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1.
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
2.
Moung-Jin Lee Jae-Won Choi Hyun-Joo Oh Joong-Sun Won Inhye Park Saro Lee 《Environmental Earth Sciences》2012,67(1):23-37
Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model. 相似文献
3.
The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea 总被引:13,自引:0,他引:13
The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility. 相似文献
4.
Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models 总被引:23,自引:5,他引:18
This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering. 相似文献
5.
基于GIS与ANN模型的地震滑坡易发性区划 总被引:1,自引:0,他引:1
基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53%与81.29%,表明ANN模型是一种高效科学的地震滑坡易发性区划模型. 相似文献
6.
Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images. 相似文献
7.
Landslide susceptibility mapping based on frequency ratio and logistic regression models 总被引:3,自引:0,他引:3
K. Solaimani Seyedeh Zohreh Mousavi Ataollah Kavian 《Arabian Journal of Geosciences》2013,6(7):2557-2569
The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning. 相似文献
8.
Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea 总被引:13,自引:0,他引:13
The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the resulting techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. A spatial database of the topography, soil type, timber cover, geology, and land cover was constructed and the landslide-related factors were extracted from the spatial database. Using these factors, the susceptibility to landslides was analyzed by artificial neural network methods. The results of the landslide susceptibility maps were compared and verified using known landslide locations at another area, Yongin, in Korea. A Geographic Information System (GIS) was used to analyze efficiently the vast amount of data and an artificial neural network turned out to be an effective tool to analyze the landslide susceptibility. 相似文献
9.
Determination and application of the weights for landslide susceptibility mapping using an artificial neural network 总被引:38,自引:0,他引:38
The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping. 相似文献
10.
Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models 总被引:28,自引:9,他引:28
The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System
(GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and
field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a
spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature,
distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution.
Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic
regression models. The results of the analysis were verified using the landslide location data and compared with probability
model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic
regression (accuracy is 90.34%) model. 相似文献
11.
Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand 总被引:2,自引:0,他引:2
Hyun-Joo Oh Saro Lee Wisut Chotikasathien Chang Hwan Kim Ju Hyoung Kwon 《Environmental Geology》2009,57(3):641-651
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
12.
Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia 总被引:20,自引:6,他引:14
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
13.
Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea 总被引:5,自引:1,他引:4
A study of landslides in Youngin, Janghung and Boeun, Korea, using the geographic information system (GIS) validates a spatial
probabilistic model for landslide susceptibility analysis. Locations were identified from aerial photographs, satellite images
and field surveys. Topography, soil-type, forest-cover and land-cover maps were constructed from spatial data sets. Landslide
occurrence is influenced by 13 factors, evidence for which was extracted from the database with the frequency ratio of each
factor computed. Landslide susceptibility maps use frequency ratios derived not only from data for each area but also ratios,
one from the probabilistic model, calculated from the other two areas (nine maps in all) as a cross-check of method validity.
For validation, analytical results were compared in each study area with actual landslide locations: Boeun based on its frequency
ratio showed the best accuracy (82.49%) whereas Janghung based on the Boeun frequency ratio showed the worst (69.53%). 相似文献
14.
Işık Yilmaz 《Environmental Earth Sciences》2010,61(4):821-836
This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability
(CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas,
Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults,
drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized
difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the
last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared
by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained
from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar.
The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS
operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes
are very simple in CP model when compared with the other methods considered in this study. 相似文献
15.
Probabilistic landslide susceptibility and factor effect analysis 总被引:18,自引:0,他引:18
The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from 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 were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area. 相似文献
16.
This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS)
and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs
and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing
tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are
topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance
from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation
amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility
was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis
were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%.
The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis
in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard
map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing
infrastructure like transportation network. 相似文献
17.
Statistical analysis of landslide susceptibility at Yongin, Korea 总被引:35,自引:1,他引:35
The aim of this study is to evaluate the susceptibility of landslides at Yongin, Korea, using a geographic information system (GIS). Landslide locations were identified in the Yongin area from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, timber cover, and geology. These data were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, age, diameter, and density of timber were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. Landslide susceptibility was analyzed using the landslide occurrence factors by probability and logistic regression methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy. The results can be used to reduce associated hazards, and to plan land use and construction. 相似文献
18.
Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models 总被引:15,自引:10,他引:5
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
19.
This study evaluates the susceptibility of landslides in the Lai Chau province of Vietnam using Geographic Information System
(GIS) and remote sensing data to focus on the relationship between tectonic fractures and landslides. Landslide locations
were identified from aerial photographs and field surveys. Topographic, geological data and satellite images were collected,
processed, and constructed into a spatial database using GIS data and image-processing techniques. A scheme of the tectonic
fracturing of crust in the Lai Chau region was established. Lai Chau was identified as a region with many crustal fractures,
where the grade of tectonic fracture is closely related to landslide occurrence. The influencing factors of landslide occurrence
were: distance from a tectonic fracture, slope, aspect, curvature, soil, and vegetative land cover. Landslide prone areas
were analyzed and mapped using the landslide occurrence factors employing the probability–frequency ratio model. The results
of the analysis were verified using landslide location data and showed 83.47% prediction accuracy. That emphasized a strong
relationship between the susceptibility map and the existing landslide location data. The results of this study can form a
basis stable development and land use planning for the region. 相似文献
20.
Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand 总被引:4,自引:2,他引:2
The purpose of this study is to produce a landslide susceptibility map for the lower Mae Chaem watershed, northern Thailand
using a Geographic Information System (GIS) and remotely sensed images. For this purpose, past landslide locations were identified
from satellite images and aerial photographs accompanied by the field surveys to create a landslide inventory map. Ten landslide-inducing
factors were used in the susceptibility analysis: elevation, slope angle, slope aspect, lithology, distance from lineament,
distance from drainage, precipitation, soil texture, land use/land cover (LULC), and NDVI. The first eight factors were prepared
from their associated database while LULC and NDVI maps were generated from Landsat-5 TM images. Landslide susceptibility
was analyzed and mapped using the frequency ratio (FR) model that determines the level of correlation between locations of
past landslides and the chosen factors and describes it in terms of frequency ratio index. Finally, the output map was validated
using the area under the curve (AUC) method where the success rate of 80.06% and the prediction rate of 84.82% were achieved.
The obtained map can be used to reduce landslide hazard and assist with proper planning of LULC in the future. 相似文献