KR101785148B1 - Empirical Formula for the Mobilization Criteria between Landslide and Debris Flow and Generation Method thereof - Google Patents

Abstract

A landslide metamorphic model and its generation method are presented. A method for generating a landslide metamorphic model of a landslide is based on a numerical map of a landslide or a site of occurrence of a meteorite and collects data through at least one of the land use map, the clinical map, the forest location soil map, the geological map, ; Analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable; And generating a landslide metamorphic model using the input parameters for statistical analysis to determine whether the occurrence of landslides is metastable.

Description

BACKGROUND OF THE INVENTION Field of the Invention The present invention relates to a landslide and a debris flow model,

The following examples relate to landslide metamorphic metamodels and methods for their generation. More particularly, the present invention relates to a landslide metamorphic model and its generation method for improving prediction performance by suggesting a transition standard from landslide to meteoroid.

Since the Industrial Revolution, there has been rapid industrial development around the world, resulting in increased emissions of greenhouse gases such as water vapor, carbon dioxide, and methane. Greenhouse gases are a major cause of global warming and since the late 19th century the temperature of the earth has been rising. During the first 100 years of the 1900s, the average surface temperature of the Earth's surface increased by approximately 0.8 ° C, and rose to 0.6 ° C for the past 30 years, especially from the 1970s to the 2000s (see Board on Atmospheric Sciences and Climate (BASC) Choices, The National Academies Press, Wahsington, DC). This continuous rise in temperature affects the frequency of abnormal weather events such as heavy rainfall, heavy snowfall, storms, and increases in precipitation.

Korea is located in the East Asia region, and the East Asia region has high climate variability due to its complex geographical characteristics. In recent years, the frequency and intensity of anomalous climatic events have increased due to climate change, and the amount of precipitation has also gradually increased (National Meteorological Research Institute, 2012).

Figure 1 is a graph illustrating an example of estimating temperature and precipitation using a climate change model. More specifically, referring to FIG. 1A, a result of analyzing the future prospect of the temperature of Korea using the climate change model can be confirmed. Referring to FIG. 1B, the future prospect of precipitation in Korea is analyzed using the climate change model You can see the results.

As a result of analyzing the future prospect of Korea 's temperature and precipitation using the climate change model, it can be predicted that it will rise more rapidly than the future temperature and precipitation increase.

Due to rising temperatures and precipitation, the occurrence of heavy rain has also increased sharply. The intensity of heavy rainfall occurred between June and September when rainfall was mostly concentrated, and the frequency of heavy rainfall more than 50mm in 1 hour increased 5.1 times in the 70s and 2.4 times in average in the 2000s (Meteorological Administration, 2011 ). If a lot of rainfall is concentrated locally in a short period of time, natural disasters such as landslides and floods are often accompanied.

In Korea, about 70% of the total land area is mountainous, and landslides occur throughout the country during the summer season where rainfall is concentrated. As a result of landslide occurrence from 1998 to 2012, landslide occurred between June and October, followed by the largest number of landslides in August (4,810 cases), followed by July (3337 cases ), September (2118 cases), June (80 cases), and October (5 cases). Considering the increasing incidence of heavy rainfall and increasing precipitation, the damage caused by landslides is expected to increase more and further, the study on the prediction and response of landslide accidents is becoming more important.

Fig. 2 is a view showing the landslide and the flow of debris; Fig.

Referring to FIG. 2, the landslide is collapsed by natural causes (such as torrential rains, earthquakes, and volcanic activity) or artificial causes (excavation, construction activities, etc.) The phenomenon of falling is collectively called. These landslides can be classified into various types depending on the type of destruction, the type of constituent material, the water content, and the flow rate.

Table 1 shows an example of classifying landslide types as follows.

In Table 1, Varnes (Varnes, DJ (1978), Slope movements, type and processes, In: Schuster, RL, Krizek, RJ, Landslides Analysis and Control, Slides (rotational & translational) and debris flow patterns in the Varenes landslide classification in Korea are shown in Fig. And the collapse of the rock fall form often occurs.

Below is the interpretation of landslides and landslides.

Here, the landslide means that the soil material that has been destroyed after the occurrence of the planar or arc destruction by the rainfall on the mountain slope does not flow down to the mountain area. Also, debris flow refers to the debris flow when soil material destroyed by landslides is mixed with water and flows down to the mountain.

The causes of debris flow are classified into several types, of which landslides are most frequently transferred to debris (Johnson, 1984; Iverson et al., 1997). Likewise, in Korea, several field surveys were conducted to observe the beginning of the occurrence of debris flow. This study was carried out by Park, DW (2014), "Simulation of Landslides and Debris Flows at Regional Scale using Coupled Model", MS Thesis, Korea Advanced Institute of Science and Technology, Korea) classified the approaches into three categories.

First, there is a method of analyzing landslide-to-metamorphic transition using geotechnical factors. A study on the developmental conditions of debris flow using the Bingham model (Johnson, 1965; 1970; Johnson and Rodine, 1984), a study on the mechanism of metamorphic development of Takahashi (1978; 1981a) And Iverson's (1997) landslide-to-metamorphic transition mechanism are the geotechnical approaches.

The second method is to analyze the geomorphological characteristics of landslide-to-meteor transition. 22, " Factors Explaining the Spatial Distribution of Hillslope Debris Flows ", Mountain Research and Development, Vol. 22, < RTI ID = 0.0 & No. 1, pp. 32-39), using terrain parameters such as altitude, slope, slope direction, slope curvature, distance from channel, contributing area, topographic index and geology, vegetation and land use, . Griswold and Iverson (2008) proposed three approaches to the metamorphic transition: (1) an upslope source area> 103 m ² , (2) slopes> 30 °, (3) slopes exceeding 30 ° within 100m ² > 95%. Chen and Yu (Chen, CY, Yu, FC, 2011, "Morphometric analysis of debris flows and their source areas using GIS, Geomorphology, Vol. 129, pp. 387-397) , stream power index (SPI), relative stream power (RSP), terrain characterization index (TCI), shape factor, topographic wetness index (TWI), relief ratio, effective watershed area And the geomorphological indices such as.

The third is an analysis method using the difference of hydrological characteristics of landslides and soils. (Caine, 1980; Cannon and Ellen, 1985), a method of analyzing the hydrological characteristics of the metamorphic rocks using physical-based interpretation, using empirical analysis of the relationship between the occurrence of debris and the intensity and duration of rainfall (Campbell, 1975), and so on.

However, all of the above methods are not models that users can easily use, and the necessary factors are complex because they need to be obtained by specialized knowledge and theory.

The embodiments describe a landslide metamorphic model and a method of generating the landslide metamorphic model, and more specifically, a description of a landslide metamorphic metamodel model and a method of generating the model of landslide metamorphic metamodel to improve the prediction performance by suggesting a metamodel transition from landslide to metamorphosis do.

The embodiments can be applied easily by the user by applying the data of the geomorphological factor, geological factor, and the land use extracted from the area where the actual earthmoving occurred to the statistical analysis and presenting the landslide- And to provide an improved landslide metamorphic model and method of generating the same.

In a method of generating a landslide-based meteorological model according to an embodiment, at least one of a land use map, a clinical map, a forest location soil map, a geological map, a satellite photograph, and location information Collecting the data through the second network; Analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable; And generating a landslide metamorphic model using the input parameters for statistical analysis to determine whether the occurrence of landslides is metastable.

The method may further include the step of verifying statistical significance of the landslide metamorphic model using a statistical verification procedure, wherein the data includes at least one of a slope, a relative elevation, a slope curvature, Top Curves, Plan Curvature, Horizontal Slope Curvature, Upslope Contributing Area, Sediment Transport Capacity Index, SPI, Terrain Characterization Index (TWI), Topographic Wetness Index).

According to another embodiment of the landslide metamorphosis model, at least one of the land use map, the clinical map, the forest location soil map, the geological map, the satellite photograph, and the location information A data collecting unit for collecting data; An input variable determining unit for analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable; A transition model generating unit for generating a landslide-unsteady transition model by using the input parameters for statistical analysis to determine whether the occurrence of the landslide is transferred to the undecomposed rocks; And statistical significance verifying means for verifying the statistical significance of the landslide metamorphic model using the statistical verification procedure.

According to the embodiments, it is possible to provide a description of a landslide-unfavorable metamorphic model and a method of generating the metamorphic metamorphic model that improve the prediction performance by suggesting a transition standard from landslide to meteoroid.

According to the embodiments, the user can easily apply the data by applying the data of the geomorphological factors, geological factors, and the land use extracted from the area where the actual earthmoving occurred to the statistical analysis and presenting the landslide- It is possible to provide a landslide-based unsteady transition model with improved prediction performance and a method of generating the model.

Figure 1 is a graph illustrating an example of estimating temperature and precipitation using a climate change model.
Fig. 2 is a view showing the landslide and the flow of debris; Fig.
FIG. 3 is a schematic diagram illustrating a landslide-unconformity model according to an embodiment; FIG.
4 is a view showing a schematic diagram of a vertical slope curvature and a horizontal slope curvature (profile curvature) according to an embodiment.
5 is a block diagram illustrating a process of acquiring the terrain data from the digital map according to an embodiment.

Hereinafter, embodiments will be described with reference to the accompanying drawings. However, the embodiments described may be modified in various other forms, and the scope of the present invention is not limited by the embodiments described below. In addition, various embodiments are provided to more fully describe the present invention to those skilled in the art. The shape and size of elements in the drawings may be exaggerated for clarity.

The embodiments describe a landslide metamorphic model and a method of generating the landslide metamorphic model, and more specifically, a description of a landslide metamorphic metamodel model and a method of generating the model of landslide metamorphic metamodel to improve the prediction performance by suggesting a metamodel transition from landslide to metamorphosis do.

In the following, we apply statistical analysis of geomorphologic factors, geologic factors, and land use data extracted from actual debris areas (for example, Woomyunsan, Yongin, Gwangju, Yeoju, , And a landslide-to-meteor transition standard with improved prediction performance that can be easily applied by users.

FIG. 3 is a schematic diagram illustrating a landslide-unconformity model according to an embodiment; FIG.

3, the landslide and unsteady transition map model 300 includes a data collection unit 310, an input variable determination unit 320, a transition model model generation unit 330, and a statistical significance verification unit 340 Lt; / RTI >

The data collecting unit 310 may collect data through at least one of the land use map, the clinical map, the forest location soil map, the geological map, the satellite photograph, and the location information based on the numerical map of the area where landslides or landslides are generated .

The input variable determining unit 320 may determine an input variable by analyzing a probability of at least one variable that affects the result of the collected data.

The transition model model generating unit 330 can generate a landslide-unfavorable transition model that determines whether the occurrence of a landslide is transferred to a meteoroid using statistical analysis through input parameters.

The statistical significance verifier 340 can verify the statistical significance of the landslide metamorphic model using the statistical verification procedure.

Data collection for landslide and / or landslide areas can be done by collecting digital maps, land use maps, clinical maps, forest location maps, geological maps, and satellite photographs in the area. In addition, it can be obtained by collecting position information data (GIS coordinates) of landslides and landslides. It is also possible to collect geotechnical property data by performing site investigation, sampling and laboratory tests.

And, in order to generate the landslide metamorphic model, it is possible to develop the landslide metamorphic model using statistical analysis by analyzing the sensitivity of each variable to the landslide metamorphic metamorphic standard. The landslide metamorphic model can be completed by verifying the statistical significance of the model using statistical validation procedures.

More specifically, the landslide metamorphic model can be generated by the following landslide metamorphic model generation method.

A method for generating a landslide metamorphic model of a landslide is based on a numerical map of a landslide or a site of occurrence of a meteorite and collects data through at least one of the land use map, the clinical map, the forest location soil map, the geological map, ; Analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable; And input variables to the statistical analysis to generate a landslide metamorphic transition model that determines whether the occurrence of landslides is transferred to the metamorphic rocks.

The method further includes the step of verifying statistical significance of the landslide metamorphic model using the statistical verification procedure, wherein the data includes at least one of a slope, a relative elevation, a slope curvature, a vertical slope curvature Plan Curvature, Horizontal Slope Curve, Upslope Contributing Area, Sediment Transport Capacity Index, SPI, Terrain Characterization Index, Topographic Wetness Index (TWI) Or more.

Also, collecting the data may include collecting at least one of a land use map, a clinical map, a forest location soil map, a geological map, and a satellite photograph based on a numerical map of a landslide or a site where the soil is generated; Collecting location information data (GIS coordinates) of a landslide or landslide occurrence area; And a step of collecting geotechnical property data by performing at least one of field investigation, sampling, and indoor experiment on lands where landslides or landslides are generated.

Therefore, by applying the data of geomorphologic factors, geologic factors, and land use extracted from the area where actual debris flow occurred to the statistical analysis, it can be easily applied by the user by presenting the landslide - It is possible to provide an improved landslide metamorphic model and method of generating the same.

In the following, we describe the landslide metamorphic model and its generation method in more detail.

Studies on the analysis of metasomatic transition after landslide have been carried out by various approaches. Through this transition standard, it is possible to predict the landslide damage zone from the predicted point of landslide occurrence to the metamorphic rocky area efficiently and efficiently. In Korea, there is a lack of research on transition standards in Korea, so it is necessary to develop appropriate transition standards in Korea.

Accordingly, the landslide metamorphic model and the method of generating the landslide metamorphic rocks according to one embodiment can provide the transition standard using the statistical analysis by analyzing the geomorphological characteristics and geological characteristics of the metamorphic rocks. In this case, since the data acquisition is easier than the physical basis analysis method, it can be considered as a topographical factor and the characteristics of various factors can be analyzed at the points where the landslide can not be transferred to the landslide and the landslides are transferred to the landslide.

Slope failures must occur at the site before the occurrence of the earthquake. For this, enough slope is required. Slope inclination is one of the most crucial factors in the metamorphic study, and many researchers analyzed the effect of slope inclination on the occurrence of unsteady stone. Takahashi (1981b) and Rickenmann and Zimmermann (1993) reported that deeper stones can occur when the slope is greater than 15 °, and generally, debris flows occur in the range of 27-38 °. In addition, Van Dine (1996) reported that the slope of 25 ° or more was required due to the occurrence of debris flow. Lorente et al. (2002) reported that the most frequent occurrences of debris are in the 25-30 ° slope, while Brayshaw and Hassan (2009) .

Thus, the landslide metamorphic model and its generation method have a wide range of landslide slopes that can generate debris, and slope slopes can be used to develop transition standards suitable for Korea.

After the landslide occurs, it should have sufficient potential energy to mix the soil and the water and flow down the mountain. Park (2014) used the elevation analysis to analyze the relationship with the occurrence of soil erosion, but there is a disadvantage that it is difficult to apply it to the area other than the analysis area because there are differences in altitude at each elevation.

Therefore, it is possible to analyze the relationship between the landslide metamorphic model and its generation method by using the difference between the altitude of each point in the watershed and the altitude after the exit point of each watershed is set to the lowest altitude. Relative elevation.

Takahashi (1981b) and Rickenmann and Zimmermann (1993) presented sedimentation potential, water inflow, and slope slope with three critical criteria related to the occurrence of debris flow. Among these, slope curvature can be used to determine sedimentation potential (Horton et al., 2008).

Thus, the landslide metamorphic model and its generation method can be classified into two types using the slope curvature, the vertical curvature in the vertical direction with respect to the slope direction, and the horizontal curvature profile in the slope direction (profile curvature) , And the starting point of debris flow.

4 is a view showing a schematic diagram of a vertical slope curvature and a horizontal slope curvature (profile curvature) according to an embodiment.

Referring to FIG. 4, a slope curvature and a slope curvature are schematically shown. The slope curvature may be a combination of two curvatures. All three curvatures can be calculated using Curvature, one of the spatial analysis functions in GIS programs, using the Digital Elevation Model (DEM).

Upslope contributing area is an indicator of the amount of water entering each cell. It is possible to obtain the flow accumulation value of each cell by the spatial analysis function in the GIS program using the DEM, and then multiplying it by the size of the cell.

On the other hand, sufficient amount of water is required to cause soil saturation and surface water discharge at the corresponding point in order to cause the earthquake. Therefore, Upslope contributing area can be used as an important factor in the study of metamorphic metamorphosis such as landslide metamorphic model and its generation method.

The shape of the topography has a great influence on the concentration and migration of surface water and groundwater, and the hydrological-geomorphic factor can be used as an important factor in the analysis of landslide and the occurrence of landslides (Oh, HJ (2010), "Landslide Detection and Landslide Susceptibility Mapping using Aerial Photos and Artificial Neural Networks ", Korean Journal of Remote Sensing, Vol.26, No. 1, pp. 47-57).

STI, SPI, TCI, and TWI are the factors that can consider the surface and groundwater flow depending on geomorphological factors. Sediment transport capacity index (STI) was derived based on the unit stream power theory of Moore and Wilson (1992) (Chen and Yu, 2011).

The soil loss is affected by slope length (L) and slope slope (S), and STI can be used as slope length - slope (LS) factor in the soil loss equation (RUSLE: Revised Universal Soil Loss Equation) have. The equation of soil loss can be expressed as follows.

Here, A _S can be an upper slope contributing area (Upslope contributing area), and? Can be a slope slope.

In addition, SPI (Stream Power Index) is a factor for estimating the extent of slope erosion due to water flow, and TCI (Terrain Characterization Index) can be a factor for sediment transport. The TWI (Topographic Wetness Index) is used to evaluate the influence of geomorphologic characteristics in the hydrological process. The larger the value, the higher the probability of metastable transition.

These SPI, TCI, and TWI expressions can be expressed as follows.

Here, A _S can be an upper slope contributing area (Upslope contributing area),? Can be a slope slope, and k can be a slope curvature.

As described above, the slope, the relative elevation, the slope curvature, the vertical slope curvature, the horizontal slope curvature, the upper slope contributing area, Data can be collected using 10 topological factors such as STI (Sediment Transport Capacity Index), SPI (Stream Power Index), TCI (Terrain Characterization Index) and TWI (Topographic Wetness Index). That is, data can be collected using at least one of the ten geographical factors.

For example, in the case of the landslide metamorphic model and its generation method according to one embodiment, the analysis was applied to the metropolitan area and the Gyeonggi-do area where the population is most concentrated, and the Gangwon-do area where the mountain area is distributed the most in the whole country. Gyeonggi-do Gwangju City, Icheon City, Yeoju City and Gangwon-do Chuncheon City, which frequently have landslide disasters recently, have been selected as data extraction areas. .

In order to collect data of the above 10 geomorphological factors, geographic information based on GIS (Geographic Information System) can be used for data collection and analysis efficiency.

5 is a block diagram illustrating a process of acquiring the terrain data from the digital map according to an embodiment.

Referring to FIG. 5, in the landslide-to-terrestrial linkage analysis process, topographic features such as slope inclination, relative altitude, slope curvature, vertical slope curvature, horizontal slope curvature, upper slope contribution area, STI, SPI, TCI, Data may be used. All of the mentioned topographic data are available using a numerical map, for example using a 1: 5000 digital map produced by the Geographical Information Service.

Horton et al. (2013) and Park (2014) used a digital elevation model (DEM) with a resolution of 10 m for landslide and debris flow analysis and noted the suitability of using a 10-m resolution DEM.

Accordingly, the DEM can be used as a cell having a size of 10 m, for example, and the resolution of the remaining data can also be made as 10 m. Ten factors can be collected for a total of 467 sites from 239 sites transferred from landslides to undisturbed lands and 228 sites not transferred to landslides.

Uncertainties in soil and rocks are inevitably involved, making it difficult to accurately determine engineering properties, geological conditions, and design constants. It is difficult to interpret stability from such distributed data or to obtain representative values for design. Therefore, probabilistic analysis methods can be applied.

The probabilistic analysis method is a method of analyzing the probability characteristics of the variables assuming the input variables to be used as random variables and analyzing them using probability theory. The probabilistic analysis is generally performed by analyzing the mean value, the standard deviation, and the dispersion coefficient, and analyzing the probability distribution characteristic of the ground parameter that determines the probability distribution function considering the distribution characteristics of the data. And a probability analysis to perform the analysis.

In the landslide metamorphic model and the method of generating the landslide metamorphic rocks according to one embodiment, the case of transition from landslide to the metamorphic rock and the case of no transition can be considered as dependent variables. These dependent variables can be entered as a value of 1 when the transition occurs from landslide to debris flow (occurrence), and a value of 0 if the transition from landslide to debris flow does not occur (not occurring).

Slope inclination, relative altitude, slope curvature, vertical slope curvature, horizontal slope curvature, Upslope contributing area, STI, SPI, TCI, TWI) affecting the landslide - Is an independent variable. Statistical analysis techniques that can be applied when there are many independent variables and have two levels of dependent variables can be adopted. In the following, statistical analysis techniques will be described in more detail.

(Regression analysis result)

Regression analysis, which is a statistical analysis technique, can be used to suggest a metamorphic metamorphic model.

If the value of the dependent variable is limited to two cases, such as presence or absence of landslide metamorphic metamorphism, it can be classified into a metamorphic group (metamorphic) and a metamorphic group (metamorphic). The ten independent variables are all continuous variables and can be grouped by linear combination of independent variables.

Regression analysis is based on the assumption that the dependent variable is Y (= 0,1), and the independent variable is considered as p discriminant variable. To convert this to a linear function, we can perform logit transform as shown in the following equation.

Using the above equation, the probability that each case belongs to a group having a dependent variable of 1 can be expressed by the following equation.

The probability of belonging to a group with a dependent variable of 0 can be expressed by the following equation.

In the case of regression analysis, the curves themselves are nonlinear, and unlike discriminant analysis, they do not have to satisfy the assumptions of the normal distribution of independent variables and the covariance matrix between the two groups (Zou, G. (2004), "A Modified Poisson Regression Approach to Prospective Studies with Binary Data ", American Journal of Epidemiology, Vol. 159, No. 7, pp. 702-706). In addition, regression analysis can be applied by converting the independent variable to a dummy variable even if it is a categorical variable as well as a continuous variable (Hur, M., Yang, KS (2013), "Multivariate data analysis" Publishing Corporation). Thus, regression analysis does not have to meet several assumptions, such as statistical analysis, such as statistical analysis.

Correlation analysis can be performed to analyze the degree of influence of 10 independent variables on the landslide metamorphic transition. For the analysis, for example, a statistical package for the social sciences (SPSS) 21, which is a statistical analysis program, can be used.

Table 2 shows the statistics for the 10 variables.

Table 3 shows the correlation analysis results.

All the factors except the slope slope influenced the landslide metamorphism in the range of significance 1%, and the degree of influence was in the order of SPI, STI, and vertical slope curvature. Also, the slope curvature, vertical slope curvature, and horizontal slope curvature were highly correlated, and SPI, STI, TPI, and TCI factors were highly correlated with each other.

Table 4 shows the significance results of the model according to one embodiment.

As the significance of the model, the significance probability is less than 0.05, it can be seen that there is more than one independent variable that affects the result. In order to secure the significance of the independent variables and to eliminate the multi-collinearity, only one of the 10 independent variables can be chosen (Lee, IH (2014), "Easy flow regression analysis", Hannarae Publishing Corporation.) .

Table 5 shows statistical analysis results according to one embodiment.

As a result of the analysis, it can be seen that all the independent variables used in the model analysis give meaningful results to the dependent variables. In addition, since the VIF (Dispersion Exponent Index) value between independent variables is less than 10, it can be seen that there is no multi-collinearity, which is statistically significant (Lee et al., 2014).

Therefore, using the coefficients of the independent variables in Table 5, the landslide meteorological equation transition from landslide to debris flow can be expressed as follows.

Here, if the transition probability P value is larger than 0.5, it means that the transition is to the metamorphic rocks. If the transition probability P is smaller than 0.5, it means that the transition does not occur. In addition, the difference of the multiplication ratio (Exp (B)) between the independent variables was not large, and the multiplication ratio of the relative altitude and STIxSPIxTWI was 1 or more, which is proportional to the dependent variable.

The Hosmer & Lemeshow test is necessary to determine the fit of the regression model.

Table 6 shows the test results of Hosmer & Lemeshow.

In order to verify the significance of the transition model derived by regression analysis, the test results of Hosmer & Lemeshow can be derived as shown in Table 6. Since the probability of significance is larger than 0.05, the landslide meteorological model of the landslide is statistically significant It can be seen that

Table 7 shows the fitness of the landslide meteorite prediction model using the regression analysis model.

As shown in Table 7, it can be seen that 93.4% of the areas with no landslides and 91.2% of the areas with no landslides have a very reliable result of 92.3%.

(Discriminant analysis result)

In addition to the regression analysis, another statistical analysis technique, discriminant analysis, can be used to present a metamorphic model.

If the value of the dependent variable is limited to two cases, such as presence or absence of landslide metamorphic metamorphic rocks, it can be classified into metamorphic and nonmetallic. In addition, the 10 independent variables are all continuous variables. Discriminant function analysis can be used for classifying and predicting groups through linear combination of independent variables. That is, the discriminant analysis can be applied when the independent variable is continuous data and the dependent variable is categorical data. In the discriminant analysis, the discriminant function can be derived as shown in the following equation so that the classification error that classifies the group is minimized.

Where Z is the discriminant function, b ₀ is the intercept, b _n is the discriminant coefficient, and X _n is the independent variable. It is the process of discrimination analysis that finds discriminant function that best compares the group while using multivariate independent variables comprehensively. That is, the discriminant score of each group case is calculated through the discriminant function formula, and the average of the discrimination scores of the cases belonging to one group is the average of the group.

The discriminant analysis tests whether there is a difference in the average value of each group. The larger the difference of the average value of each group, the greater the discriminative power of the discriminant function. In order for the discriminant analysis results to be statistically reliable, several assumptions must be met. The independent variables applied to the discriminant analysis should be normally distributed, the dispersion - covariance matrix between the two groups should be heterogeneous, and there should be no excessive collinearity between independent variables.

It is possible to perform discriminant analysis using SPSS for the development of landslide metamorphic metamorphic model, and it can be confirmed whether the factor and data used satisfy the assumption condition of discriminant analysis.

Table 8 shows the normality test results of independent variables.

The significance of Kolmogorov-Smimov and Shapiro-Wilk of all the independent variables is less than 0.05, so that independent variables are not normally distributed (Shapiro, SS, Wilk, MB (1965), "An analysis of variance test for normality (complete samples), Biometrika, Vol.52, pp. 591-611.). In order to analyze the homogeneity of the inter-group variance-covariance matrix, the probability of significance was found to be smaller than 0.05 in the Box's M statistic, which is also an important assumption condition of the discriminant analysis. It was not satisfied with homogeneity.

Although independent variables did not have a normal distribution and homogeneity of group-to-group variance-covariance matrix was not satisfied, stepwise discrimination analysis was performed using SPSS. Table 5 shows the result of discrimination analysis.

Slope, Relative Elevation, Plan Curvature, Upslope Area, and SPI factors can be selected as a result of stepwise control of the input independent variables.

Table 9 shows the coefficient values of the metric and non-metric models.

Based on the results of Table 9, the equation for the transition can be expressed as follows.

Similarly, based on the results of Table 9, the equation for the non-transition can be expressed as follows.

Therefore, it can be said that the independent variables applied to the equations (10) and (11) are substituted into the equations (10) and (11)

Table 10 shows the result of discrimination analysis.

In order to verify the significance of the transition model derived by the discriminant analysis method, Wilk's lambda test results can be derived as shown in Table 9. In this case, because the probability probability is smaller than 0.05, it can be seen that the landslide metrology model can be applied statistically significantly (Kim, H. (2013), "Statistical analysis by self-study", Hakjisa Publishing Corporation).

Table 11 shows the fitness of the landslide meteoric transition prediction model using the discriminant analysis model.

As shown in Table 11, the model fit of the developed model is shown, which shows a very reliable result of 91.6% overall.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, controller, arithmetic logic unit (ALU), digital signal processor, microcomputer, field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing apparatus may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (3)

Collecting data through at least one of a land use map, a clinical map, a forest location soil map, a geological map, a satellite photograph, and a location information based on a numerical map of a landslide or a site where the landslide is generated;
Analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable; And
A step of generating a landslide metamorphic transition model to determine whether the occurrence of landslides is transferred to a metamorphic rock using statistical analysis through the input variables
Lt; / RTI >
The data includes a slope, a relative elevation, a slope curvature, a vertical slope curvature, a horizontal slope curvature, an upper slope contributing area, an STI Sediment Transport Capacity Index (SPI), Stream Power Index (SPI), Terrain Characterization Index (TCI), and Topographic Wetness Index (TWI)
The step of generating a landslide-based unsteady transition metric model for determining whether the occurrence of landslides is transferred to a meteoroid using statistical analysis through the input variables includes:
The collected data affecting the landslide metamorphic rocks are set as independent variables and the case of transition from landslide to metamorphic rocks and landslides to metamorphic rocks are set as dependent variables and the statistical analysis technique, To generate the above landslide metamorphic model
A method of generating a landslide metamorphic model. The method according to claim 1,
Verifying the statistical significance of the landslide metamorphic model using the statistical verification procedure
Further comprising:
The step of verifying the statistical significance of the landslide metamorphic model using the statistical validation procedure comprises:
In order to analyze the degree of influence of the above independent variables on the landslide metamorphic standard, correlation analysis was performed using statistical analysis program, and the geomorphological characteristics and geological characteristics of the metamorphic sites were analyzed. Providing a model
A method of generating a landslide metamorphic model. A data collection unit for collecting data through at least one of land use map, clinical map, forest location soil map, geological map, satellite photograph, and location information based on a numerical map of a landslide or landslide occurrence area;
An input variable determining unit for analyzing a probability of at least one variable that affects a result of the collected data to determine an input variable;
A transition model generating unit for generating a landslide-unsteady transition model by using the input parameters for statistical analysis to determine whether the occurrence of the landslide is transferred to the undecomposed rocks; And
A statistical significance verifying section for verifying the statistical significance of the landslide metamorphic model using the statistical verification procedure
Lt; / RTI >
The data includes a slope, a relative elevation, a slope curvature, a vertical slope curvature, a horizontal slope curvature, an upper slope contributing area, an STI Sediment Transport Capacity Index (SPI), Stream Power Index (SPI), Terrain Characterization Index (TCI), and Topographic Wetness Index (TWI)
Wherein the transition model generating unit comprises:
The collected data affecting the landslide metamorphic rocks are set as independent variables and the case of transition from landslide to metamorphic rocks and landslides to metamorphic rocks are set as dependent variables and the statistical analysis technique, To generate the above landslide metamorphic model
A landslide metamorphic model generation system.

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