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GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) 总被引:5,自引:10,他引:5
Devrek town with increasing population is located in a hillslope area where some landslides exist. Therefore, landslide susceptibility
map of the area is required. The purpose of this study was to generate a landslide susceptibility map using a bivariate statistical
index and evaluate and compare the results of the statistical analysis conducted with three different approaches in seed cell
concept resulting in different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping
applied to the Devrek region. The data sets are created from the seed cells of (a) crowns and flanks, (b) only crowns, and
(c) only flanks of the landslides by using ten different causative parameters of the study area. To increase the data dependency
of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions
of each corresponding seed cell data set. The resultant maps of the landslide susceptibility analysis indicate that all data
sets produce fairly acceptable results. In each data set analysis, elevation, lithology, slope, aspect, and drainage density
parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are
compared using Seed Cell Area Indexes (SCAI). This comparison shows that the crown data set produces the most accurate and
successful landslide susceptibility map of the study area. 相似文献
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Gökhan Demir Mustafa Aytekin Aykut Akgün Sabriye Banu İkizler Orhan Tatar 《Natural Hazards》2013,65(3):1481-1506
The North Anatolian Fault is known as one of the most active and destructive fault zones which produced many earthquakes with high magnitudes both in historical and instrumental periods. Along this fault zone, the morphology and the lithological features are prone to landslides. Kuzulu landslide, which is located near the North Anatolian Fault Zone, was triggered by snow melting without any precursor, occurred on March 17, 2005. The landslide resulted in 15 deaths and the destruction of about 30 houses at Kuzulu village. There is still a great danger of further landslides in the region. Therefore, it is vitally important to present its environmental impacts and prepare a landslide susceptibility map of the region. In this study, we used likelihood-frequency ratio model and analytical hierarchy process (AHP) to produce landslide susceptibility maps. For this purpose, a detailed landslide inventory map was prepared and the factors chosen that influence landslide occurrence were: lithology, slope gradient, slope aspect, topographical elevation, distance to stream, distance to roads, distance to faults, drainage density and fault density. The ArcGIS package was used to evaluate and analyze all the collected data. At the end of the susceptibility assessment, the area was divided into five susceptibility regions, such as very low, low, moderate, high and very high. The results of the analyses were then verified using the landslide location data and compared with the probability model. For this purpose, an area under curvature (AUC) and the seed cell area index assessments were applied. An AUC value for the likelihood-frequency ratio-based model 0.78 was obtained, whereas the AUC value for the AHP-based model was 0.64. The landslide susceptibility map will help decision makers in site selection and the site-planning process. The map may also be accepted as a basis for landslide risk-management studies to be applied in the study area. 相似文献
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The purpose of this study is to assess the susceptibility of landslides around Yomra and Arsin towns near Trabzon, in northeast
of Turkey, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys
and the analyses of the topographical map. The landslide triggering factors are considered to be slope angle, slope aspect,
distance from drainage, distance from roads and the weathered lithological units, which were called as “geotechnical units”
in the study. Idrisi and ArcGIS packages manipulated all the collected data. Logistic regression (LR) and weighted linear
combination (WLC) statistical methods were used to create a landslide susceptibility map for the study area. The results were
assessed within the scope of two different points: (a) effectiveness of the methods used and (b) effectiveness of the environmental
casual parameters influencing the landslides. The results showed that the WLC model is more suitable than the LR model. Regarding
the casual parameters, geotechnical units and slopes were found to be the most important variables for estimating the landslide
susceptibility in the study area. 相似文献
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GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline,Hendek (Turkey) 总被引:26,自引:0,他引:26
A segment of natural gas pipeline was damaged due to landsliding near Hendek. Re-routing of the pipeline is planned, but it requires the preparation of a landslide susceptibility map. In this study, the statistical index (Wi) and weighting factor (Wf) methods have been used with GIS to prepare a landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides occur in the unconsolidated to semi-consolidated clayey unit and regolith. The Wf method gives better results than the Wi method. Lithology is found to be the most important aspect in the study area. Based on the findings obtained in this study, the unconsolidated to semi-consolidated clayey unit and alluvium should be avoided during re-routing. Agricultural activities should not be allowed in the close vicinity of the pipeline. 相似文献
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新疆巩留县广泛发育冻融降雨型滑坡地质灾害,对其现有的研究多考虑降水,而缺乏温度影响的研究,为此,本文特增加了温度因子来进行巩留县滑坡灾害危险性评价。基于巩留县已发生的682个滑坡灾害点,选取坡度、起伏度、坡向、曲率、温度、距断层距离、距河流距离、距道路距离、工程地质岩组等9个评价因子。采用信息量模型(I)、确定性系数模型(CF)、信息量模型+逻辑回归模型(I+LR)以及确定性系数模型+逻辑回归模型(CF+LR)等4种模型对巩留县滑坡危险性进行了评价,划分为极高、高、中和低4个危险等级分区并进行了精度检验与现场实际验证。结果表明:(1)温度对滑坡有较大的触发作用;(2)耦合模型极高、高危险性分区面积明显低于单一模型极高、高危险性分区面积,其中CF+LR模型的极高、高危险性分区面积最小,低危险性分区面积最大;(3)4种模型ROC精度检验AUC值分别为0.889、0.893、0.895和0.900,均能较为客观地评价巩留县滑坡危险性。CF+LR模型精度最高,且经局部地区现场检验,CF+LR模型评价结果与实际情况也最为相符,研究成果对新疆地区巩留县滑坡地质灾害的预防和治理具有一定的借鉴意义。 相似文献
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Natural Hazards - The timely and accurate assessment of casualties is the key for and basis of emergency rescue work after an earthquake. In this paper, through exponential fitting of historical... 相似文献
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Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran 总被引:33,自引:8,他引:33
The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose. 相似文献
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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
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Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand 总被引:1,自引:0,他引:1
A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved. 相似文献
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Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey) 总被引:2,自引:0,他引:2
The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly
for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of
Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different
stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning
factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides
in the period of 1955–2007 were recognised using different data sources. A ‘Sampling Circle’ approach was proposed to define
shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses,
the method of artificial neural networks was implemented. According to the performance analyses conducted using the training
and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform
the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour
of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering
factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the
execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation
hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced. 相似文献
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Landslide susceptibility mapping for a landslide-prone area (Findikli,NE of Turkey) by likelihood-frequency ratio and weighted linear combination models 总被引:17,自引:0,他引:17
Landslides are very common natural problems in the Black Sea Region of Turkey due to the steep topography, improper use of
land cover and adverse climatic conditions for landslides. In the western part of region, many studies have been carried out
especially in the last decade for landslide susceptibility mapping using different evaluation methods such as deterministic
approach, landslide distribution, qualitative, statistical and distribution-free analyses. The purpose of this study is to
produce landslide susceptibility maps of a landslide-prone area (Findikli district, Rize) located at the eastern part of the
Black Sea Region of Turkey by likelihood frequency ratio (LRM) model and weighted linear combination (WLC) model and to compare
the results obtained. For this purpose, landslide inventory map of the area were prepared for the years of 1983 and 1995 by
detailed field surveys and aerial-photography studies. Slope angle, slope aspect, lithology, distance from drainage lines,
distance from roads and the land-cover of the study area are considered as the landslide-conditioning parameters. The differences
between the susceptibility maps derived by the LRM and the WLC models are relatively minor when broad-based classifications
are taken into account. However, the WLC map showed more details but the other map produced by LRM model produced weak results.
The reason for this result is considered to be the fact that the majority of pixels in the LRM map have high values than the
WLC-derived susceptibility map. In order to validate the two susceptibility maps, both of them were compared with the landslide
inventory map. Although the landslides do not exist in the very high susceptibility class of the both maps, 79% of the landslides
fall into the high and very high susceptibility zones of the WLC map while this is 49% for the LRM map. This shows that the
WLC model exhibited higher performance than the LRM model. 相似文献
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GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest,northern Iran 总被引:11,自引:1,他引:11
A. Jaafari A. Najafi H. R. Pourghasemi J. Rezaeian A. Sattarian 《International Journal of Environmental Science and Technology》2014,11(4):909-926
This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70 %) out of 103 detected landslides were randomly selected for modeling, and the remaining 31 (30 %) cases were used for the model validation. The landslide-conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index, stream power index, sediment transport index, normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume, were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50 % of the study area. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC = 75.59 %) is slightly better in prediction than frequency ratio model (AUC = 72.68 %). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations. 相似文献
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Behera Rashmisikha Kar Abhipsa Das Manas Ranjan Panda Prachi Prava 《Natural Hazards》2019,96(2):731-751
Natural Hazards - The 485-km-long coastline of Odisha, a state in the northeastern part of the Indian peninsula, is potentially vulnerable to several disaster events that take place frequently. In... 相似文献
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Soyoung Park Chuluong Choi Byungwoo Kim Jinsoo Kim 《Environmental Earth Sciences》2013,68(5):1443-1464
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619). 相似文献
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Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey) 总被引:4,自引:2,他引:4
In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist,
geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of
this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility
mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex
slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained.
The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing
landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce
two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the
Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness
of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance
than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence
of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and
geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root
mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity
of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher
prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters
produces logical results. 相似文献