KR102473442B1 - Method for calculating flood damage and system thereof - Google Patents

Abstract

A heavy rain damage prediction system for each water area is provided. The heavy rain damage prediction system includes a damage data collection unit that collects heavy rain damage data by region, a target basin selection unit that selects a target basin, an analysis data collection unit that collects analysis data including preceding rainfall and hourly rainfall, and heavy rain disaster statistics. It includes a damage estimation unit that calculates the amount of damage by applying the based damage prediction function and a damage index analysis unit that derives correlation data between heavy rain events and damage events.

Description

Heavy rain damage prediction method and its system {METHOD FOR CALCULATING FLOOD DAMAGE AND SYSTEM THEREOF}

The present invention relates to a method for predicting damage caused by heavy rain for each water area.

Climate change is accelerating as the global warming problem due to industrialization and urbanization has recently become serious, and as a result, flood damage is repeated every year, resulting in many casualties and disaster damage, and the scale of restoration costs is also increasing accordingly. In order to protect people's lives and property from recurring disasters and to effectively manage disasters, it is necessary to establish an analysis system for advance prediction, but most disasters have many variables and uncertainties, and disaster types are becoming more complex and diverse. As a result, the information necessary for disaster prevention and recovery management is becoming relatively large, so research is continuing on the need to build a system that processes disaster information quickly and accurately in order to establish efficient disaster management and recovery measures.

In order to reduce damage caused by flooding, many disaster prevention projects such as flood control projects, flood risk map production, and flood insurance systems have been promoted. Multi Dimensional Flood Damage Assessment (MD-FDA) has been conducted. However, as these studies mainly rely on statistical data to evaluate flood damage by administrative district, there is a limit to accurately calculating flood damage for actual flooded areas.

Flood disaster information providing systems developed so far provide only basic information of the target area by year according to the user's selection, in a state where only basic information of flooded areas is accumulated in the database. Since the cause of flood damage is not just simple rainfall, but complex factors such as rainfall, characteristics of the area, and facilities included in the area, there is a limit to establishing effective countermeasures by providing basic flood-related information.

Patent Registration Publication No. 10-1741777, 2017.05.24

The problem to be solved by the present invention is to provide a method and system for predicting heavy rain damage by water district by selecting an area with a high degree of heavy rain damage among areas divided into administrative districts.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

Heavy rain damage prediction system for each water area according to an aspect of the present invention for solving the above problems is a damage data collection unit for collecting heavy rain damage data for each area, a reference area based on the damage amount and damage frequency based on the damage data for each area A target basin selection unit that selects and selects a target basin, which is a water district in the reference area, an analysis data collection unit that collects analysis data including the preceding rainfall and hourly rainfall based on the date of heavy rain damage in the target basin, the preceding rainfall and Including a damage estimation unit that calculates the amount of damage by applying hourly rainfall data to a damage prediction function based on heavy rain disaster statistics, and a damage index analysis unit that derives correlation data between heavy rain events and damage events based on the analysis data and damage amount.

According to an embodiment of the present invention, the damage data collection unit collects the disaster yearbook damage history, and the target basin selection unit selects five areas with high damage amount and frequency of damage among the areas included in the Han River area, the Geum River area, the Nakdong River area, and the Seomjin River area. A reference area can be selected.

According to an embodiment of the present invention, the target basin selector may select at least one water area included in the reference region as a target basin.

According to an embodiment of the present invention, the analysis data collection unit selects a plurality of observatories included in the target watershed and holding rainfall data for a specific period, and the observatories are Automated Synoptic Observing System (ASOS). ) and Automatic Weather System (AWS).

According to an embodiment of the present invention, the standard preceding rainfall and the hourly rainfall may be calculated by the Thyssen coefficient of the target watershed.

According to an embodiment of the present invention, the analysis data collection unit may collect a plurality of analysis data sets corresponding to a plurality of heavy rain events, and the damage estimation unit may calculate a damage amount corresponding to each heavy rain event.

According to an embodiment of the present invention, the analysis data collection unit collects facility data included in the target watershed, and the damage estimation unit calculates the amount of damage for each facility in the target basin based on the facility data.

According to an embodiment of the present invention, the target watershed is classified into one watershed characteristic area among urban areas, agricultural areas, and mountain areas based on a specific criterion, and the damage index analysis unit analyzes heavy rain events and damage events for each watershed characteristic area. can form a correlation indicator of

According to an embodiment of the present invention, the specific criterion corresponds to the urban area thematic map, the agricultural area thematic map, and the mountain area thematic map divided by the land use regulation area and district map classified by the relevant laws and regulations. can

According to an embodiment of the present invention, the damage estimation unit may estimate the damage based on the expected climate information and the correlation data collected in the target basin.

A method for predicting heavy rain damage by water zone according to an aspect of the present invention for solving the above problems includes collecting heavy rain damage data for each zone, selecting a reference area based on the damage amount and damage frequency based on the damage data for each zone, Selecting a target watershed that is a water area in the reference area, collecting analysis data including the preceding rainfall and hourly rainfall based on the date of heavy rain damage in the target basin, and using the preceding rainfall and hourly rainfall data as statistically-based damage from heavy rain Calculating the amount of damage by applying the prediction function and deriving correlation data between the heavy rain event and the damage event based on the analysis data and the amount of damage.

Other specific details of the invention are included in the detailed description and drawings.

According to the present invention, it is possible to select a water area with a high damage amount and frequency of damage, and to find out the correlation between heavy rain events and damage events through rainfall data of the corresponding water area, so that damage caused by heavy rain in the area can be more accurately predicted. .

In addition, it is possible to establish a preemptive restoration project plan because the degree of direct and indirect damage to facilities can be predicted through correlation between facilities included in the target basin and damage amount and heavy rain damage prediction by basin characteristics in terms of area utilization.

1 is a block diagram of a system for predicting heavy rain damage according to the present invention.
Figure 2a is a flow chart showing the heavy rain damage prediction method of Figure 1.
2B is a flowchart showing a method for selecting a target basin.
3A and 3B are conceptual diagrams illustrating damage amount data and damage frequency data for each region.
4 is a conceptual diagram showing a target watershed selected by mapping a municipality and a water district.
5 is a conceptual diagram for explaining analysis data collected for damage prediction.
6 is a flowchart for explaining a method for estimating damage amount for each facility.
7 is a graph of estimated damages for each facility in a target watershed.
8 is a flowchart for explaining a method for evaluating the risk of heavy rain for each watershed characteristic of a target watershed.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the skilled person of the scope of the present invention, which is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into smaller numbers of components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means any kind of hardware device including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a computer may be understood as including a smartphone, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device, but is not limited thereto.

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

Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least a part of each step may be performed in different devices according to embodiments.

1 is a configuration diagram of a heavy rain damage prediction system of the present invention, FIG. 2A is a flow chart showing a heavy rain damage prediction method of FIG. 1, and FIG. 2B is a flow chart showing a method for selecting a target basin.

The damage prediction system according to the present invention includes a damage data collection unit 11, a target basin selection unit 12, an analysis data collection unit 13, a damage estimation unit 14, and a damage index analysis unit 15.

3A and 3B are conceptual diagrams showing damage data and damage frequency data for each region, and FIG. 4 is a conceptual diagram showing target watersheds selected by mapping municipalities and districts.

1, 2a and 2b, the heavy rain damage prediction system selects a watershed to be analyzed (S110). The damage data collection unit 11 collects heavy rain damage history data (S111). The damage data collection unit 11 may collect heavy rain damage history data by region from a database such as a disaster yearbook, a disaster register, and a comprehensive storm and flood reduction plan. including damages The damage data collection unit 11 collects damage history data for the last 10 years, but the period is not limited thereto.

Referring to FIGS. 3A and 3B, the target basin selection unit 12 classifies and designates the amount of damage by city, county, or district in 10 stages based on the heavy rain damage history data, and sets the frequency of damage in 10 stages from at least 0 to 10 years. classified as The standard area is selected based on the damage amount and frequency of damage by area. In other words, the reference area is selected based on the amount of damage in the city, county and district over the last 10 years and areas with high damage frequency.

For example, Pocheon, Jecheon, Cheorwon, and Dongducheon are selected as the reference area in the Hangangwon area, Jincheon is selected as the reference area in the Geumgang area, Miryang is selected as the reference area in the Nakdong River area, and Suncheon is selected as the reference area in the Seomjin River area. , and 7 cities and counties are selected as reference areas. However, the reference area is not limited to this, and the damage amount and frequency of damage are calculated and compared at a predetermined cycle, and a city or county with a high damage amount and frequency of damage can be selected as the reference area.

The target basin selection unit 13 selects a reference area based on the reference damage amount and damage frequency among the collected heavy rain damage history data (S130). Referring to FIG. 4 , the target basin selector 13 maps regional data for each region classified as an administrative region and water district, and selects a water district corresponding to the reference region as a target basin (S113). For example, the target basin selection unit 13 selects Pocheon-si, Cheolwon-gun, Jecheon-si, Jincheon-gun, Miryang-si, and Suncheon-si, where the damage amount and frequency of damage were high, as reference areas, and Cheol-dong, Podong, Poshin, and Po-gun matched with the reference areas. , Dongdu 1, Rebar, Jesan, Jecheon 1, Jebong, Jinmun, Mildan, Milsang, Milbu, Milha, Seungwol, Seungsan, Seungseo, Seungbyeol, and Seungwoe can be selected as target watersheds.

The analysis data collection unit 13 collects analysis data of the target watershed (S130). The analysis data may correspond to an observation station corresponding to an Automated Synoptic Observing System (ASOS) and an Automatic Weather System (AWS) within the target watershed, and the observation station is the same for the last 10 years. It is desirable that it be an operating observatory. For example, the analysis data collection unit 13 may select 76 synoptic weather observation equipment and 451 automatic weather observation equipment in a total of 19 target basins, and collect data therefor.

The analysis data collection unit 13 collects rainfall data observed at the observatory. The analysis data collects rainfall information based on the damage period based on the basic data.

5 is a conceptual diagram for explaining analysis data collected for damage prediction.

Referring to FIG. 5 , the analysis data collection unit 13 collects a preceding rainfall amount D1 from 7 days before the damage date and an hourly rainfall amount D2 of the damage date. The preceding rainfall 7 days prior to the damage occurrence period corresponds to the cumulative rainfall for one day, and the hourly rainfall (D2) includes the (maximum) rainfall and total rainfall by rainfall duration from 1 to 24 hours on the day of the damage. The preceding rainfall D1 may be composed of 7 data sets for 7 days, and the rainfall by time period D2 may be composed of 24 data sets for 1 day.

The rainfall amount of the target basin is calculated using the Thiessen coefficient of the target basin and the rainfall data observed at the observation station of the target basin. The Thyssen coefficient corresponds to the ratio of the dominant area of the observatory to the target area. The rainfall amount is calculated using the Thyssen area method and the inverse distance weight (IDW) interpolation method.

The damage estimation unit 14 maps the damage prediction function to the target watershed (S140).

Table 1 is a table showing the damage function by region. It corresponds to Gangwon-do, Gyeonggi-do, Seoul Metropolitan City, Incheon Metropolitan City, and Chungcheongbuk-do, respectively, and corresponds to a statistically-based damage prediction function for heavy rain disasters using a logistic regression model. Groups with large and small damages were classified, and the predicted damages could be estimated using the principal component regression model for each group.

The heavy rain disaster statistics-based damage prediction function uses a binomial categorical variable classified based on a specific amount of damage (percentile) as a dependent variable, and presents a probability value that is classified into a group with a large amount of damage when an explanatory variable is given. The damage estimation unit 14 maps the damage prediction function to one of the five damage prediction functions based on the area of the target watershed.

The damage prediction function of the target watershed applies the area of the target watershed to the damage prediction function for each region of the reference area. tot represents the total rainfall.

The first to fourth main components are derived through principal component analysis of the collected preceding rainfall (D1) and the hourly rainfall (D2) of the damaged day. Principal component analysis is a method of finding new variables that are not correlated with each other when it is difficult to select an explanatory variable because of the large number of variables and the linear relationship between them. In Table 1, PC1.x and PC2.x are principal components 1 and 2 derived for the maximum rainfall by duration (24), and PC1.d and PC2.d are principal components 3 and 2 derived for the preceding rainfall (7). corresponds to 4.

[Table 1]

The analysis data collection unit 13 collects a plurality of heavy rain event data sets for each target watershed. Table 2 shows the antecedent rainfall (D1) for 10 heavy rain events in the Jinmun water district, and Table 3 shows the hourly rainfall (D2) and antecedent rainfall (D1) for the 10 heavy rain events in the Jinmun water district. It represents the estimated damage amount through the principal component of hourly rainfall (D2).

[Table 2]

[Table 3]

The damage index analysis unit 15 calculates the amount of damage for each heavy rain event and forms correlation data for the heavy rain event and the damage event in the target basin (S150). For example, the analysis data collection unit 13 and the damage estimation unit 14 may collect and estimate damage amounts for 685 heavy rain events in 19 target watersheds.

6 is a flow chart for explaining a damage estimation method for each facility, and FIG. 7 is a graph for estimating damage for each facility in a target basin.

6 and 7, the analysis data collection unit 13 collects facility data of the target basin, that is, data on the damaged object (S211), and the damage estimation unit 14 collects facility data for each facility in the target basin. The amount of damage is estimated (S212).

In addition, the analysis data collection unit 13 collects facility data in the target watershed in order to estimate the amount of flood damage.

Facilities subject to heavy rain damage include public buildings, river facilities, transportation facilities, pipelines, farmland, etc., and are classified as general assets and public assets. Classify and public assets consist of public buildings, river facilities, transportation facilities, and pipelines.

The analysis data collection unit 13 collects data from various data such as domestic DB provision related systems, disaster yearbooks, relevant establishment guidelines for each relevant department, and national management facilities, classifies facilities, and vehicles, crops/farmland, and loss of life. , classify the indirect damage target.

The analysis data collection unit 13 may collect data related to the flood depth and flooded area of the flood forecast map, such as the flood forecast map, application of past disaster events, and revision of construction measures, based on the target basin.

The facilities (damaged objects) in the disaster yearbook can be investigated and classified into 13 detailed categories: roads, rivers, small rivers, water supply, ports, fishing ports, schools, railways, water supply, erosion control, military facilities, small scales, and others. , Ministry of Education, Ministry of Land, Infrastructure, and Transport, Ministry of Agriculture, Forestry and Fisheries, Ministry of Security and Public Administration, Forest Service, Ministry of Environment, Cultural Heritage Administration, etc. Establishment guidelines for relevant ministries and national management facilities used to calculate restoration costs for national and local management facilities are investigated to form facility data can do.

The damage estimation unit 14 can estimate the amount of damage considering the physical damage rate of the damaged object and the loss rate of asset value by applying a loss/damage function, and the damage index analysis unit 15 can estimate the amount of damage and facilities in the target basin of relationship data can be formed. The damage estimate unit 14 analyzes the flood depth and flooded area of the flood forecast map by utilizing the flood forecast map for the target basin, past disaster event application, and flood forecast map such as construction measures. Accordingly, in order to apply the loss/damage function to the damaged object, the value obtained by multiplying the flood depth by other damage rates and the loss rate is multiplied by the value of assets for each damaged object considering the depreciation rate, which means the ratio of the depreciated value to the repurchase cost. The amount of loss can be estimated.

According to this embodiment, it is possible to more accurately predict the amount of infringement by reflecting the characteristics of the heavy rain event in the target basin and the characteristics of the facilities included in the target basin, so that efficient disaster management in terms of advance preparation can be planned and implemented.

8 is a flowchart for explaining a method for evaluating the risk of heavy rain for each watershed characteristic of a target watershed. The watershed characteristics of the target watershed are classified (S221). Here, the watershed characteristics correspond to urban areas, agricultural areas, and mountain areas according to specific standards. In addition, thematic maps of agricultural areas and thematic maps of mountainous areas can be used. One having the largest area in the target watershed may be classified as a corresponding watershed characteristic area.

Watershed characteristics for each of a plurality of target basins are classified, and the damage index analysis unit 15 forms a heavy rain risk index for each basin characteristic through rainfall data, facility data, and estimated damage per basin characteristic group (S222).

Although not specifically shown in the drawing, the damage index analysis unit can calculate the amount of damage based on newly collected climate data and predicted climate information in the target basin, and can estimate the amount of damage to the facility.

For example, the expected climate information is based on data estimated from a climate prediction system, and the amount of damage may be estimated through the amount of preceding rainfall and hourly rainfall included in the predicted climate information.

According to this embodiment, since the correlation data between the characteristics of urban areas, agricultural areas, and mountainous areas and the characteristics of damage and the scale of damage to heavy rain events are calculated, different preliminary preparation methods for each regional characteristic can be planned based on this.

The processor may include one or more cores (not shown) and a connection path (eg, a bus) through which signals are transmitted and received to and from a graphic processing unit and/or other components.

A processor according to one embodiment performs the method described with respect to FIGS. 1-8 by executing one or more instructions stored in memory.

Meanwhile, the processor may further include Random Access Memory (RAM) and Read-Only Memory (ROM) for temporarily and/or permanently storing signals (or data) processed by the processor unit. Also, the processor may be implemented in the form of a system on chip (SoC) including at least one of a graphics processing unit, RAM, and ROM.

The memory may store programs (one or more instructions) for processing and controlling the processor. Programs stored in the memory may be divided into a plurality of modules according to functions.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

10: Heavy rain damage prediction system
11: damage data collection department
12: Target basin selection department
13: analysis data collection unit
14: damage estimation unit
15: damage index analysis unit

Claims (11)

a damage data collection unit that collects heavy rain damage data for each region;
a target watershed selecting unit that selects a reference area based on the heavy rain damage data for each area, the amount of damage, and the frequency of damage, and selects a target watershed that is a water area within the reference area;
an analysis data collection unit that collects analysis data including a preceding rainfall amount and hourly rainfall amount based on a heavy rain damage date in the target basin;
a damage estimation unit for estimating a damage amount by applying the preceding rainfall amount and hourly rainfall data to a heavy rain disaster statistics-based damage prediction function; and
Including a damage index analysis unit for deriving correlation data between heavy rain events and damage events based on the analysis data and the amount of damage,
The damage estimation unit,
Using the flood forecast map for the target basin, analyze the flood depth and flood area of the flood forecast map,
The value multiplied by the damage rate according to the depth of flooding of the damaged object and the loss rate is multiplied by the property value of each damaged object considering the depreciation rate, which is the ratio of the depreciated value to the repurchase cost.
The target basin selection unit,
The damage amount and frequency of damage by all cities, counties, and districts are classified into 10 stages each according to a preset range,
Using the classification result, a reference area is selected based on an area corresponding to a classification with a large damage range and a classification with a large damage frequency range during a predetermined period,
Classify the damaged objects into general assets and public assets,
General buildings, vehicles, crops/agricultural land, and loss of life are classified as general assets, and public buildings, river facilities, transportation facilities, and pipelines are classified as public assets.
Classify the above damaged objects into detailed categories such as roads, rivers, small rivers, waterworks, ports, fishing ports, schools, railways, waterworks, erosion, military facilities, small scales, and others,
The target watershed is classified into one watershed characteristic area among urban areas, agricultural areas, and mountain areas based on specific criteria, and the damage index analysis unit forms a correlation index between heavy rain events and damage events for each watershed characteristic area,
The above specific criteria correspond to urban area thematic maps, agricultural area thematic maps, and mountain area thematic maps divided by land use regulation areas and regional maps classified by relevant laws and regulations,
Heavy rain damage prediction system for each water area, which classifies one having the largest area in the target watershed as the corresponding watershed characteristic area. According to claim 1,
The damage data collection unit collects disaster yearbook damage history,
The target basin selection unit selects five reference areas with high damage and frequency of damage among the areas included in the Han River area, the Geum River area, the Nakdong River area, and the Seomjin River area. Heavy rain damage prediction system for each water area. According to claim 2,
The heavy rain damage prediction system for each water area, characterized in that the target water area selection unit selects at least one water area included in the reference area as a target water area. According to claim 1,
The analysis data collection unit selects a plurality of observation stations included in the target basin and holding rainfall data for a specific period,
The observation station is a heavy rain damage prediction system for each water zone, characterized in that it includes an Automated Synoptic Observing System (ASOS) and an Automatic Weather System (AWS) [Claim 5] The system for predicting heavy rain damage for each water area according to claim 4, wherein the reference preceding rainfall and the hourly rainfall are calculated by the Thyssen's coefficient of the target basin. According to claim 1,
The heavy rain damage prediction system for each water area, characterized in that the analysis data collection unit collects a plurality of analysis data sets corresponding to a plurality of heavy rain events, and the damage estimation unit calculates the damage amount corresponding to each heavy rain event. According to claim 1,
The analysis data collection unit collects facility data included in the target watershed,
The heavy rain damage prediction system for each water area, characterized in that the damage estimation unit calculates the damage amount for each facility in the target basin based on the facility data. delete delete According to claim 1,
The heavy rain damage prediction system for each water area, characterized in that the damage estimation unit estimates the damage based on the expected climate information and the correlation data collected in the target basin. Collecting heavy rain damage data by region;
Selecting a reference area based on the damage amount and damage frequency based on the heavy rain damage data for each area and selecting a target watershed that is a water area within the reference area;
Collecting analysis data including preceding rainfall and hourly rainfall based on the heavy rain damage date of the target basin;
Calculating a damage amount by applying the preceding rainfall and hourly rainfall data to a heavy rain disaster statistics-based damage prediction function; and
Deriving correlation data between heavy rain events and damage events based on the analysis data and the amount of damage,
The step of calculating the amount of damage,
Using the flood forecast map for the target basin, analyze the flood depth and flood area of the flood forecast map,
Including the step of estimating the amount of flooding/loss damage for each damaged object by multiplying the value multiplied by the damage rate according to the depth of flooding of the damaged object and the loss rate by the value of the property for each damaged object considering the depreciation rate, which is the ratio of the depreciated value to the repurchase cost ,
The step of selecting the target basin is,
Classifying the damage amount and frequency of damage for each city, county, and district into 10 stages, respectively, according to a preset range; and
Using the classified result, selecting a reference area based on an area corresponding to a classification with a large damage range and a classification with a large damage frequency range during a predetermined period; including,
Classify the damaged objects into general assets and public assets,
General buildings, vehicles, crops/agricultural land, and loss of life are classified as general assets, and public buildings, river facilities, transportation facilities, and pipelines are classified as public assets.
Further comprising the step of classifying the damaged object into detailed items such as roads, rivers, small rivers, waterworks, ports, fishing ports, schools, railways, repairs, erosion, military facilities, small scales, and others,
The target watershed is classified into one watershed characteristic area among urban areas, agricultural areas, and mountain areas based on specific criteria, and the damage index analysis unit forms a correlation index between heavy rain events and damage events for each watershed characteristic area,
The above specific criteria correspond to urban area thematic maps, agricultural area thematic maps, and mountain area thematic maps divided by land use regulation areas and regional maps classified by relevant laws and regulations,
Heavy rain damage prediction method for each water area, further comprising classifying one having the largest area in the target watershed as a corresponding watershed characteristic area.

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