A rural building group flood disaster loss assessment method and system

Abstract

The present application belongs to the technical field of building group flood influence analysis, and aims at the problem that the existing building flood loss function does not fully consider the uncertainty factors in the loss evaluation process, resulting in low accuracy of the evaluation result, and provides a rural building group flood disaster loss evaluation method and system, which comprises the following steps: using a hydrodynamic model to simulate the dynamic evolution process of the flood disaster, obtaining a submerged water depth map and a maximum submerged depth map; comparing the submerged water depth map with SAR image water extraction data and social media data to obtain the flood simulation result; combining building classification information and the maximum submerged depth map to obtain the submerged depth of each building; calculating the component information of the building; performing building flood vulnerability analysis according to the component information and the submerged depth to determine the total loss of all buildings; and evaluating the effect of the current disaster prevention measures according to the total loss under different disaster prevention measures. The present application can provide a scientific basis for building flood loss evaluation and disaster prevention decision-making.

Description

Technical Field

[0001] This application relates to the field of flood impact analysis technology for building complexes, and in particular to a method and system for assessing flood disaster losses in rural building complexes. Background Technology

[0002] Rural areas exhibit high sensitivity to floods. Therefore, developing a probability-based method for assessing economic losses from floods affecting rural building complexes and an evaluation system for the effectiveness of disaster prevention decisions is crucial for formulating effective disaster mitigation measures and post-disaster recovery strategies in advance.

[0003] However, most current research focuses primarily on urban areas, and vulnerability studies largely rely on empirical and field survey data, assessing building flood damage through staged loss functions. However, these staged loss functions are typically only applicable to specific events and fail to adequately consider uncertainties in the loss assessment process. Furthermore, existing research has not achieved deep integration of flood simulation and building data, making it difficult to accurately calculate flood damage to various types of buildings within a given area, and even more difficult to comprehensively assess the effectiveness of various disaster prevention measures from the perspective of building economic losses. Summary of the Invention

[0004] In view of the above problems, this application provides a method and system for assessing flood damage to rural building complexes. It aims to quantitatively analyze the flood vulnerability and losses of buildings under extreme rainstorm disaster scenarios, thereby providing a scientific basis for the effectiveness of building flood damage assessment and disaster prevention decision-making.

[0005] In a first aspect, embodiments of this application provide a probability-based method for assessing flood damage to rural building complexes, the method comprising:

[0006] Multi-source data of the area where the rural building complex to be analyzed is located is obtained, and the dynamic evolution of flood disaster is simulated using a hydrodynamic model to obtain inundation depth map and maximum inundation depth map;

[0007] By comparing the inundation depth map with water body extraction data from SAR imagery and social media data, the hydrodynamic model was validated, and flood simulation results were obtained.

[0008] Extract the building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map through GIS to obtain the inundation depth of each building in the building complex.

[0009] Collect the structural features of various typical buildings and calculate the component information of the buildings;

[0010] Based on component information and flood depth, the flood vulnerability of buildings is analyzed to determine the total loss of all buildings;

[0011] Assess the effectiveness of current disaster prevention measures based on the total losses under different disaster prevention measures.

[0012] Optionally, the step of acquiring multi-source data of the area to be analyzed and simulating the dynamic evolution of flood disasters using a hydrodynamic model to obtain inundation depth maps and maximum inundation depth maps includes:

[0013] Obtain geographic information data, historical rainfall data, and land use data of the area where the rural building complex to be analyzed is located;

[0014] Based on the acquired data, a hydrodynamic model is constructed to simulate the dynamic evolution of flood disasters and output inundation depth maps and maximum inundation depth maps during the simulated rainfall cycle.

[0015] Optionally, the comparison of the inundation depth map with SAR image water body extraction data and social media data to verify the hydrodynamic model and obtain flood simulation results includes:

[0016] SAR images of the rural building complexes to be analyzed were extracted before, during, and after the disaster to obtain the water body extraction results before, during, and after the disaster.

[0017] The water body extraction results before, during, and after the disaster were compared with the inundation depth maps before, during, and after the disaster to verify the accuracy of the inundation range.

[0018] Web crawlers were used to collect data from social media to extract water depth information at water accumulation points;

[0019] The flood inundation depth of typical water accumulation points during the simulated rainfall cycle is determined based on the inundation depth map. This flood inundation depth is then compared with the extracted water depth information to verify the accuracy of the inundation depth.

[0020] The verified inundation depth map and the maximum inundation depth map are used as the flood simulation results.

[0021] Optionally, the step of extracting building outline data of the area where the rural building complex to be analyzed is located, classifying the buildings, and combining the building classification information with the maximum inundation depth map using GIS to obtain the inundation depth of each building in the building complex includes:

[0022] Extract the building outline data of the area where the rural building complex to be analyzed is located, and assign a building type to each building outline data corresponding to a building block.

[0023] Determine the location distribution of each type of building based on its type;

[0024] The building outline information of the building blocks with assigned building types is combined with the maximum flood depth map to determine the flood depth of each building block.

[0025] Optionally, the analysis of building flood vulnerability based on component information and flood depth, to determine the total loss of all buildings, includes:

[0026] Based on Monte Carlo simulation, the failure probability of various building components at different water depths, the vulnerability function of various buildings, and the exceedance probability of each damage state at different flooding depths were determined, thus obtaining vulnerability information of various buildings under each damage state.

[0027] Based on the vulnerability analysis of building components, the first variation relationship between the total loss of various types of buildings and the flooding depth is determined;

[0028] Using vulnerability information, a second variation relationship between total loss of various types of buildings and flood depth was determined;

[0029] The total loss of the rural building complex in the area to be analyzed is determined based on the inundation depth of various buildings in the complex and the first variation relationship, and the total loss is verified using the second variation relationship.

[0030] Optionally, the vulnerability analysis based on building components to determine the first variation relationship between the total loss of various types of buildings and the flood depth includes:

[0031] Obtain replacement cost data for each building component;

[0032] Random substitution costs based on Monte Carlo simulation-generated substitution cost data;

[0033] Based on the random substitution cost, the random loss of each building component at different flood depths is determined, and the random losses of all building components are summed to obtain the random loss of the entire building at different flood depths.

[0034] Based on the mean and variance of the random losses of each building component of various types of buildings, the first variation relationship between the total loss of various types of buildings and the flood depth is determined.

[0035] Optionally, the step of using vulnerability information to determine the second variation relationship between the total loss of various types of buildings and the flood depth includes:

[0036] Based on the vulnerability information of various types of buildings, the product of the probability of being in each damage state and the corresponding repair or replacement cost for that damage state is accumulated;

[0037] The average loss of various types of buildings at different water depths is calculated based on the cumulative results, and the second variation relationship between the total loss of various types of buildings and the flood depth is determined.

[0038] Optionally, determining the total loss of the rural building complex within the area where the rural building complex to be analyzed is located based on the first change relationship includes:

[0039] The total range of maximum flood depth for all types of buildings in the complex is divided into multiple sub-intervals, and the number of affected buildings in each sub-interval is determined.

[0040] Based on the first change relationship, determine the flood disaster loss rate of buildings corresponding to the average inundation depth in different sub-intervals;

[0041] The total loss of all buildings is determined based on the flood damage loss rate of buildings, the number of affected buildings, and the total replacement cost of various types of buildings at different water depths.

[0042] Secondly, embodiments of this application provide a probability-based rural building complex flood disaster loss assessment system, the system comprising:

[0043] The simulation module is used to acquire multi-source data of the area where the rural building complex to be analyzed is located, and to simulate the dynamic evolution of flood disaster using a hydrodynamic model to obtain inundation depth maps and maximum inundation depth maps.

[0044] The inundation analysis module is used to extract building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map through GIS to obtain the inundation depth of each building in the building complex.

[0045] The component information calculation module is used to collect the structural features of various typical buildings and calculate the component information of the buildings;

[0046] The loss analysis module is used to analyze the flood vulnerability of buildings based on component information and flooding depth, and to determine the total loss of all buildings;

[0047] The assessment module evaluates the effectiveness of current disaster prevention measures based on the total loss under different disaster prevention measures.

[0048] Optionally, a validation module is also included to compare the inundation depth map with SAR image water body extraction data and social media data to validate the hydrodynamic model and obtain flood simulation results.

[0049] Compared with the prior art, the specific beneficial effects of the present invention are as follows:

[0050] First, this invention utilizes multi-source data to conduct flood simulation. It not only incorporates SAR image water body extraction data but also integrates social media information to comprehensively verify the accuracy of the simulation results from two dimensions: inundation range and inundation depth, thereby improving the credibility of the simulation results.

[0051] Secondly, this invention constructs vulnerability curves and loss curves for 16 types of rural buildings using probabilistic methods, providing a quantitative analysis basis and data support for assessing the loss of rural buildings in flood disasters.

[0052] Furthermore, this invention classifies rural buildings into 16 categories and uses GIS technology to determine their spatial distribution. Addressing the challenge of missing building information in most rural areas, it integrates POI data with building codes to accurately infer building types and their distribution locations.

[0053] Furthermore, this invention combines flood simulation results with classified building distribution data using GIS technology, enabling the acquisition of the inundation depth of each building and, based on this, the assessment of the average loss of building clusters within the study area, thereby achieving a more refined and comprehensive risk assessment.

[0054] Finally, this invention utilizes the HEC-RAS model to integrate engineering measures such as dams into topographic data, reflecting their impact on flood processes. Simultaneously, it simulates non-engineering disaster mitigation measures by increasing the relative positions of key building components. Based on this, it calculates and compares building losses under and without flood control measures, quantitatively assessing the disaster mitigation effectiveness of flood control measures. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of the method proposed in this invention.

[0057] Figure 2 middle: Figure 2 (a) Figure 2 (d) is a schematic diagram of the building model of a single-story residential building in different orientations in an embodiment of the present invention.

[0058] Figure 3 middle: Figure 3 In Figure (a), the building vulnerability curve of a single-story residential building in an embodiment of the present invention is shown. Figure 3 (b) is the component loss curve of a single-story residential building. Figure 3 (c) is the building loss curve. Figure 3 (d) is a schematic diagram of the loss surface of a single-story residential building.

[0059] Figure 4 middle: Figure 4 (a) Figure 4(b) is a satellite image of two main waterlogged areas obtained based on information from social media platforms.

[0060] Figure 5 This is a building classification result image based on building codes and points of interest data.

[0061] Figure 6 It is a building inundation depth map obtained by combining flood simulation results with building outline data;

[0062] Figure 7 It is a map showing the representative inundation depth of 16 types of buildings in different water depth ranges. Detailed Implementation

[0063] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0064] Example 1: As Figure 1 As shown in the figure, this embodiment provides a probability-based method for assessing flood damage to rural building complexes. The method includes the following steps:

[0065] Step 1: Obtain multi-source data of the area where the rural building complex to be analyzed is located, and use a hydrodynamic model to simulate the dynamic evolution of flood disasters to obtain inundation depth maps and maximum inundation depth maps.

[0066] Optionally, step 1 includes the following sub-steps:

[0067] Step 1.1: Obtain geographic information data, historical rainfall data, and land use data of the area where the rural building complex to be analyzed is located;

[0068] In the embodiments of this application, the geographic information data specifically refers to the geographic elevation data (DEM) of the area to be analyzed; historical rainfall data refers to the rainfall data and major river flow data of the area to be analyzed during rainfall events; and land use data refers to the land cover data of the area to be analyzed.

[0069] Step 1.2: Construct a hydrodynamic model to simulate the dynamic evolution of flood disasters based on the data obtained in the previous step, and output the raster data of water depth and flow velocity that change over time during the flood event; among them, the raster data of water depth is the inundation depth map during the simulated rainfall cycle.

[0070] In the embodiments of this application, the present invention simulates the dynamic process of flood disaster spread using a hydrodynamic model based on the acquired data, and obtains raster data during the flood inundation process. This raster data is used as the causative factor in the flood vulnerability assessment index system for buildings. The hydrodynamic model is the HEC-RAS (Hydrologic Engineering Centers River Analysis System) hydrodynamic model. HEC-RAS is a river analysis system mainly used for river and flood simulation. This model can simulate the flood process of a river based on topographic, meteorological, and hydrological data, predict the propagation path and speed of flood waves, and the impact of the flood on downstream areas.

[0071] Specifically, using HEC-RAS software, the geographic elevation data (DEM) of the area where the rural building complex to be analyzed is located is imported for topographic projection, and a two-dimensional grid is created. Then, land use data is added in RasMapper. In the two-dimensional hydrodynamic model, boundary conditions and time steps are set, and finally, an inundation depth map during the simulated rainfall cycle can be obtained, as well as a map of the maximum inundation depth during the simulated rainfall cycle.

[0072] Step 2: Compare the flood depth map with SAR image water body extraction data and social media data to verify the hydrodynamic model and obtain flood simulation results.

[0073] In this embodiment, the accuracy and reliability of the hydrodynamic model are verified by comparing the regional inundation data (i.e., inundation depth map and maximum inundation depth map) obtained in step 1 with SAR image water body extraction data and social media data. The verified hydrodynamic model is then used to perform a standard flood simulation to obtain the standard flood simulation results.

[0074] In the embodiments of this application, the accuracy of the flood simulation results is verified from two aspects: the flood inundation depth and the inundation range.

[0075] Optionally, step 2 includes the following sub-steps:

[0076] Step 2.1: Obtain SAR images of the area where the rural building complex to be analyzed is located before, during, and after the disaster, and extract water bodies from them respectively to obtain the water body extraction results before, during, and after the disaster.

[0077] Specifically, high-resolution SAR images of the area to be analyzed are obtained using the Gaofen-3 satellite, and water bodies are extracted based on the SAR images;

[0078] The principle of water body extraction based on SAR imagery is that the backscattering coefficient of water bodies in the acquired SAR images is relatively low (usually appearing as dark-toned areas; to highlight the water bodies, they are processed and displayed as red areas on the image). This is one of the important technical means for monitoring flood and water conditions.

[0079] The process of water extraction before or after a disaster mainly includes:

[0080] A1: Perform data preprocessing on SAR images, such as filtering and smoothing SAR images;

[0081] A2: Perform multi-scale segmentation on the preprocessed SAR image data;

[0082] A3: Perform feature extraction and selection in the key regions after segmentation;

[0083] A4: Extracting water bodies based on the random forest method.

[0084] By using the above methods to extract water bodies from SAR images before, during, and after a disaster, we can obtain the water body extraction results for each stage.

[0085] Step 2.2: Compare the water body extraction results before, during, and after the disaster with the inundation depth maps before, during, and after the disaster to verify the accuracy of the inundation range in the flood simulation results.

[0086] Step 2.3: Use web crawlers to crawl social media data and extract water depth information at major water accumulation points;

[0087] In the embodiments of this application, the social media data mainly originates from text, images, videos, and other data with geolocation tags on Weibo. The specific process for extracting water depth information from major water accumulation points is as follows:

[0088] In the web crawler program, set keywords (such as "rainstorm", "flood", "waterlogging", "flood", "water depth", etc.), search time range (such as July 29 to August 4, 2023), and target location (such as a town in a city in a province).

[0089] Data was crawled using a web crawler to generate a data table containing information such as corresponding URLs, usernames, post times, blog content, number of likes, number of shares, and number of comments. This data was then cleaned, subjected to fuzzy matching and fusion, deduplicated, and manually filtered to ultimately obtain the water depth data for the main water accumulation points.

[0090] Step 2.4: Select typical waterlogged locations (such as schools and residential buildings), import the satellite imagery with location information of the selected waterlogged locations into the HEC-RAS software, and obtain the change data of flood inundation depth of the selected waterlogged points during the simulated rainfall cycle based on the inundation depth map. Compare the obtained flood inundation depth with the water depth information extracted from Weibo data to verify the accuracy of the inundation depth in the standard flood simulation results, and use the verified inundation depth map and the maximum inundation depth map as the flood simulation results.

[0091] Step 3: Extract the building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map using GIS to obtain the inundation depth of each building in the building complex.

[0092] First, in the embodiments of this application, typical buildings in the area to be analyzed are divided into 16 categories through methods such as site visits and questionnaires, as shown in Table 1.

[0093] Table 1. Building Type Descriptions:

[0094] ;

[0095] Secondly, Bigemap software was used to extract building outline data for the area to be analyzed, and the building outline data was classified. That is, each obtained building outline data was assigned to a building block, and each building block was assigned a building type from Table 1. Based on the building type of each building block, the location distribution of each type of building was determined. The specific operation process is as follows:

[0096] Since residential buildings are typically the most numerous and concentrated in the area being analyzed, their distribution is determined first. National and local regulations stipulate requirements for the building area and height of residential buildings; these requirements allow us to determine their distribution.

[0097] Specifically, firstly, the extracted building outline data is imported into a Geographic Information System (GIS). This building outline data includes the height attributes of building blocks, and the area of ​​each building block is calculated using GIS. Based on the area and height attributes of the building blocks, the building blocks that meet different height and area requirements are divided into different types of residential buildings, such as one-story self-built houses F1, multi-story residential buildings F3, etc., using the attribute selection function in the GIS system.

[0098] POI (Point of Interest) data is structured data in geospatial information used to describe specific locations, containing attributes such as the location, name, and type of each POI. Secondly, POI data for various building types is acquired. Based on this POI data, the distribution of building blocks for other building types (such as hospitals and clinics) is determined. The intersection of POI points for other building types with the building outline data is calculated, and the attributes of the POI points are assigned to the building blocks, thus determining the distribution of other building types. POI data for residential buildings (such as multi-story and high-rise residential buildings) is also acquired to further supplement the analysis of residential building distribution.

[0099] The building distribution data was supplemented and improved using Google satellite imagery and OpenStreetMap (OSM). For building blocks that have not yet been assigned building type attributes, their classification was determined based on satellite imagery and the building name attributes they contain.

[0100] Finally, the building outline data of the building blocks assigned to different building types are combined with the maximum inundation depth map obtained in step 1 to analyze the inundation status of the building complex. The inundation status of the building complex refers to the inundation depth information of each building block. Specifically, the maximum inundation depth map generated by HEC-RAS is imported into the GIS, and the data is converted to integer format and then raster-to-polygon processing is performed. Subsequently, through GIS intersection operations, the processed flood inundation depth results are overlaid with the attributes of the classified building blocks, assigning the water depth attribute of each location in the maximum inundation depth map to the building blocks at the same location, thereby obtaining the inundation depth of each building block.

[0101] Step 4: Collect the structural features of various typical buildings and calculate the component information of the buildings.

[0102] Optionally, step 4 includes:

[0103] Step 4.1: Obtain the structural features of various typical buildings, establish a comprehensive dataset, and build two-dimensional and three-dimensional models of each type of building based on the comprehensive dataset;

[0104] Specifically, considering the characteristics of rural architecture in China, this study systematically collected structural features of each typical building type (F1-F16) by conducting extensive field investigations and surveys of the area under analysis, combining Google satellite imagery and OpenStreetMap (OSM), and referencing relevant rural building codes. These structural features included data such as the number of floors, building area, building height, and interior design. A comprehensive dataset was constructed based on these structural features, and two-dimensional and three-dimensional models of each building type were established based on this dataset, providing a basis for calculating building component information. Figure 2 The image shown is an architectural model of a single-story self-built house. Figure 2 (a) is a floor plan of a one-story self-built house. Figure 2 (b) is its internal design drawing. Figure 2 (c) and Figure 2 The middle (d) view and the side view of the single-story self-built house are respectively. From these views, we can obtain information such as the height, area and the area or volume of the building components of the single-story self-built house.

[0105] Step 4.2: Calculate component information by creating two-dimensional and three-dimensional models of each type of building based on the comprehensive dataset.

[0106] Specifically, in order to study the flood vulnerability of buildings, information on the components of each type of building was collected through field investigations, historical data, and information published online. The component information includes information on the structural and non-structural components contained in the building block, including the type, quantity, area, volume, or length of the components (such as floor area and exterior wall volume), as well as the water resistance depth, water resistance duration, and replacement cost range of each component.

[0107] Step 5: Based on the component information and flooding depth, analyze the flood vulnerability of buildings using probability theory to determine the total loss of all buildings;

[0108] Optionally, step 5 includes the following sub-steps:

[0109] Step 5.1: Determine the failure probability of various components at different water depths, the vulnerability function of various buildings, and the exceedance probability of each damage state at different flooding depths, to obtain the vulnerability information of various buildings under each damage state. The vulnerability information refers to the vulnerability curves of various buildings.

[0110] Vulnerability curves describe the probability that a structural system or building component will reach or exceed a specified damage state at a given disaster intensity level, such as:

[0111] (1)

[0112] in, It is a vulnerability function. For disaster intensity, For the system or component at disaster intensity of Damage intensity at that time It is the damage value that the system or component can withstand; The probability of exceeding a certain specified damage state;

[0113] Vulnerability is the negative response of a disaster-bearing body to a natural disaster, that is, the degree of loss or the value of loss of the disaster-bearing body within the scope of the disaster threat.

[0114] Vulnerability functions are widely used to assess the risks of earthquakes, debris flows, landslides, and floods, and can be used to assess the physical vulnerability of buildings;

[0115] The vulnerability of structural systems and building components is typically represented by the log-normal distribution function shown in equation (2), which is defined by two parameters. The log-normal distribution function is as follows:

[0116] (2)

[0117] in, It is the median value of fragility. It is the standard deviation of the log-normal distribution. Represents the standard normal cumulative distribution function;

[0118] Initially, the building was divided into individual components, and then these components were assigned different damage states based on the ease with which they would be damaged after being subjected to a flood, ranging from minor damage (DS0) to complete damage (DS4).

[0119] Based on the component information of various buildings collected in step 4, such as the water resistance depth, water resistance duration and replacement cost range of each component, the Monte Carlo simulation is used to randomly generate the water depth that may cause damage to the component, and the failure probability of each component is calculated using equation (3).

[0120] (3)

[0121] in, and All are components of( Failure probability (at time) It is the number of simulations of component failure. This is the total number of Monte Carlo simulations;

[0122] Next, the exceedance probability of each damage state at different water depths is calculated using equation (4):

[0123] (4)

[0124] in, It is the first Damaged status exist( The probability of exceeding the threshold at (). It belongs to the first Damaged status The number of components, It is a component Replacement cost It is in a damaged state. The total replacement cost of the components included below.

[0125] Finally, based on formula (4), vulnerability curves for four damage states were plotted, as follows: Figure 3 As shown in Figure (a), based on the building vulnerability curve, the probability of a building suffering different degrees of damage at different water depths can be analyzed, thus providing a basis for building vulnerability assessment. The vulnerability curve reflects the relationship between the building's damage state and the intensity of the disaster.

[0126] Step 5.2: Based on the vulnerability analysis of building components, determine the first variation relationship between the total loss of various types of buildings and the flooding depth;

[0127] Furthermore, to calculate the total average loss of a building, this invention introduces two loss estimation methods: (1) based on the vulnerability analysis of building components, and (2) based on the overall vulnerability analysis of the building. The first variation relationship refers to the loss curve drawn based on the vulnerability analysis using method (1). The loss curve drawn based on the vulnerability analysis using method (2) represents the second variation relationship between the total loss of various types of buildings and the flood depth.

[0128] The specific steps of method (1) include:

[0129] A1: Obtain replacement cost data for each component through site visits, surveys, and collected online data.

[0130] A2: Based on the obtained replacement cost data for each component, random replacement costs are generated using Monte Carlo simulation. Equation (5) is used to calculate the random losses of each component at different water depths, such as... Figure 3 In section (b), by summing the random losses of all building components, the random losses of the entire building under different flood depths can be determined. Then, using formulas (6) and (7), the mean and variance of the building losses of each component of various types of buildings are calculated, and the average loss curve, random loss points, and confidence intervals of the buildings are plotted, such as... Figure 3 The blue curve, green interval, and blue interval are shown in (c).

[0131] (5)

[0132] in, Indicates the defined intensity ( Below, components random loss, Let i = 1, 2, ..., n be the number of digits. Components generated in sub-Monte Carlo simulations The cost of random replacement It is the first Components obtained from Monte Carlo simulation The cost of loss;

[0133] (6)

[0134] (7)

[0135] in, Indicates average building loss. It is a component The average loss, The standard deviation of building loss It is a component The standard deviation of the loss, It is the total number of Monte Carlo simulations. It represents the total number of building components.

[0136] Method (2) uses a log-normally distributed vulnerability curve to estimate total building loss. This is the simplest method for calculating flood loss. It directly uses the building vulnerability curve obtained in step 5.1 to calculate the average building loss by simple summation, as shown in equation (8). The product of the probability of being in each damage state and the corresponding repair (or replacement) cost is accumulated. The curve obtained by method (2) is shown in equation (8). Figure 3 The red loss curve in (c) shows the relationship between flood depth and total building loss.

[0137] (8)

[0138] in, It is the total building loss based on the fragility curve. It is the first Damaged status The corresponding cumulative replacement cost ratio, It is the total construction cost. It is the first The water is in a damaged state. Furthermore, all parameters are at the specified water depth. The value of .

[0139] The total building loss determined by the building loss curves obtained by methods (1) and (2) is used for mutual verification. Under normal circumstances, the loss curves obtained by the two methods are not much different. However, the loss curve obtained by method (1) is more widely used in the calculation of building group loss. Therefore, in the embodiments of this application, method (1) is used to calculate the total loss of building group in the study area. And the loss curve obtained by method (2) is used to verify the determined total loss.

[0140] Step 5.3: Based on the flooding depth information of each building in the building complex obtained in Step 3 and the first change relationship determined by method (1), estimate the total loss of all buildings;

[0141] Specifically, the total range of maximum flood depth for all types of buildings in the complex is divided into different depth intervals, and the number of different building types in each interval is calculated. The average flood depth of each interval is considered as a representative value of the flood depth of that interval. Based on these representative values, the building loss rates corresponding to different flood intervals are determined in the loss curve. Finally, the total loss of all buildings is calculated using Equation (10).

[0142] (10)

[0143] in, This represents the total loss of all buildings affected by the flood within the study area. Indicates the type of building (i.e., hospital, school). Indicates water depth horizontally. express Level 1 water depth Flood disaster loss rate of buildings of this type express underwater The number of affected buildings in the building category Indicates the first Buildings in Total replacement cost for water depth level.

[0144] Step 6: Calculate the total flood loss of the building complex after implementing different disaster prevention measures, and evaluate the effectiveness of each disaster prevention measure;

[0145] Specifically, in the face of flood disasters, different regions will take corresponding flood prevention measures. This invention evaluates the effectiveness of various flood prevention measures in terms of reducing the economic losses of floods to building complexes.

[0146] Furthermore, there are many non-engineering disaster prevention measures. This invention takes the example of people raising lightweight electrical appliances and furniture above the ground for analysis. Vulnerability curves and loss curves are plotted, the losses to the building complex after implementing this measure are calculated, and the effectiveness of the measure is evaluated.

[0147] Experimental Case: To verify the effectiveness of the method proposed in this application, this experimental case uses a town in a city of a province as the research area and selects an extreme rainfall event that occurred in the area in July 2023 as the disaster background to specifically illustrate the method proposed in this invention. Through actual case analysis, the feasibility and effectiveness of the method of this invention in assessing building damage and disaster prevention decision-making effectiveness under extreme rainfall conditions are verified.

[0148] (1) Obtain the geographic information, historical rainfall and land use data of a certain town, construct a hydrodynamic model, simulate the dynamic evolution of flood disaster, and output regional inundation data.

[0149] The geographic information data includes the geographic elevation data of the study area, the historical rainfall data includes the rainfall data and the flow data of major rivers in the study area during rainfall events, and the land use data includes the land cover data of the area to be analyzed. The data accuracy, data source, and date information of the above data are shown in Table 2.

[0150] Table 2: Flood Simulation Data Information Table

[0151] ;

[0152] Terrain projection settings: Open HEC-RAS, set the spatial unit to meters, set the projection coordinate system, add DEM data, and correct the DEM data.

[0153] Two-dimensional grid construction: Two-dimensional grid data is created. In order to reduce the computational load of the model, the grid size is set to a spatial step size of 100×100 meters, and the river section of the simulation area is densified to improve the accuracy of the simulation results. The grid size of the densified area is 20 meters × 20 meters.

[0154] Spatial roughness setting: Add land use data in RasMapper and set the spatial roughness for different land use types. The specific values ​​are shown in Table 3.

[0155] Table 3 Spatial Roughness Distribution of Land Types:

[0156] ;

[0157] Boundary condition settings: In two-dimensional hydrodynamic models, the setting of boundary conditions is one of the key factors in simulation accuracy. External boundary conditions are generally divided into two categories: inflow and outflow. Inflow includes the upstream flow data of the river and the precipitation conditions applied to the entire two-dimensional grid. Outflow is the downstream area of ​​the simulation region, including the downstream of the river and the low-lying areas of the simulation region.

[0158] Boundary condition input and time step settings: Input the time series of upstream flow and rainfall of the main river, and set the downstream data to a typical slope of 0.01. For the time step settings, both the calculation time step and the map output time step are set to 10 minutes.

[0159] By following the steps above, a map of the inundation depth of a town during a simulated rainfall cycle can be obtained.

[0160] (2) The flood simulation results of a certain town were compared with SAR image water body extraction data and social media data to verify the accuracy of the simulation results;

[0161] The accuracy of the flood simulation results was verified in terms of both flood inundation depth and inundation extent. First, water bodies were extracted using high-resolution SAR imagery from the Gaofen-3 satellite. The extraction process mainly included four steps: data preprocessing, multi-scale segmentation, feature extraction and selection, and water body extraction based on random forest. This method was used to extract water bodies from pre-disaster and post-disaster SAR images, yielding the extracted water body results. These results were then compared with the flood inundation maps from the flood simulation before and after the disaster to verify the accuracy of the inundation extent in the flood simulation results.

[0162] Data such as text, images, and videos with geotags from Sina Weibo are commonly used social media data sources in disaster research. The following are the specific steps for obtaining social media data and extracting water depth information from major flooding points using a Python-based web crawler:

[0163] Log in to your Sina Weibo account in your browser, obtain the initial URL (Uniform ResourceLocator) of the Weibo backend, and input it into your web crawler.

[0164] Set keywords ("rainstorm", "flood", "waterlogging", "flood", "water depth", etc.), search time range (July 29 to August 4, 2023), and target location (a town in a city of a province) in the program.

[0165] The generated data table after crawling contains information such as the corresponding URL, username, post time, blog post content, number of likes, number of reposts, and number of comments. This data is then cleaned, fuzzy matched and merged, deduplicated, and manually filtered to ultimately obtain the water depth data for the main water accumulation points based on Weibo posts.

[0166] In addition, based on Weibo posts by the account "Zhuozhou Traffic Police" from July 29 to August 4, 2023, regarding waterlogged areas in a certain town of a certain city, the location information of the waterlogged areas was extracted.

[0167] Finally, satellite imagery with location tags of two main waterlogging points in a certain town, such as... Figure 4 As shown in Figures 4(a) and 4(b), the simulated flood depth at that point was compared with the water depth value extracted from the Weibo data to verify the accuracy of the inundation depth in the flood simulation results.

[0168] (3) Extract the building outline data of a town, classify the buildings, and combine the flood simulation results to obtain the inundation status of the building complex;

[0169] First, based on extensive field visits and questionnaires, the typical buildings in a certain town were categorized into 16 types. Table 1 lists the names of these building types and their brief descriptions.

[0170] Secondly, the building outline data of the study area (a town) was extracted using the software Bigemap, and the building outline data was classified, that is, each building block was matched with a building type, thereby determining the distribution of each type of building. The main steps included:

[0171] Residential buildings are the most numerous and concentrated in this town, so their distribution was determined first. National and local regulations stipulate requirements for the building area and height of residential buildings; their distribution was determined based on these regulations.

[0172] Then, the building outline data is imported into a geographic information system (GIS). This building outline data includes the height attribute of the building blocks. The area of ​​each building block is calculated. According to national and local requirements for the building area and height of residential buildings, based on the area and height attributes of the building blocks, the buildings that meet different height and area requirements are divided into different types of residential buildings using the attribute selection function.

[0173] Using POI data, the distribution of building blocks for other building types is determined, supplementing the distribution of residential buildings. This invention primarily acquired eight types of POI data, as shown in Table 4. POI data includes attributes such as the location, name, and type of the POI points. The classification process is described using healthcare services as an example. By intersecting the POI points of healthcare services with the building outline data, not only is the distribution of medical buildings determined, but the attributes of the POI data are also assigned to building blocks. Using the name, type, and other attributes of the POI data, medical buildings are divided into two categories: hospitals and outpatient clinics.

[0174] Table 4 POI Data Description:

[0175] ;

[0176] Google satellite imagery and OpenStreetMap (OSM) were used to supplement and improve building distribution data. For building blocks that did not yet have building type attributes, they were classified and labeled using satellite imagery and building name attribute information. The final classification results for all buildings in a certain town were obtained, such as... Figure 5 As shown.

[0177] Therefore, the classified building outline data is combined with flood simulation results to analyze the inundation status of building complexes. Specifically, the flood simulation results generated by HEC-RAS are imported into GIS, and the data is converted to integer format and then raster-to-polygon processing is performed. Finally, by taking the intersection operation, the processed flood simulation results are overlaid with the classified building block data to obtain the inundation depth information of each building block, such as... Figure 6 As shown, it is a map showing the submersion depth of all buildings.

[0178] (4) Collect data such as the structural features, building area, and component information of typical buildings;

[0179] (5) As described in step 5 above, the flood vulnerability of buildings is analyzed based on probability theory, and vulnerability curves and loss curves for various types of buildings are generated. Figure 3 This refers to the vulnerability curve, loss curve, and loss surface of a single-story self-built house.

[0180] (6) As described in step 5 above, combine the representative flooding depths of different buildings with their loss curves to estimate the average loss of the buildings, such as... Figure 7 As shown, it is a representative inundation depth map of 16 types of buildings in different water depth ranges. Based on the representative inundation depth of each type of building, the average total loss of all buildings is estimated.

[0181] (7) Calculate the total flood loss of the building complex after implementing different disaster prevention measures, and evaluate the effectiveness of the disaster prevention measures.

[0182] The engineering measures are exemplified by the construction of a dam. In HEC-RAS, a suitable location is determined to add the dam, and in Ras, the dam is added to the terrain. Then, a flood simulation is performed on a certain city, that is, step (1) is repeated. The loss of the building complex is calculated based on the loss curve, and the loss of the building complex without engineering measures is compared to evaluate the effectiveness of the measures.

[0183] Non-engineering measures, such as raising lightweight appliances and furniture off the ground, would increase the flood-affected depth of the raised non-structural components. Therefore, the water resistance depth of these components should be modified, the failure probability recalculated, and new vulnerability and loss curves plotted. The overall building loss after implementing this measure should be calculated, and the effectiveness of the measure evaluated.

[0184] Example 2: This application also provides a probability-based rural building complex flood disaster loss assessment system, implemented based on the above method. The system may include:

[0185] The simulation module is used to acquire multi-source data of the area where the rural building complex to be analyzed is located, and to simulate the dynamic evolution of flood disaster using a hydrodynamic model to obtain inundation depth maps and maximum inundation depth maps.

[0186] The validation module is used to compare the inundation depth map with SAR image water body extraction data and social media data to validate the hydrodynamic model and obtain flood simulation results.

[0187] The inundation analysis module is used to extract building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map through GIS to obtain the inundation depth of each building in the building complex.

[0188] The component information calculation module is used to collect the structural features of various typical buildings and calculate the component information of the buildings;

[0189] The loss analysis module is used to analyze the flood vulnerability of buildings based on component information and flooding depth, and to determine the total loss of all buildings;

[0190] The assessment module evaluates the effectiveness of current disaster prevention measures based on the total loss under different disaster prevention measures.

[0191] The probability-based rural building complex flood disaster loss assessment system in this application embodiment can be a device with an operating system. This operating system can be Android, Linux, Windows, or other possible operating systems; this application embodiment does not specifically limit it.

[0192] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0193] Finally, it should be noted that in this text, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0194] The above provides a detailed description of the method and system for assessing flood damage to rural building complexes provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing flood damage to rural building complexes, characterized in that, The method includes: Multi-source data of the area where the rural building complex to be analyzed is located is obtained, and the dynamic evolution of flood disaster is simulated using a hydrodynamic model to obtain inundation depth map and maximum inundation depth map; By comparing the inundation depth map with water body extraction data from SAR imagery and social media data, the hydrodynamic model was validated, and flood simulation results were obtained. Extract the building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map through GIS to obtain the inundation depth of each building in the building complex. Collect the structural features of various typical buildings and calculate the component information of the buildings; Based on component information and flood depth, the flood vulnerability of buildings is analyzed to determine the total loss of all buildings; Based on the total loss under different disaster prevention measures, assess the effectiveness of the current disaster prevention measures; The analysis of building flood vulnerability based on component information and inundation depth, and the determination of the total loss for all buildings, includes: Based on Monte Carlo simulation, the failure probability of various building components at different water depths, the vulnerability function of various buildings, and the exceedance probability of each damage state at different flooding depths were determined, thus obtaining vulnerability information of various buildings under each damage state. Based on the vulnerability analysis of building components, the first variation relationship between the total loss of various types of buildings and the flooding depth is determined; Using vulnerability information, a second variation relationship between total loss of various types of buildings and flood depth was determined; The total loss of the rural building complex in the area where the building complex is located is determined based on the inundation depth of various buildings in the building complex and the first variation relationship, and the total loss is verified using the second variation relationship. The formula for calculating the total loss is as follows: ; in, This represents the total loss of all buildings affected by the flood within the study area. Indicates the type of building. Indicates water depth level. express Level 1 water depth Flood disaster loss rate of buildings of this type express underwater The number of affected buildings in the building category Indicates the first Buildings in Total replacement cost for water depth level.

2. The method according to claim 1, characterized in that, The process involves acquiring multi-source data for the area to be analyzed, simulating the dynamic evolution of flood disasters using a hydrodynamic model, and obtaining inundation depth maps and maximum inundation depth maps, including: Obtain geographic information data, historical rainfall data, and land use data of the area where the rural building complex to be analyzed is located; Based on the acquired data, a hydrodynamic model is constructed to simulate the dynamic evolution of flood disasters and output inundation depth maps and maximum inundation depth maps during the simulated rainfall cycle.

3. The method according to claim 2, characterized in that, The process involves comparing inundation depth maps with SAR imagery data and social media data to validate the hydrodynamic model and obtain flood simulation results, including: SAR images of the rural building complexes to be analyzed were extracted before, during, and after the disaster to obtain the water body extraction results before, during, and after the disaster. The water body extraction results before, during, and after the disaster were compared with the inundation depth maps before, during, and after the disaster to verify the accuracy of the inundation range. Web crawlers were used to collect data from social media to extract water depth information at water accumulation points; The flood inundation depth of typical water accumulation points during the simulated rainfall cycle is determined based on the inundation depth map. This flood inundation depth is then compared with the extracted water depth information to verify the accuracy of the inundation depth. The verified inundation depth map and the maximum inundation depth map are used as the flood simulation results.

4. The method according to claim 3, characterized in that, The process involves extracting building outline data of the area where the rural building complex to be analyzed is located, classifying the buildings, and combining the building classification information with the maximum inundation depth map using GIS to obtain the inundation depth of each building in the building complex, including: Extract the building outline data of the area where the rural building complex to be analyzed is located, and assign a building type to each building outline data corresponding to a building block. Determine the location distribution of each type of building based on its type; The flood depth of each building block is determined by combining the building outline information of the building blocks of the assigned building type with the maximum flood depth map.

5. The method according to claim 4, characterized in that, The vulnerability analysis based on building components determines the first variation relationship between the total loss of various types of buildings and the flood depth, including: Obtain replacement cost data for each building component; Random substitution costs based on Monte Carlo simulation-generated substitution cost data; Based on the random substitution cost, the random loss of each building component at different flood depths is determined, and the random losses of all building components are summed to obtain the random loss of the entire building at different flood depths. Based on the mean and variance of the random losses of each building component of various types of buildings, the first variation relationship between the total loss of various types of buildings and the flood depth is determined.

6. The method according to claim 5, characterized in that, The method of using vulnerability information to determine the second variation relationship between the total loss of various types of buildings and the flood depth includes: Based on the vulnerability information of various types of buildings, the product of the probability of being in each damage state and the corresponding repair or replacement cost for that damage state is accumulated; The average loss of various types of buildings at different water depths is calculated based on the cumulative results, and the second variation relationship between the total loss of various types of buildings and the flood depth is determined.

7. The method according to claim 6, characterized in that, The determination of the total loss of the rural building complex within the area where the analysis is located, based on the first change relationship, includes: The total range of maximum flood depth for all types of buildings in the complex is divided into multiple sub-intervals, and the number of affected buildings in each sub-interval is determined. Based on the first change relationship, determine the flood disaster loss rate of buildings corresponding to the average inundation depth in different sub-intervals; The total loss of all buildings is determined based on the flood damage loss rate of buildings, the number of affected buildings, and the total replacement cost of various types of buildings at different water depths.

8. A system for assessing flood damage to rural building complexes, characterized in that, Perform the method as described in claim 1, and the system comprises: The simulation module is used to acquire multi-source data of the area where the rural building complex to be analyzed is located, and to simulate the dynamic evolution of flood disaster using a hydrodynamic model to obtain inundation depth maps and maximum inundation depth maps. The inundation analysis module is used to extract building outline data of the area where the rural building complex to be analyzed is located, classify the buildings, and combine the building classification information with the maximum inundation depth map through GIS to obtain the inundation depth of each building in the building complex. The component information calculation module is used to collect the structural features of various typical buildings and calculate the component information of the buildings; The loss analysis module is used to analyze the flood vulnerability of buildings based on component information and flooding depth, and to determine the total loss of all buildings; The assessment module evaluates the effectiveness of current disaster prevention measures based on the total loss under different disaster prevention measures.

9. The system according to claim 8, characterized in that, It also includes a validation module, which compares inundation depth maps with SAR image water body extraction data and social media data to validate the hydrodynamic model and obtain flood simulation results.

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