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1.
Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria,south Italy) 总被引:3,自引:1,他引:2
Massimo Conforti Gaetano Robustelli Francesco Muto Salvatore Critelli 《Natural Hazards》2012,61(1):127-141
The Calabria (Southern Italy) region is characterized by many geological hazards among which landslides, due to the geological,
geomorphological, and climatic characteristics, constitute one of the major cause of significant and widespread damage. The
present work aims to exploit a bivariate statistics-based approach for drafting a landslide susceptibility map in a specific
scenario of the region (the Vitravo River catchment) to provide a useful and easy tool for future land planning. Landslides
have been detected through air-photo interpretation and field surveys, by identifying both the landslide detachment zones
(LDZ) and landslide bodies; a geospatial database of predisposing factors has been constructed using the ESRI ArcView 3.2
GIS. The landslide susceptibility has been assessed by computing the weighting values (Wi) for each class of the predisposing factors (lithology, proximity to fault and drainage line, land use, slope angle, aspect,
plan curvature), thus evaluating the distribution of the landslide detachment zones within each class. The extracted predisposing
factors maps have then been re-classified on the basis of the calculated weighting values (Wi) and by means of overlay processes. Finally, the landslide susceptibility map has been considered by five classes. It has
been determined that a high percentage (61%) of the study area is characterized by a high to very high degree of susceptibility;
clay and marly lithologies, and slope exceeding 20° in inclination would be much prone to landsliding. Furthermore, in order
to ascertain the proposed landslide susceptibility estimate, a validation procedure has been carried out, by splitting the
landslide detachment zones into two groups: a training and a validation set. By means of the training set, the susceptibility
map has first been produced; then, it has been compared with the validation set. As a result, a great majority of LDZ-validation
set (85%) would be located in highly and very highly susceptible areas. The predictive power of the model is considered reliable,
since more than 50% of the LDZ fall into 20% of the most susceptible areas. The reliability of the susceptibility map is also
suggested by computing the SCAI index, true positive and false positive rates; nevertheless, the most susceptible areas are
overestimated. As a whole, the results indicate that landslide susceptibility assessment based on a bivariate statistics-based
method in a GIS environment may be useful for land planning policy, especially when considering its cost/benefit ratio and
the need of using an easy tool. 相似文献
2.
Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment 总被引:31,自引:6,他引:31
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
3.
Landslides are unpredictable; however, the susceptibility of landslide occurrence can be assessed using qualitative and quantitative methods based on the technology of the Geographic Information Systems (GIS). A map of landslide inventory was obtained from the previous work in the Minamata area, the interpretation from aerial photographs taken in 1999 and 2002. A total of 160 landslides was identified in four periods. Following the construction of geospatial databases, including lithology, topography, soil deposits, land use, etc., the study documents the relationship between landslide hazard and the factors that affect the occurrence of landslides. Different methods, namely the logistic regression analysis and the information value model, were then adopted to produce susceptibility maps of landslide occurrence. After the application of each method, two resultant maps categorize the four classes of susceptibility as high, medium, low and very low. Both of them generated acceptable results as both classify the majority of the cells with landslide occurrence in high or medium susceptibility classes, which could be believed to be a success. By combining the hazard maps generated from both methods, the susceptibility was classified as high–medium and low–very low levels, in which the classification of high susceptibility level covers 6.5% of the area, while the areas predicted to be unstable, which are 50.5% of the total area, are classified as the low susceptibility level. However, comparing the results from both the approaches, 43% of the areas were misclassified, either from high–medium to low–very low or low–very low to high–medium classes. Due to the misclassification, 8% and 3.28% of all the areas, which should be stable or free of landsliding, were evaluated as high–medium susceptibility using the logistic regression analysis and the information value model, respectively. Moreover, in the case of the class rank change from high–medium susceptibility to low–very low, 35% and 39.72% of all mapping areas were predicted as stable using both the approaches, respectively, but in these areas landslides were likely to occur or were actually recognized. 相似文献
4.
Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia 总被引:20,自引:5,他引:15
Biswajeet Pradhan 《Environmental Earth Sciences》2011,63(2):329-349
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
5.
Determination and application of the weights for landslide susceptibility mapping using an artificial neural network 总被引:38,自引:0,他引:38
The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping. 相似文献
6.
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
7.
Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps 总被引:2,自引:0,他引:2
In recent years SAR interferometry has become a widely used technique for measuring altitude and displacement of the surface
of the earth. Both these capabilities are highly relevant for landslide susceptibility studies. Although there are many problems
that make the use of SAR interferometry less suitable for landslide inventory mapping, it’s use in landslide monitoring and
in the generation of input maps for landslide susceptibility assessment looks very promising. The present work attempts to
evaluate the usefulness and limitations of this technique based on a case study in the Swiss Alps. Input maps were generated
from ERS repeat pass data using SAR interferometry. A land cover map has been generated by image classification of multi-temporal
SAR intensity images. An InSAR DEM was generated and a number of maps were derived from it, such as slope-, aspect, altitude-
and slope form classes. These maps were used to generate landslide and rockfall susceptibility maps, which give fairly well
acceptable results. However, a comparison of the InSAR DEM with the conventional Swisstopo DEM, indicated significant errors
in the absolute height and slope angles derived from InSAR, especially along the ridges and in the valleys. These errors are
caused by low coherence mostly due to layover and shadow effects. Visual comparison of stereo images created from hillshading
maps and corresponding DEMs demonstrate that a considerable amount of topographic details have been lost in the InSAR-derived
DEM. It is concluded that InSAR derived input maps are not ideal for landslide susceptibility assessment, but could be used
if more accurate data is lacking. 相似文献
8.
Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey) 总被引:3,自引:3,他引:3
Ardesen is a settlement area which has been significantly damaged by frequent landslides which are caused by severe rainfalls
and result in many casualties. In this study a landslide susceptibility map of Ardesen was prepared using the Analytical Hierarchy
Process (AHP) with the help of Geographical Information Systems (GIS) and Digital Photogrametry Techniques (DPT). A landslide
inventory, lithology–weathering, slope, aspect, land cover, shear strength, distance to the river, stream density and distance
to the road thematics data layers were used to create the map. These layer maps are produced using field, laboratory and office
studies, and by the use of GIS and DPT. The landslide inventory map is also required to determine the relationship between
these maps and landslides using DPT. In the study field in the Hemsindere Formation there are units that have different weathering
classes, and this significantly affects the shear strength of the soil. In this study, shear strength values are calculated
in great detail with field and laboratory studies and an additional layer is evaluated with the help of the stability studies
used to produce the landslide susceptibility map. Finally, an overlay analysis is carried out by evaluating the layers obtained
according to their weight, and the landslide susceptibility map is produced. The study area was classified into five classes
of relative landslide susceptibility, namely, very low, low, moderate, high, and very high. Based on this analysis, the area
and percentage distribution of landslide susceptibility degrees were calculated and it was found that 28% of the region is
under the threat of landslides. Furthermore, the landslide susceptibility map and the landslide inventory map were compared
to determine whether the models produced are compatible with the real situation resulting in compatibility rate of 84%. The
total numbers of dwellings in the study area were determined one by one using aerial photos and it was found that 30% of the
houses, with a total occupancy of approximately 2,300 people, have a high or very high risk of being affected by landslides. 相似文献
9.
The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks 总被引:4,自引:2,他引:2
Işık Yilmaz 《Environmental Earth Sciences》2010,60(3):505-519
This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide
susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar
(Giresun–Turkey). Digital elevation model was first constructed using a geographical information system software and parameter
maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect,
topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage
of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell
and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results
than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility
maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling
strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the
landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood. 相似文献
10.
Subrata Mondal Sujit Mandal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2018,12(1):29-44
The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. 相似文献
11.
Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation 总被引:5,自引:2,他引:5
This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semi-quantitative
model with ten indicator maps and a cell size of 90 × 90 m. The model was designed and implemented using spatial multi-criteria
evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution
to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods,
and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region
and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest
concentration of high landslide risk index values while the Nipe–Cristal–Baracoa system has the highest absolute values, although
they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level
and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone
areas. 相似文献
12.
The random forest method was used to generate susceptibility maps for debris flows, rock slides, and active layer detachment slides in the Donjek River area within the Yukon Alaska Highway Corridor, based on an inventory of landslides compiled by the Geological Survey of Canada in collaboration with the Yukon Geological Survey. The aim of this study is to develop data-driven landslide susceptibility models which can provide information on risk assessment to existing and planned infrastructure. The factors contributing to slope failure used in the models include slope angle, slope aspect, plan and profile curvatures, bedrock geology, surficial geology, proximity to faults, permafrost distribution, vegetation distribution, wetness index, and proximity to drainage system. A total of 83 debris flow deposits, 181 active layer detachment slides, and 104 rock slides were compiled in the landslide inventory. The samples representing the landslide free zones were randomly selected. The ratio of landslide/landslide free zones was set to 1:1 and 1:2 to examine the results of different sample ratios on the classification. Two-thirds of the samples for each landslide type were used in the classification, and the remaining 1/3 were used to evaluate the results. In addition to the classification maps, probability maps were also created, which served as the susceptibility maps for debris flows, rock slides, and active layer detachment slides. Success and prediction rate curves created to evaluate the performance of the resulting models indicate a high performance of the random forest in landslide susceptibility modelling. 相似文献
13.
This paper describes the application of a well-known multi-criteria decision-making technique, called preference ranking organization method for enrichment evaluation (PROMETHEE II), in combination with fuzzy analytical hierarchy process (FAHP), as a weighting technique to explore landslide susceptibility mapping (LSM). To this end, eight landslide-related geodata layers of the Minoo Dasht located in the Gorgan province of Iran, involving slope, aspect, distance to river, drainage density, distance to fault, mean annual rainfall, distance to road and lithology have been integrated using the PROMETHEE II enhanced by FAHP technique. Afterward, the receiver operating characteristics (ROC) curves for the proposed LSM were drawn using an inventory of landslides containing 83 recent and historic landslide points, and the area under curve = 0.752 value was calculated accordingly. Additionally, to further verify the practicality of such susceptibility map, it was also evaluated against the landslide inventory using simple overlay. The outcome was that about 11 % of the occurred landslide points fall into the very high susceptibility class of the LSM, but approximately 52 % of them indeed fall into the high and very high susceptibility zones together. Also, it resulted that no recorded landslide occurred in the zone of very low susceptibility. According to the results of the ROC curves analysis and simple overlay evaluation, the produced map has exhibited good performance. 相似文献
14.
As landslides are very common in Greece, causing serious problems to the social and economic welfare of many communities,
the implementation of a proper hazard analysis system will help the creation of a reliable susceptibility map. Τhis will help
local communities to define a safe land use and urban development. The purpose of this study is to compare the implementation
of two semi-quantitative landslide assessment approaches, using landslide susceptibility maps compiled in a GIS environment.
The compared methods are rock engineering system (RES) and the analytic hierarchy process (AHP). For the landslide susceptibility
analysis, the Northeastern part of the Achaia County was examined. This area suffers from many landslides, because of its
neighborhood with the tectonically active Corinthian Gulf and its geological setting (Neogene sediments, flysch and other
bedrock formations, with local overthrusts). Ten parameters were used in both methodologies, and each one was separated into
five categories ranging from 0 to 4, representing their specific conditions derived from the investigation of the landslides
in the western part of the study area (ranking area). A layer map was generated for each parameter, using GIS, while the weighting
coefficients of each methodology were used for the compilation of RES and AHP final maps of the eastern part of the study
area (validating area). By examining these two maps, it is revealed that even though both correctly show the landslide status
of the second site, the RES map reveals a better behavior in the spatial distribution of the various landslide susceptibility
zones. 相似文献
15.
A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS 总被引:3,自引:2,他引:1
Wei-Dong Wang Jing Guo Li-Gang Fang Xin-Sheng Chang 《Environmental Earth Sciences》2012,65(6):1705-1714
The purpose of this study is to present a weighting method, integrating subjective weight with objective weight, for landslides
susceptibility mapping based on geographical information system (GIS). First, the landslide inventory, aspect, slope, proximity
to streams of drainage network, proximity to railway, proximity to road, topography, elevation, lithology, tectonic activity
and annual precipitation, including their subclasses, were taken as independent landslide causal factors. Second, objective
weights of the causal factors were calculated according to the landslide area density based on entropy weighting method, and
key factors were selected according to the rank of the objective weights. Third, trapezoidal fuzzy number weighting approach
was used to assess the sub-classes of each key factor. Finally, a case study was carried out in Guizhou province, China. A
landslide susceptibility map was created using weighted linear combination model based on GIS. Using a predicted map of probability,
the study area was classified into four categories of landslide susceptibility: low, moderate, moderate-high, and high. 相似文献
16.
Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand 总被引:2,自引:0,他引:2
Hyun-Joo Oh Saro Lee Wisut Chotikasathien Chang Hwan Kim Ju Hyoung Kwon 《Environmental Geology》2009,57(3):641-651
For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and
statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing.
Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps
of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence,
such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic
database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified
from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility
mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide
location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42%
in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover. 相似文献
17.
Landslide susceptibility zonation in Greece 总被引:7,自引:3,他引:4
The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility in Greece using a landslide inventory derived from historical archives. The effects of controlling factors on landslide susceptibility combined with multivariate statistics have been evaluated using GIS aided mapping techniques. Thousand six hundred thirty-five landslide occurrences, mainly earth slides obtained from Public Authorities archives, covering a long time period were recorded and digitally stored using a spatial relational database management system. Ten landslide predisposing factors (predictors) were identified, while digital thematic maps on the spatial distribution of those factors were generated. The correlation between the landslide locations and predictor classes was analyzed by using the Landslide Relative Frequency. R-mode factor analysis was applied to study the interrelations between predictors (independent variables) while weighting coefficients were determined. Landslide susceptibility was derived from an algorithm which modeled the influence of predictors, and a susceptibility map was compiled. The landslide susceptibility map was verified using a data set of 375 new landslide locations. It is the first comprehensive attempt to illustrate the landslide susceptibility in the total country based on the interpretation of historical data only. 相似文献
18.
Zabih Alah Rostami Seyed Ali Al-modaresi Hassan Fathizad Marzban Faramarzi 《Arabian Journal of Geosciences》2016,9(17):685
Landslides are introduced as regional movements, which influence different engineering structures such as roads, railways, and dams and cause the person’s death. Identification of landslide zones may decrease the financial losses and human injuries or deaths. This study tries to achieve a landslide susceptibility mapping in Cham-gardalan catchment by weighting the main criteria and the membership functions of fuzzy logic. For this, we applied the best relationship function between the presence and absence of landslides as well as a collection of the elements. At first, the landslide points were identified by the means of some components those of satellite images, topographical (1:50,000) and geographical (1:100,000) maps, field visits, and Google Earth software followed by the preparation of landslide distribution maps. Then, all effective landslide factors such as percentage of slope, slope aspect, height, geology, land uses, distance from roads, distance from drainages, distance from breakage, and precipitation map have been utilized in order to conduct the fuzzy analyses. Landslide susceptibility map was performed by fuzzy operators (Gamma, Product, Sum, Or, And) in the study area. After fuzzificating and weighting, the effective criteria of landslides were determined through fuzzy Gamma operators with the landaus of 0.2, 0.5, 0.8, and 0.9 and by comparing final maps for making an appropriate model of landslide susceptibility mapping. The regional susceptibility map represents the landslide-prone areas in five categories those of very low, low, moderate, high, and very high. Our results indicated that among the applied operators, Gamma with landau of 0.9 can be used as an appropriate method for mapping the landslide susceptibility due to the suitable fuzzification of given criteria based on landslide distribution maps. In addition, the elements of road, percentage of slope, distance from drainage, and geology were recognized as the most important factors for occurring the landslides. 相似文献
19.
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
20.
Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy 总被引:6,自引:3,他引:3
F. Falaschi F. Giacomelli P. R. Federici A. Puccinelli G. D’Amato Avanzi A. Pochini A. Ribolini 《Natural Hazards》2009,50(3):551-569
This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial
neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed
through statistical models (conditional analysis and logistic regression), and neural network application, in order to better
understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and
to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research
project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide
hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle
part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological,
and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means
of indirect-quantitative statistical methods and neural network software application. Experimental results from different
methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate
statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between
the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped
for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate
the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic
(ROC) curves analysis approach. 相似文献