CN106651211B - Flood disaster risk assessment method for different scale areas - Google Patents
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Abstract
The invention discloses a flood disaster risk assessment method for different scale areas. The method comprises the following steps: collecting spatial data of a region to be analyzed and attribute data associated with the spatial data as initial data, compressing the initial data and storing the compressed initial data into a created spatial database; standardizing the initial data and unifying units; determining the combined weight of each factor by using an AHP (analytic hierarchy process) and entropy weight method coupling model; and evaluating the flood disaster risk value in the research area, classifying the risk level, and generating a flood disaster risk index map. The method takes various collected natural and social data as input, adopts a plurality of algorithm coupling models to carry out weight determination, and finally generates a flood risk grade evaluation index chart, so that the accuracy of flood disaster risk evaluation is improved, and the method has the advantages of objectivity and high efficiency, and more intuitively shows the flood disaster risk condition possibly suffered by a research area under given conditions.
Description
The technical field is as follows:
the invention relates to the field of disaster information management, in particular to construction of a flood disaster risk assessment method.
Background art:
flood is a phenomenon in which the water level in low-lying areas and rivers rises sharply due to heavy rain or continuous rainfall, and the generation and development of the flood are influenced by the action of natural environmental systems. The storm flood disaster mainly has the following characteristics: (1) prevalence and persistence; (2) global and regional; (3) uncertainty of occurrence and predictability; (4) burstiness and sluggishness; (5) migration, hysteresis and reproducibility; (6) the dual nature of the consequences. Flood damages the growth of crops, damages the normal production of agriculture and the normal development of other industries, and even damages the health and safety of human life. With the instability of global climate change, the number of extreme climate events increases, and the occurrence frequency of flood disasters gradually increases. The number of casualties caused by flood disasters is more than 65% of the number of casualties caused by natural disasters every year all over the world, and the loss caused by the flood disasters is more than 30% of the loss caused by the natural disasters every year. China is a country with frequent natural disasters, and flood disasters are one of the natural disasters which have the greatest influence on the development of the whole society and economy. Along with global warming, development and utilization of natural resources are continuously expanded, planning and construction of urban and rural economy are continuously strengthened, the occurrence frequency of flood is higher and higher, the loss is larger and larger, and the safety of national economy and the safety of people's lives and properties are seriously threatened. Flood disasters have become important problems facing and paying attention to human society, and flood disaster risk evaluation research becomes a current research hotspot problem.
At present, most scholars think that the flood risk is determined by three factors of flood risk, stability and vulnerability, and in the process of forming the flood risk, besides the risk, the stability and the vulnerability, the effect of the disaster prevention and reduction capability on the magnitude of the flood risk degree is also relatively large. Therefore, some researchers in China take the disaster prevention and reduction capability into consideration when analyzing the flood disaster risk; the risk, stability, vulnerability and disaster prevention and reduction capability are considered to be the result of the comprehensive action of the four.
Danger represents a meteorological phenomenon (such as precipitation) that causes a flood disaster; stability describes topographic features and the like (such as river network density, topographic gradient and the like); the vulnerability represents the degree of influence of exposed objects in the disaster area in the flood disaster; the disaster prevention and reduction capability indicates the degree to which a disaster-affected area can recover from a disaster in a long and short period of time.
The flood disaster risk assessment is to comprehensively evaluate the disaster causing factors, the risk of the pregnant disaster environment, the vulnerability of the disaster bearing body and the stability, and provides a scientific decision basis for disaster reduction and management. One of the key points in the current flood disaster risk assessment research is how to scientifically and objectively synthesize a multi-index problem into a single-index form, and realize comprehensive assessment in a one-dimensional space, which is how to reasonably determine the weights of the evaluation indexes. The flood disaster risk assessment is to quantitatively analyze and assess the possibility of the risk area suffering from floods with different intensities and the possible consequences. The flood disaster risk evaluation relates to the influence of numerous factors such as disaster factors, pregnant disaster environments, disaster-bearing bodies and the like, and the factors have stronger spatial difference among different areas;
the flood disaster risk assessment and research methods are many, the most common method in the flood disaster risk assessment is a mathematical statistics analysis method, which is a type of traditional quantitative analysis method established on the basis of probability theory and mathematical statistics, and is suitable for the treatment of random phenomena and processes of flood disasters. The mathematical statistic analysis method comprises a correlation analysis method, a regression analysis method, a time series analysis method, a principal component analysis method and the like, wherein the regression analysis method is a mathematical statistic analysis method frequently applied in flood disaster risk assessment at present.
An Analytic Hierarchy Process (AHP) proposed by the american operational scientist, Saaty in the 70 th 20 th century is a typical decision analysis method from qualitative analysis to quantitative analysis integration, which mathematically quantizes the thinking Process of people on a complex system, quantifies the qualitative analysis mainly based on human subjective judgment, and quantifies the difference between various judgment elements to help people keep the consistency of the thinking Process. By using the method, a decision maker can decompose the complex problem into a plurality of layers and a plurality of factors, and the weights of all the factors can be obtained by simply comparing and calculating all the factors. The flood disaster risk assessment is a complex and multi-level problem, the weight of each index can be scientifically and reasonably determined by using an analytic hierarchy process, and the method is successfully applied to the flood disaster risk assessment theory and practice at present.
The comprehensive risk assessment research of the rainstorm flood disaster in China starts late, but a series of researches are also carried out. Most researches are carried out by utilizing geographic spatial information, combining with a GIS technology, constructing a disaster risk evaluation model by utilizing a research method, evaluating the contribution rate of each risk factor, generating a disaster risk zoning map of a research area by utilizing a GIS grid computing function, analyzing the zoning map and carrying out comprehensive risk evaluation on flood disasters.
The method integrates the research results at home and abroad, and the rainstorm disaster risk analysis has the following defects: (1) in the aspect of an evaluation method, a great deal of research is already carried out on the flood disaster forecasting by using one method, and the integration research of various research methods is less; (2) in the aspect of evaluating indexes, the comprehensive risk and the relative risk of a flood disaster are basically evaluated by using data such as historical rainfall, water systems, terrain and the like, and parameters such as vegetation, land utilization types and the like are not considered; (3) and (3) disaster process aspect: the method mainly has more researches on the history of flood disasters in the past, has insufficient researches on the current situation, and particularly has less researches on the rainstorm disaster process.
The invention content is as follows:
the invention aims to provide a flood risk analysis and evaluation method, which aims to establish a flood disaster risk evaluation index GIS database, establish a flood disaster risk evaluation model and solve flood disaster prevention and reduction measures for meteorological departments and relevant local governments.
In order to solve the problems, the technical scheme of the application is as follows:
the method mainly comprises the following steps:
a method for flood disaster risk assessment of different scale areas is characterized by comprising the following steps:
step one, collecting and arranging data. The method comprises the steps of collecting spatial data of an area to be analyzed and attribute data related to the spatial data as initial data, wherein the initial data comprises rainfall data, runoff data, flood disaster frequency, hydrological data, terrain data, land utilization data, vegetation data, social and economic data, population density data, hydrological water level observation station density data, labor population proportion data, local financial income data and the like, the hydrological water system data is represented by river network density, the terrain data is represented by replacing gradients with elevation relative standard deviations, and the land utilization data and the vegetation data are obtained through remote sensing information. Finally, compressing the initial data and storing the compressed initial data into a created spatial database;
step two, carrying out standardization processing on the initial data collected in the step one, and unifying units; carrying out standardization processing on the original index data in the step one and assigning values to each evaluation unit; all the data are converted into a grid data storage format, and all the data are subjected to projection conversion and resampling.
And step three, determining the weight of each influence factor. Determining the weight of each factor by using an AHP (analytic hierarchy process) and entropy weight method coupling model; the AHP analytic hierarchy process quantifies the influence degree of each influence factor by using limited quantitative data, is more systematic, clear and definite, and is simultaneously concise and practical, the entropy weight method effectively reduces the interference of artificial subjective factors in the analytic hierarchy process, and can start from the acquisition of data of each risk factor of the station facility, make full use of the change of data information per se and objectively obtain the weight of the risk factors. The analysis method integrates the advantages of the entropy weight method and the analytic hierarchy process, so that the weight determination in the multi-index comprehensive evaluation tends to be more reasonable.
And step four, establishing a comprehensive risk assessment model. According to the characteristics of flood disasters, establishing hierarchical structure models of an index layer, a criterion layer and a target layer, and determining index items corresponding to the criterion layer; determining the risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation of the research area on flood disasters, evaluating the flood disaster risk value in the research area, dividing the risk level and generating a flood disaster risk index map.
The runoff quantity calculation method of the first step adopts an SCS model curve numerical method, and the specific content comprises the following steps:
the SCS runoff model can reflect the influence of different soil types, different land utilization modes and early soil water content on rainfall runoff, and the calculation formula is as follows:
wherein: q is the runoff; p is the daily precipitation; s is the potential infiltration amount, and CN is the curve value.
The initial data standardization process in the second step comprises the following steps:
data is normalized in ARCGIS by a map algebra tool. The normalized formula is:
the new data obtained by such normalization has a maximum value of 1 and a minimum value of 0 for each element, and the remaining values are all between 0 and 1. Wherein XiIs a statistical VALUE or a VALUE of VALUE in the thematic index data, Xi(max) represents the maximum value in the index data value, Xi(min) represents the most significant of the index data valuesA small value.
Determining the weight of each influence factor in the third step, and determining the weight of each influence factor by combining an AHP (analytic hierarchy process) and an entropy weight method;
further, the specific method comprises the following steps: determining the weight of each influence factor by using an AHP (analytic hierarchy process) and an entropy weight method respectively, and then solving a combined weight;
further, the method multiplier and normalization for determining the combining weights are shown as follows:
wjrepresents the integrated weight, ujRepresents the entropy weight of information, w'jRepresenting the AHP analytic hierarchy determined weights. When the weight sequence obtained by the entropy weight method and the analytic hierarchy process is completely the same, the weight coefficient obtained by the entropy weight method is used as the final weight coefficient of each index; when the weight coefficients obtained by the two methods are not consistent according to the ranking of the importance levels of the indexes, the weight coefficient obtained by the analytic hierarchy process is the final weight coefficient of each index; in the intermediate state, a compromise approach may be employed. The analysis method combines the advantages of entropy weighting method and analytic hierarchy process.
And fourthly, establishing a comprehensive risk evaluation model, wherein the flood disaster comprehensive risk evaluation method comprises risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation.
The comprehensive risk evaluation is risk evaluation, vulnerability evaluation, stability evaluation and disaster prevention and reduction capability evaluation
The step four flood disaster comprehensive risk assessment comprises risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation, and the method further comprises the following steps:
selecting river runoff, rainfall indexes and historical flood disaster frequency for risk evaluation;
selecting DEM, land utilization type, vegetation index and river network density factor to evaluate the stability of the disaster-pregnant environment in the rainstorm disaster;
selecting population density distribution and per-capita GDP indexes to evaluate vulnerability of flood disasters;
and selecting the number density of the hydrological water level stations, the local financial income of unit land area and the proportion of labor population to evaluate the disaster prevention and reduction capability.
Establishing a risk assessment model, wherein the model further comprises:
according to the risk evaluation factor weight, carrying out IDW method interpolation, carrying out discretization on spatial data, and obtaining a flood disaster risk grade distribution map through index conversion;
according to the stability evaluation factor weight, calculating and analyzing the factor index map layer participating in evaluation by using a map algebra function to obtain a rainstorm flood disaster stability evaluation map;
determining the influence degree of each factor on flood disasters according to the principle that the larger the population density is, the higher the total density of domestic production is and the higher the vulnerability of the flood disasters is, and obtaining a vulnerability evaluation graph of the flood disasters;
and performing interpolation analysis on the participation evaluation factor indexes according to the evaluation factor weight of the disaster prevention and reduction capacity to obtain a flood disaster prevention and reduction capacity evaluation graph.
The fourth step is to evaluate the flood disaster risk value in the research area, and the content comprises the following steps:
superposing and analyzing a rainstorm flood disaster risk grade evaluation graph, a pregnant disaster environment stability grade evaluation graph, a vulnerability grade evaluation result graph and a disaster prevention and reduction capability evaluation graph in a research area; and finally obtaining a flood disaster risk evaluation result picture of the research area.
Step four, the method for grading the risk comprises the following steps:
the final flood disaster risk level is divided into five levels: a low risk zone, a lower risk zone, a medium risk zone, a higher risk zone, a high risk zone.
According to the flood disaster risk assessment method provided by the invention, the areas and the danger levels possibly causing social and economic damage due to the flood disaster phenomenon are analyzed on different spatial scales according to the characteristics of the flood disaster system and the characteristics of the spatial change rule thereof, and the danger levels are assessed.
Drawings
First, a flow chart of a flood disaster risk assessment method provided by the present application is shown
Second, the present application provides a technical route map for calculating the radial flow rate by using the SCS model
FIG. C is a flow chart of demographic data processing provided herein
FIG. four is a flow chart of economic data processing provided by the present application
FIG. V is a flow chart of AHP analytic hierarchy process in the weight determination method provided in the present application
FIG. six is a flow chart of an entropy weight method in the weight determination method provided by the present application
FIG. seven is a flowchart of a risk assessment module method provided by the present application
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The figure is a block diagram of flood disaster risk assessment process provided by the invention, and is shown as the following figure:
and step S1, collecting and arranging data including rainfall data, runoff volume, flood disaster frequency, hydrologic data, terrain data, land utilization data, vegetation data, social and economic data, population density data, hydrologic water level observation site density data, local financial income data and labor population proportion data. Compressing the initial data and storing the compressed initial data into a created spatial database;
step S1.1, in the rainfall data, the rainfall intensity, duration and range directly influence the severity of the formed flood disaster, weather station data (including longitude and latitude) is directly imported into ARCGIS software, and projection information is given; performing interpolation by adopting an IDW (inverse discrete wavelet) space difference method to obtain a rainfall grid data graph; and carrying out normalization calculation on the rainfall data to obtain a normalized rainfall influence distribution graph of the research area.
And a substep S1.2, calculating the radial flow by using a SCS model Curve numerical method (CN), and calculating based on the CN value.
The SCS runoff model can reflect the influence of different soil types, different land utilization modes and early soil water content on rainfall runoff.
Wherein: q is the runoff; p is the daily precipitation; s is the potential infiltration amount, and CN is the curve value. It can be seen from the above formula that the runoff of the catchment area depends on the rainfall and the potential infiltration amount of the catchment area before rainfall, and the potential infiltration amount is related to the soil texture of the catchment area, the land utilization mode and the soil wetting condition before rainfall, and the curve numerical method reflects the factors through an empirical synthesis. If the CN value is obtained, Q can be obtained by the formula. The path of the runoff computing technology is shown in figure two.
And step S1.3, looking up historical data to obtain the occurrence frequency of flood disasters in various places.
In substep S1.4, the terrain change is usually represented by a slope, but the current principle of performing slope calculation by GIS software is to consider only the elevation change degree of the adjacent grids, and actually the terrain change in a certain range that affects the magnitude of the flood risk degree, so the slope is replaced by the elevation relative standard deviation (using FOCALSTD function provided in module ARCGIS GRID), and a comprehensive terrain influence factor graph is obtained.
And step S1.5, the data format of the land use data preprocessing is GEOTIFF, and the research area data is extracted and converted into raster data by using an Extract by mask command in ARCGIS.
And S1.6, vegetation data processing, wherein reflectivity data can be selected for obtaining the vegetation data which are synchronous with weather conditions, and the data cloud amount in the period is very small and the data quality is high. The vegetation index is a multi-day synthetic product, and in order to prevent the influence of clouds, a maximum vegetation index Method (MVC) is adopted for processing, and finally a vegetation index distribution grid map is obtained.
And a substep S1.7 of river data processing, namely adopting a river network density processing method. The river network density is an important index of the structural characteristics of the river basin, and is defined as the total length of the river channel in a unit area, and can be expressed by the following formula:
in the formula, L is the total length of river in the river basin, A is the area of the river basin, n is the total number of sections of the river in the river basin, L is the average river length, and a is the average adjacent area. In actual calculation, the river length in each grid can be calculated, and due to different degrees of influence on flood disasters, linear rivers are weighted according to grades and then are subjected to river network density calculation, and the river network density is subjected to normalization calculation to obtain the normalized river network density.
And a substep S1.8 of acquiring the population index, wherein the common population density represents the population index of a certain place, and the acquisition method of the area population density generally adopts a statistical data calculation method, namely, the more recent population data is applied, and the area average population density is calculated by using the area total area in a statistical unit and the total population in the unit according to a formula.
ρn＝Pn/An
Where ρ isnThe population density of the residential area of the area n, P is the total population of the area n, AnIs the total residential area of the area n. The population density data processing flow chart is shown in figure three.
And S1.9, carrying out statistics on the economic data and the GDP economic data by taking a certain administrative unit as a unit, and obtaining financial income of the certain administrative unit by annual statistical inspection. The calculation flow chart is shown in the fourth figure.
And S1.10, calculating the density data of the hydrologic water level observation points according to the number of the hydrologic water level observation points in unit area.
And step S1.11, the proportion of labor population to total population is obtained from the proportion of population of 15-60 years old.
Step S2, in the data standardization module, for the convenience of spatial operation, all the data are converted into a grid data storage format, all the data are subjected to projection conversion and resampling, and the rainfall data, river network density data, terrain, population, social economy and other index data are subjected to standardization processing and assigned to each evaluation unit; and obtaining the grid data standardization through a normalization method. Data is normalized in ARCGIS by a map algebra tool. Normalized formula is
The new data obtained by such normalization has a maximum value of 1 and a minimum value of 0 for each element, and the remaining values are all between 0 and 1. Wherein XiIs a statistical VALUE or a VALUE of VALUE in the thematic index data, Xi(max) represents the maximum value in the index data value, Xi(min) represents the minimum value among the index data values.
And step S3, determining the regional flood disaster risk influence factor weight.
And a substep S3.1 of determining the weight of the influence factor by using an AHP analytic hierarchy process, wherein the specific flow is shown in a fifth figure.
And a substep S3.1.1 of establishing a hierarchical structure model, analyzing the relation and mutual influence among all the influence factors of the flood disaster risk, and establishing a hierarchical structure model of an index layer, a criterion layer and a target layer according to the characteristics of the flood disaster.
And a substep S3.1.2 of constructing a judgment matrix, comparing the factors of the same layer in the hierarchical model in pairs, comparing the importance degrees of the factors of the previous layer, and quantifying according to a scale specified in advance to form the judgment matrix. The judgment matrix result is mainly obtained through expert evaluation or historical data.
Sub-step S3.1.3, calculating a weight vector, calculating the relative weight of each factor of the decision matrix to its criteria. The judgment matrix A corresponds to the maximum eigenvalue lambdamaxThe feature vector W is normalized to be the weight W 'of the relative importance of the corresponding factor of the same level to the factor of the previous level'j. In order to avoid the interference of other factors on the judgment matrix, the judgment matrix is required to meet the substantial consistency in practice, and the consistency test is carried out according to the following formula.
In the formula, λmaxTo determine the maximum characteristic root of the matrix, n is the number of paired comparison factors. When CR is less than or equal to 0.1, the judgment matrix is satisfied, otherwise, the judgment matrix is adjusted until the consistency check meets the requirement. Wherein CI is the consistency index, RI is the average random consistency index, and CR is the random consistency ratio.
And the substep S3.2, the entropy weight method effectively reduces the interference of artificial subjective factors in the analytic hierarchy process, starts with the data acquisition of each risk factor, fully utilizes the change of data information and objectively obtains the weight of the risk factor. The main process of determining the weight by the entropy weight method is shown in figure six:
substep S3.2.1, performing normalization processing on the judgment matrix established by the analytic hierarchy process to obtain a standard matrix:
p＝(pij)n×nwherein the content of the first and second substances,
substep S3.2.2, calculating entropy of j index
Wherein e isj(0≤ej≤1)，
Are information entropy coefficients.
And a substep S3.2.3 of calculating an information entropy weight of the index.
Substep S3.3, the combining weights are determined. Common methods for determining combining weights are multipliers and normalization as follows:
when the weight sequence obtained by the entropy weight method is completely the same as the weight sequence obtained by the analytic hierarchy process, the weight coefficient obtained by the entropy weight method is used as the final weight coefficient of each index, so that the subjectivity of the index weight coefficient can be effectively eliminated; when the weight coefficients obtained by the two methods are not consistent according to the ranking of the importance levels of the indexes, the weight coefficient obtained by the analytic hierarchy process is taken as the final weight coefficient of each index, so that the error that the weight determined by the entropy weight method is contradictory to the actual importance level of the indexes can be eliminated; in the intermediate state, a compromise approach may be employed. The analysis method integrates the advantages of the entropy weight method and the analytic hierarchy process, so that the weight determination in the multi-index comprehensive evaluation tends to be more reasonable.
And step S4, establishing a risk assessment model. And evaluating the flood disaster risk value in the research area, classifying the risk level, and generating a flood disaster risk index map. Seventhly, the risk assessment module comprises four sub-modules for risk assessment, stability assessment, vulnerability assessment and disaster prevention and reduction capability assessment.
And a substep S4.1 of selecting rainfall and runoff indexes for risk evaluation, wherein one rainstorm lasts for about 3-4 days. According to historical flood data analysis, the influence of maximum rainfall for three days on the flood is the largest, so that the average maximum rainfall for three days in the month and in the concentrated rainfall month, river runoff indexes and historical flood occurrence frequency are selected as risk evaluation reflecting the influence on the flood.
And a substep S4.2, evaluating the stability of the flood disaster, which comprises the following contents: and selecting DEM, land utilization type, vegetation index and river network density factor to evaluate the stability of the disaster-pregnant environment in the rainstorm disaster. And expressing the influence of each factor index on the stability of the pregnant disaster environment based on the standardized processing of the index data and the five-stage division of the data. And determining the weight of the DEM, the land utilization type, the vegetation index and the river network density, and carrying out consistency test. And calculating and analyzing the factor index map layers participating in evaluation by using a map algebra function to obtain a rainstorm flood disaster stability evaluation map.
And a substep S4.3, evaluating the vulnerability of the flood disaster, which comprises the following contents: and selecting population density distribution and per-capita GDP indexes to evaluate vulnerability of flood disasters. And determining the influence degree of each factor on flood according to the principle that the greater the population density, the higher the total production value density in China and the higher the flood vulnerability. And obtaining a flood disaster vulnerability evaluation chart.
And a substep S4.4, evaluating the flood disaster prevention and reduction capacity of the flood disaster, wherein the evaluation contents comprise: and selecting the number density of the hydrological water level stations, the local financial income of unit land area and the proportion of labor population to evaluate the disaster prevention and reduction capability.
And a substep S4.5, wherein the flood disaster comprehensive risk evaluation content comprises the following steps: the natural disaster comprehensive risk expression is as follows: comprehensive risk (risk) is risk + vulnerability + stability + disaster prevention and reduction capability
It can more fully reflect the essential characteristics of the risk. By adopting a comprehensive index method and according to a natural disaster risk expression, the method can be further expressed as follows:
risk index:
stability index:
vulnerability index:
disaster prevention and reduction index:
ri (x) wHIHI(x)+wSISI(x)+wVIVI(x)+PI(x)
Wherein, Wj,WHI,WSI,WVIRepresenting the impact factor index weight, HIji(x)，SIji(x)，VIji(x)，PIji(x) Representing an impact factor index; hi (x) represents a flood risk index, si (x) represents a flood stability index, vi (x) represents a flood vulnerability index, pi (x) represents a disaster prevention and reduction capability index, and ri (x) represents a flood risk index.
Substep S4.6, in the regional flood disaster risk equivalent evaluation chart making, utilizing the formula to carry out superposition analysis on the flood disaster risk grade evaluation chart, the pregnant disaster environment stability grade evaluation chart, the vulnerability grade evaluation result chart and the disaster prevention and reduction capability evaluation result chart in the research region; the final risk value grades are divided into five grades: a low risk zone, a lower risk zone, a medium risk zone, a higher risk zone, a high risk zone. And finally obtaining a flood disaster risk evaluation result picture of the research area.
It should be noted that, in order to avoid duplication of written information, the scale of the area to which the method is applied is not described in the steps of embodying the present invention. It should be understood that this study applies to: areas of different dimensions in which flood disasters occur.
The flood disaster risk assessment method with different scales provided by the application is introduced in detail above. The above steps are only used to help understand the specific method and core idea of the present invention, and are not used to limit the present invention. For some researchers in the field, according to the idea of this application, the specific implementation and the application scope will be changed.
The full name of the English abbreviation in the application is:
AHP: analytic Hierarchy Process (AHP)
SCS model (Soil consistency Service, SCS)
Curve numerical method (CN)
GIS (Geographic Information System, GIS), Geographic Information System
ARCGIS geographic information System professional software developed by the American environmental systems institute (ESRI), without English acronyms
DEM (Digital Elevation Model, DEM), Digital Elevation Model
GDP (Gross Domestic Product, GDP), the Gross Domestic Product
IDW (IDW), Inverse Distance Weighted
MVC (Maximum Value Composite, MVC), Maximum vegetation index method
Claims (5)
1. A method for flood disaster risk assessment of different scale areas is characterized by comprising the following steps:
step one, collecting and arranging data; collecting spatial data of an area to be analyzed and attribute data associated with the spatial data as initial data, wherein the initial data comprises rainfall data, runoff data, flood disaster frequency, hydrological data, topographic data, land utilization data, vegetation data, social and economic data, population density data, hydrological water level observation station density data, labor population proportion data and local financial income data, the hydrological water system data is represented by river network density, the terrain data is represented by replacing gradients with elevation relative standard deviations, the land utilization data and the vegetation data are obtained through remote sensing information, and the initial data are compressed and stored in a created spatial database;
step two, carrying out standardization processing on the initial data collected in the step one, and unifying units; carrying out standardization processing on the original index data in the step one and assigning values to each evaluation unit; all the data are converted into a grid data storage format, and all the data are subjected to projection conversion and resampling;
the initial data standardization process in the second step comprises the following steps:
in the ARCGIS, data is standardized by a map algebra tool, and the standardized formula is as follows:
the new data obtained by this normalization has a maximum value of 1 and a minimum value of 0 for each element, and the remaining values are between 0 and 1, where X isiIs a statistical VALUE or a VALUE of VALUE in the thematic index data, Xi(max) represents the maximum value in the index data value, Xi(min) represents the minimum of the index data values;
thirdly, determining the weight of each influence factor; determining the weight of each factor by using an AHP (analytic hierarchy process) and entropy weight method coupling model;
determining the weight of each influence factor in the third step, and determining the weight of each influence factor by combining an AHP (analytic hierarchy process) and an entropy weight method;
further, the specific method comprises the following steps: determining the weight of each influence factor by using an AHP (analytic hierarchy process) and an entropy weight method respectively, and then solving a combined weight;
further, the method multiplier and normalization for determining the combining weights are shown as follows:
wjrepresents the integrated weight, ujRepresents the entropy weight of information, w'jRepresenting the weight determined by AHP analytic hierarchy process, and using entropy when the weight sequence obtained by entropy weight method and analytic hierarchy process is identicalTaking the weight coefficient obtained by the weight method as the final weight coefficient of each index; when the weight coefficients obtained by the two methods are not consistent according to the ranking of the importance levels of the indexes, the weight coefficient obtained by the analytic hierarchy process is the final weight coefficient of each index; when the device is in the intermediate state, a compromise method can be adopted, and the analysis method integrates the advantages of an entropy weight method and an analytic hierarchy process;
step four, establishing a comprehensive risk assessment model; according to the characteristics of flood disasters, establishing hierarchical structure models of an index layer, a criterion layer and a target layer, and determining index items corresponding to the criterion layer; determining the risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation of a research area on flood disasters of the research area, evaluating the comprehensive risk value of the flood disasters in the research area, dividing the risk level and generating a flood disaster risk index map;
establishing a comprehensive risk evaluation model, wherein the contents of the comprehensive risk evaluation model for flood disasters comprise risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation,
the comprehensive risk evaluation is risk evaluation, vulnerability evaluation, stability evaluation and disaster prevention and reduction capability evaluation;
the step four flood disaster comprehensive risk assessment comprises risk evaluation, stability evaluation, vulnerability evaluation, disaster prevention and reduction capability evaluation and comprehensive risk evaluation, and the method further comprises the following steps:
selecting river runoff, rainfall indexes and historical flood disaster frequency for risk evaluation;
selecting DEM, land utilization type, vegetation index and river network density factor to evaluate the stability of the disaster-pregnant environment in the rainstorm disaster;
selecting population density distribution and per-capita GDP indexes to evaluate vulnerability of flood disasters;
and selecting the number density of the hydrological water level stations, the local financial income of unit land area and the proportion of labor population to evaluate the disaster prevention and reduction capability.
2. The method for flood disaster risk assessment in different scale areas according to claim 1, wherein the runoff volume calculation method in the first step adopts an SCS model curve numerical method, and the specific content includes:
the SCS runoff model can reflect the influence of different soil types, different land utilization modes and early soil water content on rainfall runoff, and the calculation formula is as follows:
wherein: q is the runoff; p is the daily precipitation; s is the potential infiltration amount, and CN is the curve value.
3. The method for flood disaster risk assessment in areas of different scales according to claim 1, wherein said step four establishes a risk assessment model, which further comprises:
according to the risk evaluation factor weight, carrying out IDW method interpolation, carrying out discretization on spatial data, and obtaining a flood disaster risk grade distribution map through index conversion;
according to the stability evaluation factor weight, calculating and analyzing the factor index map layer participating in evaluation by using a map algebra function to obtain a rainstorm flood disaster stability evaluation map;
determining the influence degree of each factor on flood disasters according to the principle that the larger the population density is, the higher the total density of domestic production is and the higher the vulnerability of the flood disasters is, and obtaining a vulnerability evaluation graph of the flood disasters;
and performing interpolation analysis on the participation evaluation factor indexes according to the evaluation factor weight of the disaster prevention and reduction capacity to obtain a flood disaster prevention and reduction capacity evaluation graph.
4. The method for flood disaster risk assessment in different scale areas according to claim 1, wherein said step four assesses the integrated risk value of flood disaster in the research area, and the method comprises:
superposing and analyzing a rainstorm flood disaster risk grade evaluation graph, a pregnant disaster environment stability grade evaluation graph, a vulnerability grade evaluation result graph and a disaster prevention and reduction capability evaluation graph in a research area; and finally obtaining a flood disaster risk evaluation result picture of the research area.
5. The method for flood disaster risk assessment in areas of different scales according to claim 1, wherein said step four is a risk classification method, comprising:
the final flood disaster risk level is divided into five levels: a low risk zone, a lower risk zone, a medium risk zone, a higher risk zone, a high risk zone.
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