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1.
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   

2.
Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.  相似文献   

3.
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   

4.
Devrek town with increasing population is located in a hillslope area where some landslides exist. Therefore, landslide susceptibility map of the area is required. The purpose of this study was to generate a landslide susceptibility map using a bivariate statistical index and evaluate and compare the results of the statistical analysis conducted with three different approaches in seed cell concept resulting in different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of (a) crowns and flanks, (b) only crowns, and (c) only flanks of the landslides by using ten different causative parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each corresponding seed cell data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce fairly acceptable results. In each data set analysis, elevation, lithology, slope, aspect, and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared using Seed Cell Area Indexes (SCAI). This comparison shows that the crown data set produces the most accurate and successful landslide susceptibility map of the study area.  相似文献   

5.
GIS-based landslide susceptibility maps for the Kankai watershed in east Nepal are developed using the frequency ratio method and the multiple linear regression technique. The maps are derived from comparing observed landslides with possible causative factors: slope angle, slope aspect, slope curvature, relative relief, distance from drainage, land use, geology, distance from faults and mean annual rainfall. The consistency of the maps is evaluated using landslide density analysis, success rate analysis and spatially agreed area approach. The first two analyses produce almost identical quantitative results, whereas the last approach is able to reveal spatial differences between the maps and also to improve predictions in the agreed high landslide-susceptible area.  相似文献   

6.
Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km2 area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.  相似文献   

7.
Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.  相似文献   

8.
This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory.  相似文献   

9.
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   

10.
Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.  相似文献   

11.
van Westen  C. J.  Rengers  N.  Soeters  R. 《Natural Hazards》2003,30(3):399-419
The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.  相似文献   

12.
Pathways for adaptive and integrated disaster resilience   总被引：7，自引：2，他引：5  
The GIS-multicriteria decision analysis (GIS-MCDA) technique is increasingly used for landslide hazard mapping and zonation. It enables the integration of different data layers with different levels of uncertainty. In this study, three different GIS-MCDA methods were applied to landslide susceptibility mapping for the Urmia lake basin in northwest Iran. Nine landslide causal factors were used, whereby parameters were extracted from an associated spatial database. These factors were evaluated, and then, the respective factor weight and class weight were assigned to each of the associated factors. The landslide susceptibility maps were produced based on weighted overly techniques including analytic hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA). An existing inventory of known landslides within the case study area was compared with the resulting susceptibility maps. Respectively, Dempster-Shafer Theory was used to carry out uncertainty analysis of GIS-MCDA results. Result of research indicated the AHP performed best in the landslide susceptibility mapping closely followed by the OWA method while the WLC method delivered significantly poorer results. The resulting figures are generally very high for this area, but it could be proved that the choice of method significantly influences the results.  相似文献   

13.
Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process.  相似文献   

14.
The purpose of this study is to evaluate and to compare the results of multivariate (logical regression) and bivariate (landslide susceptibility) methods in Geographical Information System (GIS) based landslide susceptibility assessment procedures. In order to achieve this goal the Asarsuyu catchment in NW Turkey was selected as a test zone because of its well-known landslide occurrences interfering with the E-5 highway mountain pass.Two methods were applied to the test zone and two separate susceptibility maps were produced. Following this a two-fold comparison scheme was implemented. Both methods were compared by the Seed Cell Area Indexes (SCAI) and by the spatial locations of the resultant susceptibility pixels.It was found that both of the methods converge in 80% of the area; however, the weighting algorithm in the bivariate technique (landslide susceptibility method) had some severe deficiencies, as the resultant hazard classes in overweighed areas did not converge with the factual landslide inventory map. The result of the multivariate technique (logical regression) was more sensitive to the different local features of the test zone and it resulted in more accurate and homogeneous susceptibility maps.  相似文献   

15.
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   

16.
Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.  相似文献   

17.
For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.  相似文献   

18.
滑坡灾害空间预测支持向量机模型及其应用   总被引：5，自引：1，他引：4  
戴福初  姚鑫  谭国焕 《地学前缘》2007,14(6):153-159
随着GIS技术在滑坡灾害空间预测研究中的广泛应用,滑坡灾害空间预测模型成为研究的热点问题。在总结滑坡灾害空间预测研究现状的基础上,简要介绍了两类和单类支持向量机的基本原理。以香港自然滑坡空间预测为例,采用两类和单类支持向量机进行滑坡灾害空间预测,并与Logistic回归模型进行了比较。结果表明,两类支持向量机模型优于Logistic回归模型,而Logistic回归模型优于单类支持向量机模型。  相似文献   

19.
Knowing the factors that influence landslide abundance and distribution is important to evaluate landslide susceptibility and hazard. Visual interpretation of aerial photographs (API) can be used to collect spatially distributed information on bedding attitude (BA), in an area. Where a map of the location of bedding traces (BTs), i.e. lines showing the intersection of bedding planes with the local topography, is available, the map can be used to obtain BA point data and to prepare maps showing morpho-structural domains. The possibility of using BA maps to investigate the influence of morpho-structural settings on landslide abundance is hampered by the lack of understanding of the influence of the length of the BTs, and of the parameters used to interpolate the BA data on the structural zonation. To investigate the problem, we used information on 207 BTs obtained through API in the Collazzone area, Central Italy, and we prepared 150 maps showing BA information. This was accomplished using 15 different values for the segmentation length of the BTs (S), and 10 different values for the tension parameter (T) used for the interpolation. We compare the results against previous results obtained for the same area adopting a heuristic approach to the segmentation of the same set of BTs. Next, we compare the geographical distribution of old deep-seated, deep-seated and shallow landslides in five morpho-structural domains in the study area, and we analyse the influence of the structural settings on the abundance of the different types of landslides.  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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