共查询到20条相似文献，搜索用时 15 毫秒
1.
H. R. Pourghasemi H. R. Moradi S. M. Fatemi Aghda C. Gokceoglu B. Pradhan 《Arabian Journal of Geosciences》2014,7(5):1857-1878
The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
2.
Pece V. Gorsevski M. Kenneth Brown Kurt Panter Charles M. Onasch Anita Simic Jeffrey Snyder 《Landslides》2016,13(3):467-484
The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning. 相似文献
3.
Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach 总被引：2，自引：0，他引：2
This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data. 相似文献
4.
多年冻土区热融滑塌型滑坡稳定性评价方法 总被引：1，自引：0，他引：1
Landslide of thaw-slumping in permafrost regions of the Qinghai-Tibet Plateau is studied in this paper. Thaw-slumping belongs to shallow landslide with small depth-length ratio. Therefore the infinite slope analysis method is available to be applied in the stability analysis. Considering identical seepage directions to slope surface, we deduced analytical expressions of safety factor according to the effective stress principle and plotted stability analysis chart of slopes with dry or fully saturated soils.Taking an example of landslide of thaw-slumping at the mileage of K3035 of the Qinghai-Tibet Highway, we evaluated the analytical expressions. 相似文献
5.
Javed Iqbal Tanoli Chen Ningsheng Amar Deep Regmi Li Jun 《Arabian Journal of Geosciences》2017,10(13):277
The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km2 area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area. 相似文献
6.
Ogbonnaya Igwe Wilfred Mode Okechukwu Nnebedum Ikenna Okonkwo Ifeanyi Oha 《Environmental Earth Sciences》2014,71(5):2465-2480
This paper analyses the dominant mechanisms of slope failures and identifies potential obstacles to landslide-hazard reduction at the Iva Valley area, Enugu, Nigeria. The landscape is replete with landslide scars and gullies of varied sizes and the slope deposits comprise unconsolidated, friable sands inter-bedded with thin units of montmorillonitic claystone. Forty-three landslide events were identified in the study area with most being shallow, short run-out movements with slip-surface depth <2 m. The study found the landslides mainly occur in the beginning of rainy season characterized by short duration, high intensity rainfall. An integrated approach comprising field mapping, laboratory tests and numerical analyses reveals that the barren nature of the slopes prior to the outset of rainy season, high rainfall intensity, erosion, overgrazing, soil characteristics and the site’s unique lithologic sequence are the main causes of instability. Shearing tests under several conditions showed that the soils strongly strain-soften until low steady-state strength is achieved. A computer code, based on this strength reduction technique, used input parameters obtained from the field and laboratory studies to simulate a landslide with similar structure, travel distance and distribution area. It is noted that urbanization has gradually increased the vulnerability of the society’s poor to landslide hazards as they now erect unplanned residence (tents and blocks) on the slopes. This work is part of a regional study aimed at finding ways of protecting the vulnerable by generating data that could be used to build future landslide susceptibility map. 相似文献
7.
Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method.
相似文献8.
Shallow slope failure due to heavy rainfall during rainstorm and typhoon is common in mountain areas. Among the models used
for analyzing the slope stability, the rainwater infiltration model integrated with slope stability model can be an effective
way to evaluate the stability of slopes during rainstorm. This paper will propose an integrated Green–Ampt infiltration model
and infinite slope stability model for the analysis of shallow type slope failure. To verify the suitability of the proposed
model, seven landslide cases occurred in Italy and Hong Kong are adopted in this paper. The results indicate that the proposed
model can be used to distinguish failed and not-yet failed slopes. In addition, the proposed model can be used as the first
approximation for estimating the occurrence time of a rainfall-induced shallow landslide and its depth of sliding. 相似文献
9.
High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides. 相似文献
10.
A new probabilistic methodology for landslide hazard assessment in regional scale using Copula modeling technique is presented. The current probabilistic landslide hazard analyses are performed under the assumption that landslide hazard elements, such as magnitude, frequency, and location, are independent. In this paper, a general approach is proposed to consider the possible dependence among hazard elements. Part of the Seattle, WA area was selected to evaluate the competence of the presented method. A total of 357 slope failure events and their corresponding topography and geology data were included in the study to develop and test the model. Based on the results, the mean success rates of the presented model in predicting landslide occurrence are 90 % in hazardous area and 12 % in safe locations on average, while these success rates are 63 and 44 % when these hazard elements were treated as mutually independent. 相似文献
11.
赣南地区滑坡灾害点多、面广、规模小,具有群发性和突发性的特点,90%以上的滑坡是因人工切坡导致的。为研究赣南地区小型削方滑坡对易发性评价模型的适用性,以赣州市于都县银坑镇为例,基于野外地质调查成果,并利用地理探测器,选取坡度、坡体结构、岩组、断层、道路、植被等6个评价指标,分别选用信息量模型、人工神经网络模型、决策树模型和逻辑回归模型开展易发性评价。结果表明：信息量、人工神经网络、决策树和逻辑回归等模型得到的AUC值分别为0.800、0.708、0.672和0.586,信息量模型所得的易发性结果与研究区滑坡实际分布情况较吻合,高易发区和中易发区滑坡占比近80%。信息量模型较其他三个模型,更适合于赣南地区小型削方滑坡易发性评价,评价结果对该地区地质灾害易发性评价模型选取提供了参考与借鉴。 相似文献
12.
A rainfall-induced shallow landslide is a major hazard in mountainous terrain, but a time-space based approach is still an unsettled issue for mapping rainfall-induced shallow landslide hazards. Rain induces a rise of the groundwater level and an increase in pore water pressure that results in slope failures. In this study, an integrated infinite slope analysis model has been developed to evaluate the influence of infiltration on surficial stability of slopes by the limit equilibrium method. Based on this new integrated infinite slope analysis model, a time-space based approach has been implemented to map the distributed landslide hazard in a GIS (Geographic Information Systems) and to evaluate the shallow slope failure induced by a particular rainfall event that accounts for the rainfall intensity and duration. The case study results in a comprehensive time-space landslide hazard map that illustrates the change of the safety factor and the depth of the wetting front over time. 相似文献
13.
Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative
risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility
mapping methods, mapping unit, and scale. Although there are various methods of obtaining susceptibility maps, the efficiency
and performance of each method should be evaluated. In this study the effect of mapping unit and susceptibility mapping method
on landslide susceptibility assessment is investigated. When analyzing the effect of susceptibility mapping method, logistic
regression (LR) which is widely used in landslide susceptibility mapping and, spatial regression (SR), which have not been
used for landslide susceptibility mapping, are selected. The susceptibility maps with logistic and spatial regression models
are obtained using two different mapping units namely slope unit-based and grid-based mapping units. The procedure for investigation
of effect of mapping unit on different susceptibility mapping methods is applied to Kumluca watershed, in Bartin Province
of Western Black Sea Region, Turkey. 18 factor maps are prepared for landslide susceptibility assessment in the study region.
Geographic information systems and remote sensing techniques are used to create the landslide factor maps, to obtain susceptibility
maps and to compare the results. The relative operating characteristics (ROC) curve is used to compare the predictive abilities
of each model and mapping unit and also the accuracy is evaluated depending on the observations made during field surveys.
By analyzing the area under the ROC curve for grid-based and slope unit-based mapping units, it can be concluded that SR model
provide better predictive performance (0.774 in grids and 0.898 in slope units) as compared to the LR model (0.744 in grids
and 0.820 in slope units). This result is also supported by the accuracy analysis. For both mapping units, the SR model provides
more accurate result (0.55 for grids and 0.57 for slope units) than the LR model (0.50 for grids and 0.48 for slopes). The
main reason for this better performance is that the spatial correlations between the mapping units are incorporated into the
model in SR while this fact is not considered in LR model. 相似文献
14.
Wei Chen Wenping Li Enke Hou Zhou Zhao Niandong Deng Hanying Bai Danzhi Wang 《Arabian Journal of Geosciences》2014,7(11):4499-4511
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. 相似文献
15.
Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake 总被引：3，自引：1，他引：2
On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km2. The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m2. The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q4 al-pl, N, and T3 kn 1 have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau. 相似文献
16.
Mohammad Zare Hamid Reza Pourghasemi Mahdi Vafakhah Biswajeet Pradhan 《Arabian Journal of Geosciences》2013,6(8):2873-2888
Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning. 相似文献
17.
Guruh Samodra Guangqi Chen Junun Sartohadi Kiyonobu Kasama 《Environmental Earth Sciences》2017,76(4):184
There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor. 相似文献
18.
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis,support vector machines,and logistic regression 总被引：14，自引：3，他引：11
Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area. 相似文献
19.