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1.
The main goal of this paper is to generate a landslide susceptibility map through evidential belief function (EBF) model by using Geographic Information System (GIS) for Qianyang County, Shaanxi Province, China. At first, a detailed landslide inventory map was prepared, and the following ten landslide-conditioning factors were collected: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to rivers, geomorphology, lithology, and rainfall. The landslides were detected from the interpretation of aerial photographs and supported by field surveys. A total of 81 landslides were randomly split into the following two parts: the training dataset 70 % (56 landslides) were used for establishing the model and the remaining 30 % (25 landslides) were used for the model validation. The ArcGIS was used to analyze landslide-conditioning factors and evaluate landslide susceptibility; as a result, a landslide susceptibility map was generated by using EBF and ArcGIS 10.0, thus divided into the following five susceptibility classes: very low, low, moderate, high, and very high. Finally, when we validated the accuracy of the landslide susceptibility map, both the success-rate and prediction-rate curve methods were applied. The results reveal that a final susceptibility map has the success rate of 83.31 % and the prediction rate of 79.41 %. 相似文献
2.
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
3.
Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS 总被引：10，自引：0，他引：10
The purpose of this study is to assess the susceptibility of landslides in parts of Western Ghats, Kerala, India, using a
geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analysis
of the topographical maps. The landslide triggering factors are considered to be slope angle, slope aspect, slope curvature,
slope length, distance from drainage, distance from lineaments, lithology, land use and geomorphology. ArcGIS version 8.3
was used to manipulate and analyse all the collected data. Probabilistic-likelihood ratio was used to create a landslide susceptibility
map for the study area. The result was validated using the Area under Curve (AUC) method and temporal data of landslide occurrences.
The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.
As the result, the success rate of the model was (84.46%) and the prediction rate of the model was (82.38%) shows high prediction
accuracy. In the reclassified final landslide susceptibility zone map, 5.68% of the total area is classified as critical in
nature. The landslide susceptibility map thus produced can be used to reduce hazards associated with landslides and to land
cover planning. 相似文献
4.
在陕西省宝鸡市附近长寿沟地区滑坡详细调查和遥感解译的基础上,完成了1∶10000滑坡编目图。通过使用GIS的水文分析功能,运用正反DEM技术,将长寿沟地区划分为216个自然斜坡单元,其中包括123个滑坡单元和93个未发生滑坡单元,分析滑坡发生与坡高、坡度、坡向、坡形、人类工程活动和水文地质条件影响因子之间的统计规律。利用经遗传算法优化后的BP神经网络对80个滑坡样本和40个未滑坡样本进行训练学习,然后再利用训练好的网络对预测样本进行评价分析。结果表明:43个已滑坡单元中只有3个被误判为无滑坡,正确率为9302%,53个未滑坡单元中有10个被预测为滑坡,正确率为8113%,总体正确率为8646%。通过对被预测为滑坡的10个斜坡单元进行分析,发现这些单元在坡形、坡高等影响因素的组合上已经具备了发生滑坡的条件,虽然目前没有发生滑坡,但作为潜在的滑坡危险区,可以为滑坡灾害预测预报和防灾减灾工作提供参考。 相似文献
5.
6.
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
7.
Pece V. Gorsevski M. Kenneth Brown Kurt Panter Charles M. Onasch Anita Simic Jeffrey Snyder 《Landslides》2016,13(3):467-484
The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning. 相似文献
8.
Yacine Achour Abderrahmane Boumezbeur Riheb Hadji Abdelmadjid Chouabbi Victor Cavaleiro El Amine Bendaoud 《Arabian Journal of Geosciences》2017,10(8):194
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
9.
GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang,China 总被引：6，自引：2，他引：4
Shibiao Bai Guonian Lü Jian Wang Pinggen Zhou Liang Ding 《Environmental Earth Sciences》2011,62(1):139-149
Landslides have had a huge effect on human life, the environment and local economic development, and therefore they need to
be well understood. In this study, we presented an approach for the analysis and modeling of landslide data using rare events
logistic regression and applied the approach to an area in Lianyungang, China. Digital orthophotomaps, digital elevation models
of the region, geological maps and different GIS layers including settlement, road net and rivers were collected and applied
in the analysis. Landslides were identified by monoscopic manual interpretation and validated during the field investigation.
To validate the quality of mapping, the data from the study area were divided into a training set and validation set. The
result map showed that 4.26% of the study area was identified as having very high susceptibility to landslides, whereas the
others were classified as having very low susceptibility (47.2%), low susceptibility (22.21%), medium susceptibility (14.39%)
and high susceptibility (11.93%). The quality of the landslide-susceptibility map produced in this paper was validated, and
it can be used for planning protective and mitigation measures. The landslide-susceptibility map is a fundamental part of
the Lianyungang city landslide risk assessment. 相似文献
10.
Landslide susceptibility mapping and spatial prediction have been carried out for the headwater region of Manimala river basin in the Western Ghats of Kerala, India, through geographic information technology and bayesian statistics, Weights of Evidence (WofE) model. The variables such as geomorphology, slope, relative relief, terrain curvature, slope length and steepness, soil type and land use/land cover are considered as factors that translate the terrain susceptible to landsliding. The quantitative relationship between landslides and the causative factors were statistically weighted using the ArcSDM extension of ArcGIS software. The posterior probability map, produced on the basis of predictive weights for each variable by combining the weighted layers in GIS, shows a high posterior probability value of 0.1 (highly possible) with a standard deviation of 0.0025. The discrete susceptibility classes in the reclassified posterior probability map reveals that the high and moderate landslide susceptibility classes cover 0.78 and 14.93% respectively of the total study area. The result was validated using the Area Under Curve (AUC) method with a separate set of landslide locations and the validation demonstrates high prediction accuracy with a prediction rate of 81.32%. 相似文献
11.
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
12.
Wei Chen Wenping Li Enke Hou Zhou Zhao Niandong Deng Hanying Bai Danzhi Wang 《Arabian Journal of Geosciences》2014,7(11):4499-4511
The main objective of this study was to apply a statistical (information value) model using geographic information system (GIS) to the Chencang District of Baoji, China. Landslide locations within the study area were identified using reports and aerial photographs, and a field survey. A total of 120 landslides were mapped, of which 84 (70 %) were randomly selected for building the landslide susceptibility model. The remaining 36 (30 %) were used for model validation. We considered a total of 10 potential factors that predispose an area to a landslide for the landslide susceptibility mapping. These included slope degree, altitude, slope aspect, plan curvature, geomorphology, distance from faults, lithology, land use, mean annual rainfall, and peak ground acceleration. Following an analysis of these factors, a landslide susceptibility map was produced using the information value model with GIS. The resulting landslide susceptibility index was divided into five classes (very high, high, moderate, low, and very low) using the natural breaks method. The corresponding distribution area percentages were 29.22, 25.14, 15.66, 15.60, and 14.38 %, respectively. Finally, landslide locations were used to validate the results of the landslide susceptibility map using areas under the curve (AUC). The AUC plot showed that the susceptibility map had a success rate of 81.79 % and a prediction accuracy of 82.95 %. Based on the results of the AUC evaluation, the landslide susceptibility map produced using the information value model exhibited good performance. 相似文献
13.
National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea 总被引：2，自引：1，他引：1
Jangwon Suh Yosoon Choi Tae-Dal Roh Hyi-Jun Lee Hyeong-Dong Park 《Environmental Earth Sciences》2011,63(3):619-632
The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility of rock-cut slopes along expressways in Korea. A geographic information system (GIS) database was compiled based on data from topographical and geological maps, and rock-cut slope data, including the locations of past landslides. Seven factors (i.e., slope height, slope length, slope gradient, upper slope gradient, lithology, distance from nearest fault, and dip direction of slope) were extracted from the GIS database to assess the relationship between each factor and landslide events. Weight of evidence (WOE), analytic hierarchy process (AHP), and fuzzy logic methods, as well as hybrid methods, were used to establish the rating of classes for each factor, weightings for the factors, and to combine multiple factor layers into landslide-susceptibility maps. A comparison of the results obtained using several different methods, based on the area under curve technique, revealed that the WOE method showed the highest accuracy of 74%. The annual cost of traffic congestion resulting from slope failures was evaluated to identify those rock-cut slopes where detailed investigations and landslide warning systems are required. 相似文献
14.
A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models 总被引：2，自引：0，他引：2
QIQING WANG WENPING LI YANLI WU YABING PEI MAOLIN XING DONGDONG YANG 《Journal of Earth System Science》2016,125(3):645-662
The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning. 相似文献
15.
GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands, Greece 总被引：5，自引：2，他引：3
George Papathanassiou Sotiris Valkaniotis Athanassios Ganas Spyros Pavlides 《Landslides》2013,10(6):771-783
This is the first landslide inventory map in the island of Lefkada integrating satellite imagery and reports from field surveys. In particular, satellite imagery acquired before and after the 2003 earthquake were collected and interpreted with the results of the field survey that took place 1 week after this strong (Mw?=?6.3) event. The developed inventory map indicates that the density of landslides decreases from west to east. Furthermore, the spatial distribution of landslides was statistically analyzed in relation to the geology and topography for investigating their influence to landsliding. This was accomplished by overlaying these causal factors as thematic layers with landslide distribution data. Afterwards, weight values of each factor were calculated using the landslide index method and a landslide susceptibility map was developed. The susceptibility map indicates that the highest susceptibility class accounts for 38 % of the total landslide activity, while the three highest classes that cover the 10 % of the surface area, accounting for almost the 85 % of the active landslides. Our model was validated by applying the approaches of success and prediction rate to the dataset of landslides that was previously divided into two groups based on temporal criteria, estimation and validation group. The outcome of the validation dataset was that the highest susceptibility class concentrates 18 % of the total landslide activity. However, taking into account the frequency of landslides within the three highest susceptibility classes, more than 85 %, the model is characterized as reliable for a regional assessment of earthquake-induced landslides hazard. 相似文献
16.
The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity 总被引：3，自引：0，他引：3
Landslides are common natural hazards in the seismically active North Anatolian Fault Zone of Turkey. Although seismic activity, heavy rainfall, channel incisions, and anthropogenic effects are commonly the main triggers of landslides, on March 17, 2005, a catastrophic large landslide in Sivas, northeastern of Turkey, the Kuzulu landslide, was triggered by snowmelt without any other precursor. The initial failure of the Kuzulu landslide was rotational. Following the rotational failure, the earth material in the zone of accumulation exhibited an extremely rapid flow caused by steep gradient and high water content. The Agnus Creek valley, where Kuzulu village is located, was filled by the earth-flow material and a landslide dam was formed on the upper part of Agnus Creek. The distance from the toe of the rotational failure down to the toe of the earth flow measured more than 1800 m, with about 12.5 million m3 of displaced earth material. The velocity of the Kuzulu landslide was extremely fast, approximately 6 m/s. The main purposes of this study are to describe the mechanism and the factors conditioning the Kuzulu landslide, to present its environmental impacts, and to produce landslide-susceptibility maps of the Kuzulu landslide area and its near vicinity. For this purpose, a detailed landslide inventory map was prepared and geology, slope, aspect, elevation, topographic-wetness index and stream-power index were considered as conditioning factors. During the susceptibility analyses, the conditional probability approach was used and a landslide-susceptibility map was produced. 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
17.
Pre-conditioning factors and susceptibility assessments of Wenchuan earthquake landslide at the Zhouqu segment of Bailongjiang basin, China 总被引：1，自引：0，他引：1
Shibiao Bai Qiang Xu Jian Wang Pinggen Zhou 《Journal of the Geological Society of India》2013,82(5):575-582
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
18.
Slope units-based flow susceptibility model: using validation tests to select controlling factors 总被引：2，自引：0，他引：2
A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena
of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study
area, extending for approximately 90 km2, was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility
values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean
topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides
was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the
density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling
factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors,
so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility
map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope
unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated
to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index
gave unsatisfactory results. 相似文献
19.
Roland Ngwatung Afungang Carlos Valdir de Meneses Bateira Clement Anguh Nkwemoh 《Arabian Journal of Geosciences》2017,10(17):384
Landslides are a major natural hazard in the Bamenda highlands of Cameroon, and their occurrence in this region has most often been studied using qualitative methods. The aim of this research is to quantitatively assess the spatial probability of landslides using GIS and the informative value model. Landslide inventory was done through literature review, aerial photo-interpretation, participatory GIS and field survey. Six geo-environmental factors including slope, curvature, aspect, land use, lithology and geomorphology were used as landslide conditioning (static) factors. The susceptibility of the area to future landslide events was assessed by making a correlation between past landslides and geo-environmental factors using the informative value model. The landslide inventory involving 110 landslides was divided into two equal groups using random division criterion and was used to train and validate the model. The analysis showed that slope and land use are the most important causal factors of landslides in the area. The susceptibility index map predicted most landslides to occur around the steep slopes of the Bamenda escarpment that is being used for multiple anthropic activities. The training model had a success rate of 87%, and the validation model had a prediction rate of 90%. The prediction rate curve shows that 44, 32, 18 and 6% of future landslides will occur on 3, 8, 21 and 68% of the study area. The model correctly classified 89% of unstable areas and 81% of the stable areas with an accuracy rate of 0.90. This quantitative result complement other qualitative assessment results that show the Bamenda escarpment zone as a high-risk area. However, the area susceptible to landslide in this study goes beyond what earlier studies had indicated as houses and other infrastructure were found on old landslide sites whose scars have been eroded by human activities. This new input thus improves the quality of information placed at the disposal of civil protection units and land use managers during decision making. 相似文献