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1.
Aykut Akgun 《Landslides》2012,9(1):93-106
The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood
ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology,
slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced
using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides
were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy
of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under
curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood
ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method
among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood
ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning. 相似文献
2.
Landslide susceptibility consists of an essential component in the day-to-day activity of human beings. Landslide incidents are typically happening at a low rate of recurrence when compared and in contrast to other events. This might be generated into main natural catastrophes relating to widespread and undesirable sound effects. Landslide hotspot area identification and mapping are used for the regional community to secure from this disaster. Therefore, this research aims to identify the hotspot areas of landslide and to generate maps using GIS, AHP, and multi-criteria decision analysis (MCDA). MCDA techniques are applied under such circumstances to categorize and class decisions for successive comprehensive estimation or else to state possible from impossible potentiality with various landslides. Analytical hierarchy process (AHP) constructively applies for conveying influence to different criteria within multi-criteria decision analysis. The causative landslide identifying factors utilized in this research were elevation, slope, aspect, soil type, lithology, distance to stream, land use/land cover, rainfall, and drainage density achieved from various sources. Subsequently, to explain the significance of each constraint into landslide susceptibility, all factors were found using the AHP technique. Generally, landslide susceptibility map factors were multiplied by their weights to acquire with the AHP technique. The result showed that the AHP methods are comparatively good quality estimators of landslide susceptibility identification in the Chemoga watershed. As the result, the Chemoga watershed landslide susceptibility map classes were classified as 46.52%, 13.83%.18.71%, 15.39%, and 5.55% of the occurred landslide fall to very low, low, moderate, high, and very high susceptibility zones, respectively. Performance and accuracy of modeled maps have been established using GPS field data and Google earth data landslide map and area under curve (AUC) of the receiver operating characteristic curve (ROC). As the result, validation depends on the ROC specifies the accuracy of the map formed with the AHP merged through weighted overly method illustrated very good accuracy of AUC value 81.45%. In general, the research outcomes inveterate the very good test consistency of the generated maps.
相似文献3.
For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression,
and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations
were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial
database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope
gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database.
Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were
calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility
maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher
prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used
to reduce hazards associated with landslides and to land-use planning. 相似文献
4.
Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications 总被引：8，自引：0，他引：8
Lulseged Ayalew Hiromitsu Yamagishi Hideaki Marui Takami Kanno 《Engineering Geology》2005,81(4):432-445
Slope instability research and susceptibility mapping is a fundamental component of hazard management and an important basis for provision of measures aimed at decreasing the risk of living with landslides. On this basis, this paper presents the result of a comprehensive study on slope stability analyses and landslide susceptibility mapping carried out in part of Sado Island of Japan. Various types of landslides occurred in the island throughout history. Little is known about the triggering factors and severity of old landslides, but for many of the recent slope failures, the slope characteristics and stratigraphy are such that ground surfaces retain water perennially and landslides occur when additional moisture is induced during rainfall and snowmelt. A range of methods are available in literature for preparation of landslide susceptibility maps. In this study we used two methods namely, the analytical hierarchy process (AHP) and logistic regression, to produce and later compare two susceptibility maps. AHP is a semi-qualitative method, which involves a matrix-based pair-wise comparison of the contribution of different factors for landsliding. Logistic regression on the other hand promotes a multivariate statistical analysis with an objective to find the best-fitting model that describes the relationship between the presence or absence of landslides (dependent variable) and a set of causal factors (independent parameters). Elevation, lithology and slope gradient were casual factors in this study. The determinations of factor weights by AHP and logistic regression were preceded by the calculation of class weights (landslide densities) based on bivariate statistical analyses (BSA). The differences between the AHP derived susceptibility map and the logistic regression counterpart are relatively minor when broad-based classifications are considered. However, with an increase in the number of susceptibility classes, the logistic regression map gave more details but the one derived by AHP failed to do so. The reason is that the majority of pixels in the AHP map have high values, and an increase in the number of classes gives little change in the spatial distribution of susceptibility zones in the middle. To verify the practicality of the two susceptibility maps, both of them were compared with a landslide activity map containing 18 active landslide zones. The outcome was that the active landslide zones do not completely fit into the very high susceptibility class of both maps for various reasons. But 70% of these landslide zones fall into the high and very high susceptibility zones of the AHP map while this is 63% in the case of logistic regression. This indicates that despite the skewed distribution of susceptibility indices, the AHP map was better to capture the reality on the ground than the logistic regression equivalent. 相似文献
5.
Işık Yilmaz 《Environmental Earth Sciences》2010,61(4):821-836
This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability
(CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas,
Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults,
drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized
difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the
last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared
by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained
from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar.
The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS
operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes
are very simple in CP model when compared with the other methods considered in this study. 相似文献
6.
Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran 总被引：1，自引：0，他引：1
Reza Talaei 《Journal of the Geological Society of India》2014,84(1):68-86
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
7.
Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models 总被引：24，自引：0，他引：24
This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning. 相似文献
8.
Statistical modelling of Europe-wide landslide
susceptibility using limited landslide inventory data 总被引：2，自引：0，他引：2
M. Van Den Eeckhaut J. Hervás C. Jaedicke J.-P. Malet L. Montanarella F. Nadim 《Landslides》2012,9(3):357-369
In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available. 相似文献
9.
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
10.
Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment 总被引：2，自引：0，他引：2
María José Domínguez-Cuesta Montserrat Jiménez-Sánchez Ana Colubi Gil González-Rodríguez 《International Journal of Earth Sciences》2010,99(3):661-674
A new method for estimating shallow landslide susceptibility by combining Geographical Information System (GIS), nonparametric
kernel density estimation and logistic regression is described. Specifically, a logistic regression is applied to predict
the spatial distribution by estimating the probability of occurrence of a landslide in a 16 km2 area. For this purpose, a GIS is employed to gather the relevant sample information connected with the landslides. The advantages
of pre-processing the explanatory variables by nonparametric density estimation (for continuous variables) and a reclassification
(for categorical/discrete ones) are discussed. The pre-processing leads to new explanatory variables, namely, some functions
which measure the favourability of occurrence of a landslide. The resulting model correctly classifies 98.55% of the inventaried
landslides and 89.80% of the landscape surface without instabilities. New data about recent shallow landslides were collected
in order to validate the model, and 92.20% of them are also correctly classified. The results support the methodology and
the extrapolation of the model to the whole study area (278 km2) in order to obtain susceptibility maps. 相似文献
11.
Dieu Tien Bui Tran Anh Tuan Harald Klempe Biswajeet Pradhan Inge Revhaug 《Landslides》2016,13(2):361-378
Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. 相似文献
12.
Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative
risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility
mapping methods, mapping unit, and scale. Although there are various methods of obtaining susceptibility maps, the efficiency
and performance of each method should be evaluated. In this study the effect of mapping unit and susceptibility mapping method
on landslide susceptibility assessment is investigated. When analyzing the effect of susceptibility mapping method, logistic
regression (LR) which is widely used in landslide susceptibility mapping and, spatial regression (SR), which have not been
used for landslide susceptibility mapping, are selected. The susceptibility maps with logistic and spatial regression models
are obtained using two different mapping units namely slope unit-based and grid-based mapping units. The procedure for investigation
of effect of mapping unit on different susceptibility mapping methods is applied to Kumluca watershed, in Bartin Province
of Western Black Sea Region, Turkey. 18 factor maps are prepared for landslide susceptibility assessment in the study region.
Geographic information systems and remote sensing techniques are used to create the landslide factor maps, to obtain susceptibility
maps and to compare the results. The relative operating characteristics (ROC) curve is used to compare the predictive abilities
of each model and mapping unit and also the accuracy is evaluated depending on the observations made during field surveys.
By analyzing the area under the ROC curve for grid-based and slope unit-based mapping units, it can be concluded that SR model
provide better predictive performance (0.774 in grids and 0.898 in slope units) as compared to the LR model (0.744 in grids
and 0.820 in slope units). This result is also supported by the accuracy analysis. For both mapping units, the SR model provides
more accurate result (0.55 for grids and 0.57 for slope units) than the LR model (0.50 for grids and 0.48 for slopes). The
main reason for this better performance is that the spatial correlations between the mapping units are incorporated into the
model in SR while this fact is not considered in LR model. 相似文献
13.
Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran 总被引：1，自引：0，他引：1
S. M. Fatemi Aghda V. Bagheri M. Razifard 《Geotechnical and Geological Engineering》2018,36(2):915-937
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
14.
Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China 总被引：2，自引：0，他引：2
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
15.
Guruh Samodra Guangqi Chen Junun Sartohadi Kiyonobu Kasama 《Environmental Earth Sciences》2017,76(4):184
There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor. 相似文献
16.
H. R. Pourghasemi H. R. Moradi S. M. Fatemi Aghda C. Gokceoglu B. Pradhan 《Arabian Journal of Geosciences》2014,7(5):1857-1878
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
17.
Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu,Turkey) 总被引：6，自引：4，他引：2
Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives
and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the
last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work
presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km2 in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed
landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies.
To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope,
aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used
to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic
sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic
regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC
(relative operating characteristics) and cosine amplitude method (r
ij
) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by
the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic
regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy
and, or, algebraic sum maps were not sufficient to provide reliable outputs. 相似文献
18.
Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya 总被引：1，自引：1，他引：0
Ranjan Kumar Dahal 《Environmental Earth Sciences》2014,71(12):5145-5164
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
19.
Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy 总被引：4，自引：0，他引：4
The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set
of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework
targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation
of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully
applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic
(ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners,
able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20%
of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other
maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression,
linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other
techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was
around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver
operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was
applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km2 characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events. 相似文献