KR20160104788A - Decision making system corresponding to volcanic disaster - Google Patents

Abstract

The present invention relates to a decision-making support system for coping with a volcano disaster. A decision-making process of the decision-making support system for coping with a volcano disaster comprises: a simulation module including an algorithm through which a decision maker selects the most similar scenario in a scenario database during a volcanic eruption; a disaster current condition module of showing spatial information through a GIS system by using the result obtained through the algorithm; a module extracting damage estimation information from a disaster prediction and disaster estimation database, and statistically analyzing the same; and a module for the decision maker to establish a response strategy based on a situation response database, thereby quickly and accurately coping with a volcano disaster.

Description

{DECISION MAKING SYSTEM CORRESPONDING TO VOLCANIC DISASTER}

The present invention relates to a decision support system for ash settlement, more specifically, a diffusion simulation of volcanic ash due to a volcanic eruption in order to effectively cope with a volcanic disaster, Decision support system.

Natural disasters such as earthquakes, tsunamis, and heavy rainfall occur frequently due to environmental changes such as global warming. However, disasters such as volcanic eruptions have been recognized as a relatively safe area because there is almost no possibility of occurrence in Korea. However, the opinions of scholars and academics who are raising the danger of volcanic eruption in Baekdu Mountain have been increasing recently. Specifically, the Baekdusan volcano has been erupted 10 times since the big explosion in the 900s, and it has been observed that 10 to 15 earthquakes occur every month since 2002, There is a huge magma chamber that is classified as a high-risk active volcano. In this way, Korea is interested in the extent and extent of damage caused by the volcanic eruption due to the circumstances in which the explosion of Baekdu Mountain volcano is being sympathized by various experts and the fact that the scale of the damage is more than imagined. And the visualization technology of the diffusion model of volcanic ash is considered as a very important technology. In particular, the Republic of Korea is about 500 to 600 km away from Mt. Paektu, so it is expected to suffer from damage caused by heavy ash, though it is not affected by proximity disasters. It is expected to suffer from traffic obstacles and air disturbances, agricultural and livestock damage, And it is expected to have serious impact on the public health such as the environmental damage and the respiratory system. Therefore, the need for information integration and response system construction is increasing.

However, in order to cope with volcanic ash, it is common to analyze the various conditions of volcanic ash diffusion during volcanic eruption and to predict the extent and extent of damage by spreading volcanic ash by visualizing the result. In order to visually visualize the mass spectral analysis results of lattice form by time zone, which is necessary for analysis of volcanic ash, it is necessary to apply LOD (Level Of Detail) algorithm as a key element. Recent 3-D visualization technology using this algorithm It is emerging as an important technology for realizing GIS application field, virtual reality, as well as disaster simulation. However, when the visualization technique using the LOD algorithm is used, since the analysis result is very large data, there is a problem that the visualization efficiency is drastically deteriorated due to the limitation of the computer memory.

Therefore, for example, in Korean Patent Laid-Open Publication No. 2014-0110575, Korean Patent Laid-Open Publication No. 2014-0110575 discloses "LOD-based ash diffusion model visualization method, in which the ash result data is converted into a normalized three- A step of preprocessing, a step of constructing a pyramid correlation based on the amount of data, a step of adjusting the expression unit by giving detail of the particles at each level, a step of checking a hierarchical structure Constructing a three-dimensional spatial partitioning date of the ash; and creating an index to optimize the data access speed. &Quot;

Another method for preparing volcanic disasters is disclosed in Korean Patent Laid-Open Publication No. 2014-0110590 proposed by the applicant of the present invention, in which "the collection of volcanic ash, volcanic ash, flood / advection, A database storing information of geological information system (GIS) in a volcanic hazard disaster occurrence expected area, and a database storing volcanic ash and volcanic ash of the expected volcanic area, based on the collected information and the GIS information of the volcanic area, A display unit for displaying a result calculated through the simulation module as a three-dimensional image, and a display unit for displaying a result of the simulation according to the elapsed time, And a decision module for countermeasures. It has proposed a system for determining "

However, the above-described conventional technology merely suggests a method of visualizing the diffusion model of volcanic ash due to a volcanic eruption or suggesting a decision method based on the prediction of volcanic damages. In particular, in the process of predicting the diffusion of volcanic ash, Since many ash particles are affected by the atmospheric conditions, there is a problem that it takes a considerable amount of time to obtain accurate diffusion simulation results. This conventional method has a problem in that it can not be predicted not only in the early stage of a disaster but also in an effective response , The inventors of the present invention have recognized that it is not possible to provide a quick decision making method for a volcanic disaster.

Patent Document 1: Korean Patent Laid-Open Publication No. 2014-0110575 Patent Document 2: Korean Patent Laid-Open Publication No. 2014-0110590

SUMMARY OF THE INVENTION Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and it is a primary object of the present invention to provide a process for predicting the diffusion of volcanic ash due to volcanic eruption, And to provide a decision support system that responds quickly to the problem.

Another object of the present invention is to provide a new volcanic ash solution decision support system which performs simulation of volcanic ash diffusion for volcanic ash prediction and corrects the simulation results.

The present invention may also be directed to accomplishing other objects that can be easily derived by those skilled in the art from the overall description of the present specification, other than the above-described and obvious objects.

In order to accomplish the above object, the present invention provides a decision support system for ash solution, comprising:

a) a simulation module containing an algorithm for decision makers to select the most similar scenarios in the scenario database in the event of a volcanic eruption;

b) a disaster status module showing spatial information through GIS system as a result of the algorithm;

c) module for extracting and analyzing damage estimation information from disaster prediction and disaster estimation database; And

d) decision-making process of decision support system for ash-resistant solution composed of modules for decision-maker to construct a countermeasure strategy based on the situation response database.

According to another configuration of the present invention, the simulation module can be applied to various types of disasters such as ash, fireball, volcanic flood / advection, and volcanic earthquake through explosion prediction scenario-based simulation based on the explosion type and explosion information in the volcanic explosion And the simulation results of the Korean countermeasures system are provided.

According to another configuration of the present invention, the disaster status module displays the time, the 3D graphic, and the disaster state GIS system over time by avoiding the volcanic ash, the fossil fuel, the volcanic flood / And information about the damage situation.

According to another constitution of the present invention, the damage estimation module compares the damage status of ash, phlogop, volcanic flood / advection, and volcanic earthquake by time with a statistical system based on a table and a chart, And to provide information by estimating economic damage.

According to another embodiment of the present invention, the situation-handling module displays the response procedures of the volcanic ash, fossil fuels, volcanic flood / advection, and volcanic earthquakes according to the damage, Is to propagate the situation to each relevant organization after constructing a response strategy.

According to another embodiment of the present invention, the simulation module maps an ash distribution area by an ash image processing designing method.

According to still another aspect of the present invention, the ash image processing designing method is characterized by mapping an ash distribution area by modeling by using one of Pixel Matching or Interpolation or a combination thereof.

The ash decision support system of the present invention constructed as described above is constructed to construct a database of the results of the ash diffusion simulation for various explosion types and weather conditions and to search similar modeling at the early stage of the ash occurrence and apply it immediately to the corresponding system In addition, at the same time, diffusion simulation for the current weather state is performed to replace the similar modeling result applied prior to the system at the time of completion, and to convert the result to the actual modeling result, As the waiting time for prediction disappears and the modeling of the actual modeling is completed, it is possible to carry out disaster countermeasures successively with more accurate prediction results, enabling quick and accurate response to volcanic disasters. In addition, in order to predict and cope with the conventional volcanic disaster, the simulation of the diffusion of volcanic ash due to the volcanic eruption is generated in units of several kilometer grid units, which is not suitable for visualization processing. However, the present invention proposes a new method for correcting the simulation result generated in units of several km, thereby providing improved accuracy and operation speed as compared with the conventional method, thereby solving the above-mentioned conventional problems.

1 is a block diagram illustrating a business process of the ash solution support system according to a preferred embodiment of the present invention.
FIG. 2 is a detailed block diagram of a simulation module constituting the ash-resistant solution support system according to a preferred embodiment of the present invention.
3 is a detailed block diagram of a disaster status module that constitutes a decision support system for a volcanic ash solution according to a preferred embodiment of the present invention.
4 is a detailed block diagram of a damage estimation module that constitutes a decision support system for a volcanic ash solution according to a preferred embodiment of the present invention.
5 is a detailed block diagram of a situation response module constituting a decision support system for a volcanic ash solution according to a preferred embodiment of the present invention.
FIG. 6 is a screen for confirming the ash diffusion path obtained by the ash settlement countermeasure decision support system according to the preferred embodiment of the present invention.
FIG. 7 is a screen showing a mapping process of the ash diffusion region by the ash solution countermeasure decision support system according to the preferred embodiment of the present invention.
FIG. 8A is a comparative view of the ash diffusion model showing the result of designing according to the ash image processing designing method. FIG.
FIG. 8B shows a program for storing the resultant image data in the server through the generation of the ash damage area DB and the interpolation processing.
FIG. 9 is a page showing a 2D diffusion model of the diffusion of volcanic ash and analysis results of occurrence of regional disasters as a map and a bar graph.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, it is needless to say that the scope of the present invention is not limited thereto.

In this specification, the present embodiments are provided to provide a complete disclosure of the present invention and to fully disclose the scope of the invention to a person having ordinary skill in the art to which the present invention belongs. It is only defined by the claims. Accordingly, in some embodiments, well known components, well known operations, and well-known techniques are not specifically described to avoid an undesirable interpretation of the present invention.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In the present specification, the singular forms include plural forms unless otherwise specified in the specification. Also, components and acts referred to as " comprising (or comprising) " do not exclude the presence or addition of one or more other components and operations.

1 is a block diagram illustrating a business process of the ash solution support system according to a preferred embodiment of the present invention.

As shown in FIG. 1, in the decision support system for volcanic eruption response according to the present invention, when the volcanic eruption is caused by ash (for example, volcanic ash, volcanic ash, volcanic flood / (Hereinafter referred to as " file ").

The main modules for the decision-making process of the decision support system according to the present invention are composed of four major modules. In the first volcanic eruption, the decision maker chooses the most similar scenarios in the scenario database. The simulation module includes the second, the disaster status module that shows the spatial information through the GIS system, the third disaster prediction, and the disaster estimation database. A module for extracting and analyzing the estimated information, and a module for building decision strategies based on the situation response database. Details of each module are as follows.

First, the simulation module simulates the ash response system for various types of disasters such as volcanic ash, pyroclastic flow, volcanic flood / advection, and volcanic earthquake through the simulation based on the explosion prediction scenario based on the explosion type and explosion information in the volcanic explosion Results. The structure of the module may be as shown in FIG.

Second, the Disaster Status Module shows the time, the 3D graphics, the disaster situation GIS system, and the damage prediction information and the damage situation information from the volcanic ash, fossil fuels, volcanic floods / advectures and volcanic earthquakes. Show. The detailed structure can be as shown in Fig.

Third, the damage estimation module compares the damage status of volcanic ash, volcanic rocks, volcanic floods / advectures, and volcanic earthquakes by time and statistical system based on charts and charts, Damage to buildings, damage to industrial facilities, damage to agricultural land). The main structure of the damage estimation module may be as shown in FIG.

Fourth, the Situation Response Module displays the response procedures of the volcanic ash, volcanic ash, volcanic flood / advection, and volcanic earthquakes according to the damage, time, and region according to the situation response database, It spreads the situation to each relevant organization. The detailed structure of the module can be as shown in FIG.

The first simulation module according to the present invention searches for similar scenarios and can be performed through analysis of scenario variables and DB design specifications. Specifically, the program for predicting the damage of volcanic ash during the eruption of Baekdusan volcano consists of a WRF model for producing a ground model and a Fall3D model for prediction of volcanic ash diffusion. In the WRF model, the volcanic ash distribution model (Fall3D) is produced and the volcanic ash is spread according to the weather information in Fall3D. WRF is divided into WPS and ARW. WPS preprocesses ROW weather data, and ARW processes preprocessed weather data to produce a ground model. In order to carry out simulation of damage prediction of volcanic ash, the parameter values for each model should be set in namelist.wps, namelist.input, and filename.inp for the WRF model and the Fall3D model.

Input variables of WRF and FALL3D models can be divided into user variables and system variables. User variables mean setting information that user must perform input for operation of WRF and FALL3D model. The setting information does not affect the simulation result, such as setting information, and means setting information that is not changed once set. In detail, a user variable is a variable to which a user inputs a value for operation of the program, and is divided into a variable variable and a fixed variable. An input variable is a variable input by the user. It means information that can be changed every time when the simulation is performed. Fixed variable is a variable input by the user for performing the simulation. If the value is set once, it means.

Next, as a variable impact analysis and weighting measure, variables input to the program for similar scenario retrieval require as much data as presented in the input variables. Since the variables of the initial setting are diverse and their meaning is difficult, it is difficult to set values unless they are experts in related fields. Therefore, variables should be classified and simplified so that as many non-experts as possible can input them.

 User Input Variables Variables whose values continuously change each time the program is run include simulation dates, ejection volumes, volcanic grades, etc. Variables that are hardly changed when the program fixed variables are set once are, for example, And the range of the weather model. The system setting variables for operating the system variable program include, for example, an output storage path and whether or not debugging is supported. The user inputs only the user variable for executing the program, It should be based on volcanic information determined by the committee. Table 1 below lists the scenario expected variables to be applied to the system.

Item variable Variable type unit Variable Description range Minimum value Maximum value volcano
differentiation
variable Magma
type Index value - Volcanic eruption
Occurring
Types of magma Rhyolite
Basalt One 2 Volcanic eruption
type Index value - Volcanic
type 1. Explosive
2. Non-explosive One 2 Location of differentiation Index value - Volcanic
location Caldera
Outside the caldera One 2 Volcano type Index value - Classification of volcanoes
type Hawaiian
Strombolian
Vulcanian
Pel
Plinian
Ultra-Plinian
Supervolcanic One 7 Explosion index Index value - Volcano Explosion Index
Index value 0. 0-effusive
1-gentie
2-explosive
3-severe
4-cataclysmic
5-paroxysmal
6-colossal
7-super-colossal
8-mega-colossal 0 8 Minute playing
Height number m Volcanic eruption
Occurring
Min Playing height Min Playing height
Up to 60,000m
Till 0 60,
000 season number - Typical pressure arrangements of the four seasons, consideration of wind speed (most probable high probability, high vulnerability) Spring average
Summer average
Fall average
Winter average
Unusual (airborne) One 8 Ash
density number g / cm3 Volcanic eruption
Occurring
Density of volcanic ash 0.5 to 3.3 0.5 3.3 Ash
size number mm Volcanic eruption
Occurring
Size of ash 10 to 2 mm 0.001 2 volcano
Disaster
Occur
Whether Ash
Occur true /
false - Volcanic eruption
Ash generation
Whether 0. false-no occurrence
1. True-occurring 0 One Firewood
Occur true /
false - Volcanic eruption
Fireball generation
Whether 0. false-no occurrence
1. True-occurring 0 One Volcanoes
Occur true /
false - Volcanic eruption
Volcanic flow
Whether 0. false-no occurrence
1. True-occurring 0 One Volcanic
Flood occurrence true /
false - Volcanic eruption
Volcanic flood
Whether 0. false-no occurrence
1. True-occurring 0 One Volcanic
Earthquake true /
false - Volcanic eruption
Volcanic earthquake
Whether 0. false-no occurrence
1. True-occurring 0 One

For each variable, the contents of the results are clearly different, and the most similar scenarios should be searched, but the most similar scenarios should be searched for by prioritizing the weights of the season and the minutes of the season.

The following describes the design of a similar scenario search algorithm.

When searching for similar scenarios, a group of closely related variables must be found, and a group with similar items in the data set must be detected.

A similar scenario search technique uses clustering techniques. Representative clustering algorithms include the K-Means algorithm and the Expectation Maximization (EM) algorithm. Among these two algorithms, we determine a suitable model for the ash-like scenario. A clustering algorithm is similar to objects in the same cluster, and is a set of data that is different from objects in other clusters.

Among the various techniques, the K-Means algorithm and the partitioning method EM (Expectation Maximization) algorithm divide n records into k (k <= n) clusters. Each cluster must contain at least one record. Also, one record is included in one cluster, but when using a statistical technique, one record can belong to several clusters with a weight.

The K-Means algorithm takes K as an input value and divides a set of n objects into k clusters so that the similarity in the cluster is high and the similarity between the clusters is low. Cluster similarity is measured by averaging the distance between the centroid of the cluster and the objects it belongs to. The K-Means algorithm selects k arbitrary values as the center point of the cluster. The remaining objects calculate the distance from the selected k center points and assign them to the closest cluster. Then, a new cluster center (average) is obtained for each cluster. This process is repeated until there is no movement of the center point, that is, until the center point converges. In general, a squared-error criterion is used.

The algorithm produces k segments to minimize the squared error function. This technique is relatively efficient and extensible for dealing with large datasets because the total number of data is n, the number of clusters is k, and the number of iterations is t, because the complexity of the algorithm is O (nkt). However, the K-Means technique can only be applied when the mean of the cluster is defined. It can not be applied when the data contains categorical properties. The user has to set the number k of clusters in advance and it is not suitable for finding clusters having different cluster sizes. In addition, since a small number of data may actually affect the average value, there is a disadvantage in that it is sensitive to noise values or outlier data and can not be properly processed.

K-Means and EM are similar in terms of finding optimal clusters through repetitive model refinement processes. However, the K-Means algorithm uses the Euclidean distance calculation method to calculate the distance between data, but the EM algorithm uses the log-likelihood function to evaluate the fitness of the model. In other words, EM is called probability-based clustering, whereas K-Means is distance-based clustering. The EM algorithm uses a similar approach to the K-Means algorithm as a clustering algorithm belonging to the partitioning scheme. However, unlike K-Means, which uses distance to evaluate fitness, the EM algorithm uses probability to evaluate fitness.

Probability-based clusters use a Mixture Model for the distribution of data. Here, one cluster means one data distribution, and in probability-based clustering, one record can belong to several models. The degree of belonging at this time is given as a weight (probability). The initial model is not suitable for the distribution of data, but it is useful for extracting the most similar model by searching the appropriate model for given data through Iteration process.

As described above, we have confirmed that the K-Means algorithm can be applied only when the average of the clusters is defined using the distance calculation method between data. It can not be applied when the data includes categorical characteristics and the user has to set the number k of clusters in advance and it is not suitable for finding clusters having different cluster sizes. It is not possible to define the average of the clusters of each variable even when searching for similar scenarios by the ash, and the EM algorithm can search the similar scenarios more effectively because the size of the clusters such as the attribute variable and the ejection amount variable are different.

To use this algorithm, you need to enter K, which you want to divide into several groups. Then, the algorithm assigns K average points, and all the data is looked at one by one and assigned to the group corresponding to the nearest average point. After that, we reassign the data to the nearest group again by changing the averaging points little by little. This process is repeated until the scale function representing the cluster state no longer changes. When the change does not change any more, the group of states is set as the result of clustering.

The hierarchical approach is a bottom-up approach in which each data point is initially set as a cluster and then split or merged based on the distance between the two pairs. When all the points belong to one large cluster This is the method of clustering near objects. In this algorithm, it is assumed that all n data are n different groups, then the process of merging the two closest similar groups and reducing the number of groups by looking at the similarity between the groups is called K By repeating until the K groups are found. In addition, the process of merging or separating the clusters is simplified by using the Dendrogram of the two-dimensional drawing, and once an object belongs to another cluster in the clustering process, it can not belong to another cluster again. The similarity formation matrix (Matrix) selects the constants of magma type, volcanic eruption type, eruption location, volcanic type, explosion exponent, minus height, and seasons.

Next, analyzing the received netCDF file, you can see the Fall3D information, which can be analyzed to implement the 2D ash diffusion model. Analyzing the netCDF file of 649,803,600 capacity based on the worst case scenario, we obtain Dimension, Global Attributes and Variables property information, and estimate the ash diffusion path and the region of ash area by each property information

If we describe the ash diffusion path, in order to display the volcanic ash diffusion path on the 2D GIS screen with time, lon, and lat data in the Variables, it is necessary to unitize the color by time, lon, lat. Let's try to assign the mass value of C_MASSPM10 among several variables. The units to be compared are as follows.

Comparison unit: 0.1, 1.0, 10.0, 100.0, 500.0, 1000.0, 5000.0, 10000.0, 50000.0, 100000.0 The comparison value is stored step by step, color is applied to the time, lon and lat areas and the ash diffusion path can be checked have.

Next, we describe regional ash area estimation.

It is necessary to judge how much volcanic ash and volcanic ash are present in the Korean region, and the damage area can be derived. Grid is displayed on the GIS screen as shown in FIG. 7A with minlon, minlat, maxlonl, maxat values among volcanic ash Global AttributesVariables.

We map the legal boundary data to the grid data to derive the volcanic ash area. The mapping overlaps the grid polygon area and the administrative boundary polygon area and displays the overlapped area as shown in FIG. 7B.

Mapping the Grid data and the legal boundary data to the city and county area to calculate the area for damage prediction by city and district. Mapping of Gyeongsangnam-do, as shown in Fig. 7c, indicates the grid mapped by each city group. In the case of Miryang City, 11 Grids are mapped to find the mean value of concentration and thickness of 11 Grid regions in order to know the volcanic ash and volcanic ash thickness of Miryang City.

When the area and the grid area are mapped, the ash area is mapped. Since the volcanic ash has a concentration or thickness value of volcanic ash, mapping to the GIS screen can predict the level of damage corresponding to the volcanic ash range. As shown in FIG. 7d, Gyeonggi-do, Chungcheongbuk-do, Gyeongsangbuk-do and Gangwon-do enter the volcanic ash area and the volcanic ash concentrations are displayed differently for each city group.

If we describe the extraction of damage DB by volcanic ash area, Fall3D content is composed of netCDF file as the result of ash simulation, and the volcanic material distribution area is mapped to the topography of Korean peninsula to extract the data . The extracted data is utilized in the damage history and damage estimation by sector / region.

Next, when describing the ash image processing design, the ash diffusion area of the disaster status page developed in the previous year was calculated by drawing a polygon as a particle value for each 10 Km coordinate. As a result, the contents on the screen become angular, and the color is uniformly displayed. There is no error value for the contents, but the value within 10Km of left and right was inevitably expressed.

Therefore, the present invention has a need to upgrade the conventional products. You should smooth out the diffuse results that were right-angled and change the color per pixel to avoid uniform values. As a solution, we use the Pixel Matching method to divide the value into pixels, which draws the value of the area containing the pixels, and the Concave Hull method, which draws the outline of the same area. Finally, we use a method that uses interpolation. The interpolation method is applied to the values xi (i = 1, 2, ..., n) of two or more variables with unknown shape of the function f (x) of the real variable or with any interval (irregular or unequal interval) If the sum f (xi) is known, it means to estimate the function value for any x between them. It is used when estimating a value from an observed value obtained by an experiment or an observation or obtaining a function value not in the table by a function table such as a log table. The simplest method is to obtain the function value to be obtained by concatenating the points of the variable with the x coordinate and the known function value of the variable with the y coordinate.

model characteristic Polygon draw
(Polygon Draw) Lack of visibility due to staircase phenomena and contents uniformity (result of train year) Concave Hull Modeling range distortion and slow speed Pixel Matching Weak staircase phenomenon that lacks visibility but speed is fast Interpolation Maintain modeling results, good visibility, slow speed

Table 2 shows the ash image processing design method and its characteristics, and FIG. 8A is a comparative view of the ash diffusion model showing the result of designing according to the ash image processing designing method.

As shown in Table 2, interpolation is the best, but if it is slow, it is not easy to apply to the system. Except for the polygonal draw, which is the result of the Tank Year, the Concave Hull, which is also a modeling range distortion, is excluded. Excluding the outer edge of the concave shell because it improves the distortion to logic, it slows down the speed. In this paper, we developed a modeling method based on the method of Pixel Matching and Interpolation.

Specifically, we have implemented a method of using pixel matching method and interpolation method to enhance the diffusion model of volcanic ash. The pixel matching is implemented by matching the latitude and the longitude to the screen coordinates and applying the image color to the part. Image creation processing speed was fast and image processing was better than I thought. However, as the size of the generated image increases, the staircase becomes stronger and the color of the image changes uniformly. Next, I implemented the interpolation method. The interpolation method is not uniform as in the case of the isosceles data, and the larger the image size, the better the data can be generated. However, the speed problem still has a big problem.

Pixel matching and interpolation are the same as applying color by dividing the latitude and the longitude by pixels. Pixel matching is to color the nearest data and apply it to the pixels. The interpolation method sets the average value of the set peripheral data at that point, . Therefore, there is a difference in speed. However, the NetCDF file is a file containing a lot of data in grid units, and only 3x3 or 5x5, 7x7, and 9x9 data are taken around the point to perform local interpolation. As I thought, 3x3 was the fastest and from 7x7 it was faster than the first, but I had problems using it. Basically, 3x3 is used to store image data in DB server through interpolation method of particle concentration, fine dust, and sediment data. In addition, mapping data of regional particle concentration, fine dust, and sediment for damage estimation is created, and DB of damage area is created and damage estimation of volcanic ash is possible. FIG. 8B shows a program for storing the resultant image data in the server through the generation of the ash damage area DB and the interpolation processing.

Using the results obtained in accordance with the preferred embodiment of the present invention, a volcanic hazard situation can be implemented by a 2D diffusion model of volcanic ash diffusion and a page providing a map and bar graph of the results of the occurrence of regional disasters. These volcanic disasters show DB analysis results through the data entered in the damage prediction page. After selecting the detail area, the map can be enlarged and displayed, and the legend and bar graph information can be placed on the map to provide the map as wide as possible. Bar graphs can be minimized when selected. Such an execution screen may appear as shown in FIG.

As described above, according to the preferred embodiment of the present invention, a new method of correcting the simulation result generated in several km units can be provided to provide improved accuracy and operation speed as compared with the conventional method, , It is possible to suggest a method of predicting the damage scale by region (administrative district) for regional response. The prediction of damage scale by region maps the spread of volcanic ash in the unit of cell to the area (administrative district) by using the corrected volcanic ash diffusion information in the way mentioned above, so that more precise local disaster occurrence situation can be provided.

It is appreciated that the present disclosure may be embodied in many different forms, all without departing from the spirit and scope of the invention, as embodied and broadly described herein, in order to provide a complete description of the methodology, system, and / Although a person skilled in the art may make numerous modifications to the disclosed embodiments without departing from the spirit or scope of the description herein. Since many embodiments can be made without departing from the spirit and scope of the present disclosure, appropriate ranges are set forth in the appended claims.

Claims (8)

a) a simulation module containing an algorithm for decision makers to select the most similar scenarios in the scenario database in the event of a volcanic eruption;
b) a disaster status module showing spatial information through GIS system as a result of the algorithm;
c) module for extracting and analyzing damage estimation information from disaster prediction and disaster estimation database; And
d) Decision-making process of decision support system of volcanic ash-resistant decision support system composed of module that decision-maker constructs countermeasure strategy based on the situation response database.
2. The method of claim 1, wherein the simulation module is adapted to simulate various types of disasters such as volcanic ash, firestone, volcanic flood / advection, and volcanic earthquake through simulation based on damage prediction scenario based on explosion type and explosion information in volcanic explosion And the simulation result of the harmonic response system is provided.
[Claim 3] The method according to claim 1, wherein the disaster status module displays the time, the 3D graphic, and the disaster state GIS system by time to avoid volcanic ash, fossil fuels, volcanic floods / And the information about the damage situation is displayed.
The damage estimation module as set forth in claim 1, wherein the damage estimation module compares the damages of the ash, the fire hydrant, the volcanic flood / advection, and the volcanic earthquake with the statistical system using the charts and charts, And the information is displayed by estimating the information.
The system according to claim 1, wherein the situation-handling module displays the response procedure of the ash, time series, and region of the volcanic ash, flare, volcanic flood / advection, and volcanic earthquake on the basis of the situation response database, And then spreading the situation to each relevant organization.
3. The system as claimed in claim 1 or 2, wherein the simulation module maps an ash distribution area by an ash image processing designing method.
The ash treatment system according to claim 6, wherein the ash image processing designing method maps the ash distribution area by modeling by using any one of pixel matching or interpolation or a combination thereof, Support system.
The system as claimed in claim 1, wherein, in the simulation module, each variable is changed and applied in accordance with the result of the actual modeling.

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