The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil t...
The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.
The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.
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문제 정의
Government and research institutions worldwide have attempted for years to assess the hazard of landslides, estimate their risk, and show their spatial distribution. In this study, a statistical approach to estimating the susceptibility of an area to landslides using aerial photography and the GIS is presented. For the landslide susceptibility analysis, landslide location was detected using aerial photographs, and a landslide-related database was constructed for the study area of Boun and Yoimgin, Ko- ieFor the landslide susceptibility analysis, logistic multiple-regression methods were applied and validated for the study area of Youngin, Korea, using the spatial database.
가설 설정
One is that landslides are related to spatial information such as topography, soil, forest, geology, and land-use, and the other is that future landslides will be precipitated by a specific impact factor, such as rainfall or an earthquake. In this study, the two assumptions are satisfied because the landslides are related to the spatial information, and the landslides were precipitated by one cause, heavy rainfall in the Bonn and Yotmgin areas.
제안 방법
Following selection of the study area, the places where landslides had occurred in the Boun area were identified by interpretation of aerial photographs and field surveys. A map of recent landslides was developed from 1:20, 000 scale aerial photographs, in combination with the GIS, and this was used to evaluate the frequency and distribution of shallow landslides in the area. Topography, soil, forest, geology, and land-use databases were constructed as part of the analysis.
A statistical program was used to calculate the correlation of a landslide event to each factor. Firstly, all of the factors that were constructed in the database were considered, and then logistic multiple-regression coefficients of the factors were calculated. The coefficients of the logistic multiple-regression model were estimated using the maximum-likelihood method.
Where the dependent variable is binary, the logistic link function is applicable (Atkinson and Massari, 1998). For this study, the dependent variable must be input as either 0 or 1, so the method applies well to landslide occurrence possibility analysis. Logistic regression coefficients can be used to estimate odds ratios for- each of the independent variables in the model.
In this study, Geographic Information System (GIS) software, Arc View 3.2 and ARC/INFO 8.1 NT software, and the statistical software SPSS 12.0 were used as the basic analysis tools for spatial management and data manipulation.
Using these formulae, a landslide susceptibility map was made. Logistic multiple-regression analysis was performed by dividing the study area into a 5 m x 5 m sized grid, and the factors were divided into a 5 m x 5 m array and converted to an ASCII file to use in the statistical package. In the study area, the total cell number was 2, 729, 160 and the cell number where landslides occurred was 483.
The comparisons are performed using the logistic multiple-regression method at the cases of success rate and prediction rate. The success rates illustrate how well the estimators perform with respect to the leftside landslides used in constructing those estimators. The prediction rates, on the other hand, are used as measurements of how well the probability model and its estimators predict the distribution of future landslides (Chungand Fabbri, 1999).
The grid data was converted into ASCII file and then imported to the statistical program used. Then, using a logistic multiple-regression model, the spatial relationships between the landslide location and each landslide-related factor, such as topography, soil, forest, geology, and land-use, were analyzed, and a formula of landslide occurrence possibility was extracted using the relationships in the statistical program. This formula was used to calculate the landslide susceptibility index and was mapped using the grid.
The DEM has 5 m resolution. Using the DEM, the slope angle, slope aspect, and slope curvature were calculated. The topography, texture, drainage, material, and thickness of soil were acquired from the soil map, and the type, diameter, and density of forest were obtained from the forest maps.
00030). Using the coefficients and formulas (1) and (3), the other study area, Youngin, was analyzed for cross-validation of landslide susceptibility. Logistical multiple regression analysis is performed for the Youngin area.
대상 데이터
The Boun area of study had much landslide damage following heavy rain in 1998 and was selected as a suitable case to evaluate the frequency and distribution of landslides. The site lies between the latitudes 36 °25' 21" N and 36° 30' 00” N, and longitudes 127° 39' 36” E and 127° 45'00 E, and covers an area of 68.43km2. The bedrock geology of the study area consists mainly of biotite granite.
The Ybungin study area had high landslide damage after heavy rain in 1991 and was selected as a suitable case to evaluate the frequency and distribution of landslides. The site lies between the latitudes 37.14° N and 37.19° N. and longitudes 127.11° E and 127.23° E, and covers an area of 66 km2. In the study area, the landslides were mainly debris flows and shallow soil slips that occurred during 3—4 hours of high intensity rainfall, or shortly afterwards.
데이터처리
The statistic사 method used was logistic multiple-regression analysis. A statistical program was used to calculate the correlation of a landslide event to each factor. Firstly, all of the factors that were constructed in the database were considered, and then logistic multiple-regression coefficients of the factors were calculated.
이론/모형
In this study, a statistical approach to estimating the susceptibility of an area to landslides using aerial photography and the GIS is presented. For the landslide susceptibility analysis, landslide location was detected using aerial photographs, and a landslide-related database was constructed for the study area of Boun and Yoimgin, Ko- ieFor the landslide susceptibility analysis, logistic multiple-regression methods were applied and validated for the study area of Youngin, Korea, using the spatial database. Generally, the validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.
Firstly, all of the factors that were constructed in the database were considered, and then logistic multiple-regression coefficients of the factors were calculated. The coefficients of the logistic multiple-regression model were estimated using the maximum-likelihood method. In other words, coefficients that make the observed results most likely are selected.
The validation method was performed by comparison of existing landslide data and landslide susceptibility analysis results for the Bonn study area. The comparisons are performed using the logistic multiple-regression method at the cases of success rate and prediction rate. The success rates illustrate how well the estimators perform with respect to the leftside landslides used in constructing those estimators.
The prediction rate validation results have found by comparing the susceptibility calculation results and landslide occurrence locations using the logistic multipleregression method. The prediction rate validation results are divided into classes with accumulated area percentage according to landslide susceptibility index value.
Using the logistic multiple-regression method, the spatial relationship between landslide-occurrence location and landslide-related factors was calculated. The statistic사 method used was logistic multiple-regression analysis. A statistical program was used to calculate the correlation of a landslide event to each factor.
, 2002). The success rate validation is from the landslide susceptibility analysis result validated in the Boun area using the landslide occurrence locations and logistic multiple-regression methods. Therefore, strictly speaking, the success rate is not a perfect validation method.
The above procedure also was adapted for the Yoimgin area by comparing the classes obtained with the distribution in Youngin to obtain the prediction rate. The success rate validation results have obtained by comparing the susceptibility calculation resuits and landslide occurrence location using the logistic multiple-regression method. The success rate validation results are divided into classes of accumulated area ratio percentage according to the landslide susceptibility index value.
Geomorphologic, lithological, structural geologic, soil, forest, and land-use data should be available for the entire area. To apply the logistic multiple-regression method, maps relevant to landslide occurrence were constructed to a vector-type spatial database using the GIS software ARC/INFO. These included 1:5,000 scale topographic maps, 1:25,000 scale soil maps, 1:25,000 scale forest maps, and 1:50, 000 scale geological maps.
Using the logistic multiple-regression method, the spatial relationship between landslide-occurrence location and landslide-related factors was calculated. The statistic사 method used was logistic multiple-regression analysis.
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