CN113191670B - Fine lightning disaster risk evaluation and division method - Google Patents
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Abstract
The application discloses a refined lightning disaster risk evaluation and division method, which is characterized in that; the method is implemented as follows; firstly, establishing an evaluation model; (II) determining index weights; third, disaster-bearing body data are spatially changed; and (IV) risk classification. The application has the following advantages: 1. remote sensing data such as DMSP/OLS night light, NDVI vegetation index, land coverage and the like and DEM data are based on inversion of population and GDP spatial distribution, and the remote sensing data has high degree of coincidence with actual conditions in general trend and local characteristics, so that a fine and reliable data source can be provided for evaluating vulnerability of disaster-bearing bodies; 2. corresponding evaluation indexes are selected from three aspects of disaster factors, disaster-causing environments and disaster-bearing bodies, and a lightning disaster risk evaluation structural model is built based on targets, criteria and index layers, so that refined lightning disaster risk division is realized.
Description
Technical Field
The application relates to the technical field related to lightning disaster risk evaluation, in particular to a refined lightning disaster risk evaluation and division method.
Background
Lightning disasters act as one of the ten most serious natural disasters and act on disaster-bearing bodies mainly in two aspects of casualties and economic property loss, and depend on local economy and population density. However, the demographic-economic related data is usually counted by each level of administrative units, and has the defects of large spatial unit scale, low resolution and the like, so that the requirement of risk decision is difficult to meet. Thus, creating a continuous population, economic data surface across an area, implementing data spatialization is critical to solving the problem. At present, three spatial methods for redistributing social and economic data by using land utilization/coverage data, namely Gao Chengdai, gradient zone, slope zone, highway, railway, water system, land coverage, residential points and the like are combined with social and economic data, and inversion is carried out on DMSP/OLS night light remote sensing data.
The research on the risk of the lightning disaster mainly comprises a risk analysis method based on historical disaster condition data and simulated disaster forming process. The historical disaster data itself is used as the result of the actions of the natural disasters and the disaster-bearing body, so that the loss degree of the disaster-bearing body in the face of the natural disasters with certain intensity is reflected, and the method has the advantages of intuitionism and credibility, but the historical disaster data is difficult to complete and is greatly discounted. The lightning disaster forming process is simulated, and the method is quite scientific from the aspects of disaster-tolerant environment, disaster-causing factors, disaster-bearing bodies and disaster prevention and reduction capabilities, but in the actual implementation process, detailed socioeconomic background data of a research area are difficult to obtain, so that the spatial distribution of disaster-tolerant environment and disaster-bearing body indexes is realized. For example, the population and economic statistics mainly based on administrative units at different levels cannot display the internal differences of areas, and if lightning disaster risk evaluation is performed on areas of city and state or smaller areas, the precision of the regional and economic statistics cannot meet government decision and scientific research requirements.
Disclosure of Invention
Accordingly, in order to solve the above-described drawbacks, the present application provides a method for evaluating and dividing risk of lightning disaster.
The application is realized in such a way, and constructs a method for evaluating and dividing the risk of the lightning disaster, which is characterized in that; the method is implemented as follows;
firstly, establishing an evaluation model: simulating a lightning disaster risk forming process, selecting corresponding evaluation indexes from three aspects of disaster factors, disaster-tolerant environments and disaster-bearing bodies, and establishing a lightning disaster risk evaluation structural model based on targets, criteria and index layers; the ground flash density and the ground flash intensity are generated by adopting density analysis and IDW interpolation of lightning positioning monitoring data in the last 10 years, and lightning with the lightning current amplitude value larger than 100kA is selected by the strong lightning current density for density analysis and generation;
index factors are selected from three aspects of disaster causing factors (H), disaster pregnancy environments (E) and disaster bearing bodies (S), and a lightning disaster risk evaluation model is established;
wherein R is lightning disaster risk, H i 、E j 、S k Respectively evaluating the ith index, the jth index and the kth index of the disaster-causing factor risk, the disaster-tolerant environment sensitivity and the disaster-bearing body vulnerability, and X Hi 、X Ej 、X Sk A, b and c are index weights corresponding to H, E, S respectively;
(II) determining index weights:
adopting an objective method of projection pursuit fuzzy clustering (Projection Pursuit Classification, called PPC), directly driving by sample data to perform data mining, searching an optimal projection direction through genetic iteration, projecting multi-dimensional data into a low-dimensional space, and finally obtaining the optimal projection direction of each index as the weight of each index;
the modeling process is as follows:
(1) Index processing; eliminating the difference between the dimensions and unifying the variation range;
(2) Linear projection; randomly extracting a plurality of initial projection directions a (a 1 ,a 2 ,…,a m ) Calculating, determining the solution corresponding to the maximum index as the optimal projection direction and projecting the characteristic value Z according to the index selection principle i The expression of (2) is:
(3) Optimizing a projection objective function; projection value Z i The distribution characteristics of (2) should be such that: the projected dot clusters are scattered as much as possible; the local projection points are aggregated into single point clusters as much as possible; the objective function T (a) is defined as the product of the inter-class distance L (a) and the intra-class density d (a), i.e., T (a) =l (a) ·d (a):
wherein the method comprises the steps ofThe larger the average value of the sequence { Z (i) i=1, 2, …, n }, the more spread. Let the distance r between projection characteristic values ij ＝|Z i -Z j I (i, j=1, 2, …, n), then
f (t) is a first-order unit step function, and when t is more than or equal to 0, the value is 1; when t is less than 0, the value is 0;
r is a window width parameter, and the selection principle is that at least one scattered point is included in the width. Reasonable value range is r max R is less than or equal to 2m, wherein R max ＝max(r ik ) (i, k=1, 2, …, n); the larger the intra-class density d (a), the more pronounced the classification;
when T (a) is the maximum value, the corresponding projection direction is the searched optimal projection direction; the problem of finding the optimal projection direction can thus be translated into the following optimization problem:
(4) Substituting the optimal projection direction into the corresponding index weight;
and (III) spatialization of disaster-bearing body data:
(1) Spatialization of demographic data
In recent years, with the development and application of remote sensing and geographic information technology, DMSP/OLS night light remote sensing data are used for simulating human activities. The supersaturation phenomenon of the DMSP/OLS night light data can be effectively reduced by fusing the NDVI index. Population data is inverted according to the population index model, and specifically, the population index (HSI) can be corrected according to geographical information data such as regional topography (such as altitude and gradient), river water system, road network and the like.
Wherein: HIS is human habitat index, NDVI max At the maximum of NDVI, OLS nor Is normalized night light (0-1), OLS max 、OLS min Respectively, the maximum value and the minimum value of night light data.
Demographic data are distributed to each pixel through a human population index, and a county level demographic count is adopted for linear adjustment to correct a population density distribution map for reducing errors, wherein the specific formula is as follows:
(2) Economic data spatialization
And combining economic (GDP) data classification characteristics, and mutually combining land coverage data and night light data to realize GDP data spatialization.
First industry model: the first industrial data G1 has one-to-one correspondence with the agricultural, forest, pasture, fish and farmland, forest land, grassland and water land utilization data, thereby establishing a space model
Wherein G1 i First industry GDP value and Area related to four land utilization data of cultivated land, woodland, grassland and water Area respectively ij For the j-th county i land utilization data corresponding area, A i The coefficients corresponding to the four land utilization data are obtained.
Second, three industry models: 2. the three industries mainly relate to industry, construction industry and various service industries, and have obvious correlation with night light data indirectly representing social and economic conditions. Extracting a DMSP/OLS night lamplight data intensity value (0 < DN less than or equal to 63), and establishing a spatial distribution model.
Wherein Int j And (3) the light intensity value of the night in the jth county, and B is a fitting coefficient.
Spatialization of economic data: in order to ensure that the GDP error after the spatialization is controlled in the administrative region of county level, k1 and k2 are adopted to respectively correct the first industry and the second industry.
G＝k1 j ×G1+k2 j ×G2/3 (13)
Wherein G is ground average GDP, k1 j For the j-th county ratio of the actual value of the first industry to the calculated value of the model, k2 j And calculating the ratio of the actual value of the second industry to the model value for the jth county.
(IV) risk classification:
the lightning disaster risk evaluation grade is determined by adopting a natural breakpoint grading method, which is also called a Jenks optimization method. The principle is that a data clustering method is adopted, so that the variance of the classes is reduced, and the difference between the classes is improved to the maximum extent; the calculation adopts a repeated iterative process, and different data sets are repeatedly calculated to determine the minimum class variance until the sum of the deviations reaches the minimum value; the formula is as follows:
wherein the method comprises the steps of
Wherein: SSD is variance, A is an array (array length N), mean i-j Is the average value in each class.
The method for evaluating and dividing the risk of the refined lightning disaster is characterized by comprising the following steps of; the method also comprises result analysis, in particular; establishing a risk evaluation set based on risk evaluation factors (including disaster factors, disaster-causing environments and disaster-bearing bodies) and corresponding indexes thereof in a rasterization process, acquiring the average value of the evaluation indexes (risk factors) of each county area by using a GIS technology, substituting a projection pursuit model based on a genetic algorithm, and calculating the optimal projection direction vector one by one to obtain the weight of an evaluation index system; establishing a population and economic data spatialization model based on DMSP/OLS night light remote sensing data, NDVI vegetation index, land coverage data and the like to obtain population density and land average GDP distribution;
and (3) carrying out raster data calculation and analysis according to the established risk evaluation model and index weight value, and obtaining an interruption value by adopting a natural breakpoint grading method, wherein the risk of the lightning disaster is divided into 5 grades of a low risk zone, a sub-low risk zone, a medium risk zone, a sub-high risk zone and a high risk zone.
The application has the following advantages: 1. remote sensing data such as DMSP/OLS night light, NDVI vegetation index, land coverage and the like and DEM data are based on the fact that population and GDP spatial distribution are inverted, the remote sensing data and the DEM data are matched with actual conditions to a higher degree in overall trend and local characteristics, and a fine and reliable data source can be provided for disaster-bearing vulnerability assessment.
2. Corresponding evaluation indexes are selected from three aspects of disaster factors, disaster-causing environments and disaster-bearing bodies, and a lightning disaster risk evaluation structural model is built based on targets, criteria and index layers, so that refined lightning disaster risk division is realized.
Drawings
FIG. 1 is a graph of the Guizhou lightning-saving activity;
FIG. 2 is a flow chart of a demographic data spatialization technique;
FIG. 3 is a graph of stabilized night light data normalized;
FIG. 4 normalized vegetation index profile;
FIG. 5 shows a slope distribution of Guizhou province;
FIG. 6 is a plot of average slope and average population density index function fit for each county;
FIG. 7 is a regression analysis chart of the accumulated value of the population index of each city and county and the statistical population;
FIG. 8 Guizhou province population density profile (resolution 1 km);
FIG. 9 is a flow chart of a GDP data spatialization technique;
FIG. 10 shows the distribution of water (a), farmland (b), woodland (c), grasslands (d) in Guizhou province;
FIG. 11 shows a GDP profile (1 km) for Guizhou province;
FIG. 12 is a technical flow chart for lightning disaster risk assessment;
FIG. 13 is a structural model diagram for lightning disaster risk assessment;
fig. 14 is a view of a lightning disaster risk zone in Guizhou province.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below with reference to fig. 1 to 14, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a refined lightning disaster risk evaluation and division method by improving, and the detailed implementation and description of the rush method are carried out by taking Guizhou province as an example;
1. the study combines the natural environment characteristics of mountain land in Guizhou province, establishes a spatial model of population and GDP data to invert the spatial distribution based on statistical annual-differentiation data, land coverage data, vegetation index (NDVI), DMSP/OLS night light and DEM data, and provides a refined and reliable data source for evaluating vulnerability of disaster-bearing bodies. And meanwhile, corresponding evaluation indexes are selected from three aspects of disaster causing factors, disaster pregnancy environments and disaster bearing bodies, a lightning disaster risk evaluation structural model is built based on targets, criteria and index layers, and lightning disaster risk division of 1km multiplied by 1km in Guizhou province is realized. The method not only can provide basis for scientific decisions of lightning protection and disaster reduction, but also can provide reference for developing refined risk evaluation of other disasters.
2. Study area overview and data sources
2.1 study area overview: the Guizhou province is located in the southwest part of China, belongs to mountain provinces with typical karst landform development, and is characterized by crisscross mountain ranges, staggered and serpentine rivers and complex topography, so that the vertical climate change of mountain regions and valleys is particularly obvious, and the theory that the mountain regions are not three days and sunny is one mountain to four mountains is presentedThe scene of ten days in season. The province is full of 6 district cities (Guiyang, zunyi, six-disc water, anshun, copper, pichia) and 3 minority autonomous states (Qian nan, qian southwest, qian southeast), 88 county administrative areas. According to the 'Guizhou statistical annual book', the model is shown to stop 2015 at the end of the year, and the population is 3529.50 ten thousands people in the whole province, and the GDP reaches 10502.56 hundred million yuan. The annual average lightning density of Guizhou province is between 0.16 and 7.15 times/km 2 The distribution is regional difference, the western part is higher than the eastern part and the southern part is higher than the north part, and the whole distribution is gradually decreased from the western part to the eastern part. Wherein the north-west of Puan, northeast of water city, west of Puding, north-west of Zhangjin, and north-middle of Wangmu are all regions with high lightning density, and the annual average lightning density is higher than 7.00 times/km 2 . Fig. 1 shows the lightning activity profile in Guizhou province.
2.2 sources of data
2.2.1DMSP/OLS night light data: DMSP/OLS night light data is derived from the national geophysical data center (NGDC, the National Geophysical Data Center). The united states national defense weather satellite program Defense Meteorological Satellite Program (DMSP) is the united states department of defense polar orbiting satellite program on which the linear scanning business system Operational Linescan System (OLS) can detect the visible-near infrared (VNIR) band, using optical telescope heads in the daytime and optical multipliers at night. The photomultiplier is originally designed for meteorological purposes for detecting clouds under moon illumination, and is gradually applied to detecting urban lights, aurora, lightning, fishing fires, fire and other surface activities due to its strong photoelectric amplifying capability.
2.2.2 land cover data: the land cover data is derived from MODIS data obtained by a medium resolution imaging spectrometer on a TERRA, AQUA satellite, and belongs to MODIS three-level data (MCD 12Q 1). Land Cover type product (Land Cover data) is a type describing Land Cover by processing data obtained from Terra and Aqua observations of one year.
Project extraction MODIS terra+Aqua three-level land cover types Global 500-meter annual global product IGBP global vegetation classification scheme in MCD12Q1, the land cover dataset contains 17 main land cover types according to the International territory biosphere program (IGBP), which includes 11 natural vegetation types, 3 land development and mosaic land classes and 3 non-vegetation land type definition classes.
2.2.3 normalized vegetation index (NDVI): the vegetation index (NDVI) is a measure of vegetation growth, vegetation coverage, and elimination of some radiation errors, etc. NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, coarse and excessive, etc., and is related to vegetation coverage. The data is from the national academy of sciences resource environment science data center (http:// www.resdc.cn).
2.2.4 lightning monitoring data: the lightning monitoring data is derived from a lightning monitoring network in Guizhou province, the monitoring is built in 2006, the operation is continued for more than 10 years at present, the construction comprises the following steps of security, chinese white fungus, daozhen, red water, pichia, kai, rich, from river, beacon, xingzhi, wangzhi and Sinan 12 ADTD lightning positioning detection substations, the effective detection efficiency can reach 200km, and after the monitoring is carried out together with peripheral sites, the real-time monitoring of the lightning activity in most areas of the whole province can be realized. Recording parameters such as time, longitude and latitude position, intensity, polarity, steepness and the like of the impact.
2.2.5 soil conductivity data: the soil conductivity data is derived from a world soil database (HWSD for short), and is commonly issued by the United nations grain and agriculture organization (FAO), international application System analysis institute (IIASA), the Netherlands ISRIC-World Soil Information, the national academy of sciences Nanjing soil Institute (ISSCAS), the European Committee Joint Research Center (JRC) in 3 months 2009, and the resolution can reach 1 km. The database provides information of soil types (FAO-74, 85, 90), soil phases, physical and chemical properties (16 indexes such as clay content, USDA soil texture classification, cation exchange capacity of soil, carbonate or lime content, sulfate content, exchangeable sodium salt, conductivity and the like) of 1X 1km lattice points and the like. The paper extracts the conductivity therein as an index for related disaster analysis.
2.2.6DEM data: the altitude and the gradient are obtained through a digital elevation model, a GTOP30 data set from an EROS data center of the United states geological survey is spliced through grids after being downloaded according to slices, and the resolution is about 30m; the gradient is generated on the basis of the dem data.
3. Spatialization of demographic data
3.1 technical route: in recent years, with the development and application of remote sensing and geographic information technology, DMSP/OLS night light remote sensing data are used for simulating human activities. According to the human settlement index concept and model, and combining with the environmental characteristics of Guizhou mountain land, slope distribution is introduced, and the human settlement index formula is corrected. According to population data spatialization, population density distribution is inverted according to population index distribution conditions, the technical flow is shown in fig. 2, and the population data spatialization technical flow in fig. 2 is realized.
The NDVI index and the DMSP/OLS night light data have good complementarity in reflecting human activities, and the oversaturation phenomenon of the night light data can be effectively reduced after fusion. Accordingly, LU et al propose concepts and models of human living index (Human settlement index, HSI).
Wherein: NDVI max Is the maximum value of NDVI, R is the density index of the road network; OLS (organic light emitting diode) nor Is normalized night light (0-1), OLS max 、OLS min Respectively, the maximum value and the minimum value of night light data.
Demographic data is assigned to each pel according to the model by the modified human population index model. In order to control the generated errors in the administrative region at the county level, the population density distribution map is linearly adjusted and corrected by adopting county level population statistics, and the specific formula is as follows:
3.2 population distribution: the processed night light index (figure 3) has a good spatial correspondence with the NDVI index (figure 4), and the NDVI value in the area with high night light value is low. Considering that Guizhou province belongs to a typical mountain province, the influence of gradient difference changes on population distribution is obvious. According to the gradient distribution, the human settlement index formula provided by LU and the like is corrected. And directly adopting DEM data to perform gradient analysis to obtain the gradient distribution condition of Guizhou province (figure 5). Further, exponential regression was used to analyze the relationship between average gradient and population density for each county (fig. 6).
Goodness of fit R of regression equation 2 = 0.6725, exhibiting a better positive correlation. The human habitat index (Human settlement index, HSI) was constructed as follows:
based on DMSP/OLS night lights and NDVI vegetation indexes, there is still a strong linear correlation between human settlement index and demographic data after slope distribution correction (FIG. 7), the decision coefficient is improved (R) 2 = 0.7182). To ensure that the simulated and statistical population are equal on the county level administrative unit, the population density map is modified with the ratio of the statistical and simulated population, and inversion generates a Guizhou population density distribution with a resolution of 1km x 1km as shown in FIG. 8. The population distribution depends on the central distribution of administrative areas, and dense areas are mainly concentrated in Guiyang to Zunyi, guiyang to Anshuan.
4. Economic data spatialization
4.1 technical route: GDP is an important index reflecting the social economic development condition and has close relation with social production activities. And combining three industrial characteristics in GDP, and mutually combining land coverage data and night light data to realize GDP data spatialization. FIG. 10 is a schematic diagram of a GDP data spatialization technique.
1. First industry model: the first industry is directly affected by natural productivity, depends on natural resources, especially land resource output, and has close relation with land coverage data. According to the principle of 'no land use and no GDP', the global vegetation classification scheme of IGBP in MODIS three-level data MCD12Q1 is adopted to extract coverage data related to agriculture, forestry, grazing, fishing and the like in the first industry from 17 main land coverage types, so as to form land, forest land, grassland and water land use data. A first industrial GDP spatial distribution model is established, and the GDP value is denoted by G1.
Wherein G1i is a first industry GDP value related to four land use data of cultivated land, woodland, grassland, and water area, area j is an area corresponding to i land use data of the jth county, and Ai is a coefficient corresponding to the four land use data.
2. Second, three industry models: the three industries mainly relate to industry, construction industry and various service industries, have little dependence on natural resources and have obvious correlation with night light data indirectly representing social and economic conditions. Accordingly, the intensity value (0 < DN less than or equal to 63) of the DMSP/OLS night light data is extracted, and a spatial distribution model is built.
Wherein, intj is the light intensity value of the night in the jth county, and B is the fitting coefficient. In order to ensure that the GDP error after the spatialization is controlled in the administrative region of county level, k1 and k2 are adopted to respectively correct the first industry and the second industry.
G＝k1 j ×G1+k2 j ×G2/3 (7)
G is ground average GDP, k1 j For the j-th county ratio of the actual value of the first industry to the calculated value of the model, k2 j And calculating the ratio of the actual value of the second industry to the model value for the jth county.
4.2GDP distribution: under the SPSS environment, the first industrial statistical value of each county and the areas of cultivated land, woodland, grassland and water area are subjected to multiple stepwise regression analysis by adopting the steps (4) and (5), the coefficients corresponding to the cultivated land, woodland, grassland and water area are respectively 0.53, 1.30, 0.22 and 15.42, and the goodness of fit R 2 = 0.7714, and passed the significance test.
Grid analysis calculation processing was performed in the Arcgis system, and the first industrial data of each county after the regional statistics and the spatial statistics were compared to each other, and the obtained correction coefficients are shown in table 1.
TABLE 1 first Industrial spatially correction factors for counties
And (3) linearly fitting the second industry, the third industry and the second industry values with night light intensity by adopting a formula (6), wherein the fitting goodness is 0.7188, 0.4996 and 0.6989 respectively, and the significance test is passed. Because the fitting condition of the third industry is poor, the night light intensity value is adopted to directly carry out the space assignment processing on the sum of the two industry and the three industry.
TABLE 2 fitting of second industry and night light
TABLE 3 second three Industrial spatially correction factors for each county
And (3) according to the established first industry and second industry space distribution model and the established third industry space distribution model, correcting the first industry and the second industry space distribution model, limiting the control error to the inside of the administrative region of the county level, and further obtaining a GDP distribution map (figure 11) of Guizhou province by adopting superposition analysis of raster data. The space distribution situation is matched with the regional economy of Guizhou features, a space structure type of 'center-periphery' taking Guiyang as an economic politics center is formed, the economic subdeveloped area is mainly distributed at the place where the urban government in each place is located and around Guiyang, the economic underdeveloped county is mainly located at the periphery of Guiyang and the developed area in the following city, the southeast part is mainly concentrated by minority nations, ecological resources are rich, the economic level is lower, and the fitting result is mostly below 10 ten thousand yuan.
5. The lightning disaster risk assessment was performed as follows:
5.1 technical route: lightning disaster refers to the event of loss of human beings and socioeconomic performance due to strong current, high voltage and strong electromagnetic radiation equivalent generated in the lightning discharge process, and lightning disaster risk refers to the degree of loss due to the effect of lightning in a future period of time. The application establishes a Guizhou lightning disaster risk division technical flow (as shown in figure 12) by taking district and county administrative areas as minimum research units by means of the strong geographic information processing and analyzing capability of Arigis 10.2.
Disaster causing factors are a potential hazard to life or property, and are distinguished from disasters. Disaster causing factors act as rare or extreme events that adversely affect activities of life, property or human beings and reach the extent of causing disasters, the existence and production of which is directly controlled to a great extent by natural effects. Factors for determining the disaster-causing factors mainly comprise time, intensity, frequency, density and the like, and by analyzing lightning parameter characteristics and a lightning accident occurrence mechanism and combining lightning current parameter attributes recorded by a Guizhou lightning-saving monitoring network, the ground flash density, the ground flash intensity and the strong current density are used as indexes for analyzing the disaster-causing factor dangers.
The disaster-causing environment is taken as a natural environment in which a disaster is generated, and generally refers to a disaster-causing factor, a climate condition in which a disaster-bearing body is located, a geographical geological condition, an altitude, water-system river distribution, vegetation coverage and the like. The sensitivity degree is a quantitative index for representing the disaster-tolerant environment of a certain area, and plays a decisive role in the intensity of a lightning disaster system and the disaster range. The thunderstorm is used as the most direct trigger factor for triggering the thunder and lightning disasters, but the lightning triggering scope and the lightning triggering strength are different due to different disaster-pregnant environments of the underlying surface. For example, areas with lower soil resistivity are more conducive to accumulation of charged particles in thunderstorm clouds, resulting in air breakdown triggering of pilot discharge; in a certain range, the area with high altitude is closer to thunderstorm cloud, and forms atmospheric electric field distortion to cause lightning. From this, it is determined that the major factors affecting the disaster recovery environment include soil conductivity, water system density, terrain elevation (DEM) and gradient distribution.
Disaster causing factors are necessary conditions for causing disasters, but the existence of risks must enable the disaster causing factors to act on disaster bearing bodies, namely human beings and social and economic activities thereof. The disaster-bearing body is a human social body directly affected by lightning disasters, and covers various aspects of human self safety and social development, various wealths accumulated by human, and the like. The method is mainly characterized by two aspects of human casualties and economic property loss, and is analyzed by adopting two indexes of population density and ground average GDP according to the spatial result of the economic data of the front part of population.
5.2 research methods
5.2.1 evaluation model: and simulating a lightning disaster risk forming process, selecting corresponding evaluation indexes from three aspects of disaster factors, disaster-tolerant environments and disaster-tolerant bodies, and establishing a lightning disaster risk evaluation structural model based on targets, criteria and index layers as shown in fig. 7. The ground flash density and the ground flash intensity are generated by adopting density analysis and IDW interpolation of lightning positioning monitoring data in the last 10 years (2006-2015), and the strong lightning current density is generated by selecting lightning with the lightning current amplitude value larger than 100kA for density analysis. Fig. 13 lightning disaster risk evaluation structural model.
Index factors are selected from three aspects of disaster causing factors (H), disaster pregnancy environments (E) and disaster bearing bodies (S), and a lightning disaster risk evaluation model is established.
Wherein R is lightning disaster risk, H i 、E j 、S k Respectively evaluating the ith index, the jth index and the kth index of the disaster-causing factor risk, the disaster-tolerant environment sensitivity and the disaster-bearing body vulnerability, and X Hi 、X Ej 、X Sk For the corresponding index weights, a, b and c are the corresponding index weights of H, E, S respectively.
5.2.2 determination of index weights: the objective method of projection pursuit fuzzy clustering (Projection Pursuit Classification, called PPC) is adopted, data mining is directly carried out by driving sample data, the optimal projection direction is sought through genetic iteration, multi-dimensional data is projected to a low-dimensional space, and finally the optimal projection direction of each index is the weight of each index.
Projection pursuit (projection pursuit) was proposed by Kruskal in the early 70 s, aims to mine a clustering structure of data, solves the problem of fossil classification, corrects Friedman and other scholars on the basis of the problem, proposes to combine the degree of walk and the degree of local aggregation, and formally proposes a concept of projection pursuit clustering. The principle is used as a clustering and classifying method which is directly driven by sample data to carry out data mining analysis, high (multi) dimension data are projected to a low dimension subspace, nonlinear problems such as multi-index classification and the like are solved to a certain extent, artificial subjectivity control is reduced, and the modeling process is as follows:
(1) And (5) index processing. Eliminating the difference between the dimensions and unifying the variation range.
(2) And (5) linear projection. Randomly extracting a plurality of initial projection directions a (a 1 ,a 2 ,…,a m ) Calculating, determining the solution corresponding to the maximum index as the optimal projection direction and projecting the characteristic value Z according to the index selection principle i The expression of (2) is:
(3) The projection objective function is optimized. Projection value Z i The distribution characteristics of (2) should be such that: the projected dot clusters are scattered as much as possible; the locally projected spots are aggregated as much as possible into a single cluster of spots. The objective function T (a) is defined as the product of the inter-class distance L (a) and the intra-class density d (a), i.e., T (a) =l (a) ·d (a):
wherein the method comprises the steps ofThe larger the average value of the sequence { Z (i) i=1, 2, …, n }, the more spread. Let the distance r between projection characteristic values ij ＝|Z i -Z j I (i, j=1, 2, …, n), then
f (t) is a first-order unit step function, and when t is more than or equal to 0, the value is 1; when t < 0, the value is 0.
R is a window width parameter, and the selection principle is that at least one scattered point is included in the width. Reasonable value range is r max R is less than or equal to 2m, wherein R max ＝max(r ik ) (i, k=1, 2, …, n). The larger the intra-class density d (a), the more pronounced the classification.
When T (a) is the maximum value, the corresponding projection direction is the searched optimal projection direction. The problem of finding the optimal projection direction can thus be translated into the following optimization problem:
(4) Substituting the optimal projection direction into the corresponding index weight.
5.2.3 risk classification: the lightning disaster risk evaluation grade is determined by adopting a natural breakpoint grading method, which is also called a Jenks optimization method. The principle is that a data clustering method is adopted, so that the variance of the classes is reduced, and the difference between the classes is improved to the maximum extent. The calculation takes a iterative process by repeatedly calculating different data sets to determine the smallest class variance until the sum of the deviations reaches a minimum. The formula is as follows:
wherein the method comprises the steps of
Wherein: SSD is variance, A is an array (array length N), mean i-j Is the average value in each class.
5.3 analysis of results
Establishing a risk evaluation set based on risk evaluation factors (including disaster causing factors, disaster-tolerant environments and disaster-bearing bodies) and corresponding indexes thereof in a rasterization process, acquiring the average value of the evaluation indexes (risk factors) of each county area by using a GIS technology, substituting a projection pursuit model based on a genetic algorithm, and calculating the optimal projection direction vectors one by one to obtain the weight of an evaluation index system as shown in table 4. The index weights of the disaster causing factors and the disaster bearing bodies in the lightning disaster risk are larger and are 0.4286 and 0.3950 respectively, and the disaster is reflected as the result of the interaction between the disaster causing factors and the disaster bearing bodies.
TABLE 4 lightning disaster risk assessment index weight
According to the established risk evaluation model and index weight values, carrying out raster data calculation analysis by substituting the established risk evaluation model and index weight values into the (8), wherein the risk values are 0.1817-1.7480, adopting a natural breakpoint grading method to obtain 0.4642, 0.5871, 0.7222 and 0.9065 interruption values, dividing the risk of a lightning disaster into 5 grades of a low risk area, a sub-low risk area, a medium risk area, a sub-high risk area and a high risk area, and dividing a lightning disaster risk area division diagram of Guizhou province is shown in fig. 14. Fig. 14 is a view of a lightning disaster risk zone in Guizhou province.
The lightning disaster high-risk areas are distributed in six-disc water, southeast of the Pichia, northeast of the southwest of the Qian, northwest of the Zunyi and other lightning activity high-incidence areas, and are all economic political centers in various cities and states.
The method has the following specific effects;
1. realize Guizhou province population-GDP data spatialization:
the traditional population economic data is usually counted by each level of administrative units, and has the defects of large space unit scale, low resolution and the like, so that the requirements of risk decision are difficult to meet. The method comprises the steps of searching documents, sorting data, integrating the current common land utilization/coverage data to redistribute social and economic data, integrating multi-source data such as Gao Chengdai, gradient zone, slope zone, highway, railway, water system, land coverage, residential points and the like with social and economic fusion, and inverting three spatial methods of DMSP/OLS night light remote sensing data, and inverting the population and GDP spatial distribution conditions of Guizhou province by combining the natural environment characteristics of Guizhou mountain land.
Remote sensing data such as DMSP/OLS night light, NDVI vegetation index, land coverage and the like and DEM data are based on the fact that population and GDP spatial distribution are inverted, the remote sensing data and the DEM data are matched with actual conditions to a higher degree in overall trend and local characteristics, and a fine and reliable data source can be provided for disaster-bearing vulnerability assessment.
2. And (3) finishing the fine evaluation research of the risk of the Guizhou lightning-saving disaster:
by establishing a population and GDP data spatialization model, the vulnerability situation of the disaster bearing body in the area is reflected in a refined mode, lightning disaster risk evaluation research is carried out, the basis can be provided for lightning protection and disaster reduction scientific decision making, and reference can be provided for carrying out refined risk evaluation on other disasters.
Corresponding evaluation indexes are selected from three aspects of disaster causing factors, disaster pregnancy environments and disaster bearing bodies, a lightning disaster risk evaluation structure model is established based on targets, criteria and index layers, the disaster causing factors and the disaster bearing bodies in the lightning disaster risk are relatively large in index weight and 0.4286 and 0.3950 respectively, and the disaster occurrence is reflected as the interaction result between the disaster causing factors and the disaster bearing bodies.
The technical innovation points are as follows:
1. remote sensing data such as DMSP/OLS night light, NDVI vegetation index, land coverage and the like and DEM data are based on the fact that population and GDP spatial distribution are inverted, the remote sensing data and the DEM data are matched with actual conditions to a higher degree in overall trend and local characteristics, and a fine and reliable data source can be provided for disaster-bearing vulnerability assessment.
2. Corresponding evaluation indexes are selected from three aspects of disaster factors, disaster-causing environments and disaster-bearing bodies, a lightning disaster risk evaluation structural model is built based on targets, criteria and index layers, and lightning disaster risk division of 1km multiplied by 1km in Guizhou province is achieved.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. A method for evaluating and dividing risk of refined lightning disasters is characterized in that the method comprises the following steps of; the method is implemented as follows;
firstly, establishing an evaluation model: simulating a lightning disaster risk forming process, selecting corresponding evaluation indexes from three aspects of disaster factors, disaster-tolerant environments and disaster-bearing bodies, and establishing a lightning disaster risk evaluation structural model based on targets, criteria and index layers; the ground flash density and the ground flash intensity are generated by adopting historical lightning positioning monitoring data density analysis and IDW interpolation, and lightning with the lightning current amplitude value larger than 100kA is selected for density analysis and generation by strong lightning current density;
index factors are selected from three aspects of disaster causing factors H, disaster inducing environments E and disaster bearing bodies S, and a lightning disaster risk evaluation model is established;
wherein R is lightning disaster risk, H i 、E j 、S k Respectively evaluating the ith index, the jth index and the kth index of the disaster-causing factor risk, the disaster-tolerant environment sensitivity and the disaster-bearing body vulnerability, and X Hi 、X Ej 、X Sk A, b and c are index weights corresponding to H, E, S respectively;
(II) determining index weights:
adopting an objective method of projection pursuit fuzzy clustering (Projection Pursuit Classification, called PPC), directly driving by sample data to perform data mining, searching an optimal projection direction through genetic iteration, projecting multi-dimensional data into a low-dimensional space, and finally obtaining the optimal projection direction of each index as the weight of each index;
the modeling process is as follows:
(1) Index processing; eliminating the difference between the dimensions and unifying the variation range;
(2) Linear projection; randomly extracting a plurality of initial projection directions a (a 1 ,a 2 ,…,a m ) Calculating, determining the solution corresponding to the maximum index as the optimal projection direction and projecting the characteristic value Z according to the index selection principle i The expression of (2) is:
(3) Optimizing a projection objective function; projection value Z i The distribution characteristics of (2) should be such that: the projected dot clusters are scattered as much as possible; the local projection points are aggregated into single point clusters as much as possible; the objective function T (a) is defined as the inter-class distance L(a) The product of the density d (a) in class, i.e., T (a) =l (a) ·d (a):
wherein the method comprises the steps ofThe larger the average value of the sequence { Z (i) |i=1, 2, …, n }, the more spread; let the distance r between projection characteristic values ij ＝|Z i -Z j I (i, j=1, 2, …, n), then
f (t) is a first-order unit step function, and when t is more than or equal to 0, the value is 1; when t is less than 0, the value is 0;
r is a window width parameter, and the selection principle is that the window width parameter at least comprises one scattered point; reasonable value range is r max R is less than or equal to 2m, wherein R max ＝max(r ik ) (i, k=1, 2, …, n); the larger the intra-class density d (a), the more pronounced the classification;
when T (a) is the maximum value, the corresponding projection direction is the searched optimal projection direction; the problem of finding the optimal projection direction can thus be translated into the following optimization problem:
(4) Substituting the optimal projection direction into the corresponding index weight;
and (III) spatialization of disaster-bearing body data:
(1) Spatialization of demographic data;
inverting population data according to a population index model, and particularly correcting the population index HSI according to geographical information data such as regional topography, river water systems, road networks and the like;
wherein: HIS is human habitat index, NDVI max At the maximum of NDVI, OLS nor Is normalized night light (0-1), OLS max 、OLS min Respectively the maximum value and the minimum value of night lamplight data;
demographic data are distributed to each pixel through a human population index, and a county level demographic count is adopted for linear adjustment to correct a population density distribution map for reducing errors, wherein the specific formula is as follows:
(2) Economic data spatialization
By combining the economic GDP data classification characteristics, the land coverage data and night light data are combined with each other to realize GDP data spatialization;
first industry model: the first industrial data G1 has one-to-one correspondence with the agricultural, forest, pasture, fish and farmland, forest land, grassland and water land utilization data, thereby establishing a space model
Wherein G1 i First industry GDP value and Area related to four land utilization data of cultivated land, woodland, grassland and water Area respectively ij For the j-th county i land utilization data corresponding area, A i Coefficients corresponding to four kinds of land utilization data;
second, three industry models: 2. the third industry mainly relates to industry, construction industry and various service industries, and has obvious correlation with night light data indirectly representing social and economic conditions; extracting a DMSP/OLS night lamplight data intensity value (0 < DN less than or equal to 63), and establishing a spatial distribution model;
wherein Int j The light intensity value of the night in the jth county is represented by B, and the fitting coefficient is represented by B;
spatialization of economic data: in order to ensure that the GDP error after the spatialization is controlled in the administrative region of county level, adopting k1 and k2 to respectively correct the first industry and the second industry;
G＝k1 j ×G1+k2 j ×G2/3 (13)
wherein G is ground average GDP, k1 j For the j-th county ratio of the actual value of the first industry to the calculated value of the model, k2 j Calculating the ratio of the actual value of the second industry to the model of the jth county;
(IV) risk classification:
the lightning disaster risk evaluation grade is determined by adopting a natural breakpoint grading method, which is also called a Jenks optimization method; the principle is that a data clustering method is adopted, so that the variance of the classes is reduced, and the difference between the classes is improved to the maximum extent; the calculation adopts a repeated iterative process, and different data sets are repeatedly calculated to determine the minimum class variance until the sum of the deviations reaches the minimum value; the formula is as follows:
wherein the method comprises the steps of
Wherein: SSD is variance, A is an array, array length N, mean i-j Is the average value in each class;
the method further comprises; establishing risk evaluation factors based on rasterization processing, wherein the risk evaluation factors comprise disaster factors, disaster-tolerant environments, disaster-tolerant bodies and indexes corresponding to the disaster factors, acquiring the average value of the evaluation indexes of each county area by using a GIS technology, substituting the average value into a projection pursuit model based on a genetic algorithm, and calculating the optimal projection direction vectors one by one to obtain the weight of an evaluation index system; establishing a population and economic data spatialization model based on DMSP/OLS night light remote sensing data, NDVI vegetation index, land coverage data and the like to obtain population density and land average GDP distribution;
and (3) carrying out raster data calculation and analysis according to the established risk evaluation model and index weight value, and obtaining an interruption value by adopting a natural breakpoint grading method, wherein the risk of the lightning disaster is divided into 5 grades of a low risk zone, a sub-low risk zone, a medium risk zone, a sub-high risk zone and a high risk zone.
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