operating characteristics curve. The results revealed that the AdaBoost classifier was the best in terms of the statistical measures, followed by the random forest and rotation forest models. A flood susceptibility map was prepared based on the result of each classifier and classified into five zones: very low, low, moderate, high and very high. For the model with the best performance, i.e., the AdaBoost model, these zones were distributed over an area of 6002 km2 (44%) for the very low–low zone, 2477 km2 (18%) for the moderate zone and 5048 km2 (40%) for the high–very high zones. This study proved the high capabilities of ensemble machine learning classifiers to decipher flood susceptibility zones in an arid region.  相似文献   

9.
Hausmann  Peter 《Natural Hazards》2016,86(1):197-198

As a leading global reinsurer, Swiss Re deals with many hazards and risks for which geospatial data are crucial in order to obtain reliable assessments of expected insured losses or large losses from catastrophes. Typically, such data are used in combination with insurance data either in pricing tools to calculate premiums, tail risks and more, or in mapping tools. In natural perils pricing applications—the most important group of tools—geospatial data are usually “not visible” but are instead used to create probabilistic event sets. For example, a flood event set may define spatially if and how frequently a given location is flooded. Mapping tools, such as Swiss Re’s CatNet® (www.swissre.com/catnet), visualize the data in the form of maps which include many useful attributes per geographic location.

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10.
Groundwater recharge is an important process for the management of both surface and subsurface water resources. The present study utilizes the application of analytical hierarchical process (AHP) on geospatial analysis for the exploration of potential zones for artificial groundwater recharge along Vaigai upper basin in the Theni district, Tamil Nadu, India. The morphology of earth surface features such as geology, geomorphology, soil types, land use and land cover, drainage, lineament, and aquifers influence the groundwater recharge in either direct or indirect way. These thematic layers are extracted from Landsat ETM+ image, topographical map, and other collateral data sources. In this study, the multilayers were weighed accordingly to the magnitude of groundwater recharge potential. The AHP technique is a pair-wise matrix analytical method was used to calculate the geometric mean and normalized weight of individual parameters. Further, the normalized weighted layers are mathematically overlaid for preparation of groundwater recharge potential zone map. The results revealed that 21.8 km2 of the total area are identified as high potential for groundwater recharge. The gentle slope areas in middle-east and central part have been moderately potential for groundwater recharge. Hilly terrains in south are considered as unsuitable zone for groundwater recharge processes.  相似文献   

11.
The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.  相似文献   

12.
Gengma region, Sanjiang district is known to have some large-scale gold deposits. GIS predictive model for hydroghermal gold potential was carried out in this region using weights of evidence modeling technique. Datasets used include large-scale hydroghermal gold deposit records, geological, geophysical and remote sensing imagery. Based on the geological and mineral characteristics of areas with known gold occurrences in Sanjiang, several geological features were thought to be indicative of areas with potential for the occurrence of hydroghtermal gold deposits. Indicative features were extracted from geoexploration datasets for use as input in the predictive model. The features include host rock lithology, geologic structures, wallrock alteration and associated (volcanic-plutonic) igneous rocks. To determine which of the indicative geological features are important spatial predictors of area with potential for gold deposits, spatial analysis was done through the modeling method. The input maps were buffered and the optimum distance of spatial association for each geological feature was determined by calculating the contrast and studentized contrast. Five feature maps were converted to binary predictor patterns and used as evidential layers for predictive modeling. The binary patterns were integrated in two combinations, each of which consists of four patterns in order to avoid over prediction due to the effect of duplicate features in the two structural evidences. The two produced potential maps define almost similar favorable zones. Areas of intersections between these zones in the two potential maps placed the highest predictive favorable zones in the region.  相似文献   

13.
Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.  相似文献   

14.
Zhao  Yapeng  Kong  Liang  Liu  Lele  Liu  Jiaqi 《Natural Hazards》2022,110(1):719-740

Urban pluvial flash floods have become a matter of widespread concern, as they severely impact people’s lives in urban areas. Hydrological and hydraulic models have been widely used for urban flood management and urban planning. Traditionally, to reduce the complexity of urban flood modelling and simulations, simplification or generalization methods have been used; for example, some models focus on the simulation of overland water flow, and some models focus on the simulation of the water flow in sewer systems. However, the water flow of urban floods includes both overland flow and sewer system flow. The overland flow processes are impacted by many different geographical features in what is an extremely spatially heterogeneous environment. Therefore, this article is based on two widely used models (SWMM and ANUGA) that are coupled to develop a bi-directional method of simulating water flow processes in urban areas. The open source overland flow model uses the unstructured triangular as the spatial discretization scheme. The unstructured triangular-based hydraulic model can be better used to capture the spatial heterogeneity of the urban surfaces. So, the unstructured triangular-based model is an essential condition for heterogeneous feature-based urban flood simulation. The experiments indicate that the proposed coupled model in this article can accurately depict surface waterlogged areas and that the heterogeneous feature-based urban flood model can be used to determine different types of urban flow processes.

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15.
利用我国海量地质标准基础数据库中的数字地质图和矿产图,通过基于GIS的地质解译空间集成地质信息,将其用于综合信息矿产预测。以地质解译系统对内蒙大兴安岭南段1∶20万成矿预测的应用为案例,阐述地质信息的空间提取与集成过程：首先在建立地质字典库实现地质空间信息共享的基础上,通过矿化密集区对地质模型的分类图层进行空间分析,建立地质成矿空间信息库和图库;然后,基于典型矿床圈定模型单元,通过模型单元与地质成矿空间信息库和图库的空间分析,建立地质找矿模型;最后,基于地质单元对地质成矿空间信息库和图库的二次空间集成,完成预测模型的地质空间信息提取与集成。将本方法应用在银矿案例的综合信息矿产预测靶区评价上,得到可供进一步查证的新增靶区比已知靶区增加了近5倍。  相似文献   

16.
极端洪水灾害损失评估方法及应用   总被引：3，自引：0，他引：3       下载免费PDF全文
极端洪水灾害具有频率低、影响范围大、损失高等特点,一般常遇洪水的灾害损失评估方法难以适用。分析了极端洪水灾害的自然属性与社会属性,认为极端洪水灾害损失具有时空分布的特性,因此借鉴空间信息格网技术,分别形成了极端洪水水文特性格网与社会经济特性格网,并将其叠加得到具有空间拓扑关系和属性信息的基于GIS的极端洪水损失空间信息格网模型,从而有效地反映了极端洪水灾害的时空特性。结合极端洪水损失率数据库,可评估极端洪水灾害损失。利用该方法评估了1998年特大洪水造成哈尔滨市江南主城区的直接经济损失,实证说明该方法可用于极端洪水灾害损失的评估。  相似文献   

17.
Rajabi  Ahmad  Shabanlou  Saeid  Yosefvand  Fariborz  Kiani  Afshin 《Natural Hazards》2021,109(1):871-901

Flood has always been a destructive natural hazard during the recent years. Hence, this research aimed to predict the potentiality and probability of flood phenomenon by using the two well-known models, i.e., the MARS algorithm (multivariate adaptive regression splines) and MaxEnt (maximum entropy) method in the Saliantapeh catchment, Golestan province, Iran, covering 4515.47 km2. First, documentary sources report and field surveys were used to provide a flood database map. Then, to prepare the flood spatial potentiality map (FSPM), we select sixteen influential variables as predictors. Furthermore, the relative contributions of predicting factors are estimated using the MaxEnt method. For the analysis of data sensitivity and the uncertainty of the proposed models, different scenarios including the sample size (50%/50%, 80%/20%, and 70%/30%, respectively, for training and validation), and the number of replications (5, 10, and 20) were used. Along with the area under the ROC curve (AUC), the highest accuracy for both models corresponds to the first scenario of sample size (80/20%). Contrarywise, it can be concluded that for this scenario, the MARS technique indicated higher predictive skill (AUC?=?98.51%). Regarding the second scenario, which is corresponding to the replicate, the MARS model with 20 replications still has the highest accuracy (94.70%) compared to the other scenarios and the MaxEnt model. The findings of robustness demonstrated that the scenarios with the greatest AUC value have the highest robustness. This work demonstrates that the utilization of the best accurate model with high certainty along with FSPM may be useful to identify and manage the areas that are most susceptible to flood.

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18.
Identifying and/or predicting the geography of malaria will help decision makers locate the particular area with the health problem, and to design area-specific interventions. Using GIS (ArcMap 10.1), a spatial analysis of environmental factors that contribute to the spread of malaria vector was conducted to develop a malaria susceptibility model that could be used in effective malaria control planning. The study first determined malaria susceptibility index and combined it with geospatial modelling to predict malaria susceptibility. Clinical malaria cases were then geocoded and tested to determine the accuracy of the prediction. The results show that 72.3, 24.5, 3.1 and 0.1 % of the clinical malaria incidence were found in areas that were predicted to have very high, high, low and very low susceptibility levels. Hence, the model, to a large extent, predicted malaria occurrences. The conclusion is that modelling such as this can help determine spatio-temporal prediction and mapping of malaria incidence to aid in the design and administration of appropriate interventions.  相似文献   

19.
The objective of this paper is to develop a spatial temporal runoff modelling of local rainfall patterns effect on the plant cover hilly lands in Kelantan River Basin. Rainfall interception loss based on leaf area index, loss/infiltration on the ground surface, and runoff calculation were considered as the main plant cover effects on the runoff volume. In this regard, a hydrological and geotechnical grid-based regional model (integrated model) was performed using Microsoft Excel® and GIS framework system for deterministic modelling of rainfall-induced runoff by incorporating plant cover effects. The infiltration process of the current model was integrated with the precipitation distribution method and rainfall interception approach while the runoff analysis of integrated model was employed based on loss/infiltration water on the ground surface with consideration of water interception loss by canopy and the remaining surface water. In the following, the spatial temporal analysis of rainfall-induced runoff was performed using 10 days of hourly rainfall events at the end of December 2014 in Kelantan River Basin. The corresponding changes in pressure head and consequent rate of infiltration were calculated during rainfall events. Subsequently, flood volume is computed using local rainfall patterns, along with water interception loss and the remaining surface water in the study area. The results showed the land cover changes caused significant differences in hydrological response to surface water. The increase in runoff volume of the Kelantan River Basin is as a function of deforestation and urbanization, especially converting the forest area to agricultural land (i.e. rubber and mixed agriculture).  相似文献   

20.
空间三维滑坡敏感性分区工具及其应用   总被引：1，自引：0，他引：1  
对于滑坡敏感性分区目前有三种方法:定性法、统计法和基于岩土定量模型的确定性方法。定性法基于对滑坡敏感性或灾害评估的人为判断;统计法用一个来源于结合了权重因子的预测函数或指标;而确定性法,或者说是物理定量模型法以质量、能量和动量守恒定律为基础。二维确定性模型广泛用于土木工程设计,而无限边坡模型(一维)也用于滑坡灾害分区的确定性模型。文中提出了一个新的基于GIS(地理信息系统)的滑坡敏感性分区系统,这个系统可用于从复杂地形中确认可能的危险三维(3-D)滑坡体。所有与滑坡相关的空间数据(矢量或栅格数据)都被集成到这个系统中。通过把研究区域划分为边坡单元并假定初始滑动面是椭球的下半部分,并使用Monte Carlo随机搜索法,三维滑坡稳定性分析中的三维最危险滑面是三维安全系数最小的地方。使用近似方法假定有效凝聚力、有效摩擦角和三维安全系数服从正态分布,可以计算出滑坡失稳概率。3DSlopeGIS是一个计算机程序,它内嵌了GIS Developer kit(ArcObjects of ESRI)来实现GIS空间分析功能和有效的数据管理。应用此工具可以解决所有的三维边坡空间数据解问题。通过使用空间分析、数据管理和GIS的可视化功能来处理复杂的边坡数据,三维边坡稳定性问题很容易用一个友好的可视化图形界面来解决。将3DSlopeGIS系统应用到3个滑坡敏感性分区的实例中:第一个是一个城市规划项目,第二个是预测以往滑坡灾害对临近区域可能的影响,第三个则是沿着国家主干道的滑坡分区。基于足够次数的Monte Carlo模拟法,可以确认可能的最危险滑坡体。这在以往的传统边坡稳定性分析中是不可能的。  相似文献   

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