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1.
The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.  相似文献   

2.
Landslides are one of the major natural disasters that occur in the Himalayan range with recurring frequency, causing enormous loss of life and property every year. Preparation of landslide inventory maps and landslide susceptibility zonation maps are the important tasks to be taken into account initially for safe mitigation measures. The present paper focuses on landslide susceptibility maps of the Ghurmi–Dhad Khola area, east Nepal, using Geographic Information System. For this purpose, the landslide susceptibility maps are prepared by using the heuristic and bivariate statistical methods. The parameters considered for the study are slope angle, slope aspect, elevation, distance from drainage, geology, land cover, rock and soil type, and distance from faults and folds. The landslide susceptibility zonation map produced from the heuristic method shows that 42.59 % of the observed landslide falls under the very high susceptible zone and 33.00 % under the high susceptible zone. Likewise, the landslide susceptibility zonation map produced from the bivariate method depicts that 44.19 % of the observed landslide falls under the very high susceptible zone and 31.59 % under the high susceptible zone. Both the landslide susceptibility zonation maps are identical, and success rates of both the maps are above 80 %. While comparing the landslide susceptibility maps obtained from two different methods, about 78 % of the study area falls in the identical susceptible zones. Special attention should be taken into consideration for the construction works in the areas which have been spatially agreed as very high and high susceptible zones from both techniques. Moreover, these maps can be used for slope management, land use planning, disaster management planning, etc., by the concerned authorities.  相似文献   

3.
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   

4.
The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.  相似文献   

5.
The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km2 area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.  相似文献   

6.
Mehrabi  Mohammad 《Natural Hazards》2022,111(1):901-937

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

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7.
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   

8.
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   

9.
Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.  相似文献   

10.
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   

11.
The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.  相似文献   

12.
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   

13.
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   

14.
A Luoi is a Vietnamese–Laotian border district situated in the western part of Thua Thien Hue province, central Vietnam, where landslides occur frequently and seriously affect local living conditions. This study focuses on the spatial analysis of landslide susceptibility in this 263-km2 area. To analyze landslide manifestation in the study area, causative factor maps are derived of slope angle, weathering, land use, geomorphology, fault density, geology, drainage distance, elevation, and precipitation. The analytical hierarchical process approach is used to combine these maps for landslide susceptibility mapping. A landslide susceptibility zonation map with four landslide susceptibility classes, i.e. low, moderate, high, and very high susceptibility for landsliding, is derived based on the correspondence with an inventory of observed landslides. The final map indicates that about 37% of the area is very highly susceptible for landsliding and about 22% is highly susceptible, which means that more than half of the area should be considered prone to landsliding.  相似文献   

15.
This study aimed to investigate the parameter effects in preparing landslide susceptibility maps with a data-driven approach and to adapt this approach to analytical hierarchy process (AHP). For this purpose, at the first stage, landslide inventory of an area located in the Western Black Sea region of Turkey covering approximately 567?km2 was prepared, and a total of 101 landslides were mapped. In order to assess the landslide susceptibility, a total of 13 parameters were considered as the input parameters: slope, aspect, plan curvature, topographical elevation, vegetation cover index, land use, distance to drainage, distance to roads, distance to structural elements, distance to ridges, stream power index, sediment transport capacity index, and wetness index. AHP was selected as the major assessment methodology since the adapted approach and AHP work in data pairs. Adapted to AHP, a similarity relation?Cbased approach, namely landslide relation indicator (LRI) for parameter selection method, was also proposed. AHP and parametric effect analyses were performed by the proposed approach, and seven landslide susceptibility maps were produced. Among these maps, the best performance was gathered from the landslide susceptibility map produced by 9 parameter combinations using area under curve (AUC) approach. For this map, the AUC value was calculated as 0.797, while the others ranged between 0.686 and 0.771. According to this map, 38.3?% of the study area was classified as having very low, 8.5?% as low, 15.0?% as moderate, 20.3?% as high, and 17.9?% as very high landslide susceptibility, respectively. Based on the overall assessments, the proposed approach in this study was concluded as objective and applicable and yielded reasonable results.  相似文献   

16.
Landslides are natural geological disasters causing massive destructions and loss of lives, as well as severe damage to natural resources, so it is essential to delineate the area that probably will be affected by landslides. Landslide susceptibility mapping (LSM) is making increasing implications for GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. It is considered to be an effective tool to understand natural disasters related to mass movements and carry out an appropriate risk assessment. This study is based on an integrated approach of GIS and statistical modelling including fuzzy analytical hierarchy process (FAHP), weighted linear combination and MCE models. In the modelling process, eleven causative factors include slope aspect, slope, rainfall, geology, geomorphology, distance from lineament, distance from drainage networks, distance from the road, land use/land cover, soil erodibility and vegetation proportion were identified for landslide susceptibility mapping. These factors were identified based on the (1) literature review, (2) the expert knowledge, (3) field observation, (4) geophysical investigation, and (5) multivariate techniques. Initially, analytical hierarchy process linked with the fuzzy set theory is used in pairwise comparisons of LSM criteria for ranking purposes. Thereafter, fuzzy membership functions were carried out to determine the criteria weights used in the development of a landslide susceptibility map. These selected thematic maps were integrated using a weighted linear combination method to create the final landslide susceptibility map. Finally, a validation of the results was carried out using a sensitivity analysis based on receiver operator curves and an overlay method using the landslide inventory map. The study results show that the weighted overlay analysis method using the FAHP and eigenvector method is a reliable technique to map landslide susceptibility areas. The landslide susceptibility areas were classified into five categories, viz. very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The very high and high susceptibility zones account for 15.11% area coverage. The results are useful to get an impression of the sustainability of the watershed in terms of landsliding and therefore may help decision makers in future planning and mitigation of landslide impacts.  相似文献   

17.
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   

18.
Landslides in the three studied basins of the Siwalik Hills are not random in distribution; they tend to cluster in certain areas implying the control of certain in situ factors or their combination. Landslide controlling in situ factors were reviewed and analyzed from maps, aerial photos and imageries using GIS. Chi square analysis was carried out to test the significance of landslide distribution vis-à-vis in situ factors. Slope gradient and relative relief were consistently significant in landslide distribution. Geology, dip-topography relation, land use and land cover, and vegetation conditions appeared important in landslide occurrence in all three basins either in terms of area or count in any two basins. Slope aspect and altitude tested significant for landslide occurrence in at least two basins. However, upslope flow contributing area, drainage density and distance to lineament were found insignificant in all three basins. In situ factors that tested significant in any two basins were used for landslide susceptibility analysis using a bivariate statistical approach. The distribution of landslides strongly correlates with susceptibility indices. With in situ factors, landslide susceptibility had good correlation with slope gradient and relative relief. Incorporating calculated factor weight values from one basin to the other two basins, proxy susceptibility index maps were also prepared. A moderate to good positive correlation appeared between them implying certain range of confidence for replicating the result for whole of the Siwalik Hills. Slope gradient and relative relief can be used as proxy indicators of landslide susceptible areas in the Siwalik Hills.  相似文献   

19.
The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.  相似文献   

20.
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   

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