KR102308748B1 - Heavy Rain Disaster Vulnerability Analysis Advanced System - Google Patents

Abstract

The present invention relates to a heavy rain disaster vulnerability analysis advanced system, and includes an output unit that divides the current vulnerability rating analysis unit and the future vulnerability rating analysis unit into a current vulnerability rating analysis unit and a future vulnerability rating analysis unit for each analysis unit, and selects and outputs a lower grade among the ratings of each analysis unit, wherein the current vulnerability The rating analysis unit consists of a current climate exposure analysis unit and a current urban sensitivity analysis unit, and the result of each analysis unit is calculated as an average of two grades in any one of 1 to 4 grades, and the future vulnerability grade analysis unit is a future climate exposure It consists of an analysis unit and a future city sensitivity analysis unit, and the result of each analysis unit is characterized in that it is calculated as an average of two grades in any one of 1 to 4 grades.
By improving the spatial analysis unit from the existing aggregate unit unit to the grid-based unit, disaster vulnerability rating information is provided in units of parcels of the continuous cadastral map, resulting in excellent accuracy and effectiveness, and local rainwater runoff in the current vulnerability index of heavy rain disaster vulnerability analysis It has the characteristic of linking with the vulnerable population by applying the cumulative CN index and the relative lowland index, which are urban sensitivity indexes that can reflect characteristics.

Description

Heavy Rain Disaster Vulnerability Analysis Advanced System

The present invention relates to an advanced system for analyzing heavy rain disaster vulnerability, and it is an advanced system to which an improved spatial analysis unit and sensitivity index are applied.

As the risk of natural disasters is increasing due to climate change, etc., disaster prevention-type urban planning measures that comprehensively consider not only disaster prevention facilities but also urban planning measures are being institutionalized. ), as one of the basic surveys when establishing city/gun basic plans and management plans, the 「Urban Climate Change Disaster Vulnerability Analysis」 has become mandatory (Jan. 2015).

There are six types of disasters targeted for disaster vulnerability analysis: heavy rain, drought, heatwave, heavy snow, strong wind, and sea level rise. Although the vulnerability analysis has been established through institutionalization, problems in spatial analysis units and regional characteristics are insufficiently reflected, and the related system improvement of local governments is insufficient. There is a limit to the establishment of an effective disaster prevention type urban disaster prevention plan.

Because the existing spatial analysis unit is determined by the size of the target for about 500 residents, the area of the aggregation district is small in urban areas. In the case of counting districts in some regions, the population is concentrated in a certain area, so there is a limit to presenting the same disaster prevention-type urban planning plan for each counting district.

Therefore, in the present invention, the analysis unit of disaster vulnerability analysis is upgraded from the aggregation district to the grid unit (100 m), and it is due to the need to develop the cumulative CN and relative lowland index to reflect the characteristics of the region.

An object of the present invention is to provide an advanced system for disaster vulnerability analysis in which the spatial unit of disaster vulnerability analysis due to heavy rain is improved from the resident's aggregate to the regional unit.

In addition, the present invention is to provide a disaster vulnerability analysis advanced system that reflects the improved urban sensitivity index (cumulative CN, relative lowlands) in the vulnerability analysis to heavy rain disasters.

In order to solve the above problems, the present invention is a heavy rain disaster vulnerability analysis advanced system, divided into a current vulnerability rating analysis unit and a future vulnerability rating analysis unit for each analysis unit, and selects and outputs the lower grade among the grades of each analysis unit Including an output unit, wherein the current vulnerability rating analysis unit is composed of a current climate exposure analysis unit and a current urban sensitivity analysis unit, and the result of each analysis unit is calculated as an average of two grades in any one of 1 to 4 grades, the The future vulnerability rating analysis unit is composed of a future climate exposure analysis unit and a future city sensitivity analysis unit, and the result of each analysis unit is one of 1 to 4 grades, which is characterized by being calculated as an average of two grades. An upgrade system is provided.

In addition, the present invention is a database in which observation data for the average number of precipitation days and average time maximum precipitation provided by the Korea Meteorological Administration provided by the Korea Meteorological Administration are stored in the current climate exposure analysis unit; Spatial interpolation of the observation data stored in the database. Provides an advanced system for analysis of vulnerability to heavy rains characterized by a data conversion unit that converts point-type climate data into area-type data using

In addition, the present invention provides an advanced system for analysis of vulnerability to heavy rain disasters, which is composed of a potential vulnerable area and urban vulnerable components in the current urban sensitivity analysis unit, and is characterized by constructing analysis index data for each component as an analysis unit.

In addition, the present invention provides a database for classifying and storing data on the number of days of precipitation more than 80 mm/day per annum using climate change scenario data (RCP 8.5) from the Climate Change Information Center in the future climate exposure analysis unit; Heavy rain disaster characterized in that it consists of a data conversion unit that converts point-type climate data into area-type data and zonal statistics grid-specific indicators by using spatial interpolation of the observed data stored in the database. Vulnerability analysis advanced system is provided.

In addition, the present invention provides an advanced system for analysis of vulnerability to heavy rain, characterized in that the future city sensitivity analysis unit builds urbanization area, population increase, and development area planned district data for a certain period as analysis index data for each analysis unit. .

In addition, the present invention provides an advanced system for analysis of vulnerability to heavy rain, characterized in that the analysis unit has a grid area of 100 m × 100 m in size.

In addition, the present invention adds the cumulative CN index and the relative low land index, which are improved indexes to the urban sensitivity analysis unit, with the same weight, and converts them into standard indexes that can be summed with the existing analysis indexes to calculate the average value index, the cumulative CN The index consists of a basic data processing step and a step of processing the surface in a subwatershed unit using the processed surface data, wherein the basic data are numerical land cover, hydrological soil group, numerical elevation data, and subwatershed boundary data, The basic data processing step includes pre-processing of data and calculation of CN values for each grid, deriving CN values for each sub-watershed, and calculating accumulated CN values using the derived CN values in the direction of stormwater runoff. Vulnerability to heavy rain disasters It provides an analysis advancement system.

In addition, the present invention is a result of the calculation of the elevation value and the analysis of the elevation cluster with 100 m grid data on the relative low land index. Low) or other (Non Significant), the 100m grid elevation value calculation is calculated using ArcMap's Zonal Statics as Table, Join function, and the elevation cluster analysis is analyzed using ArcMap's Local Moran's I function. It provides an advanced system for analysis of vulnerability to heavy rain disasters characterized by

The present invention configured as described above can provide disaster vulnerability rating information in units of lots of continuous cadastral maps by improving the spatial analysis unit from the existing aggregate unit unit to the grid-based unit, so that the accuracy and effectiveness of the results are excellent.

In addition, the present invention applies the cumulative CN index and the relative lowland index, which are urban sensitivity indexes that can reflect the local stormwater runoff characteristics, to the current vulnerability index of heavy rain disaster vulnerability analysis, so that the disaster vulnerability analysis system linked with the vulnerable population can be advanced There is a characteristic.

1 shows the current climate exposure analysis index construction process.
2 is an area-type data showing the number of days of precipitation of 80 mm/day or more per year.
3 is a grid-type data showing the number of days of precipitation over an average of 80 mm/day per year.
4 is an annual average time maximum precipitation area type data.
5 is a grid-type data showing the number of precipitation days of 80 mm/day or more per year.
6 shows a map of the current state of the damaged area in the past.
7 is a grid-type data of damaged areas for the last 10 years.
8 is a diagram showing the status of a natural disaster risk improvement district.
9 is a natural disaster risk improvement zone analysis index data for each grid area of a natural disaster risk improvement zone.
10 is a diagram of the current state of the main riverside lowlands.
11 is a grid-type data of the riverside low-lying area, FIG. 12 is a diagram of the current state of the storm and flood risk zone, and FIG.
14 is a grid-type data of potential vulnerable areas.
15 is a diagram of the status of vulnerable urban infrastructure (heavy rain).
16 is a road grid type data, FIG. 17 is a water supply facility grid type data, FIG. 18 is an electricity supply facility grid type data, FIG. 19 is a gas supply facility grid type data, FIG. 20 is a water pollution grid type data, FIG. It is an urban infrastructure grid type data.
22 is a diagram of the status of vulnerable buildings (heavy rain).
23 is an aged-only grid type data, and FIG. 24 is a semi-underground housing grid type data.
25 is a grid data of the elderly population, and FIG. 26 is a grid data of a citizen population.
26 is a grid-type data of the number of citizens.
27 shows the number of precipitation days of 80 mm/day or more on average in the future.
28 is a grid-type data showing the number of precipitation days of 80 mm/day or more per year.
29 is a diagram showing the current state of urbanization/drying areas for the last 10 years.
30 is a grid-type data of urbanization areas for the last 10 years.
31 is a diagram of the current status of the district in which the development project is to be carried out.
32 is a grid-type data of development project progress and planned districts.
33 is a data grid for population growth.
34 is a missing area of a hydrological soil group, and FIG. 35 is a grid of missing numerical land cover diagrams.
36 is a CN value calculation for each grid.
37 is a sub-basin division of Jeju Island.
38 is an example of Flow Direction analysis.
39 is a method for indicating an outflow direction.
40 is a flow accumulation window screen (example).
41 is a cumulative CN map of Jeju Island.
42 is a comparison of the riverside lowlands and flooded trace areas.
43 is an example of the Cluster and Outlier Analysis Tool Window window.
44 is a cluster and outlier analysis analysis range (example).
45 is a cluster analysis result of the numerical elevation model according to the range of the surrounding area.
46 shows the current climate exposure.
47 is a current urban sensitivity.
48 is a vulnerability rating matrix.
49 is a current storm disaster vulnerability analysis result.
50 is future climate exposure.
51 is a future city sensitivity.
52 is a vulnerability rating matrix, and FIG. 53 is a future storm disaster vulnerability analysis result.
54 is an urban comprehensive disaster vulnerability rating matrix, and FIG. 55 is a heavy rain-related urban comprehensive disaster vulnerability analysis result.
56 is a block diagram of the present inventor's system for analyzing heavy rain disaster vulnerability.
57 is a block diagram of the current climate exposure analysis unit.
58 is a block diagram of a future climate exposure analysis unit.

Hereinafter, preferred embodiments of the present invention will be described in detail. First, in describing the present invention, detailed descriptions of related known functions or configurations are omitted so as not to obscure the gist of the present invention.

As used herein, the terms 'about', 'substantially', etc. are used in or close to the numerical value when a tolerance is given in the stated meaning, and to help the understanding of the present invention, accurate or absolute The figures are used to prevent unscrupulous infringers from taking advantage of the referenced disclosure.

First, the analysis method used in the present invention will be described.

Standardization of analysis indicators

In order to compare, analyze, and overlap indicators with different observation values, standardization is required. In general, the Z-score standardized by the normal distribution is used. After obtaining the Z-score value, it is standardized again to a value between 0 and 1. . The analysis index standardization formula is shown in Table 1 below.

Current Disaster Vulnerability Analysis Method

① Current climate exposure

The current climate exposure is analyzed using past-current weather observation data (manned observatories, AWS) to analyze the degree of influence by current climatic factors,

After performing spatial interpolation using the meteorological observation data of the target local government and neighboring cities/guns, the results are given as climate exposure values for each aggregate district, and graded (I~ IV).

Using ArcGIS Spatial Analyst's Zonal Tool, the average value of climate exposure for each aggregate is extracted.

② Current Urban Sensitivity

Potentially vulnerable areas are aggregated and classified by synthesizing the damage areas over the past 10 years, legal risk areas stipulated in the relevant laws and regulations for each type of target disaster, and vulnerable areas considering the characteristics of disaster damage.

Potentially vulnerable area score for each counting district is calculated as the ratio of the potential vulnerable area to the area of the aggregated district, and areas that have repeatedly suffered damage twice or more in the past are considered redundantly.

The urban vulnerable components are aggregated by synthesizing the vulnerable population, urban infrastructure, and buildings within the minimum spatial range including the potential vulnerable areas, and the city sensitivity is calculated by synthesizing the potential vulnerable areas and the urban vulnerable components and using ArcGIS’s natural classification method (Jenks). optimization method) to grade (I-IV).

The urban sensitivity score is calculated as the average of the potential vulnerable area score and the urban vulnerable component score.

Table 2 below shows the current urban sensitivity calculation method.

③ Current vulnerability to disasters (grading)

The current disaster vulnerability is graded (grade I to IV) into a matching grade on the disaster vulnerability rating matrix for the current climate exposure (grade I to IV) and the current urban sensitivity (grade I to IV), and the current disaster vulnerable area (I, II) grades are derived, and the current climate exposure, current urban sensitivity, and current vulnerability to disasters are more vulnerable to I grade.

Table 3 below is the current disaster vulnerability classification table by metrics.

Future disaster vulnerability analysis method

① Future climate exposure

For future climate exposure, the impact of future climatic factors is analyzed using forecasts based on climate change scenarios. Using ArcGIS Spatial Analyst's Zonal Tool, the average value of climate exposure for each minimum spatial range is extracted, and the natural classification method (Jenks' optimization method) is used. ) to grade (grade I to IV).

② Future city sensitivity

In consideration of future urban development prospects such as urbanization areas, population growth, development project progress/planned districts, etc., future urban sensitive areas are derived, and points are given for each counting district. It is calculated as the area ratio of the urban sensitive area. The score for each indicator is standardized and averaged to calculate the future city sensitivity score.

The future urban sensitive area scores for each minimum spatial extent are graded (I-IV) by ArcGIS's natural classification method (Jenks' optimization method). Table 4 below is a method for calculating future city sensitivity.

③ Vulnerability to future disasters (grading)

Future disaster vulnerability is graded (grade I-IV) by matching grades on the disaster vulnerability rating matrix for future climate exposure (grade I-IV) and sensitivity of future cities (grade I-IV) (Level I, II), and future climate exposure, future city sensitivity, and future disaster vulnerability are more vulnerable to I grade. Table 5 below is a classification table of future disaster vulnerability by matrix.

Urban Comprehensive Disaster Vulnerability

① Calculation of urban comprehensive disaster vulnerability

By superimposing the current disaster vulnerability analysis result and the future disaster vulnerability analysis result, the urban comprehensive disaster vulnerability (draft) is created by calculating the higher grade, and an example of the urban comprehensive disaster vulnerability analysis is shown in Table 6 below.

Current Vulnerability Level 1 + Future Vulnerability Level 2 = Level 1

Current Vulnerability Level 3 + Future Vulnerability Level 1 = Level 1

How to build future climate exposure indicators

① RCP Scenario

IPCC AR5, which means sea level rise due to the RCP (Representative Concentration Pathways) scenario, uses the Representative Concentration Pathways RCP as a new scenario in the CC 5th Assessment Report (AR5, 2013).

The greenhouse gas concentration was determined by the amount of radiation that human activity exerts on the atmosphere. For one representative radiative forcing, the expression ‘representative’ is used in the sense that there can be many socio-economic scenarios. And it includes the meaning of 'Pathways' to highlight changes over time in greenhouse gas emission scenarios.

In the RCP scenario, four representative greenhouse gas concentrations of 2.6, 4.5, 6.0, and 8.5 were used to reflect the recent trend of changes in greenhouse gas concentrations. In the process of calculating the greenhouse gas concentration, the socioeconomic assumption is changed from the basis of the structure of the future society (SRES scenario) to whether or not to implement the climate change response policy. Table 7 below shows the RCP scenario characteristics.

According to the IPCC 5th Assessment Report (AR5, 2013), if greenhouse gases are emitted without reduction at the current trend (RCP 8.5), the global average temperature at the end of this century (2081-2100) will rise by 3.7 ℃ and sea level will rise by 63 cm. do.

If the greenhouse gas reduction policy is substantially realized (RCP 4.5), the global average temperature by the end of this century is projected to rise by 1.8 °C and sea level by 47 cm.

The IPCC (AR4, 2007) predicts that if the mass consumption society (A1FI) fueled by fossil fuels continues, the global average temperature at the end of this century (2090-2099) will rise by up to 6.4 °C and sea level by 59 cm compared to 1980 to 1999. do.

Since the amount of sea level rise according to the IPCC 5th Assessment Report (AR5, 2013) is the result of predicting global sea level rise, in order to predict regional sea level rise, the analysis data should be reflected as regional characteristic data.

Analysis unit improvement

As the unit of analysis of the present invention, the old aggregation district is calculated as the area ratio of the potential vulnerable area, so the analysis result is distorted due to the aggregate area area.

A census district is a spatial unit divided using semi-permanent topographical features such as surrounding roads, rivers, railroads, and mountain fortresses based on a population of about 500 people during the census survey. The spatial scope of the disaster vulnerability analysis refers to the census counts of the relevant city and county included within 1 km of the coastline.

In the unit analysis of aggregate districts, large aggregate districts are included in the eastern and western regions of Jeju Island, where population development is relatively small. Therefore, since the present invention is calculated as the area ratio of the potential vulnerable area, in order to improve the distortion of the analysis result due to the area of the aggregation district, the spatial analysis unit is improved from the aggregation district to the grid unit (100 × 100m), and the disaster vulnerability analysis result is converted to the parcel unit. It is intended to promote practical utility when establishing a disaster-preventing urban plan.

56 is a block diagram of the present inventor's system for analyzing heavy rain disaster vulnerability. The present invention relates to a heavy rain disaster vulnerability analysis advanced system, and includes an output unit that divides the current vulnerability rating analysis unit and the future vulnerability rating analysis unit into a current vulnerability rating analysis unit and a future vulnerability rating analysis unit for each analysis unit, and selects and outputs a lower grade among the ratings of each analysis unit, wherein the current The vulnerability rating analysis unit consists of a current climate exposure analysis unit and a current urban sensitivity analysis unit, and the result of each analysis unit is calculated as an average of two grades in any one of 1 to 4 grades, and the future vulnerability grade analysis unit is the future climate It consists of an exposure analysis unit and a future city sensitivity analysis unit, and the result of each analysis unit is one of 1 to 4 grades, and has a characteristic that is calculated as an average of two grades.

1. Current Climate Exposure Analysis Department

57 is a block diagram of the current climate exposure analysis unit. A current climate exposure analysis unit of the present invention includes: a database in which observation data on the average annual average precipitation days of 80 mm/day or more and the average annual time maximum precipitation provided by the Korea Meteorological Administration are stored; It is characterized in that it is composed of a data conversion unit that converts point-type climate data into area-type data and zonal statistics grid-specific indicators by using spatial interpolation for the observation data stored in the database. 1 shows the current climate exposure analysis index construction process.

In order to derive data on the number of days of precipitation over 80 mm/day and the maximum amount of precipitation per year on average in Jeju Special Self-Governing Province, a total of 40 observatories (4 manned, 36 unmanned) were applied, and although the number of manned observatories is small, the data collection period This length makes the statistics more reliable. Although the data collection period of the unmanned observatory is short, it is possible to supplement the data of the manned observatory because it is located at various points. data can be obtained.

The number of days of precipitation of 80 mm/day or more per year on average is constructed with data on the number of days of precipitation of 80 mm/day or more on average per meteorological station. The station with the lowest annual average of 80mm/day or more is about 0.5 days at Woljeong, and the highest station at Seongpanak is about 15.26 days.

The number of precipitation days (point type data) of 80 mm/day or more per year per observation point is derived as area type data using IDW spatial interpolation, and the observation values between points are derived using the IDW analysis technique of Arcmap (GIS program).

The number of days of precipitation over 80 mm/day tends to increase near Mt. Halla, and the southern part of Jeju Island (Seogwipo-si area) tends to have a relatively higher value. The average number of precipitation days (area data) of 80mm/day or more is constructed using the section statistical method to construct data for each 100m X 100m grid. 2 is an area-type data showing the number of days of precipitation of 80 mm/day or more per year.

The annual average hourly maximum precipitation data was constructed by meteorological station. The observatory with the smallest annual maximum hourly precipitation is about 32.25 mm at Odeung, and the highest is about 92.75 mm at Yeongsil. 3 is a grid-type data showing the number of days of precipitation over an average of 80 mm/day per year.

The average annual time maximum precipitation (point-type data) for each observation point is derived as area-type data using IDW spatial interpolation. Hourly maximum precipitation tends to increase nearer Mt. Halla, and the western side of Jeju Island tends to have a relatively low value and the eastern side tends to have a relatively high value. 4 is an annual average time maximum precipitation area type data. The average annual time maximum precipitation index (area data) can be constructed using the section statistical method to construct data for each 100m X 100m grid.

2. Current Urban Sensitivity Analysis Department

The present urban sensitivity analysis unit of the present invention is composed of a potential vulnerable area and urban vulnerable components, and it is characterized in that each component is an analysis unit to construct an analysis index data.

The analysis index related to urban sensitivity is largely composed of potential vulnerable areas and urban vulnerable components, and the analysis index data is constructed based on the aggregate area of each component compared to the area of each component. It consists of a risk improvement district (flooding, loss, fragility prevention, collapse), a landslide vulnerable area (landslide risk area), a low-lying area along major rivers (a region lower than the planned flood level), and a risk district of the Comprehensive Flood and Flood Reduction Plan.

Urban vulnerable components are composed of citizens, urban infrastructure, and buildings, and in the case of the grid, the area is the same, so the analysis index data is constructed with the area included in the grid. In the case of citizens, information on the vulnerable class is derived using the population data of 100*100m from the National Geographic Information Service, and the relative low-lying areas, cumulative CN, and urban sensitivity indicators are added and averaged with the same weight to calculate the indicators.

Areas affected in the last 10 years Information on areas affected by Jeju Special Self-Governing Province over the past 10 years is constructed based on flood damage areas and flood traces managed by the National Disaster Management Information System (NDMS). 6 shows a map of the current state of the damaged area in the past. The area of the damaged area for the last 10 years by grid can be constructed as an analysis index data for the damaged area for the last 10 years, and FIG. 7 is a grid-type data of the damaged area for the last 10 years.

Natural disaster risk improvement districts are 35 districts in total designated as natural disaster risk improvement districts (flooding, loss, vulnerable disaster prevention, collapse) related to heavy rain disasters in Jeju Special Self-Governing Province, and 14 districts in Jeju City are natural disaster risk improvement districts. In the case of Seogwipo City, 17 out of 21 districts are designated as flood risk zones and 4 are designated as collapse risk zones. 8 is a diagram of the status of the natural disaster risk improvement district, and FIG. 9 is the natural disaster risk improvement district analysis index data for each grid area of the natural disaster risk improvement district.

The low-lying areas along the main rivers are lower than the planned flood level, and the analysis indicators for the low-lying areas along the rivers are derived from 17 major rivers in Jeju Special Self-Governing Province.

In the Jeju Special Self-Governing Province Comprehensive Plan for Flood and Flood Damage Reduction, low-lying areas lower than the planned flood level are derived for 17 major rivers when selecting a storm and flood risk zone. 10 is a diagram of the current state of the main riverside lowlands.

As for the storm and flood risk zones in the Comprehensive Flood and Flood Reduction Plan, there are a total of 120 zones designated by the Jeju Special Self-Governing Province Comprehensive Plan for Flood and Flood Damage Reduction in 2014. There are 42 river disaster risk zones, 38 inland water disaster risk zones, 7 slope disaster risk zones, and 33 coastal disaster risk zones. In the case of coastal disaster risk zones, there is little relevance to heavy rain disasters, so data were constructed for 87 locations excluding 33 locations. do. 11 is a grid-type data of the riverside low-lying area, FIG. 12 is a diagram of the current state of the storm and flood risk zone, and FIG.

The grid data of potential vulnerable areas such as damaged areas, natural disaster risk improvement areas, major riverside lowlands, and risk areas of the Comprehensive Flood and Flood Reduction Plan was constructed.

Urban vulnerable components are largely composed of citizens, urban infrastructure, and buildings. Urban infrastructure related to the vulnerability analysis of heavy rain disasters is roads, railways, water supply facilities, electricity supply facilities, gas supply facilities, heat supply facilities, and oil. It includes storage and oil transmission facilities and water pollution prevention facilities.

The area within the aggregated district for each facility is derived, and the facility area data compared to the aggregated area is used as an analysis index, and the infrastructure-related data is extracted from the Real Estate Comprehensive Study System (KRAS) to construct the analysis data. 15 is a diagram of the status of vulnerable urban infrastructure (heavy rain).

The urban infrastructure area for each grid can be built as an urban infrastructure analysis index data, FIG. 16 is road grid data, FIG. 17 is water supply facility grid data, FIG. Supply facility grid-type data, FIG. 20 is water pollution grid-type data, and FIG. 21 is urban infrastructure grid-type data.

Data on buildings related to heavy rain disasters are built based on old single-family houses and semi-underground houses in the tally. For building-related information, the building register is used to provide information on old single-family houses and single and multi-family houses that are over 20 years old (approval date for use before 1999). Data is constructed by deriving information on semi-underground houses with the number of stories below the ground level. 22 is a diagram of the status of vulnerable buildings (heavy rain). It is possible to construct the building area for each grid as a building analysis index data, and FIG. 23 is a grid-type data for an aging individual, and FIG. 24 is a grid-type data for a semi-underground house.

Citizen data related to heavy rain disasters use data from the National Geographic Information Service, and the 100-meter grid of the National Geographic Information Service is used to derive the population of the elderly over 65 and children under the age of 5 and use it as an index. 25 is a grid data of the elderly population, and FIG. 26 is a grid data of a citizen population.

3. Future Climate Exposure Analysis Department

58 is a block diagram of a future climate exposure analysis unit. The future climate exposure analysis unit of the present invention includes: a database for classifying and storing data on the number of days of precipitation over an average of 80 mm/day per year using climate change scenario data (RCP 8.5) from the Climate Change Information Center; It is characterized in that it is composed of a data conversion unit that converts point-type climate data into area-type data and zonal statistics grid-specific indicators by using spatial interpolation for the observation data stored in the database.

Future climate exposure data related to heavy rain disasters can be constructed using data on the number of days of precipitation over an average of 80 mm/day using climate change scenario data (RCP 8.5). , the daily average data were processed to construct a 30-year average data of the number of precipitation days of 80 mm/day or more, and used as future climate exposure data. and calculated as the average value. In the southeastern part of Jeju Special Self-Governing Province, such as Seogwipo City, it is high, whereas Jeju City is relatively low. 27 shows the number of precipitation days of 80 mm/day or more on average in the future.

The area of the future annual average of 80 mm/day or more for each grid can be constructed as an analysis index data for the future average annual average of 80 mm/day or more.

4. Future City Sensitivity Analysis Department

The future city sensitivity analysis unit of the present invention is characterized by constructing urbanization area, population increase number, and planned district data for each analysis unit as analysis index data for a certain period.

Urbanization areas in the last 10 years are used as analysis indicators by deriving areas that have changed due to urbanization in the last 10 years by using the middle classification data of the land cover of the Environmental Geospatial Information Service.

Using the 2005 and 2014 medium-class land cover data (resolution 5m) of the Environmental Geospatial Information Service, data on areas newly changed to urbanization and dry areas were derived. and dry areas. 29 is a diagram showing the current state of urbanization/drying areas for the last 10 years.

It is possible to construct the urbanized and dried area area for the last 10 years by grid as the urbanized and dried area data for the last 10 years, and FIG.

Development project progress and planned district Urban development project progress and planned district data are derived using computerized data from the Korea Land Information System. Currently, there are five urban development-related business areas and planned districts registered in the Jeju Special Self-Governing Province Real Estate Comprehensive Study System (KRAS). 37 is a diagram of the current state of the district in which the development project is to be carried out.

It is possible to construct the urban development project progress and planned district area by grid as urban development project progress and planned district data, and FIG.

The number of population growth in the last 10 years can be constructed using five-year data due to the limitation of data. In the present invention, due to data limitations, the population increase data is constructed by calculating the difference between the number of the population in October 2014 and the number of the population in April 2019, which is the most recent data. 33 is a data grid for population growth.

5. Improved Indicators for Urban Sensitivity Analysis Department

The cumulative CN index and the relative lowland index, which are improved indexes, are added to the current urban sensitivity analysis unit of the present invention with the same weight.

It is converted into a standard index that can be combined with the existing analysis index and calculated as an average value index. The basic data are numerical land cover, hydrological soil group, numerical elevation data, and subwatershed boundary data. It is characterized by calculating the accumulated CN by using the CN value in the direction of the runoff.

(1) Cumulative CN Indicator

The method of calculating the amount of direct runoff minus the loss from rainfall is to select a method for calculating stormwater runoff to reflect the characteristics of stormwater runoff. There are five methods of law.

In order to calculate the direct runoff in the target area, it is necessary to accurately analyze the rainfall-runoff data that have already occurred and calculate the rainfall loss. There are many difficulties in estimating the direct outflow.

In Korea, the NRCS method, which is one of the standard rainfall-runoff curve methods proposed by the U.S. Bureau of Natural Resources Conservation (NRCS), is widely used. Caution should be exercised when applying to watersheds with different hydrological characteristics.

The NRCS method does not take into account the effect of rainfall intensity on the loss, the estimated penetration rate does not match the unsaturated layer flow theory, and if the rainfall continues for a long time, it becomes the same as the permeability coefficient in the saturated state. It is judged that the runoff coefficient according to the rational equation can be applied in reality because the surface of the earth is saturated. The practical guidelines of the Ministry of Public Administration and Security for the preparation and review of the pre-disaster impact assessment report stipulates the application method of NRCS as follows, but the direct runoff (effective rainfall) is calculated using the NRCS method.

For the soil map, use a precise soil map, but use a rough soil map when unavoidable.

For the runoff curve index (CN: Curve Number), the average runoff curve index of the watershed is calculated by assigning a CN value to the area of the same soil type-coverage type and then averaging the area.

Antecedent Moisture Condition (AMC) is calculated by applying AMC-Ⅲ conditions.

The steps to construct the cumulative CN index are largely composed of the basic data processing part and the step of processing the index in subwatershed units using the processed index data. There are counties, numerical elevation data, and subwatershed boundary data.

The basic data processing part includes data pre-processing and calculation of CN values for each grid. In the stage of constructing the cumulative CN indicator, the CN value is derived for each subwatershed, and the process of calculating the accumulated CN by considering the flow direction of the stormwater runoff. include

The first step of processing the basic data is to check the missing data by examining the soil group and land cover data, and assign the relevant values considering the surrounding values. 34 is a missing area of a hydrological soil group, and FIG. 35 is a grid of missing numerical land cover diagrams.

After supplementing missing data, data such as numerical land cover and hydrological soil group are converted into the same format as the 100m grid of population data, and in the case of numerical land cover and hydrological soil group data for CN value calculation, the population data Since the 100m grid and the starting point and size are different, it is necessary to unify them.

When processing basic data with 100m grid data, the characteristic that takes up the most area in the grid is given as the representative characteristic of the grid, and programs such as ArcMap and Excel are used for this. functions, etc. The next step is to process the spatial format of the numerical elevation model in the same format as the 100m grid of the population data, calculate the average elevation value in the 00m grid data, and assign it to the 100m grid. Assign numerical elevation values to each grid.

The CN value for each grid is calculated using data such as numerical land cover and hydrological soil group, and the CN value is calculated by applying the numerical land cover and hydrological soil group values for each grid in AMC-III condition. 36 is a CN value calculation for each grid.

The steps to build the cumulative CN indicator index are to classify the analysis data by subwatershed, convert polygon grid data to raster data, analyze the outflow direction for each subwatershed, calculate the cumulative CN value, integrate the calculated raster data for each subwatershed, convert the raster grid data to polygon Convert to data.

To calculate the cumulative CN for each subwatershed in Jeju Island, the numerical elevation model data is divided by subwatershed and the subwatershed unit map and 100m grid unit numerical elevation model data are used to classify the cumulative CN value for each of the 16 subwatersheds.

To classify numerical elevation model data by subwatershed, AcrMap's Spatial Join and Split by Attributes functions are used. 37 is a sub-basin division of Jeju Island.

Convert CN values and numerical elevation model data constructed in polygon form to raster data to enable hydrological analysis such as storm runoff direction analysis for each subwatershed, and CN values and numerical elevation model data for each grid by using ArcMap’s Polygon to Raster analysis. Analyze the outflow direction by subcatchment using 16 100m grid raster data for each subcatchment as an outflow direction analysis for each subwatershed and calculate the runoff direction for each subcatchment by using the Flow Direction function of ArcMap. It can be seen that the outflow direction is a total of 8 directions. 38 is an example of Flow Direction analysis. 39 is a method for indicating an outflow direction.

Calculating the accumulated CN value considering the runoff direction for each subcatchment is to calculate the accumulated CN value by using the Flow Accumulation function of ArcMap considering the runoff direction for each grid. When using the Flow Accumulation function, the Input Weight Raster is calculated for each grid. Enter the CN value. 40 is a flow accumulation window screen (example).

It converts the accumulated CN values built for each 16 subwatersheds into one raster data, and uses the Mosaic to New Raster function of ArcMap to convert the accumulated CN values built for each 16 subwatersheds into one raster data. Converting one cumulative CN raster data to polygon data converts the accumulated CN value built from raster data into polygon data for ease of disaster vulnerability analysis and utilizes the Zonal Statistics as Table, Join function of ArcMap.

As a result of the cumulative CN index, the cumulative CN value is calculated to be high along the downstream of the river, and the highest CN value is 209574. 41 is a cumulative CN map of Jeju Island.

(2) relative lowlands

The relative low land indicator of the present invention is a result of the elevation value calculation and elevation cluster analysis using 100 m grid data, and is a result of relative high-low, high-high, low-high, low-low. Alternatively, as any one of the other (Non Significant), the 100m grid elevation value calculation is calculated using the Zonal Statics as Table, Join function of ArcMap, and the elevation cluster analysis is analyzed using ArcMap's Local Moran's I function. There is this.

Due to the need to develop a relative low-lying index, in the disaster vulnerability analysis, the area lower than the planned flood level can be used as a low-lying area along the river. Among the current urban sensitivity indicators of disaster vulnerability analysis, it is possible to use major riverside low-lying indicators for potential vulnerable areas. However, when looking at the areas where damage actually occurred, damage frequently occurred not only in the low-lying areas along the rivers, but also in other areas. When judging low-lying areas by considering only numerical values, it is limited to explain areas where flooding occurs in areas other than riverside low-lying areas.

Therefore, in the present invention, to improve this problem, a “relative low-lying index” is developed to improve the vulnerability analysis method for heavy rain disasters, and the relative low-lying area means an area having a relatively low elevation compared to the surrounding area. Vulnerability does not have to exactly match the flooded area because climate exposure and various urban sensitivity indicators are used. 42 is a comparison of the riverside lowlands and flooded trace areas.

Using the Spatial Clustering Analysis technique to build the relative lowlands,

Spatial cluster analysis is an analysis method used to statistically determine whether spatial clusters are formed by analyzing spatial patterns of data. Spatial clustering analysis largely includes a global clustering pattern that determines the presence or absence of a spatial clustering pattern in the entire region and a local clustering pattern that determines the presence or absence of a spatial clustering pattern in specific places. Higher (lower) than surrounding areas can be analyzed, which can be analyzed using the Local Moran's I analysis method.

When constructing and analyzing the relative lowland data by calculating the local peony index for the surrounding 1km by grid, it is necessary to select the area to be analyzed (the grid) and the range of the surrounding area. The wider the range of the surrounding area, the longer the analysis time. , it is possible to overload the computer when there is an abnormality in a certain space. In the case of Jeju Island, it is composed of a total of 184,973 grids. 43 is an example of the Cluster and Outlier Analysis tool Window window. 44 is a cluster and outlier analysis analysis range (example).

The relative lowland construction method consists of calculating the elevation value with 100m grid data and analyzing the elevation cluster. The calculation of the 100m grid elevation value is the same as when constructing the cumulative CN, using ArcMap's Zonal Statics as Table, Join function.

Elevation cluster analysis is analyzed using ArcMap's Local Moran's I function, and the analysis results are largely as shown in Table 8 below: Relative High-Low, High-High, Relative Low-High, and Low. -Low) and other (Non Significant). 45 is a cluster analysis result of the numerical elevation model according to the range of the surrounding area.

6. Comparison of analysis results of heavy rain disaster vulnerability

In the 'Jeju-type disaster vulnerability analysis system' developed in the present invention, it is possible to add indicators and apply weights, but in the present invention, the accumulated CN and relative low-land indicators developed in this study are added to the existing heavy rain disaster vulnerability analysis index to add a heavy rain disaster. Analyze the vulnerability and review the results.

(1) Current storm disaster vulnerability analysis result

As a result of the current climate exposure analysis, grades 1 and II are analyzed to be about 32%, and the current climate exposure is analyzed using the average annual average of 80 mm/day or more of precipitation days and the average annual maximum time precipitation index. 46 shows the current climate exposure.

As a result of the current urban sensitivity analysis, grades 1 and II are analyzed to be about 28%, and the current urban sensitivity analysis is analyzed using the index of potential vulnerable areas and urban vulnerable components. 47 is a current urban sensitivity.

As a result of the current vulnerability analysis to heavy rain disasters, grades 1 and II are analyzed to be about 8%. In the current urban sensitivity analysis, current climate exposure and current urban civic sensitivity data are classified according to the vulnerability grade matrix, and the climate exposure is distributed around the Hallasan area. However, as for the urban sensitivity, there is no area classified as an I grade as grade I is distributed around the coast. 48 is a vulnerability rating matrix. 49 is a current storm disaster vulnerability analysis result. Table 11 below shows the current vulnerability analysis results for heavy rain disasters.

(2) Future heavy rain disaster vulnerability analysis result

As a result of the analysis of future climate exposure, grades 1 and II are about 25.67%, and future climate exposure is analyzed using an index of the number of precipitation days with an average annual average of 80 mm/day or more. 50 is future climate exposure. Table 12 below is a table of future climate exposure.

As a result of the analysis of future city sensitivity, grades 1 and II are analyzed to be about 2.6%, and the analysis of future city sensitivity is analyzed using indicators of development project progress/planned districts, urbanization areas over the past 10 years, and population growth index over the past 5 years. 51 is a future city sensitivity. Table 13 shows the results.

As a result of the vulnerability analysis for future heavy rain disasters, grades 1 and II are about 10%, and the future city sensitivity analysis classifies future climate exposure and future city sensitivity data by the vulnerability rating matrix. 52 is a vulnerability rating matrix, and FIG. 53 is a future storm disaster vulnerability analysis result. Table 14 shows the results.

(3) Result of analysis of vulnerability to comprehensive disasters in heavy rain

As a result of the vulnerability analysis for future heavy rain disasters, grades 1 and II are about 10%, and the future city sensitivity analysis classifies future climate exposure and future city sensitivity data by the vulnerability rating matrix. 54 is an urban comprehensive disaster vulnerability rating matrix, and FIG. 55 is a heavy rain-related urban comprehensive disaster vulnerability analysis result. Table 15 shows the results.

The present invention described above is not limited by the above-described embodiments and the accompanying drawings, and it is common in the technical field to which the present invention pertains that various substitutions, modifications and changes are possible within the scope without departing from the technical spirit of the present invention. It will be clear to those who have the knowledge of

Claims (8)

In the heavy rain disaster vulnerability analysis advanced system,
Divided into a current vulnerability rating analysis unit and a future vulnerability rating analysis unit for each analysis unit, including an output unit that selects and outputs the lower level among the ratings of each analysis unit,
The current vulnerability rating analysis unit consists of a current climate exposure analysis unit and a current urban sensitivity analysis unit, and the result of each analysis unit is calculated as an average of two grades in any one of 1 to 4 grades,
The future vulnerability rating analysis unit is composed of a future climate exposure analysis unit and a future city sensitivity analysis unit, and the result of each analysis unit is calculated as an average of two grades in any one of 1 to 4 grades,
The current urban sensitivity analysis unit consists of a potential vulnerable area and urban vulnerable components, and constructs analysis index data for each component as an analysis unit,
The cumulative CN index and the relative lowland index, which are improved indexes, are added to the urban sensitivity analysis unit with the same weight,
It is converted into a standard index that can be summed with the existing analysis index and calculated as an average value index,
The cumulative CN index consists of a basic data processing step and a step of processing the index in a subwatershed unit using the processed index data,
The basic data are numerical land cover, hydrological soil group, numerical elevation data, and subwatershed boundary data,
The basic data processing step includes pre-processing of data and calculation of CN values for each grid,
The CN value is derived for each subwatershed, and the accumulated CN is calculated using the derived CN value using the stormwater runoff direction.
The step for constructing the cumulative CN index consists of processing the index in a subwatershed unit using the basic data processing part and the processed index data. , numerical elevation data and subwatershed boundary data,
The basic data processing part includes data pre-processing and calculation of CN values for each grid, and in the cumulative CN index construction stage, CN values are derived for each subwatershed, and the derived CN values are used to calculate the accumulated CN in consideration of the stormwater runoff direction. includes,
The relative low land indicator is a result of the elevation value calculation and elevation cluster analysis with 100 m grid data. (Non Significant) either,
Elevation value calculation using the 100m grid data is calculated using ArcMap's Zonal Statics as Table, Join function,
The elevation cluster analysis is characterized by analyzing using the Local Moran's I function of ArcMap, a heavy rain disaster vulnerability analysis advanced system.
The method of claim 1,
The current climate exposure analysis department
A database for storing observation data on the number of days of precipitation over 80 mm/day and the average annual maximum precipitation for each station provided by the Korea Meteorological Administration;
a data conversion unit that converts point-type climate data into area-type data and zonal statistics grid-specific indicators by using spatial interpolation for the observation data stored in the database;
An advanced system for analysis of vulnerability to heavy rain disasters characterized by being composed of
delete The method of claim 1,
The future climate exposure analysis department
A database that classifies and stores data on the number of days of precipitation with an average annual average of 80 mm/day or more using climate change scenario data (RCP 8.5) from the Climate Change Information Center;
a data conversion unit that converts point-type climate data into area-type data and zonal statistics grid-specific indicators by using spatial interpolation for the observation data stored in the database;
An advanced system for analysis of vulnerability to heavy rain disasters characterized by being composed of
2. The method of claim 1
The future city sensitivity analysis unit is characterized in that the urbanization area, the number of population increase, and the planned district data of the development area for a certain period are constructed as analysis index data for each analysis unit.
The method of claim 1,
The analysis unit is a heavy rain disaster vulnerability analysis advanced system, characterized in that the grid area of 100m × 100m size.
delete delete

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