Plain region flood comprehensive risk evaluation method

Abstract

The invention relates to a comprehensive flood risk evaluation method for a plain area, which comprises the following steps of: constructing a three-order progressive flood risk evaluation index system comprising four criteria and fifteen indexes through information arrangement of a target area, establishing a fuzzy consistent judgment matrix by utilizing a fuzzy analytic hierarchy process, and scientifically quantifying index weights, so as to evaluate the flood risk of the target area. And carrying out overlay analysis and comprehensive risk grading on each risk factor index according to each calculated index weight by utilizing a GIS tool, and finally generating a flood risk zoning map of the target region. According to the method, risk assessment can be converted from qualitative description to quantitative and visual partition, so that the high-risk area of the target area is accurately identified, a methodological template is provided for risk assessment of similar areas, and the application range is wide.

Description

Technical Field

[0001] This invention relates to the field of flood risk assessment technology in plain areas, and specifically to a comprehensive flood risk assessment method for plain areas. Background Technology

[0002] As a vital agricultural and population center globally, plains bear the core functions of regional production and life. However, due to complex climatic conditions, their own natural topography and geomorphological features, and the superposition of extreme rainfall events, urban flooding and farmland waterlogging disasters caused by short-duration heavy rainfall are becoming increasingly frequent. Floods have become one of the main disasters restricting high-quality development in plains. Based on the scenario of multiple disaster coupling and complex human-land relationships in plains, higher requirements are placed on flood risk assessment.

[0003] Flood risk is the probability of floods of varying intensities occurring and the potential for losses caused by floods. From the evolution of existing flood risk assessment theories, current technologies have undergone a significant shift from single-factor analysis to multi-dimensional system coupling. Early single-factor assessments focused only on quantifying the natural attributes of disasters using hydrological and hydrodynamic models, neglecting the spatial heterogeneity of the disaster-prone environment and the disaster-bearing bodies, making it difficult to comprehensively reflect the risk composition. Although the "risk triangle" theory incorporates the three-dimensional framework of "hazard-exposure-vulnerability," it does not fully incorporate the dimension of "disaster prevention and mitigation capabilities," failing to accurately analyze the interaction mechanism between natural disaster-causing factors and human adaptation measures in plain areas, and making it difficult to reflect the regulatory role of human interventions such as water conservancy projects and emergency management on risk.

[0004] In traditional evaluation methods, the analytic hierarchy process (AHP) has become mainstream due to its advantage of combining qualitative and quantitative approaches. However, this method has significant drawbacks, such as strong subjectivity, with judgment matrices relying on expert experience and inconsistencies of 20%-30% in the weights of indicators derived by different researchers. Consistency testing is complex, and when the number of evaluation indicators exceeds nine, the consistency test criteria lack scientific basis. Furthermore, its spatial data processing capabilities are weak, making it difficult to integrate multi-source spatial data such as meteorological, topographical, and socioeconomic data, thus failing to meet the quantitative and spatial visualization requirements of risk assessment. Although some studies have attempted hybrid methods such as "AHP + fuzzy mathematics" and "AHP + GIS spatial analysis," they have still failed to effectively balance subjectivity and objectivity, complexity and operability, and there is room for improvement in areas such as multi-source data fusion and fuzzy indicator quantification.

[0005] Therefore, researching a comprehensive flood risk assessment method for plain areas has become an urgent need to solve the problem of regional flood prevention and control. Summary of the Invention

[0006] Therefore, the purpose of this invention is to provide a comprehensive flood risk assessment method for plain areas, which effectively solves the problem that existing technologies are unable to meet the actual needs of refined flood prevention and control in plain areas due to the limitations of traditional flood risk assessment theories, the technical shortcomings of assessment methods, and the new challenges brought about by climate change and urbanization.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a comprehensive flood risk assessment method for plain areas, comprising the following steps: Step 1: Establishment of Evaluation Index System Based on the geographical characteristics and historical disaster data of the target area, a three-level progressive flood risk assessment index system is established. The system includes a target layer, a criterion layer and an index layer. The target layer is for flood risk decision-making in the target area. The criterion layer includes four dimensions: the hazard of disaster-causing factors, the sensitivity of the nurturing environment, the vulnerability of disaster-bearing bodies and the disaster prevention and mitigation capacity. The index layer is the quantitative carrier corresponding to each criterion layer. Step 2: Standardization of indicator data Based on the multi-source basic data in the indicator layer, preprocessing, raster conversion and standardization are performed on them respectively to output standardized indicator data, so as to eliminate the differences in magnitude and unit between different indicator data and ensure the spatial consistency of indicator data. Step 3: Calculate the indicator weights Based on the standardized index data, the index weights of the target layer and each criterion layer are calculated: First, a judgment matrix with a scale of 1-9 is constructed, then the judgment matrix is ​​converted into a fuzzy complementary matrix, then the fuzzy complementary matrix is ​​converted into a fuzzy consistent judgment matrix that satisfies complete consistency, and finally the weight vector is solved by the least squares method. Step 4: Risk Value Classification The standardized indicator data generated in step 2 and the weights in step 3 are coupled and calculated. First, the comprehensive evaluation value of each criterion layer is calculated by GIS raster overlay analysis. Then, the comprehensive flood risk value is obtained by coupling and the comprehensive risk value is classified into levels. Finally, the comprehensive flood risk assessment map of the target area is output.

[0008] The beneficial effects of the above technical solution are as follows: The comprehensive flood risk assessment method for plain areas proposed in this invention starts from four dimensions by collecting data, including incorporating disaster prevention and mitigation capabilities into the traditional flood assessment framework. It breaks through the limitation of the traditional assessment method's "risk triangle" theory in focusing on human adaptive measures, and constructs a four-dimensional system that includes the hazard of disaster-causing factors, the sensitivity of the disaster-prone environment, the vulnerability of disaster-bearing bodies, and disaster prevention and mitigation capabilities, thereby achieving a more comprehensive assessment of the factors affecting flood risk.

[0009] This invention employs fuzzy hierarchical analysis (AHP) to calculate the weights of each dimension, thereby effectively solving the problems of fuzziness, subjectivity, and consistency of indicators in traditional AHP. Specifically, it solves the complex problem of consistency testing in traditional AHP by constructing a fuzzy consistency judgment matrix, significantly improving the scientific nature of weight assignment. At the same time, this invention integrates GIS technology to achieve rasterization processing and spatial overlay analysis of multi-source data, transforming risk assessment from qualitative description to quantitative and visualized zoning. This enables accurate identification of high-risk areas, facilitating targeted professional decisions in flood control planning, emergency management, and watershed ecological protection in different regions. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the overall process of the method of the present invention; Figure 2 This is a hierarchical structure diagram of the flood risk assessment index system of this invention; Figure 3 This is a schematic diagram of the four-petal theoretical framework structure of the present invention; Figure 4 A comprehensive assessment map of flood risk in the Yinchuan Plain; Figure 5 A comprehensive assessment map of flood sensitivity in the Yinchuan Plain; Figure 6 A comprehensive assessment map of flood vulnerability in the Yinchuan Plain; Figure 7 A comprehensive evaluation map of the disaster prevention and mitigation capabilities of the Yinchuan Plain; Figure 8 This is a comprehensive assessment map of flood risk in the Yinchuan Plain. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1: This example aims to provide a comprehensive flood risk assessment method for plain areas. It is mainly used for the quantification and spatial visualization of flood risk assessment in plain areas, and to realize the zoning analysis of flood risk in plain areas. This example takes the Yinchuan Plain as an example. As an important oasis irrigation agricultural area and core economic area in the Yellow River Basin of Ningxia, the Yinchuan Plain has long faced complex flood risks caused by a combination of mountain torrents, river floods, and inland waterlogging, which seriously restricts the high-quality development of the region.

[0012] This embodiment proposes a comprehensive flood risk assessment method for plain areas. Based on the risk four-petal theory and fuzzy hierarchical analysis, a comprehensive analysis is conducted to construct a flood risk assessment model for plain areas. The final output is a chart, which facilitates the analysis of the spatial distribution characteristics and causes of high-risk areas. Furthermore, the risk zoning results can be combined with the actual situation in the region and applied to flood control planning and emergency management, thus providing a systematic solution for flood prevention and control in plain areas.

[0013] like Figure 1 As shown, a comprehensive flood risk assessment method for plain areas includes the following steps: Step 1: Selection of evaluation indicators and establishment of the system Based on the geographical characteristics of the target area, by analyzing the natural conditions and socio-economic carrying capacity characteristics of past floods, historical disaster data, DEM data, soil type data, land use type data, precipitation data, GDP data, population data, hydrological data, and social data of the target area are obtained; then based on, such as Figure 3 The four-petal theoretical framework shown selects indicators from four dimensions: disaster cause, disaster preparation, disaster bearing, and disaster prevention.

[0014] Specifically, in this embodiment, the average annual flood season rainfall, the average number of rainstorm days per year, and the number of historical flood disasters are selected as indicators of disaster-causing factors. Among them, the average annual flood season rainfall is the core indicator for measuring the potential energy of regional floods, the average number of rainstorm days per year reflects the frequency and intensity of extreme precipitation, and the number of historical flood disasters reflects the cumulative effect and potential pattern of flood occurrence. By focusing on the direct driving factors of flood formation, the disaster-causing potential is comprehensively characterized.

[0015] Based on the natural background conditions for flood occurrence, this invention selects distance from water bodies, absolute elevation, topographic slope, and soil type as indicators for the disaster-prone environment. Among them, distance from water bodies directly represents the risk of flood overflow and river channel risk; absolute elevation is the topographic basis for flood collection and discharge capacity; topographic slope is a key factor in runoff velocity and confluence efficiency; and soil type is a regulatory element of infiltration capacity and surface runoff. These four indicators systematically cover the key natural elements of the disaster-prone environment: distance from water bodies quantifies the spatial range of flood threat; absolute elevation and topographic slope characterize the dynamic mechanism of floods; and soil type reflects the surface's response to precipitation, providing a solid natural foundation for the spatial differentiation analysis of flood risk.

[0016] In the flood assessment of the target area, population density, crop area ratio, GDP per capita, and land use type are selected as indicators for the disaster-bearing body. Among them, population density is a direct exposure indicator of flood losses, crop area ratio is a key quantitative factor of agricultural losses, GDP per capita is a comprehensive reflection of economic vulnerability, and land use type is the spatial differentiation basis of the exposure of the disaster-bearing body. The above four indicators not only continue the classic framework of bearing body = human + economy + assets in flood risk assessment, but also strengthen the impact assessment of agricultural losses and land use changes, so as to provide a basis for risk avoidance for population layout and industrial planning in the target area.

[0017] The disaster prevention and mitigation capacity of the target area is assessed using indicators such as the number of hospital beds per unit area, public budget expenditure, GDP, and the proportion of water conservancy, environmental protection, and public facilities management. The main focus is on the role of human intervention in risk control. The number of hospital beds per unit area directly reflects the emergency medical security capacity, which can reflect the accessibility of regional medical resources and emergency treatment capacity after a flood. Public budget expenditure is a quantitative representation of local government's disaster prevention investment. GDP is a comprehensive reflection of economic foundation and post-disaster recovery capacity. Regions with higher GDP have stronger economic resilience and faster post-disaster recovery and infrastructure repair capabilities. The proportion of water conservancy, environmental protection, and public facilities management can reflect the region's professional investment in water conservancy project management (such as irrigation area drainage maintenance and river dredging), ecological environment governance (such as wetland protection and vegetation restoration), and public facilities construction (such as flood control dikes and rainwater pipe networks).

[0018] Next, based on the selected indicators, a hierarchical system of flood risk assessment indicators suitable for the target area is constructed, such as... Figure 2 As shown, the system comprises a three-tiered progressive hierarchy: the target layer, the criteria layer, and the indicator layer. The target layer is used for flood risk decision-making in the target area, assessing the overall flood risk level of the region and identifying high-risk areas. The criteria layer covers four major factors: the hazard of disaster-causing factors, the sensitivity of the disaster-prone environment, the vulnerability of disaster-bearing bodies, and disaster prevention and mitigation capabilities. Each dimension of the factors corresponds to different indicators mentioned above, thus corresponding to the evaluation scope of each criterion layer. The indicator layer includes the aforementioned 15 specific evaluation indicators, serving as the quantitative carrier of the criteria layer to achieve an operational assessment of the flood risk of the target area across various dimensions.

[0019] Step 2: Rasterization and Standardization of Indicator Data Based on the flood risk assessment index hierarchy constructed in step 1 above, corresponding data are found from different data sources according to the determined risk factor indicators. GIS technology is used to preprocess, rasterize, and standardize the basic data of each index layer. The inverse distance weighting method is used to solve the data interpolation accuracy problem in sparse areas of meteorological stations, so as to eliminate the differences in magnitude and unit between different indicators and ensure data spatial consistency. Specifically, this includes the following: 1) Data preprocessing First, the multi-source basic data obtained in step 1 above is preprocessed in a targeted manner to clarify the data format, time span and spatial range, so as to ensure data integrity and validity; (1) Preprocessing of meteorological data: Based on the target area, the daily value dataset (2000-2023) of 22 ground meteorological stations obtained from the meteorological data center of the China Meteorological Administration was used to screen out valid records with daily rainfall ≥ 50 mm, and the annual average number of rainstorm days for each station was calculated cumulatively; monthly precipitation data (2000-2023) was obtained from the National Earth System Science Data Center, and the annual flood season precipitation data was extracted as the basic data for calculating the annual average flood season rainfall.

[0020] (2) Preprocessing of historical disaster data: After collecting flood disaster data of the target area, extract core information such as the number of historical floods and the scope of impact of each administrative unit to form a structured historical flood disaster dataset.

[0021] (3) Preprocessing of natural environment data: ASTER GDEM 30M resolution digital elevation data (DEM) was obtained from the geospatial data cloud platform for subsequent topographic elevation and slope extraction in the target area; raster soil type data was downloaded from the National Earth System Science Data Center and land use type data was obtained from the National Geographic Information Public Service Platform. Both were initially cropped according to the boundary of the target area to retain the effective spatial range; remote sensing images were obtained through the geospatial data cloud platform of the Computer Network Information Center of the Chinese Academy of Sciences, and water body information was extracted using ENVI software to generate vector format water system data.

[0022] (4) Socioeconomic data preprocessing: Data such as population density, per capita GDP, proportion of crop area, public budget expenditure, regional GDP, proportion of water conservancy and public facilities management industry, and number of hospital beds per unit area are obtained from the statistical yearbook of the target area. All data are organized according to the district and county administrative units to form a standardized text format dataset to ensure that the data is associated with the spatial units.

[0023] 2) Rasterization of indicator data Based on GIS technology, the various types of preprocessed data are uniformly converted into raster format. All raster data have the same resolution as the DEM data to ensure spatial matching. The processing method is as follows: (1) Rasterization of station discrete data: For station discrete data such as the annual average number of rainstorm days and the annual average rainfall during the flood season, spatial interpolation is performed using the inverse distance weighting method (IDW). The specific steps are as follows: First, the station data is converted into vector point data with target area projection information. Meteorological stations near the area boundary are extended to participate in interpolation to improve the accuracy of edge areas. Then, the raster cell value is calculated based on the following interpolation formula: In the above formula, Z The estimated value represents the interpolation point; Zi Represents the measured sample value; n Represents the number of measured samples involved in the calculation; D i For the interpolation point and the first i The distance between stations; P It is a power of the distance.

[0024] Finally, using the target region boundary as a mask, the interpolation results are cropped to generate complete rasterized data.

[0025] (2) Rasterization of district and county statistical data: For district and county-level statistical data such as population density, GDP per capita, proportion of crop area, and public budget expenditure, the statistical data is first associated with the district and county administrative division vector map, and each administrative unit is assigned a corresponding indicator value. Then, the GIS vector to raster tool is used to take the average value of the indicator within the district and county administrative unit as the value of the corresponding raster unit to realize the spatial expression of statistical data. For historical flood disaster data, additional raster overlay processing is performed to generate a historical flood inundation frequency raster map.

[0026] (3) Rasterization of raw vector data: Based on the extracted vector water system data, a buffer zone is constructed according to the river level and absolute elevation. In this embodiment, the Yinchuan Plain area is used as an example. The buffer zone width takes into account factors such as river level and elevation. The specific rules are set as shown in Table 1 below: Table 1. Buffer widths of rivers and lakes at different elevations Secondly, different soil types have different infiltration capacities and surface runoff, therefore their flood sensitivity varies. Taking the Yinchuan Plain as an example, based on the GIS platform, the Yinchuan Plain region was extracted using a mask. The Yinchuan Plain has seven soil types, mainly alluvial soil and tidal soil. Sensitivity values ​​were then assigned to each soil type, as shown in Table 2 below. Table 2 Sensitivity of each soil type Furthermore, different land use types experience varying degrees of damage during floods. Taking the Yinchuan Plain as an example, based on a GIS platform, the Yinchuan Plain region was extracted using a mask. The land use in the Yinchuan Plain includes seven types: cultivated land, grassland, shrubland, wetland, water bodies, artificial surfaces, and bare land. Vulnerability values ​​were then assigned to each land use type, as shown in Table 3 below. Table 3 Vulnerability of different land use types Finally, based on the DEM data, a terrain elevation distribution map was drawn using GIS, and the terrain slope raster was directly generated in GIS by using the slope function built into GIS.

[0027] 3) Standardization of indicator data To eliminate differences in magnitude and unit between different indicators, such as rainfall measured in mm and population density measured in people / km² 2 Therefore, all the above-mentioned rasterized indicator data are standardized, as detailed below: First, define the number of raster samples in the target region as... M The number of evaluation indicators is N The sample measured matrix is ​​constructed as follows: In the formula, x ij The representative is the first j In the nth sample i The actual measured value of the indicator.

[0028] Then, the standard normalization formula is used to construct the sample standardization matrix: For indicators that are positively correlated with decision-making objectives (such as the average number of days with heavy rain per year, population density, etc.), the following formula is used: For indicators that are negatively correlated with the decision-making objective (such as absolute elevation, distance from water bodies, etc.), the following formula is used: Furthermore, considering the need to avoid the interference of a zero value on the overall evaluation in the calculation of the flood risk index, the initial standardized value was adjusted to the range of 0.5-1.0, and the final standardized formula is: In the above formula, y ij Indicates the first j In the nth sample i The standardized value of the indicator satisfies 0 ≤ y ij ≤1, x min (i) For the first i The minimum value among all sample data corresponding to the item indicator. x max (i) For the first i The maximum value among all sample data corresponding to the item indicator.

[0029] 4) Spatial consistency guarantee To ensure that all indicator data can be used for subsequent overlay analysis, all vector and raster data use the same coordinate system to avoid spatial offset; all raster data can be resampled to a resolution of 30m×30m to maintain consistency with the DEM data and ensure accurate alignment of raster cells; all indicator data are masked and cropped according to the target area boundary to remove invalid external areas and ensure the adaptability of the data spatial range.

[0030] Step 3: Calculate the indicator weights After step 2 above completes the rasterization and standardization of multi-source data using GIS technology, a complete indicator dataset covering the target area, with a unified format, comparable magnitude, and spatial consistency can be formed. Based on the processed indicator dataset, this step uses the fuzzy hierarchical analysis method (FAHP) to calculate the weights for the target layer and the criterion layer. This method improves the hierarchical analysis method by introducing a fuzzy consistency matrix, thus obtaining a practical and effective fuzzy hierarchical analysis method that can ensure the complete consistency of the judgment matrix without complex consistency checks and realizes the ranking of the importance of risk factors.

[0031] Specifically, a complex problem or decision-making objective is represented as an ordered hierarchical structure. For indicators at the same level and with the same hierarchical relationship, their relative importance is compared pairwise. Based on the actual situation, data collection, and the decision-maker's experience, an importance scale of 1-9 and its reciprocals is used to construct a judgment matrix. In the above formula, a ij for v i and v j Compare the importance scale relative to the upper-level elements, satisfying a. ii =1, a ij =1 / a ji The meanings of each scale are shown in Table 4 below: Table 4 Scale Meaning Based on the above scaling transformation formula, the above judgment matrix is ​​transformed. A n×n Fuzzy complementary matrix B n×n The conversion formula is as follows: In the above formula, b ij for v i and vj The transition scale relative to the upper-level element satisfies: when b ij When =0.5, v i and v j Both are equally important; when b ij When >0.5, v i Comparison v j More importantly; when b ij When <0.5, v j Comparison v i More importantly; Simultaneously satisfy b ij =0.5, b ij + b ji =1, a ≥81, used to ensure 0≤ b ij ≤1, parameter a It can be used to adjust the degree of weight difference between the final indicators, and its selection value is set according to the actual situation.

[0032] Next, the fuzzy complementary judgment matrix will be... B n×n Convert to fuzzy consistency judgment matrix R n×n The conversion formula is: In the above formula, r ij for v i and v j Relative to the FAHP importance scale of the upper-level elements, it satisfies r ij =0.5, r ij + r ji =1, r ij = r ik - r jk +0.5 ( i, j, k = 1, 2, ..., n ).

[0033] The fuzzy consistency judgment matrix after the above transformation has complete consistency, thus ensuring that the consistency problem is handled and guaranteeing consistency with the decision-maker's subjective thinking.

[0034] Finally, the weights of the fuzzy consistency judgment matrix that satisfies complete consistency are calculated using the least squares method to obtain the final weight vector. W 1×n The objective function is constructed to minimize the sum of squared deviations between the weight vector and the matrix elements. The mathematical model is as follows: Objective function: Constraints: By solving the above constrained optimization problem, the weight vector with the highest accuracy is obtained: The present invention, through the above steps, can transform the judgment matrix in the hierarchical structure into a fuzzy consistent judgment matrix, and then solve for the corresponding weight vector. The comprehensive weight of the underlying indicators to the decision objective is calculated from top to bottom. Finally, through weighted comprehensive calculation, the evaluation criteria value of the sample to be decided can be obtained.

[0035] The specific implementation process includes the following: 1) Calculation of index weights under the disaster-causing factor criterion Based on the pairwise comparison of the impact of indicators on flood formation under this criterion, first define... D 1 This represents the average annual rainfall during the flood season. D 2 This represents the average number of days with heavy rain per year. D 3 For historical flood disasters, a judgment matrix is ​​constructed using the 1-9 scale method (numbers 1-9 and their reciprocals): In the formula, a ij for D i and D j Compare the importance scale relative to the upper-level disaster-causing factors, satisfying a ii =1, a ij =1 / a ji The matrix is ​​constructed based on the following: (1) Rainfall during the flood season D 1 Average number of rainy days per year D 2Comparison: The average number of rainstorm days per year directly reflects the frequency and intensity of extreme precipitation, and has a more direct impact on flood triggering. Therefore, the average number of rainstorm days per year is slightly more important than the average annual flood season rainfall. Hence, a 12 =1 / 3, that is D 2 Compare D 1 Importance scale is 3. D 1 Compare D 2 The importance scale is 1 / 3.

[0036] (2) Rainfall during the flood season D 1 Compared with historical floods D 3 Comparison: Historical disaster occurrences reflect the cumulative patterns and potential dangers of regional floods, encompassing the combined effects of topography and water systems. They are more significant indicators of risk assessment and are therefore considerably more important than average annual flood season rainfall. Therefore, a... 13 =1 / 5, that is D 3 Compare D 1 Importance scale is 5. D 1 Compare D 3 The importance scale is 1 / 5.

[0037] (3) Average number of rainstorm days per year D 2 Compared with historical floods D 3 Comparison: Historical disaster occurrences encompass the cumulative effects of long-term disaster-causing factors, while the average annual number of rainstorm days focuses on short-term triggering factors under current climate conditions. Therefore, the former is more important for long-term regional risk assessment, hence a 23 =1 / 3, that is D 3 Compare D 2 Importance scale is 3. D 2 Compare D 3 The importance scale is 1 / 3.

[0038] According to the scaling conversion formula, take a =243, transform the judgment matrix of the disaster-causing factor criterion layer into a fuzzy complementary matrix: In the above formula, the fuzzy complementary judgment matrix of the disaster-causing factor criterion layer is... B 1 Satisfying properties bij + b ji =1, b ii =0.5.

[0039] Furthermore, the fuzzy complementary judgment matrix of the disaster-causing factor criterion layer is transformed into a fuzzy consistent judgment matrix, which is as follows: In the above formula, r ij for v i and v j The FAHP importance scale relative to the upper-level elements satisfies r ij =0.5, r ij + r ji =1, r ij = r ik - r jk +0.5 ( i, j, k = 1, 2, ..., n ).

[0040] Therefore, the transformed fuzzy consistency judgment matrix satisfies complete consistency.

[0041] Finally, the weights of the fuzzy consistent judgment matrix that satisfies complete consistency are calculated using the least squares method to obtain the weight vector of the final disaster factor criterion layer: That is, the weight of the average annual flood season rainfall is 0.2178, the weight of the average annual number of rainstorm days is 0.3297, and the weight of the number of historical floods is 0.4525.

[0042] 2) Calculation of indicator weights under the disaster-prone environment criteria Based on the pairwise comparison of the sensitivity of indicators to floods under this criterion, the distance from the water body is first defined as... D 1 The absolute elevation is D 2 The terrain slope is D 3 Soil type is D 4 The judgment matrix is ​​constructed using scaling: In the above formula, a ij for D iand D j Compare the importance scale relative to the upper disaster-prone environment, and satisfy a ii =1, a ij =1 / a ji The matrix is ​​constructed based on the following: (1) Distance from water body D 1 and absolute elevation D 2 Comparison: The distance from the water body has a more significant direct impact on flood risk. For example, the probability of flooding within 1 km of the Yellow River in the Yinchuan Plain area is 50% higher than in low-elevation non-riverside areas. Therefore... D 1 Compare D 2 Clearly important, scaled to 5.

[0043] (2) Distance from water body D 1 With terrain slope D 3 Comparison: Distance from water is a triggering condition for flood risk, while slope is an accelerating condition; the former is far more important than the latter. D 1 Compare D 3 Strongly important, scaled to 7.

[0044] (3) Distance from water body D 1 With soil category D 4 Comparison: Distance from water directly determines whether flooding occurs, while soil type affects the severity of the disaster. D 1 Compare D 4 Extremely important, scaled to 9.

[0045] (4) Absolute elevation D 2 With terrain slope D 3 Comparison: The risk of flood accumulation in low elevation areas is higher than the impact of slope, therefore D 2 Compare D 3 Slightly more important, scaled to 3.

[0046] (5) Absolute elevation D 2 With soil category D 4Comparison: Soil elevation determines the likelihood of flooding, while soil type determines the losses after flooding; the former is slightly more important. D 2 Compare D 4 Clearly important, scaled to 5.

[0047] (6) Terrain slope D 3 With soil category D 4 Comparison: Slope affects flood flow velocity, while soil type affects disaster resistance; both are of similar importance, therefore... D 3 and D 4 Equally important, scaled to 1.

[0048] According to the scaling conversion formula, take a =243, transform the judgment matrix of the disaster-prone environment criterion layer into a fuzzy complementary matrix: In the above formula, the fuzzy complementary judgment matrix of the disaster-prone environment criterion layer is... B 2 Satisfying properties b ij + b ji =1, b ii =0.5.

[0049] Furthermore, the fuzzy complementary judgment matrix of the disaster-prone environment criterion layer is transformed into a fuzzy consistent judgment matrix, which is as follows: In the above formula, r ij for v i and v j The FAHP importance scale relative to the upper-level elements satisfies r ij = r ik - r jk +0.5, r ij =0.5, r ij + r ji =1, ( i, j, k = 1, 2, ..., n ), if optional i =1, j =2, k=3 verifies complete consistency, left side r 13 =0.739, and the right side 0.592+0.647-0.5=0.739, which are completely consistent.

[0050] Therefore, the transformed fuzzy consistency judgment matrix satisfies complete consistency.

[0051] Finally, based on the fuzzy consistent judgment matrix that satisfies complete consistency, the weights are calculated using the least squares method to obtain the final weight vector of the disaster-prone environment criterion layer: Therefore, the weights for distance from water bodies are 0.3285, absolute elevation is 0.2547, topographic slope is 0.2172, and soil type is 0.2006.

[0052] 3) Calculation of index weights under the disaster-bearing body criterion Based on this criterion, pairwise comparisons of flood vulnerability are made using indicators. First, population density is defined as... D 1 Land use type is D 2 The area of ​​crops accounted for D 3 GDP per capita is D 4 The judgment matrix is ​​constructed using scaling: In the formula, a ij for D i and D j Compare the importance scale relative to the upper disaster-bearing body, satisfying a ii =1, a ij =1 / a ji The matrix is ​​constructed based on the following: (1) Population density D 1 With land use type D 2 Comparison: Population density determines the breadth of flood impact, i.e., the number of people threatened; land use type determines the depth, i.e., loss per unit area. Both are of similar importance, but population safety is limited. Therefore... D 1 Compare D 2 Slightly more important, scaled to 3.

[0053] (2) Population density D 1proportion of crop area D 3 Comparison: Population security is the primary goal, while crop losses are secondary. D 1 Compare D 3 Clearly important, scaled to 5.

[0054] (3) Population density D 1 GDP per capita D 4 Comparison: Population density is directly related to life safety, while GDP per capita reflects economic disaster resilience; the former is far more important than the latter. D 1 Compare D 4 Strongly important, scaled to 7.

[0055] (4) Land use type D 2 proportion of crop area D 3 Comparison: Land use types encompass all land-use types, while crop area refers only to agricultural land; therefore... D 2 Compare D 3 Clearly important, scaled to 5.

[0056] (5) Land use type D 2 GDP per capita D 4 Comparison: Land use type directly determines the type of loss, such as damage to infrastructure on man-made surfaces; GDP per capita is an indirect indicator of disaster resilience. D 2 Compare D 4 Slightly more important, scaled to 3.

[0057] (6) Proportion of crop area D 3 GDP per capita D 4 Comparison: Crop acreage reflects the risk of agricultural losses, while GDP per capita reflects economic recovery capacity; both are of similar importance, therefore... D 3 and D 4 Equally important, scaled to 1.

[0058] According to the scaling conversion formula, take a =243, transform the judgment matrix of the disaster-bearing body criterion layer into a fuzzy complementary matrix: In the above formula, the fuzzy complementary judgment matrix of the disaster-bearing body criterion layer is... B 3 Satisfying properties b ij + b ji =1, b ii =0.5.

[0059] Furthermore, the fuzzy complementary judgment matrix of the disaster-bearing body criterion layer is transformed into a fuzzy consistent judgment matrix, which is as follows: In the above formula, r ij for v i and v j The FAHP importance scale relative to the upper-level elements satisfies r ij = r ik - r jk +0.5, r ij =0.5, r ij + r ji =1, ( i, j, k = 1, 2, ..., n ), if optional i =1, j =2, k =3 verifies complete consistency, left side r 13 =0.739, and the right side 0.592+0.647-0.5=0.739, which are completely consistent.

[0060] Therefore, the transformed fuzzy consistency judgment matrix satisfies complete consistency.

[0061] Finally, based on the fuzzy consistency judgment matrix that satisfies complete consistency, the weights are calculated using the least squares method to obtain the final weight vector of the disaster-bearing body criterion layer: Therefore, the weights for population density are 0.3562, land use type are 0.2868, crop area ratio is 0.1785, and GDP per capita are 0.1785.

[0062] 4) Calculation of indicator weights under the disaster prevention and mitigation criteria First, define the proportion of the water conservancy, environment, and public facilities management industry as...D 1 Public budget expenditure is D 2 The region's GDP is D 3 The number of beds per unit area in medical institutions is D 4 Based on the indicators under this criterion, pairwise comparisons of flood prevention and mitigation capabilities are made, and a judgment matrix is ​​constructed using the 1-9 scaling method: In the above formula, a ij for D i and D j Compare the importance ratio scale relative to the upper-level disaster prevention and mitigation capabilities, satisfying a ii =1, a ij =1 / a ji The matrix is ​​constructed based on the following: (1) Proportion of water conservancy, environment and public facilities management industry D 1 With public budget expenditure D 2 Comparison: Water conservancy facilities are the core of flood control projects, while the public budget provides financial security; the former is slightly more important, therefore... D 1 Compare D 2 Slightly more important, scaled to 3.

[0063] (2) Proportion of water conservancy, environment and public facilities management industry D 1 With regional GDP D 3 Comparison: Water conservancy facilities directly determine disaster prevention capabilities, while GDP is the economic foundation; the former is clearly more important. D 1 Compare D 3 Clearly important, scaled to 5.

[0064] (3) Proportion of water conservancy, environment and public facilities management industry D 1 Number of beds per unit area of ​​medical institutions D 4 Comparison: Water conservancy facilities are used to prevent floods, while medical beds are used for disaster relief. The former is far more important than the latter. D 1 Compare D 4 Strongly important, scaled to 7.

[0065] (4) Public budget expenditure D 2 With regional GDP D 3 Comparison: Disaster prevention expenditures in the public budget have a more direct impact on disaster prevention effectiveness than overall GDP. D 2 Compare D 3 Slightly more important, scaled to 3.

[0066] (5) Public budget expenditure D 2 Number of beds per unit area of ​​medical institutions D 4 Comparison: The public budget covers disaster prevention projects and emergency medical expenditures, including medical beds, therefore... D 2 Compare D 4 Clearly important, scaled to 5.

[0067] (6) Gross Regional Product D 3 Number of beds per unit area of ​​medical institutions D 4 Comparison: GDP reflects economic scale, while medical bed capacity reflects emergency response capability; both are of similar importance, therefore... D 3 and D 4 Equally important, scaled to 1.

[0068] According to the scaling conversion formula, take a =243, transform the judgment matrix of the disaster-bearing body criterion layer into a fuzzy complementary matrix: In the above formula, the fuzzy complementary judgment matrix of the criterion layer B 4 Satisfying properties b ij + b ji =1, b ii =0.5.

[0069] Furthermore, the fuzzy complementary judgment matrix of the disaster prevention and mitigation criterion layer is transformed into a fuzzy consistent judgment matrix, which is as follows: In the above formula, r ij for v i and vj The FAHP importance scale relative to the upper-level elements satisfies r ij = r ik - r jk +0.5, r ij =0.5, r ij + r ji =1, ( i, j, k = 1, 2, ..., n ), if optional i =1, j =2, k =3 verifies complete consistency, left side r 14 =0.749, and the right side 0.592+0.657-0.5=0.749, which are completely consistent.

[0070] Therefore, the transformed fuzzy consistency judgment matrix satisfies complete consistency.

[0071] Finally, the weights of the fuzzy consistency judgment matrix that satisfies complete consistency are calculated using the least squares method to obtain the weight vector of the final disaster prevention and mitigation capability criterion layer: Therefore, the weights for water conservancy, environment and public facilities management are 0.3560, public budget expenditure is 0.2865, GDP is 0.1880, and the number of hospital beds per unit area is 0.1695.

[0072] 5) Calculation of indicator weights at the target layer First, define the disaster-causing factor as... D 1 The environment for pregnancy is D 2 The disaster-bearing body is D 3 Disaster prevention and mitigation capabilities are D 4 Based on the indicators under this criterion, pairwise comparisons of flood prevention and mitigation capabilities are made, and a judgment matrix is ​​constructed using the 1-9 scaling method: In the above formula, a ij for D i and D j Compare the importance scale relative to the flood disaster decision-making objective layer, satisfying a ii =1, a ij =1 / a ji The matrix is ​​constructed based on the following: (1) Disaster-causing factors D 1 Environment related to pregnancy D 2 Comparison: In this embodiment, the floods in the Yinchuan Plain were mainly triggered by torrential rain. The causative factors were the direct driving force behind the flood formation, while the disaster-prone environment was the basic condition for flood accumulation. The short-term suddenness of the causative factors had a more direct triggering effect on the risk. Therefore... D 1 Compare D 2 Slightly more important, scaled to 3.

[0073] (2) Disaster-causing factors D 1 With the disaster-bearing body D 3 Comparison: The disaster-bearing body determines the severity of the flood's consequences, but the source of the risk from the disaster-causing factors is also crucial. D 1 Compare D 3 Slightly more important, scaled to 3.

[0074] (3) Disaster-causing factors D 1 and disaster prevention and mitigation capabilities D 4 Comparison: Disaster prevention and mitigation capabilities are regulatory factors for reducing risk, but disaster-causing factors directly determine the potential scale of risk. D 1 Compare D 3 Clearly important, scaled to 5.

[0075] (4) Environment that breeds disaster D 2 With the disaster-bearing body D 3 Comparison: The disaster-prone environment is the natural background condition, while the disaster-bearing body is the amplification factor of human activities on risk. Therefore, the disaster-prone environment is less important than the disaster-bearing body, and the scale is taken as 1 / 3.

[0076] (5) Environment that breeds disasters D 2 and disaster prevention and mitigation capabilities D 4 Comparison: The disaster-prone environment is the natural basis for flood occurrence, while disaster prevention and mitigation capabilities are human intervention methods; both have a comparable degree of impact. Therefore... D 2 and D 4 Equally important, scaled to 1.

[0077] (6) The disaster-bearing body is D 3 and disaster prevention and mitigation capabilities D 4 Comparison: Disaster prevention and mitigation capabilities directly determine the effectiveness of risk control, while disaster-bearing bodies are the result of risk exposure. The proactive regulatory role of disaster prevention and mitigation capabilities is better than the passive exposure of disaster-bearing bodies. Therefore, disaster-bearing bodies are less important than disaster prevention and mitigation capabilities, and the scale is taken as 1 / 3.

[0078] According to the scaling conversion formula, take a =243, transform the judgment matrix of the disaster-bearing body criterion layer into a fuzzy complementary matrix: In the above formula, the fuzzy complementary judgment matrix of the target layer B 5 Satisfying properties b ij + b ji =1, b ii =0.5.

[0079] Furthermore, the fuzzy complementary judgment matrix of the target layer is transformed into a fuzzy consistent judgment matrix, which is as follows: In the above formula, r ij for v i and v j The FAHP importance scale relative to the upper-level elements satisfies r ij = r ik - r jk +0.5, r ij =0.5, r ij + r ji =1, ( i, j, k = 1, 2, ..., n ), if optional i =1, j =2, k =3 verifies complete consistency, left side r 12 =0.6822, and the right side 0.6488-4667+0.5=0.6821, which are completely consistent.

[0080] Therefore, the transformed fuzzy consistency judgment matrix satisfies complete consistency.

[0081] Finally, the weights of the fuzzy consistent judgment matrix that satisfies complete consistency are calculated using the least squares method to obtain the weight vector of the final flood disaster decision target layer: Therefore, the weight of the disaster-causing factor is 0.4333, the weight of the disaster-prone environment is 0.1536, the weight of the disaster-bearing body is 0.2029, and the weight of disaster prevention and mitigation capacity is 0.2102.

[0082] In summary, this step uses the inverse distance weighting method to solve the interpolation problem in sparse areas of meteorological stations, ensuring data spatial consistency. Furthermore, by introducing fuzzy hierarchical analysis and constructing a fuzzy consistency judgment matrix and solving for the weights, the problems of fuzziness and subjectivity of indicators can be effectively addressed.

[0083] Step 4: Risk Value Classification Based on the weighting results calculated in step 3 above, this step uses a weighted comprehensive evaluation method to couple the standardized indicator data with the corresponding weights, transforming multi-dimensional and dispersed risk factors into a single quantitative comprehensive risk value. The present invention employs the weighted comprehensive evaluation method, as shown in the following formula: In the above formula, C Represents the overall evaluation value. n Represents the number of indicators. W i The weights of each indicator affecting the overall evaluation value are: D i These are the normalized values ​​of the indicators that affect the overall evaluation value; After calculating the normalized value, the weighted comprehensive evaluation method is used again to construct a flood risk assessment model for the target area: In the above formula, FDRI Represents the overall risk value of flooding; H , S , V , A The values ​​are, in order: the calculated hazard of the disaster-causing factor, the sensitivity of the disaster-prone environment, the vulnerability of the disaster-bearing body, and the disaster prevention and mitigation capacity. W H , W S , W V , W A These are the weights of four influencing factors. The higher the weight value, the greater the impact on flood disasters.

[0084] The specific implementation process includes the following: (1) Risk assessment of flood-causing factors Combining steps 2 and 3 above, and comprehensively analyzing the factors of average annual flood season rainfall, average annual number of rainstorm days, and historical flood severity, the raster maps of each evaluation indicator and the weights of each indicator are input into ArcGIS raster overlay calculations. The calculation formulas are as follows: In this formula, H The hazard value of the disaster-causing factor. D 1 This is the normalized value of the average annual rainfall during the flood season. D 2 This is the normalized value of the average number of days with heavy rain per year. D 3 This is the normalized value of historical flood disasters.

[0085] The calculated raster map is reclassified using GIS tools to classify natural discontinuities into five levels: high risk, relatively high risk, moderate risk, relatively low risk, and low risk areas. This yields a comprehensive flood risk assessment map for the target area. This embodiment uses the Yinchuan Plain as an example. Figure 4 The image shown is a comprehensive assessment map of flood risk in the Yinchuan Plain.

[0086] As can be seen from the map, the high-risk areas of the Yinchuan Plain are located in the central part, with the entire counties of Xingqing District and Xixia District being high-risk areas. The risk gradually decreases from the central to the north of the Yinchuan Plain, with the entire counties of Huinong District and Dawukou District being low-risk areas. In the area south of the central part of the Yinchuan Plain, the risk decreases from the center to both sides, with the western parts of Lingwu City, Yongning County, and Qingtongxia City being relatively low-risk areas.

[0087] (2) Sensitivity assessment of the flood-prone environment Combining steps 2 and 3 above, a comprehensive analysis of the absolute elevation factor, distance from water body factor, topographic slope factor, and soil type factor is performed. The raster maps of each evaluation index and the weights of each index are then input into the raster overlay calculation in ArcGIS. The calculation formula is as follows: In this formula, S This represents the environmental sensitivity value for disaster-prone areas. D 1 This is the normalized value of the distance from the water body. D 2 The normalized value of the absolute elevation. D 3 This is the normalized value of the terrain slope. D 4 This represents the normalized value for soil type.

[0088] The calculated raster map is reclassified using GIS tools to classify areas into five levels based on natural discontinuities: high sensitivity, relatively high sensitivity, moderate sensitivity, relatively low sensitivity, and low sensitivity. This yields a comprehensive flood sensitivity assessment map for the target area. This example uses the Yinchuan Plain as a case study. Figure 5 The image shown is a comprehensive assessment map of the flood sensitivity of the Yinchuan Plain.

[0089] As can be seen from the map, when comparing the districts and counties within the Yinchuan Plain, the entire area of ​​Dawukou District is basically a highly sensitive area, as is the northern part of Huinong District. Apart from Dawukou District and Huinong District, the sensitivity of the Yinchuan Plain generally shows a trend of high in the east and low in the west. The western parts of Helan County, Yongning County, and Qingtongxia City are low-sensitivity areas, and most areas of the Yinchuan Plain are medium-sensitive or low-sensitivity areas.

[0090] (3) Flood disaster vulnerability assessment Combining steps 2 and 3 above, and comprehensively analyzing the population density factor, crop area ratio factor, per capita GDP factor, and land use type factor, the raster maps of each evaluation indicator and the weights of each indicator are input into ArcGIS raster overlay calculations. The calculation formulas are as follows: In this formula, V The vulnerability value of the disaster-bearing body. D 1 This is the normalized value of population density. D 2 Normalized values ​​for land use types D 3 This is the normalized value of the crop area ratio. D 4 This is the normalized value of GDP per capita.

[0091] The calculated raster map is reclassified using GIS tools to classify areas into five levels based on natural discontinuities: high vulnerability, relatively high vulnerability, moderate vulnerability, relatively low vulnerability, and low vulnerability. This yields a comprehensive flood vulnerability assessment map of the target area. This example uses the Yinchuan Plain as a case study. Figure 6 The image shown is a comprehensive assessment map of the flood vulnerability of the Yinchuan Plain.

[0092] As can be seen from the map, when comparing the districts and counties within the Yinchuan Plain, the entire areas of Xingqing District and Xixia District are basically high-vulnerability areas, with flood vulnerability gradually decreasing from Xingqing District and Xixia District outwards. Next, Xixia District, Helan County, Pingluo County, Huinong District, Yongning County, and parts of Lingwu City are relatively vulnerable. The northeastern corner of Qingtongxia City is a mixed area of ​​low and low vulnerability. Most areas of the Yinchuan Plain are relatively vulnerable.

[0093] (4) Disaster prevention and mitigation capacity assessment Combining steps 2 and 3 above, a comprehensive analysis is conducted on the factors of the number of hospital beds per unit area, public budget expenditure, regional GDP, and the proportion of water conservancy, environmental, and public facilities management industries. The raster maps of each evaluation indicator and the weights of each indicator are then input into the raster overlay calculation in ArcGIS. The calculation formula is as follows: In this formula, A The value represents the disaster prevention and mitigation capability. D 1 This is the normalized value of the proportion of the water conservancy, environment and public facilities management industry. D 2 The normalized value of public budget expenditures. D 3 The normalized value of regional GDP D 4 This is the normalized value of the number of beds per unit area in medical institutions.

[0094] The calculated raster map is reclassified using GIS tools to classify areas into five levels based on natural discontinuities: high disaster prevention and mitigation capacity, relatively high disaster prevention and mitigation capacity, medium disaster prevention and mitigation capacity, relatively low disaster prevention and mitigation capacity, and low disaster prevention and mitigation capacity. This yields a comprehensive evaluation map of the disaster prevention and mitigation capacity of the target area. This embodiment uses the Yinchuan Plain as an example. Figure 7 The image shown is a comprehensive evaluation map of the disaster prevention and mitigation capabilities of the Yinchuan Plain.

[0095] As can be seen from the map, when comparing the various districts and counties within the Yinchuan Plain, Yongning County has a low disaster prevention and mitigation capacity. The disaster prevention and mitigation capacity increases from Yongning County as the center outwards. Qingtongxia City, Lingwu City, and Xingqing District have high disaster prevention and mitigation capabilities. Excluding Yongning County, the overall disaster prevention and mitigation capacity of the Yinchuan Plain shows a spatial distribution that is high in the south and low in the north. The entire plain is mainly characterized by medium and high disaster prevention and mitigation capabilities.

[0096] (5) Comprehensive flood risk assessment of the target area Based on the above evaluation results, a comprehensive analysis of the hazard of disaster-causing factors, the sensitivity of the disaster-prone environment, the vulnerability of disaster-bearing bodies, and disaster prevention and mitigation capabilities is performed. The respective raster maps and index weights are then input into the ArcGIS raster calculator, and the calculation formula is as follows: In this formula, FDRI Represents the overall risk value of flooding; H The hazard value of the disaster-causing factor. S This represents the environmental sensitivity value for disaster-prone areas. V The vulnerability value of the disaster-bearing body. A This represents the disaster prevention and mitigation capability value.

[0097] The calculated raster map is reclassified using GIS tools to classify areas into five levels based on natural discontinuities: high-risk, relatively high-risk, medium-risk, relatively low-risk, and low-risk areas. This yields a comprehensive flood risk assessment map for the target area. The comprehensive risk is the combined effect of various factors, including disaster-causing factors, the disaster-prone environment, the disaster-bearing body, and the combined effects of disaster prevention and mitigation. This embodiment uses the Yinchuan Plain as an example. Figure 8 The image shown is a comprehensive evaluation map of the disaster prevention and mitigation capabilities of the Yinchuan Plain.

[0098] As can be seen from the map, when comparing and dividing the districts and counties within the Yinchuan Plain, the basic data of the entire Xingqing District, Helan County, and Xixia District are high-risk areas. The eastern parts of Pingluo County and Yongning County are also high-risk areas. The western parts of Qingtongxia City and Dawukou District, as well as the eastern part of Lingwu City, are low-risk areas. The overall flood risk of the Yinchuan Plain shows a spatial distribution of high in the middle and low in the north and south, with the entire plain mainly consisting of high-risk and low-risk areas.

[0099] Finally, based on the above risk assessment results, the spatial distribution characteristics and causes of high-risk areas can be analyzed in conjunction with the actual situation. By combining the risk zoning results with the actual situation, targeted flood control and disaster reduction countermeasures can be proposed and applied to flood control planning and emergency management.

[0100] This invention proposes a comprehensive flood risk assessment method for plain areas. By integrating the four-petal theory and fuzzy hierarchical analysis, it provides a new paradigm for flood risk assessment and early warning in plain areas. It improves upon the subjectivity and consistency deficiencies of traditional AHP through FAHP, significantly enhancing the scientific rigor of indicator weight calculation. Simultaneously, it utilizes GIS technology to achieve rasterization and standardization of multi-source data, ensuring spatial consistency through unified projection, resolution, and range. Furthermore, it optimizes the accuracy of small sample area data using the inverse distance weighting method. The selected 15 indicators are precisely adapted to the terrain, water system, industry, and population characteristics of plain areas, achieving full-dimensional risk coverage. Ultimately, it can quantitatively identify the spatial distribution patterns of risks, providing differentiated disaster prevention and mitigation strategies for areas with different risk levels, and offering precise and efficient decision-making support for flood control planning, emergency management, and watershed ecological protection in plain areas.

[0101] The embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. The basic concept of the present invention lies in constructing a four-dimensional risk assessment model based on the four-petal risk theory framework, combined with fuzzy hierarchical analysis and GIS technology, thereby conducting flood risk zoning analysis in plain areas and providing a scientific basis for regional flood prevention and disaster reduction. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for comprehensive risk assessment of flood in plain area, characterized in that: The method comprises the following steps: Step 1: evaluation index system establishment Based on the geographical features of the target region and historical disaster data, a three-level progressive flood disaster risk evaluation index system is established, which comprises a target layer, a criterion layer and an index layer. The target layer is for the target region flood disaster risk decision, the criterion layer comprises four dimensions of disaster-causing factor risk, disaster-birth environment sensitivity, disaster-bearing body vulnerability and disaster prevention and mitigation capacity, and the index layer is the quantitative carrier corresponding to each criterion layer. Step 2: index data standardization processing Based on the various multi-source basic data in the index layer, the data is pre-processed, raster-converted and standardized to eliminate the differences in magnitude and unit between different index data and ensure spatial consistency of the index data. Step 3: index weight calculation Based on the standardized index data, the index weights under the target layer and each criterion layer are calculated: first, a 1-9 scale judgment matrix is constructed, then the judgment matrix is converted into a fuzzy complementary matrix, then the fuzzy complementary matrix is converted into a fuzzy consistent judgment matrix that meets complete consistency, and finally the least square method is used to solve the weight vector. Step 4: risk value grading The standardized index data generated in step 2 and the weight of step 3 are coupled and calculated: first, the comprehensive evaluation value of each criterion layer is calculated through GIS raster overlay analysis, then the comprehensive risk value is obtained by coupling, and finally the target region flood comprehensive risk evaluation map is output.

2. The method according to claim 1, wherein: In step 1, the indexes of the disaster-causing factor risk criterion layer include annual average flood season rainfall, annual average rainstorm days and historical flood disaster times; the indexes of the disaster-birth environment sensitivity criterion layer include distance from water body, absolute elevation, terrain slope and soil type; the indexes of the disaster-bearing body vulnerability criterion layer include population density, crop area ratio, per capita GDP and land use type; and the indexes of the disaster prevention and mitigation capacity criterion layer include the number of medical institution beds per unit area, public budget expenditure, regional gross product and the proportion of water conservancy, environment and public facilities management industry.

3. The method according to claim 1, wherein: In step 2, the corresponding data of the risk factor indexes under the criterion layer is collected, and the obtained multi-source basic data is pre-processed, including specifying data format, time span and spatial range; then, based on GIS technology, the pre-processed data of various types is uniformly converted into raster format to ensure consistent data resolution, and then the standardized processing is performed on all rasterized index data to ensure spatial consistency.

4. The method according to claim 3, wherein: The data standardization processing process is as follows: First, the target area grid sample quantity is defined as M , the evaluation index quantity is N , and the sample measurement matrix is constructed as: In the formula, x ij represent the actual measurement value of the i-th index in the j-th sample; j represent the actual measurement value of the i-th index in the j-th sample; i represent the actual measurement value of the i-th index in the j-th sample; Then, the standard normalization formula is used to construct a sample standardization matrix: For indexes positively correlated with the decision target, the following formula is used: For indexes negatively correlated with the decision target, the following formula is used: In the above formula, y ij Indicates the first j In the nth sample i The standardized value of the indicator satisfies 0 ≤ y ij ≤1, x min (i) For the first i The minimum value among all sample data corresponding to the item indicator. x max (i) For the first i The maximum value among all sample data corresponding to the item indicator; The range of the standardized value is 0.5-1.

0.

5. The method according to claim 1, wherein: In step 3, the judgment matrix is constructed using the 1-9 scale method, and the scale meaning is: 1 indicates that the importance of both sides is the same, 3 indicates that one side is slightly more important than the other, 5 indicates that one side is obviously more important than the other, 7 indicates that one side is strongly more important than the other, 9 indicates that one side is extremely more important than the other, 2, 4, 6 and 8 indicate that the importance is between the adjacent judgments, and the reciprocal indicates the opposite comparison result.

6. The method according to claim 5, wherein: In step 3, the formula of the judgment matrix is as follows: In the above formula, a ij is v i compared with v j the importance scale of the upper element, a ii = 1, a ij = 1 / a ji ; Converting the judgment matrix into a fuzzy complementary matrix B n×n The conversion formula is as follows: In the above formula, b ij is v i with v j a transition scale relative to the upper element, a ij is a judgment matrix element, b ij is a fuzzy complementary matrix element, a is an adjustment parameter, and a ≥ 81; After that, the fuzzy complementary judgment matrix B n×n is converted into a fuzzy consistent judgment matrix R n×n The conversion formula is: In the above formula, r ij For v i With v j Relative to the FAHP importance scale of the upper element, which satisfies r ij = 0.5, r ij + r ji = 1, r ij = r ik - r jk + 0.5( i, j, k = 1, 2, …, n ).

7. The method according to claim 6, wherein: In step 3, the fuzzy consistent judgment matrix is calculated by the least square method to obtain the minimum weight vector W 1×n The objective function is constructed to minimize the square sum of the deviation of the weight vector and the matrix elements, and the mathematical model is as follows: Objective function: Constraint condition: Solve the above constraint optimization problem to obtain the weight of different indexes: in W i For the first i The weight of each indicator.

8. The method according to claim 1, wherein: In step 4, based on the weight result calculated in step 3, the standardized index data is coupled with the corresponding weight by using the weighted comprehensive evaluation method to convert the multi-dimensional and dispersed risk factors into a single quantitative comprehensive risk value. The formula is as follows: In the above formula, C representing the comprehensive evaluation value, n representing the number of indexes, W i is the weight of each index affecting the comprehensive evaluation value, D i is the normalized value of each index affecting the comprehensive evaluation value; After calculating the normalized value, the weighted comprehensive evaluation method is used again to build the target area flood risk assessment model: In the above formula, FDRI represent the comprehensive risk value of flood disaster; H , S , V , A are the calculated hazard of disaster-causing factors, sensitivity of disaster-conducive environment, vulnerability of disaster-bearing body and ability of disaster prevention and mitigation, respectively; W H , W S , W V , W A are the weights of the four influence factors, respectively. The higher the weight value is, the greater the influence on flood disaster is.

9. The method according to claim 8, wherein: In step 4, the natural breakpoint classification method of GIS tool is used to divide the comprehensive evaluation value under different criteria, including: under the criteria of disaster-causing factors, it is divided into five levels of high risk, higher risk, medium risk, lower risk and low risk area; under the criteria of disaster environment, it is divided into five levels of high sensitivity, higher sensitivity, medium sensitivity, lower sensitivity and low sensitivity area; under the criteria of disaster-bearing body, it is divided into five levels of high vulnerability, higher vulnerability, medium vulnerability, lower vulnerability and low vulnerability area; under the criteria of disaster prevention and mitigation, it is divided into five levels of high disaster prevention and mitigation ability, higher disaster prevention and mitigation ability, medium disaster prevention and mitigation ability, lower disaster prevention and mitigation ability and low disaster prevention and mitigation ability area.

10. The method according to claim 9, wherein: In step 4, the results of the grade division of the risk of disaster-causing factors, the sensitivity of disaster environment, the vulnerability of disaster-bearing body and the ability of disaster prevention and mitigation are analyzed comprehensively, and the coupled flood comprehensive risk is output. Then, the GIS tool is used to divide the target area flood comprehensive risk into five levels, i.e. high risk, higher risk, medium risk, lower risk and low risk area, and finally the target area flood comprehensive risk assessment map is output.

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