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1.
利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文) 总被引：3，自引：0，他引：3
Biswajeet Pradhan Saro Lee 《地学前缘》2007,14(6):143-152
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。 相似文献
2.
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
3.
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
4.
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
5.
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
6.
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
7.
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
8.
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
9.
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
10.
Subrata Mondal Sujit Mandal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2018,12(1):29-44
The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. 相似文献
11.
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
12.
Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models 总被引：24，自引：0，他引：24
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
13.
Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System: a case study at Boun,Korea 总被引：10，自引：0，他引：10
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. 相似文献
14.
Krishna Chandra Devkota Amar Deep Regmi Hamid Reza Pourghasemi Kohki Yoshida Biswajeet Pradhan In Chang Ryu Megh Raj Dhital Omar F. Althuwaynee 《Natural Hazards》2013,65(1):135-165
Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
15.
Chong Xu Xiwei Xu Fuchu Dai Zhide Wu Honglin He Feng Shi Xiyan Wu Suning Xu 《Natural Hazards》2013,68(2):883-900
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
16.
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
17.
Validation of an artificial neural network model for landslide susceptibility mapping 总被引：6，自引：3，他引：3
The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the
geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature,
and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and
density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and
Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the
artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the
landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural
network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight,
whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%). 相似文献
18.
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures. 相似文献
19.
The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling 总被引：11，自引：3，他引：8
Chong Xu Xiwei Xu Yuan Hsi Lee Xibin Tan Guihua Yu Fuchu Dai 《Environmental Earth Sciences》2012,66(6):1603-1616
The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between
96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system
(GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total
of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake,
and verified by selected field checking before a final landslide-inventory map of the study area could be established using
GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628
landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated
controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from
main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from
drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were
calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to
obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement
between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate
is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate,
low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction
work. 相似文献
20.
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