A flood inundation range analysis method based on multi-source data and evidence weight

Abstract

This invention provides a multi-source data flood inundation range analysis method based on weight of evidence. The method comprises: acquiring data, spatially overlaying and analyzing multi-source data, classifying inundation characteristics of the multi-source data, calculating inundation characteristic weight coefficients, calculating the logarithmic posterior probability of grid-level flood inundation, calculating the fused grid-level flood inundation probability, and determining a flood inundation probability threshold. This method utilizes a weight of evidence approach as a method for calculating multi-source data fusion weights, integrating flood model simulation, remote sensing monitoring interpretation, and social survey inundation data. This method overcomes issues such as unreasonable or incomplete inundation range coverage caused by errors and methodological flaws in a single data source, thereby improving the accuracy of inundation range analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood disaster management and planning, and in particular relates to a flood inundation range analysis method based on multi-source data of evidence weight. Background Art

[0002] After a flood disaster occurs, quickly locating the affected area and determining the inundated area is essential for understanding the disaster's impact, assessing losses, and carrying out flood prevention, emergency response, and disaster relief efforts. This provides critical support for developing emergency relief plans and deploying personnel and supplies. Currently, a variety of methods exist for assessing flood inundation, including flood model analysis, remote sensing imagery analysis, and simplified GIS methods.

[0003] Flood models enable rapid analysis and timely identification of flooded or flooded areas within a modeled region. However, they place high demands on modeling data. Model calculations often exhibit significant deviations when basic data is scarce, current operating conditions are unclear, and future trends are unclear. Furthermore, flood models rely on historical flood data for parameter calibration.

[0004] Remote sensing image analysis can interpret large-scale inundation extent data. However, image acquisition is limited by the frequency of satellite passes. Furthermore, factors such as obstructions such as clouds and crops on the ground can also lead to large errors in the interpreted inundation extent.

[0005] The simplified GIS analysis method is mainly based on DEM data, using GIS to perform spatial analysis to obtain results. This method has shortcomings in terms of timeliness, accuracy, and the degree of reflection of working conditions.

[0006] In addition to the aforementioned technical means, multimedia photos and videos circulating online, as well as related discussions on Weibo, can also provide some additional information on flood inundation. Furthermore, my country has established a disaster reporting system for grid workers. After a flood occurs, grid workers across the country will report key affected areas within key areas. The data content and format of these reports are relatively standardized, serving as a valuable supplement to flood inundation information at various stages.

[0007] During flood disasters, both technical means and social information reporting lay the foundation for determining the inundation area. However, due to flaws in the inherent methods and limitations of external basic data, one-sided flood source information suffers from problems such as incomplete coverage and large errors in inundation area.

[0008] Therefore, there is an urgent need for a method that can effectively integrate multiple flood and inundation information sources, make up for the shortcomings of incomplete information source coverage, reduce the error of inundation range, and thus obtain the inundation range analysis method that best matches or is closest to the actual situation in a timely and accurate manner when flood disasters occur. Summary of the Invention

[0009] In response to the problems existing in the prior art, the purpose of the present invention is to provide a flood inundation range analysis method based on multi-source data of evidence weight. This method effectively integrates multiple flood inundation information sources through specific mathematical methods, strengthens the real area, reduces or weakens the false and erroneous areas, and thus obtains the inundation range that best matches or is closest to the actual situation. It can solve the problems of missing, incomplete or excessive errors in the inundation range during the development of flood disasters, and provide more accurate data for flood prevention, emergency rescue and disaster relief.

[0010] The purpose of the present invention is achieved through the following technical solutions:

[0011] The present invention provides a method for analyzing flood inundation range based on multi-source data based on weight of evidence, comprising the following steps:

[0012] Step 1, get the data:

[0013] Determine the study area, obtain flood model simulation data, remote sensing inundation range data, social survey data and currently known flood inundation true value data of the study area.

[0014] Step 2: Multi-source data spatial overlay analysis:

[0015] Using the grid of flood model simulation data obtained in step 1 as a benchmark, remote sensing inundation range data and social survey data are superimposed on it, including:

[0016] S21, Spatial overlay analysis of remote sensing inundation range data and flood model simulation data

[0017] The remote sensing flood inundation range map is cut according to the grid size of the flood model, and the grid numbers are used to identify the inundation range blocks cut out by the grids, so as to establish the association between the remote sensing flood inundation range map and the grids, and form a spatial layer of remote sensing image inundation or not indexed by the grid numbers;

[0018] S22, Spatial overlay analysis of social survey data and flood model simulation data

[0019] The social disaster data map is spatially superimposed with the flood model grid, the grid serial number of the social disaster data is marked, and the relationship between the social disaster data and the grid is established.

[0020] Step 3: Multi-source data flood feature classification:

[0021] The flood model simulation data, remote sensing inundation range data, and social survey data obtained in step 1 are classified into different levels of inundation characteristics as evidence for flood inundation classification, including:

[0022] S31, Classification of flood model simulation data

[0023] The data calculated by the flood model includes various information such as the flood depth, flood duration and flood velocity of each grid. The flood depth of each grid is used as a characteristic indicator, and the data is divided into several different levels to establish evidence information.

[0024] S32, Classification of Remotely Sensed Inundation Extent Data

[0025] Remote sensing inundation range data refers to the flood inundation or waterlogging range of the study area obtained by remote sensing monitoring. The data is divided into several different levels based on whether it is inundated or not, and evidence information is established.

[0026] S33, Classification of Social Survey Data

[0027] Socialized survey data refers to the standard data reported by communities, villages or grid workers. The data attributes include seven levels of description: flooding points above the knees, above the waist, above the chest, above the head, above the windows, above the beams and above the roof. The data are divided into several different levels to establish evidence information.

[0028] Step 4: Calculation of flooding characteristic weight coefficient:

[0029] Calculate the weight coefficients for the different levels of flooding characteristics after classification in step 3, including positive weights and negative weights The calculation formula is:

[0030]

[0031] Where i is the flooding characteristic classification of multi-source data, A1 is the area that reaches a certain classification in a certain flooding characteristic data and is actually flooded, and A2 is the total area that is actually flooded but not in the classification corresponding to A1. It is the area that reaches a certain level in a certain flood characteristic data and is not actually flooded. For The corresponding total area that is not classified and is not actually flooded.

[0032] Step 5: Calculate the logarithmic posterior probability of grid flooding:

[0033] Taking the grid of the two-dimensional flood model as the unit, the logarithmic posterior probability L of flood inundation in each grid is calculated as follows:

[0034]

[0035] Where P(D) refers to the prior probability of flooding in the region, W iPoints to all feature classifications, and takes values ​​based on whether the multi-source data attributes of the grid meet certain feature classifications. If a special classification is met, the corresponding value is If it does not meet the requirements, the corresponding value is i is the inundation characteristic classification of multi-source data, and n is the total number of inundation characteristic classifications.

[0036] Step 6: Calculate the flood inundation probability of the fused grid:

[0037] Taking the grid of the two-dimensional flood model as the unit, the flood inundation probability of each grid is calculated as follows:

[0038] P k =exp(L k ) / (1+exp(L k ))

[0039] Where, P k Refers to the flood inundation probability after the grid integrates multi-source data, L k Refers to the logarithmic posterior probability of flood inundation of each grid, and k refers to the grid number of the two-dimensional flood model.

[0040] Step 7: Determine the flood probability threshold:

[0041] Using the social survey data obtained in step 1 as verification points, the flood inundation probability map of each grid after fusion is compared with the verification points. A certain inundation probability value is taken as the flood inundation threshold. When the flood inundation situation of a certain proportion of verification points is consistent with the fused flood inundation range, the inundation range is considered to meet the requirements. The grids with inundation probability greater than or equal to the threshold are selected as the flood inundation range.

[0042] During the development of the flood, the above steps 1-7 are repeated for the flood model simulation data, remote sensing inundation range data, and social survey data obtained at each stage, and an iterative analysis is performed to obtain the flood inundation range in each time period, thereby realizing the flood inundation range analysis of the study area.

[0043] The beneficial effects of the present invention compared to the prior art are:

[0044] 1. This invention uses the weight of evidence method as a calculation method for multi-source data fusion weights, integrating flood model simulation, remote sensing monitoring interpretation, and social survey inundation data. This method overcomes the problems of unreasonable inundation range or incomplete coverage caused by errors and methodological defects in a single data source, and can improve the accuracy of inundation range analysis.

[0045] 2. The weight of evidence method is a statistical analysis method based on Bayes' theorem and probabilistic uncertainty. This method sorts out the inundation characteristics of various flood data sources, uses the weight of evidence method to establish the weight value of each inundation characteristic, characterizes the contribution of each inundation evidence to flood inundation prediction, and integrates the factors that have important contribution value to the prediction results to obtain the logarithmic posterior probability of flood inundation, which is used to represent the possibility of each grid unit in the study area being inundated. The posterior probability of the fused area is further calculated to determine the inundation range that best matches or is closest to the actual situation, providing solid support for flood prevention, emergency rescue and disaster relief. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings and examples:

[0047] Figure 1 Schematic diagram of the process of the multi-source data flood inundation range analysis method based on weight of evidence according to the present invention;

[0048] Figure 2 Shows the basic situation of the flood storage area and the hydrological process of Dongci Village of Beigou River and Beihedian of Nanjuma River;

[0049] Figure 3 Showing the multi-source data of the maximum flooding range described in the embodiment;

[0050] Figure 4 Overlaying remote sensing monitoring maps for flood analysis grids;

[0051] Figure 5 Simulate the inundation range water depth classification map for flood model;

[0052] Figure 6 A classification map of flooded water depth for socialized survey points;

[0053] Figure 7 Shows the logarithmic posterior probability of flood inundation after multi-source data fusion;

[0054] Figure 8 This is a comparison chart of multi-source data fusion effects. DETAILED DESCRIPTION

[0055] To make the purpose, technical solutions and advantages of the present invention more clear, the following will further describe the disclosed embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] Example 1

[0057] like Figure 1As shown, this embodiment provides a method for analyzing flood inundation range based on multi-source data of evidence weights, taking the Langgouwa Flood Storage Area in the Haihe River Basin as an example area. Starting from July 31, 2023, the Langgouwa Flood Storage Area was put into use for flood diversion, with a total of 7 flood diversions at Xiaoying Hengdi, Xici Village (2 locations), Dongxinzhuang, Zhuzhuang, Dongwu and Liyibakou. As the flood levels of Beigou River and Nanjuma River dropped, the flood storage area receded at Dongmayingbakou at 18:00 on August 6. By August 22, the flood had basically receded, with only a small amount of water remaining in some low-lying areas. During the use of the Langgouwa flood diversion, flood simulation, remote sensing monitoring and social investigation were continuously carried out. This embodiment takes the fusion of the maximum flood inundation range during the flood diversion process as an example.

[0058] The multi-source data flood inundation range analysis method based on evidence weight includes the following steps:

[0059] Step 1, get the data:

[0060] Determine the study area, obtain flood model simulation data, remote sensing inundation range data, social survey data and currently known flood inundation true value data of the study area; among them, the true value data is the currently known flood inundation range obtained by actual measurement or other means and confirmed to be accurate.

[0061] The flood model simulation data is the flood inundation range of the study area calculated using the hydrodynamic model, including the inundation depth, inundation duration and inundation flow rate of each grid during the flood process, as well as the maximum inundation depth of each grid during a single simulation. The data format is vector surface data. In this embodiment, in order to determine the flood evolution and water retreat during the flood diversion process, one- and two-dimensional flood analysis models of the Baigou River, Nanjuma River, Daqing River and flood storage area are established. In the one-dimensional model, the measured and predicted flow data of the Dongci Village Station for the Baigou River and the Beihedian Station for the Nanjuma River are used as boundary conditions ( Figure 2 The lower boundary of the Daqing River adopts the water level-discharge relationship; the modeling range is from Xiaoying Hengdi in the north, the right bank of Baigou River in the east, the left bank of Nanjuma River in the south, and the western boundary is the current design flood storage level. The modeling area is approximately 523km 2 ,like Figure 3 As shown, the area covered by the grid is the modeling scope and is also the research area mentioned in this example. Using the measured and predicted hydrological processes at Dongci Village Station and Beihedian Station, we can continuously simulate the inundation range and water depth distribution of the flood storage area at different times.

[0062] The remote sensing flooding range data is the flooding or waterlogging range obtained by interpreting remote sensing images, and the data format is vector surface data. This embodiment collects satellite remote sensing monitoring data during flood diversion in the flood storage area, merges the remote sensing flooding ranges of each stage, and obtains the maximum flooding range of the remote sensing monitoring flood diversion, such as Figure 3 shown.

[0063] The social survey data refers to data obtained by surveys, etc., and the data format is vector point data. This embodiment collects social survey data from the northern and southern parts of the flood storage area, a total of 164 points, and the main attribute reflecting flood inundation is the depth of the survey point, such as Figure 3 shown.

[0064] Collect the currently known flood inundation range data layer during the flood diversion process of the flood storage and detention area as the current inundation true value, which is vector surface data.

[0065] Step 2: Multi-source data spatial overlay analysis:

[0066] Using the grid of flood model simulation data obtained in step 1 as a benchmark, remote sensing inundation range data and social survey data are superimposed on it, including:

[0067] S21, Spatial overlay analysis of remote sensing inundation range data and flood model simulation data

[0068] The remote sensing flood inundation range map (i.e., the maximum flooding range of the remote sensing monitoring flood diversion in this embodiment) is spatially superimposed with the grid of the flood model simulation data. The remote sensing flood inundation range map is cut by the edges of each grid and divided into blocks no larger than the original grid size. Each block is labeled and assigned a grid number. The association between the remote sensing flood inundation range map and the grid is established to form a spatial layer of remote sensing image flooding or not indexed by the grid number. Figure 4 .

[0069] S22, Spatial overlay analysis of social survey data and flood model simulation data

[0070] A certain percentage of survey points are selected for data fusion, while the remaining survey points are used to select the flood probability threshold and verify the flood range after fusion. In this example, 80% of the survey points are used for fusion, and the remaining 20% ​​are used for verification. The social survey data used for fusion is spatially overlaid with the flood model grid, and the grid numbers of the social survey data points are analyzed.

[0071] Step 3: Multi-source data flood feature classification:

[0072] The flood model simulation data, remote sensing inundation range data, and social survey data obtained in step 1 are classified into different levels of inundation characteristics as evidence for flood inundation classification, including:

[0073] S31, Classification of flood model simulation data

[0074] The data calculated by the flood model include various information such as the inundation depth, inundation duration and inundation velocity of each grid. The inundation depth of each grid is used as a characteristic indicator to classify the data into several different levels to establish evidence information. Specifically, the inundation depth of each grid is used as a characteristic indicator, and the data are divided into 6 flood inundation levels according to the inundation depth h < 0.05; 0.05 ≤ h < 0.5; 0.5 ≤ h < 1; 1 ≤ h < 2; 2 ≤ h < 3 and h ≥ 3 (unit: m). Figure 5 .

[0075] S32, Classification of Remotely Sensed Inundation Extent Data

[0076] Remote sensing inundation range data refers to the flooded or waterlogged range of the study area obtained by remote sensing monitoring. Whether it is inundated is used as a characteristic indicator, and the data is divided into several different levels to establish evidence information. Specifically, in this embodiment, the remote sensing flood inundation range map is superimposed with the grid to be divided into two levels: inundation and non-inundation. The inundation attribute of the grid in the area covered by the inundation range is assigned a value of 1, indicating that the remote sensing monitoring believes that the area is inundated; the inundation attribute of the grid in the uncovered area is assigned a value of 0, indicating that the remote sensing monitoring believes that the area is not inundated. Figure 4 The block grid attributes of the area outside the remote sensing inundation range are assigned to 0.

[0077] S33, Classification of Social Survey Data

[0078] Social survey data refers to the standard data reported by communities, villages or grid workers. The data attributes include seven levels of inundation points: above the knees, above the waist, above the chest, above the head, above the windows, above the beams and above the roof. The water depths are divided into seven inundation levels: h < 0.4; 0.4 ≤ h < 1.0; 1.0 ≤ h < 1.5; 1.5 ≤ h < 1.8; 1.8 ≤ h < 2.0; 2.0 ≤ h < 3.0 and h ≥ 3.0 (unit: m). Figure 6 As stated.

[0079] Step 4: Calculation of flooding characteristic weight coefficient:

[0080] The weight coefficients are calculated for the different levels of inundation characteristics after classification in step 3 (including 6 levels of flood model simulation data, 2 levels of remote sensing inundation range data, and 7 levels of social survey data), including positive weights. and negative weights

[0081] S41, randomly sample the study area and calculate the prior probability of flooding

[0082] Randomly sample N grids in the study area, calculate the flooding situation of these N sampling points based on the flooding information of the currently known true flooding data, and calculate the prior probability of flooding. The formula is as follows:

[0083] P(D)=A 淹 / A 总

[0084] Where P(D) refers to the prior probability of flooding in the region, A 淹 Refers to the total flooded area of ​​the sampling grid, A 总 Refers to the total area of ​​the sampling grid.

[0085] This embodiment uses the model grid area as the evaluation area, randomly samples 3% of the total number of grids, and performs statistics on the inundation conditions of the true inundation values ​​before fusion to calculate the prior probability of flooding in the study area, which is about 48%.

[0086] S42, statistics on positive information of flooding

[0087] In N sampling grids, for flood model simulation data, remote sensing inundation range data, and social survey data, calculate the area A where the corresponding inundation characteristics of each type of data reach a certain level and are actually inundated. 1淹j_i , thereby calculating the total area actually submerged by floods in a certain type of data Sum_A 1淹j , the formula is as follows:

[0088]

[0089] In the formula, i refers to the submerged feature classification of multi-source data, j refers to the data type, and m is the number of feature classifications of a certain type of data in the multi-source data;

[0090] Calculate the total area A that is not classified and actually flooded 2淹j_i , the formula is as follows:

[0091] A 2淹j_i =Sum_A 1淹j -A 1淹j_i

[0092] The calculation results are shown in the following table:

[0093]

[0094]

[0095] S43, statistics of negative flood inundation information

[0096] In N sampling grids, for flood model simulation data, remote sensing inundation range data, and social survey data, calculate the area where the inundation characteristics of each type of data reach a certain level and are actually not inundated. Thus, the total area not actually flooded by flood in a certain type of data can be calculated. The formula is as follows:

[0097]

[0098] Calculate the total area that is not classified and is not actually flooded The formula is as follows:

[0099]

[0100] The calculation results are shown in the following table:

[0101]

[0102] S44 calculates the positive and negative weights of 3 types of data, a total of 15 feature classifications, namely and The calculation formula is:

[0103]

[0104]

[0105] Where i is the flooding characteristic classification of multi-source data, A1 is the area that reaches a certain classification in a certain flooding characteristic data and is actually flooded, and A2 is the total area that is actually flooded but not in the classification corresponding to A1. It is the area that reaches a certain level in a certain flood characteristic data and is not actually flooded. For The corresponding total area that is not classified and is not actually flooded.

[0106] The calculation results are shown in the following table:

[0107]

[0108] Step 5: Calculate the logarithmic posterior probability of grid flooding:

[0109] Taking the grid of the two-dimensional flood model as the unit, the logarithmic posterior probability L of flood inundation in each grid is calculated. The calculation results are as follows: Figure 7 As shown, the formula is as follows:

[0110]

[0111] Where P(D) refers to the prior probability of flooding in the region, W iPoints to all feature classifications, and takes values ​​based on whether the multi-source data attributes of the grid meet certain feature classifications. If a certain special classification is met, the corresponding value is If it does not meet the requirements, the corresponding value is i is the inundation characteristic classification of multi-source data, and n is the total number of inundation characteristic classifications.

[0112] Step 6: Calculate the flood inundation probability of the fused grid:

[0113] Taking the grid of the two-dimensional flood model as the unit, the flood inundation probability of each grid is calculated using the following formula:

[0114] P k =exp(L k ) / (1+exp(L k ))

[0115] Where, P k Refers to the flood inundation probability after the grid integrates multi-source data, L k Refers to the logarithmic posterior probability of flood inundation of each grid, and k refers to the grid number of the two-dimensional flood model.

[0116] Step 7: Determine the flood probability threshold:

[0117] The social survey data obtained in step 1 are used as verification points. The flood inundation probability map of each grid after fusion is compared with the verification points. A certain inundation probability value is taken as the flood inundation threshold. When the flood inundation situation of a certain proportion of verification points is consistent with the fused flood inundation range, it is considered that the inundation range meets the requirements. The grids with inundation probability greater than or equal to the threshold are selected as the flood inundation range.

[0118] During the development of the flood, the above steps 1-7 are repeated for the flood model simulation data, remote sensing inundation range data, and social survey data obtained at each stage, and an iterative analysis is performed to obtain the flood inundation range in each time period, thereby realizing the flood inundation range analysis of the study area.

[0119] Compare the flood survey points with the inundation ranges under different inundation probabilities, take P>20% as the flood inundation threshold, and generate the flood inundation layer, such as Figure 8 shown.

[0120] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and is not limiting. Although the present invention is described in detail with reference to the preferred arrangement scheme, ordinary technicians in this field should understand that the technical solution of the present invention (such as the use of various formulas, the sequence of steps, etc.) can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A flood inundation range analysis method based on multi-source data based on evidence weight, characterized by: The method comprises the following steps: Step 1, get the data: Determine the study area and obtain flood model simulation data, remote sensing inundation range data, social survey data and currently known flood inundation true value data for the study area; Step 2: Multi-source data spatial overlay analysis: Using the grid of flood model simulation data obtained in step 1 as a benchmark, remote sensing inundation range data and social survey data are superimposed on it, including: S21, Spatial overlay analysis of remote sensing inundation range data and flood model simulation data The remote sensing flood inundation range map is cut according to the grid size of the flood model, and the grid numbers are used to identify the inundation range blocks cut out by the grids, so as to establish the association between the remote sensing flood inundation range map and the grids, and form a spatial layer of remote sensing image inundation or not indexed by the grid numbers; S22, Spatial overlay analysis of social survey data and flood model simulation data The social disaster data map is spatially superimposed with the flood model grid, the grid sequence of the social disaster data is marked, and the relationship between the social disaster data and the grid is established; Step 3: Multi-source data flood feature classification: The flood model simulation data, remote sensing inundation range data, and social survey data obtained in step 1 are classified into different levels of inundation characteristics as evidence for flood inundation classification, including: S31, Classification of flood model simulation data The data calculated by the flood model includes various information such as the flood depth, flood duration and flood velocity of each grid. The flood depth of each grid is used as a characteristic indicator, and the data is divided into several different levels to establish evidence information. S32, Classification of Remotely Sensed Inundation Extent Data Remote sensing inundation range data refers to the flood inundation or waterlogging range of the study area obtained by remote sensing monitoring. The data is divided into several different levels based on whether it is inundated or not, and evidence information is established. S33, Classification of Social Survey Data Social survey data refers to standard data reported by communities, villages, or grid workers. Data attributes include seven levels of description: inundation points above the knees, above the waist, above the chest, above the head, above the windows, above the beams, and above the roof. Data are classified into several different levels to establish evidence information. Step 4: Calculation of flooding characteristic weight coefficient: Calculate the weight coefficients for the different levels of flooding characteristics after classification in step 3, including positive weights and negative weights The calculation formula is: Where i is the flooding characteristic classification of multi-source data, A1 is the area that reaches a certain classification in a certain flooding characteristic data and is actually flooded, and A2 is the total area that is actually flooded but not in the classification corresponding to A1. It is the area that reaches a certain level in a certain flood characteristic data and is not actually flooded. For The corresponding total area that is not classified and is not actually flooded; Step 5: Calculate the logarithmic posterior probability of grid flooding: Taking the grid of the two-dimensional flood model as the unit, the logarithmic posterior probability L of flood inundation in each grid is calculated as follows: Where P(D) refers to the prior probability of flooding in the region, W i Points to all feature classifications, and takes values ​​based on whether the multi-source data attributes of the grid meet certain feature classifications. If a certain special classification is met, the corresponding value is If it does not meet the requirements, the corresponding value is i is the inundation characteristic classification of multi-source data, and n is the total number of inundation characteristic classifications; Step 6: Calculate the flood inundation probability of the fused grid: Taking the grid of the two-dimensional flood model as the unit, the flood inundation probability of each grid is calculated as follows: P k =exp(L k ) / (1+exp(L k )) Where, P k Refers to the flood inundation probability after the grid integrates multi-source data, L k Refers to the logarithmic posterior probability of flood inundation of each grid, and k refers to the grid number of the two-dimensional flood model; Step 7: Determine the flood probability threshold: Using the social survey data obtained in step 1 as verification points, the flood inundation probability map of each grid after fusion is compared with the verification points. A certain inundation probability value is taken as the flood inundation threshold. When the flood inundation situation of a certain proportion of verification points is consistent with the fused flood inundation range, the inundation range is considered to meet the requirements. The grids with inundation probability greater than or equal to the threshold are selected as the flood inundation range. During the development of the flood, the above steps 1-7 are repeated for the flood model simulation data, remote sensing inundation range data, and social survey data obtained at each stage, and an iterative analysis is performed to obtain the flood inundation range in each time period, thereby realizing the flood inundation range analysis of the study area.

2. The method according to claim 1, characterized in that In step 3 S31, when grading the flood model simulation data, the flood depth grading standards of each grid are divided into 6 levels: h<0.05; 0.05≤h<0.5; 0.5≤h<1; 1≤h<2; 2≤h<3 and h≥3; In step 3, S32, when classifying the remote sensing flood range data, after the remote sensing flood range map is superimposed on the grid, the grid flood attribute of the area covered by the flood range is assigned a value of 1, indicating that the remote sensing monitoring believes that the area is flooded; the grid flood attribute of the uncovered area is assigned a value of 0, indicating that the remote sensing monitoring believes that the area is not flooded; In step 3 S33, when grading the social survey data, the flooding point is described in seven levels: over the knee, over the waist, over the chest, over the head, over the window, over the beam, and over the roof, and the water depths correspond to h<0.4, 0.4≤h<1.0, 1.0≤h<1.5, 1.5≤h<1.8, 1.8≤h<2.0, 2.0≤h<3.0, and h≥3.0, respectively.

3. The method according to claim 1, characterized in that In step 4 S41, the weight coefficient calculation specifically includes: S41, random sampling of the study area and calculation of flooding probability Randomly sample N grids in the study area, calculate the inundation situation of these N sampling points based on the flood inundation information of the currently known true flood inundation data, and calculate the prior probability of regional flood inundation. The formula is as follows: P(D)=A 淹 / A 总 Where P(D) refers to the prior probability of flooding in the region, A 淹 Refers to the total flooded area of ​​the sampling grid, A 总 Refers to the total area of ​​the sampling grid; S42, statistics on positive information of flooding In N sampling grids, for flood model simulation data, remote sensing inundation range data, and social survey data, calculate the area A where the corresponding inundation characteristics of each type of data reach a certain level and are actually inundated. 1淹j_i , thereby calculating the total area actually submerged by floods in a certain type of data Sum_A 1淹j , the formula is as follows: In the formula, i refers to the submerged feature classification of multi-source data, j refers to the data type, and m is the number of feature classifications of a certain type of data in the multi-source data; Calculate the total area A that is not classified and actually flooded 2淹j_i , the formula is as follows: A 2淹j_i =Sum_A 1淹j -A 1淹j_i S43, statistics of negative flood inundation information In N sampling grids, for flood model simulation data, remote sensing inundation range data, and social survey data, calculate the area where the inundation characteristics of each type of data reach a certain level and are actually not inundated. Thus, the total area not actually flooded by flood in a certain type of data can be calculated. The formula is as follows: Calculate the total area that is not classified and is not actually flooded The formula is as follows:

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