首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 297 毫秒
1.
Desalegn  Hunegnaw  Mulu  Arega  Damtew  Banchiamlak 《Natural Hazards》2022,113(2):1391-1417

Landslide susceptibility consists of an essential component in the day-to-day activity of human beings. Landslide incidents are typically happening at a low rate of recurrence when compared and in contrast to other events. This might be generated into main natural catastrophes relating to widespread and undesirable sound effects. Landslide hotspot area identification and mapping are used for the regional community to secure from this disaster. Therefore, this research aims to identify the hotspot areas of landslide and to generate maps using GIS, AHP, and multi-criteria decision analysis (MCDA). MCDA techniques are applied under such circumstances to categorize and class decisions for successive comprehensive estimation or else to state possible from impossible potentiality with various landslides. Analytical hierarchy process (AHP) constructively applies for conveying influence to different criteria within multi-criteria decision analysis. The causative landslide identifying factors utilized in this research were elevation, slope, aspect, soil type, lithology, distance to stream, land use/land cover, rainfall, and drainage density achieved from various sources. Subsequently, to explain the significance of each constraint into landslide susceptibility, all factors were found using the AHP technique. Generally, landslide susceptibility map factors were multiplied by their weights to acquire with the AHP technique. The result showed that the AHP methods are comparatively good quality estimators of landslide susceptibility identification in the Chemoga watershed. As the result, the Chemoga watershed landslide susceptibility map classes were classified as 46.52%, 13.83%.18.71%, 15.39%, and 5.55% of the occurred landslide fall to very low, low, moderate, high, and very high susceptibility zones, respectively. Performance and accuracy of modeled maps have been established using GPS field data and Google earth data landslide map and area under curve (AUC) of the receiver operating characteristic curve (ROC). As the result, validation depends on the ROC specifies the accuracy of the map formed with the AHP merged through weighted overly method illustrated very good accuracy of AUC value 81.45%. In general, the research outcomes inveterate the very good test consistency of the generated maps.

  相似文献   

2.
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   

3.
This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.  相似文献   

4.
The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.  相似文献   

5.
Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.  相似文献   

6.
Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.  相似文献   

7.
国道212线陇南段是我国地质灾害最发育的地区之一,绘制该区的滑坡危险等级地图对灾害管理和发展规划是极其必要的。基于滑坡的野外调查、机理研究和室内试验等工作,分析了滑坡与各种要素的相关性,选择控制滑坡的9个重要要素作为评价要素,利用GIS和二元统计的信息值模型和滑坡先验风险要素模型绘制了研究区的滑坡危险等级地图。最后,选用区内11个具有明显滑动位移的活动滑坡与滑坡危险等级地图比较,检验其可靠度。结果表明,活动的滑坡绝大部分都位于危险等级很高和高的范围内,说明两种模型的评价结果与研究区实际情况相吻合,同时也反映出信息值模型与实际情况更加相符。  相似文献   

8.
For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.  相似文献   

9.
Landslides are introduced as regional movements, which influence different engineering structures such as roads, railways, and dams and cause the person’s death. Identification of landslide zones may decrease the financial losses and human injuries or deaths. This study tries to achieve a landslide susceptibility mapping in Cham-gardalan catchment by weighting the main criteria and the membership functions of fuzzy logic. For this, we applied the best relationship function between the presence and absence of landslides as well as a collection of the elements. At first, the landslide points were identified by the means of some components those of satellite images, topographical (1:50,000) and geographical (1:100,000) maps, field visits, and Google Earth software followed by the preparation of landslide distribution maps. Then, all effective landslide factors such as percentage of slope, slope aspect, height, geology, land uses, distance from roads, distance from drainages, distance from breakage, and precipitation map have been utilized in order to conduct the fuzzy analyses. Landslide susceptibility map was performed by fuzzy operators (Gamma, Product, Sum, Or, And) in the study area. After fuzzificating and weighting, the effective criteria of landslides were determined through fuzzy Gamma operators with the landaus of 0.2, 0.5, 0.8, and 0.9 and by comparing final maps for making an appropriate model of landslide susceptibility mapping. The regional susceptibility map represents the landslide-prone areas in five categories those of very low, low, moderate, high, and very high. Our results indicated that among the applied operators, Gamma with landau of 0.9 can be used as an appropriate method for mapping the landslide susceptibility due to the suitable fuzzification of given criteria based on landslide distribution maps. In addition, the elements of road, percentage of slope, distance from drainage, and geology were recognized as the most important factors for occurring the landslides.  相似文献   

10.
In this study, we present a landslide susceptibility assessment carried out after the devastating 2008 Wenchuan earthquake. For the Zhouqu segment in the Bailongjiang basin in north-western China landslide susceptibility was computed by a logistic regression method. This region has been experiencing landslides for a long time, and numerous additional slope failures were triggered by the 2008 Wenchuan earthquake. The data used for this study consists of slope failures attributed to the 2008 earthquake, the 878 post Wenchuan earthquake landslides and collapses inventory build up by combination the field investigation, monoscopic manual interpretation, image classification and texture analysis using SPOT 5 and ALOS remote-sensing image data. All data derived from remote sensing images are validated during field investigations. The landslide pre-disposing factor database was constructed. A digital elevation model (DEM) with a 30 × 30 m resolution, orthophotos, geological and land-use maps and information on peak ground acceleration data from the 2008 earthquake is used. The statistical analysis of the relation between Wencuan earthquake-triggered landslides and pre-disposing factors show the great influence of lithological and topographical conditions for earthquake-triggered slope failures. The quality of susceptibility mapping was validated by splitting the study area into a training and validation set. The prediction capability analysis showed that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities in this region.  相似文献   

11.
In this study a Wenchuan earthquake-induced landslide susceptibility assessment was carried out in the Longnan area in northwestern China using a GIS-based logistic regression model. This region has frequently been affected by landslides in the past, and was intensively affected by the 5.12 Wenchuan earthquake which received considerable international attention. The data used for this study consist of the landslides triggered by the Wenchuan earthquake and a landslide pre-disposing factor database. Information regarding the landslide causative factors came from additional data sources, such as a digital elevation model (DEM) with a 30 × 30 m2 resolution, orthophotos, geological and land-use maps, precipitation records, and information on peak ground acceleration data from the 2008 earthquake. The statistical analysis of the relationship between the Wenchuan earthquake-triggered landslides and pre-disposing factors showed the great influence of lithological and topographical conditions on slope failures. The quality of susceptibility mapping was validated by splitting the study area into training and validation sections. The prediction capability analysis demonstrated that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities.  相似文献   

12.
In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.  相似文献   

13.
The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.  相似文献   

14.
The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.  相似文献   

15.
This research work deals with the landslide susceptibility assessment using Analytic hierarchy process (AHP) and information value (IV) methods along a highway road section in Constantine region, NE Algeria. The landslide inventory map which has a total of 29 single landslide locations was created based on historical information, aerial photo interpretation, remote sensing images, and extensive field surveys. The different landslide influencing geoenvironmental factors considered for this study are lithology, slope gradient, slope aspect, distance from faults, land use, distance from streams, and geotechnical parameters. A thematic layer map is generated for every geoenvironmental factor using Geographic Information System (GIS); the lithological units and the distance from faults maps were extracted from the geological database of the region. The slope gradient, slope aspect, and distance from streams were calculated from the Digital Elevation Model (DEM). Contemporary land use map was derived from satellite images and field study. Concerning the geotechnical parameters maps, they were determined making use of the geotechnical data from laboratory tests. The analysis of the relationships between the landslide-related factors and the landslide events was then carried out in GIS environment. The AUC plot showed that the susceptibility maps had a success rate of 77 and 66% for IV and AHP models, respectively. For that purpose, the IV model is better in predicting the occurrence of landslides than AHP one. Therefore, the information value method could be used as a landslide susceptibility mapping zonation method along other sections of the A1 highway.  相似文献   

16.
Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km2 area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.  相似文献   

17.
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   

18.
A Luoi is a Vietnamese–Laotian border district situated in the western part of Thua Thien Hue province, central Vietnam, where landslides occur frequently and seriously affect local living conditions. This study focuses on the spatial analysis of landslide susceptibility in this 263-km2 area. To analyze landslide manifestation in the study area, causative factor maps are derived of slope angle, weathering, land use, geomorphology, fault density, geology, drainage distance, elevation, and precipitation. The analytical hierarchical process approach is used to combine these maps for landslide susceptibility mapping. A landslide susceptibility zonation map with four landslide susceptibility classes, i.e. low, moderate, high, and very high susceptibility for landsliding, is derived based on the correspondence with an inventory of observed landslides. The final map indicates that about 37% of the area is very highly susceptible for landsliding and about 22% is highly susceptible, which means that more than half of the area should be considered prone to landsliding.  相似文献   

19.
Quantitative landslide susceptibility mapping at Pemalang area,Indonesia   总被引：3，自引：0，他引：3  
For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.  相似文献   

20.
Aykut Akgun 《Landslides》2012,9(1):93-106
The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号