KR20160118797A - Debris flow prediction apparatus using digital terrain model and the predicting method thereof - Google Patents

Abstract

The present invention relates to an apparatus for predicting an area in which debris flow occurs using a digital terrain model, which generates a digital terrain model by using information on a contour map of an area in which debris flow occurs, and then predicts an area in which debris flow occurs by using the generated digital terrain model, and a method using the same. The apparatus comprises: a debris flow terrain information DB storing contour map information of an area to be analyzed as basic information to predict an area in which debris flow is to be generated; a digital terrain model generating unit generating a digital terrain model by using the basic information stored in the debris flow terrain information DB; and a digital terrain model analyzing unit selecting a topographic wetness index (TWI), a stream power index (SPI), a slope length (SL) or two or more of the TWI, the SPI, and the SL by using the digital terrain model of the area to be analyzed, generated by the digital terrain model generating unit, adding the values, analyzing one or more topographic index values of the added values by locations, calculating a distribution of respective topographic index values, and drawing an area in which the debris flow is to be generated. The apparatus and the method can significantly simplify operation processing for predicting the occurrence of the debris flow, and can easily predict the generation of the debris flow by accurately predicting the generation of the debris flow only with area information and altitude information of the area to be analyzed included in the digital terrain model.

Description

TECHNICAL FIELD [0001] The present invention relates to an apparatus and method for estimating a debris flow region using a digital terrain model,

More particularly, the present invention relates to a method for predicting a meteorological terrain, and more particularly, to a method for predicting a meteorological region using a digital terrain model using a topographical information of a debris- The present invention relates to an apparatus and a method for predicting a metamorphic terrestrial region using a digital terrain model for predicting occurrence regions.

Typhoons and local storms are increasing in Korea due to global climate change.

In recent years, torrential rains in 2011, typhoon in 1998, Rusa in 2002, and cicada in 2003 caused damage to people and property, especially in landslides, especially due to landslides. Especially, in the year of 2011, there was a lot of earthquakes due to heavy rainfall in the Woomyunsan area in Seoul, and typhoon Ewonia, Billys and Ants in 2006. In 2006, it recorded rainfall more than twice that of normal (758mm), and it was reported that the property damage of 1.5 trillion won nationwide occurred in July for one month (Water Journal, 2007).

In Gangwon - do, heavy rain of 675mm took 95.4% of the total damages over 2 days, and many landslides occurred in Inje - gun and Pyeongchang - gun.

As such, it is one of the topographic and geological phenomena that cause damage to human life and infrastructure. It is irregularly occurring in the earth system. Therefore, it is necessary Do.

In addition, due to the development of advanced technology and equipment, real geospatial data related to topography and geological phenomena are becoming more and more complex. Therefore, a geographic information system (GIS) is needed to effectively detect and analyze information related to topography and geological phenomena in the flood of such data. GIS is a powerful tool for efficient storage, management and integrated analysis of vast geospatial data and serves as a support for expert decision making. However, GIS itself does not have enough functions to grasp the spatial relevance of the events occurring in large-scale multi-dimensional data or predict future events. So, we use various analysis techniques using GIS to predict future events.

Therefore, it is necessary to develop a technology that can minimize the damage of the debris flow by analyzing the geographical information based on the GIS and predicting the area where the debris flows quickly and accurately.

According to this need, Korean Patent Laid-Open Publication No. 2009-0102311 (Patent Document 1, published on 09.30., 2009) discloses a method and apparatus for generating a combination attribute by combining a plurality of layers of a numerical map and attribute information and precipitation data included in each layer A table is input to a neural network algorithm to generate a digital map that measures the occurrence of a landslide to predict a landslide occurrence area.

The neural network algorithm of Patent Document 1 is a method in which a plurality of nodes are arranged in respective layers so that they are generally completely interconnected in the next layer but not in the same layer and the output values from all layers and interlayer weights are multiplied Is used as an input value of a node to perform the training by applying the method of expanding or reducing the output of the nodes from one layer through the interlayer weight, wherein a plurality of numerical maps having attributes input for landslide prediction The landslide is estimated by calculating the output value by weighting the attribute information included in the layer of the landslide, and the landslide is predicted by comparing the predicted landslide information with the occurrence of the actual landslide to adjust the weight.

Next, Korean Patent Publication No. 10-2014-0046358 (Patent Document 2, published on Apr. 18, 2014) discloses a method of extracting items related to occurrence of landslides such as soil level, geology, , A landslide risk analysis system that derives the landslide vulnerability by applying spatial statistical methods such as frequency ratio, logistic regression, and neural network after correcting and normalizing geographical information and attribute information related to landslide occurrence based on the extracted items / RTI >

Japanese Unexamined Patent Application Publication No. 2002-148082 (Patent Document 3, published on May 22, 2002) discloses a so-called " earth-and-stone detection system ", which detects a soil stone after installing a plurality of detectors 10, .

However, in the case of the conventional technique disclosed in Patent Document 1, since the attribute table for each node of the layer included in the digital map is subjected to the arithmetic processing by applying the weight for performing the neural network algorithm to the input information of the attribute values, Have problems to be performed.

In the case of the conventional technique of Patent Document 2, since the statistical method is applied after performing the arithmetic processing for normalizing the included items, including the operation of extracting the individual items such as the soil map, the geological map, the clinical map, There is a problem that a lot of arithmetic processing must be performed.

In addition, in the case of the conventional technique disclosed in Patent Document 3, there is a problem that the occurrence of unsteady stones can not be predicted in an area where no detector is installed.

Due to the development of advanced technology and equipment, real geospatial data related to topography and geological phenomena are becoming more and more complex. Therefore, a geographic information system (GIS) is needed to effectively detect and analyze information related to topography and geological phenomena in the flood of such data. GIS is a powerful tool for efficient storage, management and integrated analysis of vast geospatial data and serves as a support for expert decision making. However, GIS itself does not have enough functions to grasp the spatial relevance of the events occurring in large-scale multi-dimensional data or predict future events. So, we use various analysis techniques using GIS to predict future events.

Therefore, it is necessary to develop a technology that can minimize the damage of the debris flow by analyzing the geographical information based on the GIS and predicting the area where the debris flows quickly and accurately.

Patent Document 1: Korean Patent Laid- 2009. 09. 30. Disclosure) Patent Document 2: Korean Patent Laid- 2014. 04. 18. Disclosure) Patent Document 3: JP-A- May 22,2002. open)

Therefore, in order to solve the problems and necessity of the above-described conventional technology, the present invention creates a digital terrain model through digital terrain analysis of a debris flow generating region based on GIS information, The results of terrestrial index analysis such as TWI (Topographic Wetness Index), SPI (Stream Power Index) and SL (Slope length) And to provide a method and apparatus for predicting a meteoroid occurrence region using a digital terrain model that makes it possible to predict a meteoroid occurrence region regardless of a region by comparing the reference value and a regional topographical index value.

According to an aspect of the present invention, there is provided an apparatus for predicting a terrestrial current generation region using a digital terrain model, the terrestrial current terrain information prediction apparatus comprising: a landmark terrain information DB storing topographical information of a region to be analyzed; A digital terrain model generation unit for generating a digital terrain model using the terrestrial current generation area prediction basic information of the analysis target area stored in the terrain digital terrain information DB; And a topographic wetness index (TWI), a stream power index (SPI), a slope length (SLI), or two or more of TWI, SPI, and SL using the digital terrain model of the analysis target area generated by the digital terrain model generation unit And a digital terrain model analyzer for analyzing at least one of the selected landmark values by location to calculate a distribution of each of the landmark values and deriving a terrestrial destruction prediction area.

And the digital terrain model includes the area and altitude information of the area to be analyzed.

The digital terrain model analyzing unit may include a TWI analyzing unit for calculating and calculating a distribution of TWI values that are topographical indicators reflecting the role of the terrain including the slope stability from the digital terrain model, An SPI analyzer for analyzing the digital terrain model to calculate and calculate a distribution of SPI values, which are topographical indicators indicating stability in a target area including erosive power of a river, And an SL analyzer for analyzing the digital terrain model to calculate and calculate a distribution of SL (slope length), which is a topographic index indicating the length of the slope, and to predict a location of the unsteady flow.

는 한 점을 통해 흐르는 누적된 위쪽 사면 면적이고,

는 그 점에서의 경사도(Degree)인 경우

에 의해 TWI의 분포를 도출하도록 구성될 수 있다.
The TWI analyzing unit,

Is the cumulative upper slope area flowing through a point,

Is a degree of degree at that point (Degree)

Gt; TWI < / RTI >

는 한 점을 통해 흐르는 누적된 위쪽 사면 면적이고,

는 그 점에서의 경사도(Degree)인 경우

에 의해 SPI의 분포를 도출하도록 구성될 수 있다.
The SPI analyzing unit,

Is the cumulative upper slope area flowing through a point,

Is a degree of degree at that point (Degree)

Gt; SPI < / RTI >

에 의해 SL의 분포를 도출하도록 구성될 수 있다.
The SL analyzing unit may be configured such that, when the elevation height of the two points is h and the inclination angle is?

Gt; SL < / RTI >

The digital terrain model analyzing unit may include an integrated analyzing unit for calculating an integrated analysis value for determining whether or not the occurrence of the earth stone is generated by integrally applying the TWI, SPI, or SL values calculated by the TWI analysis unit, the SPI analysis unit, And the like.

The digital terrain model analyzing unit classifies the terrestrial stars according to the standard deviation of the distributions of the terrain index values to classify whether the terrestrial stars are generated in each step, And estimating the debris flow generation area based on the derived reference grade.

In order to achieve the above object, there is provided a method of predicting a terrestrial current generation area using a digital terrain model of the present invention, comprising the steps of: Terrestrial digital terrain information D Digital terrain model generation process to generate digital terrain model using basic information of terrestrial occurrence area for analysis target area; And at least one of the TWI distribution, the SPI distribution, the SL distribution, or the sum of two or more of TWI, SPI, and SL selected from the digital terrain model generated in the digital terrain model generation process, And a digital terrain model analysis process for predicting the area.

The terrestrial-destruction-occurring-area-prediction-based basic information includes aerial photograph information, terrestrial occurrence location information, GIS spatial information data including altitude information by location, and topographic map information.

)과 경사도(

)를 이용하여 사면 안정성을 나타내는 TWI의 분포를 산출하고 토석류발생지역을 예측하는 TWI분석과정; 상기 분석대상 지역의 사면 면적(

)과 경사도(

)를 이용하여 사면의 침식력을 나타내는 SPI의 분포를 산출하고 토석류발생지역을 예측하는 SPI분석과정; 또는 상기 분석 대상 지역의 두 위치의 고도차인 높이(h)와 경사각(θ)을 이용하여 사면길이인 SL의 분포를 산출하고 토석류 발생지역을 예측하는 SL분석과정(S45); 중 하나 이상의 분석 과정을 포함하여 이루어질 수 있다.
In the digital terrain model analysis process, the slope area of the area to be analyzed

) And slope (

) To calculate the distribution of TWI showing slope stability and to predict the location of the meteorite; The slope area of the area to be analyzed (

) And slope (

) To calculate the distribution of SPI which represents the erosion power of the slope, Or an SL analysis process (S45) for calculating a distribution of slopes SL by using height difference h and slope angle? And / or < / RTI >

The above-mentioned terrestrial-destruction-area prediction is classified according to the standard deviation of the distributions of the above-mentioned landmark values by classifying the occurrence of the terrestrial rocks at each stage, and comparing with the actual terrestrial- And then estimating the area where the debris flow is generated based on the derived reference grade.

In the digital terrain model analysis process, the TWI, SPI, and SL calculated in each of the TWI analysis unit, the SPI analysis unit, and the SL analysis unit are selected from two or more of the TWI, SPI, and SL, This is an integrated analysis process that predicts the occurrence area of the unstable rocks based on the derived reference class after classifying the occurrence of the unstable rocks at each step, As shown in FIG.

As described above, according to the present invention, it is possible to accurately predict the occurrence of debris flow only by the area information and the altitude information of the analysis target area included in the digital terrain model, thereby remarkably simplifying the calculation process for prediction of the occurrence of the debris flow, .

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a functional block diagram of a terrestrial destruction region prediction device using the digital terrain model of the present invention. FIG.
2 is a conceptual diagram of a TWI (Topographic Wetness Index) and SPI (Stream Power Index) analysis method using a digital terrain model.
3 is a conceptual diagram of a Slope Length (SL) analysis method.
4 is a flowchart showing a process of a method for predicting a terrestrial destruction area using the digital terrain model of the present invention.
5 is a view showing an aerial photograph of an analysis target area for predicting a meteorite occurrence area.
6 is a view showing a digital terrain model including altitude information of an analysis target area generated using an aerial photograph of an analysis target area by the digital terrain model generation unit 115;
FIG. 7 is a view showing a TWI distribution predicted and displayed by a TWI analysis process (S41) during the above-described process, and a region where the undisturbed region is generated; FIG.
FIG. 8 is a diagram showing an SPI distribution predicted and displayed by the SPI analysis process (S43) during the process described above, and showing a region where the undrawn stones are generated.
FIG. 9 is a diagram showing an SL distribution in which the area where the earths are produced is predicted and analyzed by the SL analysis process (S45) in the above-described process. FIG.
Fig. 10 shows the distribution of the sum of the TWI value and the SPI value at the corresponding positions of the derived TWI distribution and the SPI distributions on the aerial photograph.
11 shows the distribution of the sum of the TWI value, the SPI value and the SL value at the corresponding positions of the derived TWI distribution, the SPI distribution and the SL distributions on the aerial photograph.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings showing embodiments of the present invention.

In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

The embodiments according to the concept of the present invention can be variously modified and can take various forms, so that specific embodiments are illustrated in the drawings and described in detail in the specification or the application. It is to be understood, however, that the intention is not to limit the embodiments according to the concepts of the invention to the specific forms of disclosure, and that the invention includes all modifications, equivalents and alternatives falling within the spirit and scope of the invention.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ",or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

FIG. 1 is a functional block diagram of an apparatus 100 for predicting a terraced area using a digital terrain model of the present invention.

As shown in FIG. 1, the apparatus for predicting a terraced area 1 according to the present invention includes an input unit 101, an output unit 103, a display unit 105, a communication unit 107, a terrain type terrain information DB 110, A digital topography model generating unit 115 and a topographic wetness index (TWI) analyzing unit 121, a SPI (Stream Power Index) analyzing unit 123, an SL (Slope Length) analyzing unit 125, And a digital terrain model analysis unit 120 including the digital terrain model analysis unit 120. In the description of the present invention, 'terrain indicator' is defined as a combination of two or more of TWI, SPI, SL and TWI, and SPI and SL.

The input unit 101 is configured to be able to input GIS information for predicting the unburst zone, terrestrial occurrence information, user control commands, and the like. The input unit 101 includes a reader for inputting a large amount of data such as a magnetic reader and an optical reader, And an input port of a user interface means for inputting a user interface command.

The output unit 103 outputs the analytical values of the digital terrain model analysis unit 120 and the predicted result values of the unburst locality predicting unit 130 to the printer port, the magnetic recording apparatus, the optical recording apparatus And an output port for outputting data to the recording apparatus.

The display unit 105 displays an LCD display for displaying information on the driving state of the terrestrial-destressed-area generating apparatus 100, the results of the analysis performed by the digital terrain model analyzing unit 120, And a port for transmitting image data to a display device such as a device.

The communication unit 107 includes at least one of wired communication means such as a LAN card for performing data communication with the outside or a wireless communication means such as a wireless communication card such as an optical system such as an RF system such as WIFI or Bluetooth or an infrared port do.

The terrain type terrain information DB 110 may include at least one of an aerial photograph information of a region to be analyzed, a terrestrial occurrence location information, a terrestrial occurrence related factor information, GIS spatial information data including altitude information by location, And stores it.

The digital terrain model generation unit 120 generates a digital terrain model using the aerial photograph information of the analysis target area and the altitude information for each location stored in the terrain model information DB 110.

The digital terrain model analyzer 120 performs the TWI and the SPI using the digital terrain model of the analysis target area generated by the digital terrain model generator 115. [ And slope length (SL) values are analyzed by location, and integrated processing is performed to detect an area which is greater than or equal to the reference value of the occurrence of the undrained stones.

The TWI analysis unit 121 of the digital terrain model analyzer 120 analyzes the distribution of TWI values, which is a factor reflecting the terrain's role in the diversity of hydrological conditions such as soil moisture, groundwater flow, and slope stability from the digital terrain model And calculates it.

The SPI analyzing unit 123 analyzes the digital terrain model to indicate the erosion power of the river, and calculates and calculates the distribution of the SPI values as a factor indicating the stability in the target area.

The SL analyzer 125 analyzes the digital terrain model and calculates and calculates the distribution of SL (Slope Length), which is a topological index indicating the slope length.

The integrated analysis unit 127 integrates TWI, SPI, and SL values calculated by the TWI analysis unit 121, the SPI analysis unit 123, and the SL analysis unit 125 to calculate an integrated analysis Lt; / RTI >

FIG. 2 is a conceptual diagram of a TWI (Topographic Wetness Index) and SPI (Stream Power Index) analysis method using a digital terrain model, and FIG. 3 is a conceptual diagram of a SL (Slope Length) analysis method.

The TWI analyzer 121, the SPI analyzer 123, and the SL analyzer 125 will be described in more detail with reference to FIGS. 2 and 3. FIG.

The above-described TWI is calculated by the following equation (1).

는 한 점을 통해 흐르는 누적된 위쪽 사면 면적이고,

는 그 점에서의 경사도(Degree) 이다.here

Is the cumulative upper slope area flowing through a point,

Is the degree at that point.

)을 반영하고, 물의 사면 아래로 움직이려는 중력의 경향(

)을 나타낸다. 물의 침투는 주로 침투율, 간극압, 토성강도에 대한 영향 같은 물질의 특성에 좌우된다. 이러한 TWI 분포는 시각적 해석을 위해 표준 편차 값을 기준으로 등급화된 후 도시된다.
The TWI in Equation (1) is a topographic index that reflects the diversity of hydrological conditions such as topographic soil moisture, groundwater flow, and slope stability. Specifically, the TWI index indicates the tendency of water to accumulate at any point in the water body

), And the tendency of gravity to move down the slope of the water (

). Penetration of water mainly depends on the properties of the material, such as permeability, pore pressure, and impact on the strength of the soil. This TWI distribution is shown after being graded based on the standard deviation values for visual interpretation.

Next, the SPI is calculated by the following equation (2).

The indicator indicated by the SPI is used to indicate the spatial distribution of the soil function.

As described above, the SPI is considered as a factor that measures the erosion power of the river and contributes to the stability in the analysis target area. This SPI distribution is also shown after being graded based on standard deviation values for visual interpretation, such as TWI distribution.

Next, as shown in FIG. 3, the SL calculates the altitude difference h between the two positions and the inclination angle? By applying Equation (3).

The distribution of SLs calculated as described above is also shown after being graded based on the standard deviation value for visual analysis.

The TWI analyzing unit 121 and the SPI analyzing unit 123, the SL analyzing unit 125 and the unifying analyzing unit 127 calculate the sum of the TWI distribution, the SPI distribution, the SL distribution, and the TWI distribution, After deriving the distribution, the sum of the TWI distribution, the SPI distribution, the SL distribution, the TWI distribution, the SPI distribution, and the SL distribution is derived for each visual analysis, The standard deviation is added to the minimum value of the surface once, and the step is added to the first step, the second step, and the second step.

In this case, since the geographical index values applied in the present invention are different relative values by region, they are derived for each region to be analyzed and given a rating based on the standard deviation value. Then, a grade value As a result, it is possible to predict regions having a grade higher than or equal to the corresponding value as the occurrence region of the soil, and the respective grades are different according to the region to be analyzed.

FIG. 4 is a flowchart showing a process of a method for predicting a terrazzo generation area using the digital terrain model of the present invention.

As shown in FIG. 4, the method for predicting a meteorological terrain generation region using the digital terrain model of the present invention includes the steps of selecting a region to be analyzed S10, a soil-and-soil topography DB construction process S20, a digital terrain model generation process S30, The digital terrain model analysis process S40 includes a TWI analysis process S41, an SPI analysis process S43, an SL analysis process S45, and an integration process S40. The digital terrain model analysis process S40 includes a model analysis process S40 and an analysis result display process S50. And a detailed process of the analysis process S47.

4, the process of the method for predicting the occurrence area of the meteoroids using the digital terrain model of the present invention will be described in more detail.

First, an analysis target region selection process for predicting the occurrence area of the undisturbed region is performed (S10).

Thereafter, after the region to be analyzed for predicting the undisturbed region is selected, topographic information of the selected region to be analyzed is input as the undisturbed region-based prediction basic information through the input unit 101 and stored as a structured data structure, A terrain type terrain information DB construction step S20 for constructing the information DB 110 is performed.

In the present invention, the information used for the meteoric area requires only the numerical data to be calculated from the topographic map. However, for the display of the terrestrial-destruction area, the terrestrial terrain topography information DB 110 stores the aerial photograph information of the analysis target area, the terrestrial occurrence location information, the GIS spatial information data including the altitude information by location, And can be further stored as information.

FIGS. 5 to 11 are diagrams showing the comparison of the occurrence of soil erosion with that of the actual undisturbed area after analyzing the occurrence of soil erosion with respect to the area of the right field.

FIG. 5 is a view showing an aerial photograph of the Umyeon mountain area as an analysis target area for predicting a meteorite occurrence area, and is an example of an aerial photograph of an analysis target area stored in the above-described soil erosion topography information DB construction process (S20).

As described above, the digital terrain model generation unit 115 extracts the altitude information from the topographic map information stored in the terrain model information DB 110 to generate a digital terrain model after the digital terrestrial terrain information DB 110 is constructed. The terrain model generation process S30 is performed. 6 is a diagram showing a digital terrain model including altitude information of an analysis target area generated by using the aerial photograph of the analysis target area by the digital terrain model generation unit 115. The generated digital terrain model includes an analysis target area As a high-level distribution so that area and altitude information can be grasped.

After the digital terrain model is generated by the digital terrain model generation process (S30), the digital terrain model analysis unit 120 receives the generated digital terrain model and calculates a digital terrain model Perform model analysis. The digital terrain model analysis may be used to predict the occurrence of a metamorphic rock by deriving the distribution of each TWI, SPI, or SL value, or to perform an integrated analysis using one or more values of the TWI, SPI, And performs an analysis process for improving the accuracy of the predicted value.

)과 경사도(

)를 이용하여 사면 안정성을 나타내는 TWI의 분포를 산출하고 분포의 표준편차를 등급화한 후 실제 토석류 발생 지역들을 포함하는 표준편차의 등급을 도출하여 토석류발생지역을 예측하는 TWI분석과정(S41)과, 분석대상 지역의 사면 면적(

)과 경사도(

)를 이용하여 사면의 침식력을 나타내는 SPI의 분포를 산출하고 분포의 표준편차를 등급화한 후 실제 토석류 발생 지역들을 포함하는 등급을 도출하여 토석류발생지역을 예측하는 SPI분석과정(S43)과, 분석 대상 지역의 두 위치의 고도차인 높이(h)와 경사각(θ)을 이용하여 사면길이인 SL의 분포를 산출하고 분포의 표준편차를 등급화한 후 실제 토석류 발생 지역들을 포함하는 등급을 도출하여 토석류 발생지역을 예측하는 SL분석과정(S45)을 포함한다.Specifically, in the digital terrain model generation process (S30), the slope area of the region to be analyzed

) And slope (

), A TWI analysis process (S41) for calculating a distribution of TWIs showing slope stability and classifying the standard deviation of the distribution, deriving a class of standard deviation including actual land masses, , Slope area of analysis area (

) And slope (

An SPI analysis process (S43) for calculating the distribution of SPIs representing the erosion power of the slope, classifying the standard deviation of the distribution, deriving a class including the actual landslide occurrence regions, The distribution of the slope length SL is calculated using the height difference (h) and the slope angle (θ) of the two locations in the analysis area, and the standard deviation of the distribution is graded. And an SL analysis process (S45) for predicting the area where the undrawn seawater is generated.

FIG. 7 is a diagram showing a TWI distribution predicted and displayed by the TWI analysis process (S41) during the above-described process, and the area where the undisturbed area is estimated.

In the case of FIG. 7, the standard deviation value is classed as the grade interval for the TWI distribution of the undisturbed region for the prediction of the undisturbed region using the TWI values derived by applying Equation (1) . That is, in the case of FIG. 7, the TWI values are in the range of 0.42348 to 21.93650, and when compared with the undisturbed areas, the TWI value in the area where the undisturbed area is greater than 3.44606 indicates that the TWI value is 3.44606 or more. Predicted areas were marked in red.

FIG. 8 is a diagram showing an SPI distribution which is analyzed by the SPI analysis process (S43) during the above-described process and is predicted and displayed in the region where the earth is present.

In the case of FIG. 8, the standard deviation value is applied to the SPI distribution of the undisturbed region for prediction of the undulation area using the SPI values derived by applying Equation (2) . That is, in the case of FIG. 8, the SPI values are in the range of 0 to 10.46179, and when compared with the undisturbed area, the SPI value of the undisturbed is 6.68189 or more, As shown in Fig.

FIG. 9 is a diagram showing an SL distribution which is analyzed by the SL analysis process (S45) during the above-described process and predicted and displayed the area where the earth is present.

In the case of FIG. 9, a standard deviation value is applied to the SL distribution of the undisturbed region for prediction of the undulation area using the SL values derived by applying Equation (3) . That is, in the case of FIG. 9, the SL values showed a distribution of values of 0 to 108.17172, and the SL value of the debris flow was 1.61897 or more, The area is marked in red.

In addition, the digital terrain model generation process S30 may be performed by selecting two or more of the TWI distribution, the SPI distribution, and the SL distribution to improve the accuracy of the predicted value of the undisturbed generation, and then adding the standard deviation of the distribution of the summed values An integrated analysis process (S47) for performing an integrated analysis for predicting the occurrence area of the undrained stones can be selectively performed by deriving a grade including the actual unfavorable regions of the stones after being graded at intervals of the grade interval.

10, the distribution of the sum of the TWI value and the SPI value at the corresponding positions of the derived TWI distribution and the SPI distributions is shown on the aerial photograph. In the case of FIG. 10, the sum of the TWI value and the SPI value is in the range of 1.03305 to 23.88321. As a result of comparison with the undisturbed area, the undisturbed area occurs in the area of 10.24301 or more. Therefore, the sum of the TWI value and the SPI value is 10.24301 or more The area was marked in red as a predicted area for the occurrence of the earths.

11, the TWI distribution, the SPI distribution, and the distribution of the sum of the SPI value and the SL value at the corresponding positions of the SL distributions are shown on aerial photographs. 11, the sum of the TWI value, the SPI value and the SL value is 1.52473 to 115.65656. As a result of comparison with the undisturbed area, The area with the sum of the values of 10.45047 or more is indicated in red as the predicted area for the occurrence of the devastating earth.

As shown in FIG. 5 to FIG. 11, in the case of the Woomyunsan area, the range of the upper three levels of the six stages in the case of TWI coincides well with the distribution of the unstable rocks. In other words, when the step is classified by the standard deviation, it takes the value of the upper three steps (the step with a large standard deviation among the distributions) to predict the location of the earth.

In the case of SPI, the range of the first stage value of the five levels agrees well with the distribution of the unstimulated ones. In other words, when the step is classified by the standard deviation, it takes the upper first step value to predict the area where the debris flows.

In the case of SL, the range of the upper 3 levels of 7 levels agrees well with the distribution of the unsteady sediments. In other words, when the step is classified by the standard deviation, it takes the upper three steps and predicts the location of the earth.

When the SPI and TWI values are summed up, the range of the upper two levels of the six stages agrees well with the distribution of the unsteady flow. In other words, when the step is classified by the standard deviation, the upper two step values are taken to predict the area where the debris flows.

When SPI, TWI, and SL values are all summed, the range of the upper two levels of the six levels agrees well with the distribution of unsteady flow. In other words, when the step is classified by the standard deviation, the upper two step values are taken to predict the area where the debris flows.

In the case of forecasting of debris flow, the distribution of TWI distribution, SP distribution, SL distribution, TWI distribution, SPI distribution, and SL distribution is derived for each digital terrain model of the analyzed area. The TWI distribution, the SPI distribution, the SL distribution, the TWI distribution, the SPI distribution, and the SL distribution are derived as the sum of one or more of the topographic indices, and the standard deviation is repeatedly added to the minimum value of each topological index And then compare it with the value of the actual debris flow area, it is possible to derive a reference step for predicting the debris flow area.

As described above, the TWI analysis process (S41), the SPI analysis process (S43), and the SL analysis process (S45) in the digital terrain model analysis process (S40) are performed irrespective of the order. The integrated analysis process S47 may include one or more of TWI, SPI, or SL values calculated and derived after at least one of the TWI analysis process S41, the SPI analysis process S43, and the SL analysis process S45. To predict the area where the earth is formed.

Next, the analysis result display process S50 of the process shown in FIG. 4 is performed after performing the TWI analysis process S41, the SPI analysis process S43, the SL analysis process S45 or the integrated analysis process S47 Distribution of standard deviation values of TWI, SPI, and SL results, distribution of standard deviation values of two or more selected TWI, SPI, or SL values, And displays it.

As described above, according to the present invention, it is possible to accurately predict the occurrence of debris flow only by the area information and the altitude information of the analysis target area included in the digital terrain model, thereby remarkably simplifying the calculation process for debris flow generation prediction, So that it can be easily performed.

The present invention can be broadly applied to a disaster response technology industry field to prepare for a disaster due to various flows of accumulated water such as an avalanche as well as a debris.

100: Debris locating area prediction device 120: digital terrain model analysis unit

Claims (13)

A landmark terrain information DB for storing the topographic map information of the analysis target area as the destructive region prediction basic information;
A digital terrain model generation unit for generating a digital terrain model using the terrestrial current generation area prediction basic information of the analysis target area stored in the terrain digital terrain information DB; And
A topographic wetness index (TWI), a stream power index (SPI), a slope length (SLI), or two or more of TWI, SPI, and SL are selected using the digital terrain model of the analysis target area generated by the digital terrain model generation unit And a digital terrain model analyzing unit for analyzing at least one of the landmark values of the distribution of the sum values and calculating the distribution of the respective landmark values, A Model for Prediction of Devastated Land Area.
The digital terrain model according to claim 1,
And an area and an altitude information of the area to be analyzed.
The digital terrain model analysis unit according to claim 1,
A TWI analysis unit for calculating and calculating a distribution of TWI values that are topographical indicators reflecting the role of the terrain including slope stability from the digital terrain model,
An SPI analyzer for analyzing the digital terrain model to calculate and calculate a distribution of SPI values, which are topographical indicators indicating stability in a target area including erosive power of a river, And
And an SL analyzer for analyzing the digital terrain model to calculate and calculate a distribution of SL (Slope Length), which is a topographic index indicating the slope length, An Estimation System for the Devastated Rocks Using.
4. The apparatus of claim 3,

Is the cumulative upper slope area flowing through a point,

Is a degree of degree at that point (Degree)

Wherein the TWI distribution is derived from the digital terrain model.
4. The apparatus of claim 3,

Is the cumulative upper slope area flowing through a point,

Is a degree of degree at that point (Degree)

Wherein the distribution of the SPI is derived by the digital terrain model.
4. The apparatus of claim 3,
When the elevation height of the two points is h and the inclination angle is?

To derive the distribution of SL by using the digital terrain model.
4. The digital terrain model analyzing apparatus according to claim 3,
And an integrated analyzer for calculating an integrated analysis value for determining whether or not a malodorous soil is generated by integrally applying the TWI, SPI, or SL values calculated by the TWI analysis unit, the SPI analysis unit, or the SL analysis unit, A meteorological terrain model prediction system using the digital terrain model.
The digital terrain model analyzing apparatus according to claim 1,
The classifications are given based on the standard deviation of the distributions of the landmark values and compared with the class of the actual occurrence area of the earths, Wherein the digital terrain model is predicted using the digital terrain model.
A soil erosion topographical information DB construction process for storing the topographic map information of the analyzed area as the debris flow origin prediction basic information;
Terrestrial digital terrain information D Digital terrain model generation process to generate digital terrain model using basic information of terrestrial occurrence area for analysis target area;
From the digital terrain model generated in the digital terrain model generation process, at least one of the TWI distribution, the SPI distribution, the SL distribution, or the distribution of the sum of the TWI, the SPI, and the SL, And a digital terrain model analysis process for predicting a region where the terrestrial current is generated.
[12] The method of claim 9,
Wherein the geomorphological information includes at least one of aerial photograph information, terrestrial occurrence location information, GIS spatial information data including altitude information by location, and topographic map information.
10. The digital terrain model analysis method according to claim 9,
The slope area of the area to be analyzed (

) And slope (

) To calculate the distribution of TWI showing slope stability and to predict the location of the meteorite;
The slope area of the area to be analyzed (

) And slope (

) To calculate the distribution of SPI which represents the erosion power of the slope, or
An SL analysis process for calculating a distribution of slopes SL by using height difference h and slope angle? The method comprising the steps of: analyzing at least one of at least one of the following:
12. The method of claim 11,
A classification is made on the basis of the standard deviation of the distributions of the above landmark values to classify whether or not the occurrence of the unsteady stone is generated in each step, And estimating the area where the meteoroids are generated based on the grade.
The digital terrain model analysis method according to claim 11,
The distribution of the combined analysis values obtained by selecting two or more of the TWI, SPI, and SL calculated in each of the TWI analysis unit, SPI analysis unit, and SL analysis unit is graded based on the standard deviation, And an integrated analysis process for deriving a meteorite origin generation standard in a specific area by comparing the actual meteoroid origin occurrence area with an actual land mass generation area and then predicting a meteorite origin occurrence area based on the derived reference class, A Method for Predicting Debris Flows Using Digital Terrain Model.

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