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1.
A digital landslide database has been created for Nicaragua to provide the scientific community and national authorities with
a tool for landslide-hazard assessment, emergency management, land-use planning, development of early warning systems, and
the implementation of public and private policies. The Instituto Nicaragüense de Estudios Territoriales (Nicaraguan Geosciences
Institute, INETER) began to compile the database in a digital format in 2003 as part of a comprehensive geographical information
system for all types of geohazards. Landslide data have been obtained from a variety of sources including newspapers, technical
reports, and landslide inventory maps. Inventory maps are largely based on fieldwork and aerial-photo analyses conducted by
foreign development agencies in collaboration with INETER and other Nicaraguan institutions. This paper presents the sources
of landslide information, introduces the database, and presents the first analyses of the data at national and regional scales.
The database currently contains spatial information for about 17,000 landslides that occurred in mountainous and volcanic
terrains. Information is mainly recorded for the period 1826–2003, with a large number of events that occurred during the
disastrous Hurricane Mitch in October 1998. The oldest historical event is dated at 1570, some events are recorded as prehistorical,
and other events have unknown dates of occurrence. Debris flows have been the most common types of landslides, both in volcanic
and nonvolcanic areas, but other types, including rockfalls and slides, have also been identified. Intense and prolonged rainfall,
often associated with tropical cyclones, and seismic and volcanic activity represent the most important landslide triggers.
At a regional scale, the influence of topographic (elevation, slope angle, slope aspect) and lithologic parameters on the
occurrence of landslides was analyzed. The development of the database allowed us to define the state of knowledge on landslide
processes in the Nicaragua and to provide a preliminary identification of areas affected by landslides. 相似文献
2.
Plan curvature and landslide probability in regions dominated by earth flows and earth slides 总被引：4，自引：0，他引：4
Damaging landslides in the Appalachian Plateau and scattered regions within the Midcontinent of North America highlight the need for landslide-hazard mapping and a better understanding of the geomorphic development of landslide terrains. The Plateau and Midcontinent have the necessary ingredients for landslides including sufficient relief, steep slope gradients, Pennsylvanian and Permian cyclothems that weather into fine-grained soils containing considerable clay, and adequate precipitation. One commonly used parameter in landslide-hazard analysis that is in need of further investigation is plan curvature. Plan curvature is the curvature of the hillside in a horizontal plane or the curvature of the contours on a topographic map. Hillsides can be subdivided into regions of concave outward plan curvature called hollows, convex outward plan curvature called noses, and straight contours called planar regions. Statistical analysis of plan-curvature and landslide datasets indicate that hillsides with planar plan curvature have the highest probability for landslides in regions dominated by earth flows and earth slides in clayey soils (CH and CL). The probability of landslides decreases as the hillsides become more concave or convex. Hollows have a slightly higher probability for landslides than noses. In hollows landslide material converges into the narrow region at the base of the slope. The convergence combined with the cohesive nature of fine-grained soils creates a buttressing effect that slows soil movement and increases the stability of the hillside within the hollow. Statistical approaches that attempt to determine landslide hazard need to account for the complex relationship between plan curvature, type of landslide, and landslide susceptibility. 相似文献
3.
Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China 总被引：3，自引：2，他引：1
In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations. 相似文献
4.
《地学前缘(英文版)》2020,11(4):1203-1217
Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province. 相似文献
5.
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
6.
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
7.
2008年汶川地震滑坡详细编目及其空间分布规律分析 总被引：3，自引：0，他引：3
最新研究成果表明, 2008年5月12日汶川MS 8.0级地震触发了超过197000处滑坡。首先,基于GIS与遥感技术构建了汶川地震滑坡的3类编目图,分别为单体滑坡面分布数据、滑坡中心点位置和滑坡后壁点位置。构建方法为基于地震前后高分辨率遥感影像的目视解译方法,区分单体滑坡并圈定其边界,对滑坡后壁进行识别与定点,并开展了部分滑坡的野外验证工作。这些滑坡分布在一个面积大约为110000km2的区域内,滑坡总面积约为1160km2。选择一个面积约为44031km2的区域作为研究区,区内滑坡数量为196007个,滑坡面积为1150.622km2,这是最详细完整的汶川地震滑坡编录成果,也是单次地震事件触发滑坡最多的记录。其次,开展研究区内的地震滑坡空间分布规律的研究。基于滑坡面与滑坡中心点分别构建滑坡空间分布面积密度图与点密度图,结果表明:滑坡多沿着映秀北川断裂分布,多发生在断裂的上盘。滑坡的高密度区位于映秀北川同震地表破裂的南西段(映秀镇与北川县之间)的上盘区域,这一区域恰对应着逆冲分量为主的断裂上盘,表明逆冲断裂对上盘区域发生滑坡的极强烈的控制作用,而该区域正是形变最大的区域,因此说明是地震滑坡发生的强烈控制作用。基于滑坡面密度(LAP)、滑坡中心点密度(LCND)与滑坡后壁点密度(LTND)这3个衡量指标,使用统计分析方法,评价了汶川地震滑坡与地震参数、地质参数、地形参数的关系。结果表明:LAP、LCND与LTND这3个衡量指标与坡度、地震烈度与PGA存在明显的正相关关系; 与距离震中、距离映秀北川同震地表破裂存在负相关关系; 斜坡曲率越接近0,滑坡越不易发生; LAP、LCND与LTND的高值高程区间为1200～3000m; 滑坡发生的优势坡向为E、SE、S方向; 滑坡发育的易发岩性为砂岩与粉砂岩(Z)、花岗岩; 滑坡与坡位的相关关系不太明显。统计结果还表明LCND与LTND两个衡量指标的差异对地震与地质因子不敏感,而对地形因子较敏感。最后将本文的统计结果与以往的汶川地震滑坡空间分布规律统计成果进行了一些对比,对比结果表明,对于某些因子,如高程、岩性、距离震中、距离映秀北川断裂的统计分析结果,采用不完整的滑坡分布数据或点数据,与采用较完整的滑坡分布面数据会有一定的差异,这种差异并未出现在针对坡度与坡向等因子的统计对比结果中。总之,作者认为一个完备、详细的地震滑坡分布面要素编目图是地震滑坡空间分布规律定量分析、危险性定量分析与滑坡控制的地震区地貌演化研究的重要基础,否则,与实际情况相比,得到统计结果会有一定的偏差,本文的研究成果与以往成果的对比结果证明了这一点。 相似文献
8.
Quanfu Niu Xinghai Dang Yuefeng Li Yingxue Zhang Xiaolin Lu Wenxing Gao 《Environmental Earth Sciences》2018,77(7):294
Loess Plateau is one of the ecologically fragile regions in China. It is one of the slippery strata of which landslides often developed. The formation and development of landslides are mainly affected by various natural environments, triggering factors, the vulnerability of landslide-bearing bodies, and topography has a controlling effect on landslides and determines landslide distribution. As important environmental elements, the selection and reclassification of topographic factors are the basis for loess landslide vulnerability map. In this study, our research suggests an effective workflow to select and analyze the topographic factors in the loess landslides. Nine hazard-formative environmental factors [e.g., slope, aspect, slope shape (SS), slope of slope (SOS), slope of aspect (SOA), surface amplitude (SA), surface roughness (SR), incision depth (ID) and elevation variation coefficient (EVC)] are prepared for landslide suitability analysis. The models of certainty factor, sensitivity index and correlation coefficient are combined to select and analyze the suitability of these factors. Four topographic factors (i.e., slope, SOS, SS and SR) were ultimately selected to carry out the landslide vulnerability mapping with other factors. Our results showed that most of the landslides were located in medium and high classes and accounting for 75.3%, and these places also coincided with higher economies and intense human activities. Our research also suggested that in situ measurements are necessary to determine how to reclassify these topographic factors and how many grades these topographic factors divided, which would further improve the reliability of landslide vulnerability map for the decision makers to deal with the possible future landslides in terms of safety and human activities. 相似文献
9.
Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek,South Korea 总被引：2，自引：0，他引：2
The objective of this study was to produce and evaluate a landslide susceptibility map for weathered granite soils in Deokjeok-ri Creek, South Korea. The relative effect (RE) method was used to determine the relationship between landslide causative factors (CFs) and landslide occurrence. To determine the effect of CFs on landslides, data layers of aspect, elevation, slope, internal relief, curvature, distance to drainage, drainage density, stream power index, sediment transport index, topographic wetness index, soil drainage character, soil type, soil depth, forest type, timber age, and geology were analyzed in a geographical information system (GIS) environment. A GIS-based landslide inventory map of 748 landslide locations was prepared using data from previous reports, aerial photographic interpretation, and extensive field work. A RE model was generated from a training set consisting of 673 randomly selected landslides in the inventory map, with the remaining 75 landslides used for validation of the susceptibility map. The results of the analysis were verified using the landslide location data. According to the analysis, the RE model had a success rate of 86.3 % and a predictive accuracy of 88.6 %. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. The results of this study can therefore be used to mitigate landslide-induced hazards and to plan land use. 相似文献
10.
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
11.
Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression 总被引：12，自引：2，他引：10
The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression
methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide
inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last
10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature.
Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map,
and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated
from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological
and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land
Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from
topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and
to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area
under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an
almost equal predicting capacity. 相似文献
12.
Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran 总被引：41，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
13.
A. Rosi V. Tofani L. Tanteri C. Tacconi Stefanelli A. Agostini F. Catani N. Casagli 《Landslides》2018,15(1):5-19
In this paper, the updating of the landslide inventory of Tuscany region is presented. To achieve this goal, satellite SAR data processed with persistent scatter interferometry (PSI) technique have been used. The updating leads to a consistent reduction of unclassified landslides and to an increasing of active landslides. After the updating, we explored the characteristics of the new inventory, analysing landslide distribution and geomorphological features. Several maps have been elaborated, as sliding index or landslide density map; we also propose a density-area map to highlight areas with different landslide densities and sizes. A frequency-area analysis has been performed, highlighting a classical negative power-law distribution. We also explored landslide frequency for lithology, soil use and several morphological attributes (elevation, slope gradient, slope curvature), considering both all landslides and classified landslide types (flows, falls and slides). 相似文献
14.
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
15.
Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus 总被引：1，自引：0，他引：1
D. D. Alexakis A. Agapiou M. Tzouvaras K. Themistocleous K. Neocleous S. Michaelides D. G. Hadjimitsis 《Natural Hazards》2014,72(1):119-141
This study considers the impact of landslides on transportation pavements in rural road network of Cyprus using remote sensing and geographical information system (GIS) techniques. Landslides are considered to be one of the most extreme natural hazards worldwide, causing both human losses and severe damages to the transportation network. Risk assessment for monitoring a road network is based on the combination of the probability of landslides occurrence and the extent and severity of the resultant consequences should the disasters (landslides) occur. Factors that can trigger landslide episodes include proximity to active faults, geological formations, fracture zones, degree and high curvature of slopes, water conditions, etc. In this study, the reliability and vulnerability of a rural network are examined. Initially, landslide locations were identified from the interpretation of satellite images. Different geomorphological factors such as aspect, slope, distance from the watershed, lithology, distance from lineaments, topographic curvature, land use and vegetation regime derived from satellite images were selected and incorporated in GIS environment in order to develop a decision support and continuous landslide monitoring system of the area. These parameters were then used in the final landslide hazard assessment model based on the analytic hierarchy process method. The results indicated good correlation between classified high-hazard areas and field-confirmed slope failures. The CA Markov model was also used to predict the landslide hazard zonation map for 2020 and the possible future hazards for transportation pavements. The proposed methodology can be used for areas with similar physiographic conditions all over the Eastern Mediterranean region. 相似文献
16.
This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system
(GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides
in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology,
rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances
from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified
form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote
sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence
were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river
and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard
zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation
map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes
were suggested by using the GIS technology. 相似文献
17.
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
18.
Landslides susceptibility mapping based on geographical information system,GuiZhou, south-west China 总被引：6，自引：0，他引：6
The purpose of this study is to assess the susceptibility of landslides around the area of Guizhou province, in south-west
of China, using a geographical information system (GIS). The base map is prepared by visiting the field area and mapping individual
landslide at a scale of 1:500,000 topographic maps. In the study, slope, lithology, landslide inventory, tectonic activity,
drainage distribution and annual precipitation were taken as independent causal factors. Therefore, six causal factors maps
are prepared by collecting information from various authorized sources and converting them in to GIS maps. The susceptibility
assessment is based on the qualitative map combination model and trapezoidal fuzzy number weighting (TFNW) approach. Using
a predicted map of probability, the study area was classified into four categories of landslide susceptibility: low, moderate,
high and very high. In addition, the weighting procedure showed that the TFNW is an efficient method for landslide causal
factors weighting. 相似文献
19.
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
20.
Ogbonnaya Igwe Wilfred Mode Okechukwu Nnebedum Ikenna Okonkwo Ifeanyi Oha 《Environmental Earth Sciences》2014,71(5):2465-2480
This paper analyses the dominant mechanisms of slope failures and identifies potential obstacles to landslide-hazard reduction at the Iva Valley area, Enugu, Nigeria. The landscape is replete with landslide scars and gullies of varied sizes and the slope deposits comprise unconsolidated, friable sands inter-bedded with thin units of montmorillonitic claystone. Forty-three landslide events were identified in the study area with most being shallow, short run-out movements with slip-surface depth <2 m. The study found the landslides mainly occur in the beginning of rainy season characterized by short duration, high intensity rainfall. An integrated approach comprising field mapping, laboratory tests and numerical analyses reveals that the barren nature of the slopes prior to the outset of rainy season, high rainfall intensity, erosion, overgrazing, soil characteristics and the site’s unique lithologic sequence are the main causes of instability. Shearing tests under several conditions showed that the soils strongly strain-soften until low steady-state strength is achieved. A computer code, based on this strength reduction technique, used input parameters obtained from the field and laboratory studies to simulate a landslide with similar structure, travel distance and distribution area. It is noted that urbanization has gradually increased the vulnerability of the society’s poor to landslide hazards as they now erect unplanned residence (tents and blocks) on the slopes. This work is part of a regional study aimed at finding ways of protecting the vulnerable by generating data that could be used to build future landslide susceptibility map. 相似文献