A method for predicting flood submerged areas based on land survey data

Abstract

The present invention discloses a method for predicting flood inundation areas based on land survey data, and relates to the technical field of flood inundation area prediction. When the dam breach risk coefficient of a target dam is not less than a preset dam breach risk coefficient, a plurality of preliminary inundation sub-areas are simulated according to the flood water flow, and the first flood water storage capacity of each preliminary inundation sub-area is obtained; and the current vegetation density, groundwater level and soil permeability coefficient of each preliminary inundation sub-area are used as a data set, and the data set is clustered to determine the water storage capacity impact index of each preliminary inundation sub-area; a second flood water storage capacity is obtained according to the water storage capacity impact index and the first flood water storage capacity, and a final flood inundation area is determined according to the second flood water storage capacity. In this way, the flood inundation area of ​​the future cycle can be predicted, and the normality of flood control planning and emergency plans can be ensured, so that effective prevention and evacuation can be carried out before the flood comes, and casualties and property losses can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of flood inundation area prediction, and in particular to a flood inundation area prediction method based on land survey data. Background Art

[0002] A dam is a protective structure built along a river, lake or coast to resist the impact of water and prevent flooding. The main function of a dam is to control the flow of water and protect nearby areas from flooding. However, once a dam breaks, since it loses the ability to control the flow of water, a large amount of water will quickly gush out, which may cause serious flooding disasters and submerge towns, farmland and infrastructure downstream. Especially in rainy weather, the possibility of dam breaking is greater, causing flooding to submerge downstream areas, causing casualties and property losses;

[0003] The existing flood inundation areas are often determined after the flood occurs. There is a lack of predictions for flood inundation areas in future cycles, which leads to delays in flood control planning and emergency plans, and inability to effectively prevent and evacuate before the flood arrives, resulting in large casualties and property losses. Summary of the invention

[0004] The purpose of the present invention is to solve the problem and propose a flood inundation area prediction method based on land survey data.

[0005] The present invention proposes a flood inundation area prediction method based on land survey data, the method comprising:

[0006] The following steps are involved:

[0007] Obtain the water level, maintenance time and dam cracks of the target dam, and obtain the dam breach risk coefficient based on the water level, maintenance time and dam cracks;

[0008] When the dam breach risk coefficient is not less than the preset dam breach risk coefficient, a first flood area is simulated according to the flood flow, and the first flood area is divided into a number of preliminary flood sub-areas, and the flood water storage capacity of each preliminary flood sub-area is obtained as the first flood water storage capacity;

[0009] The current vegetation density, groundwater level and soil permeability coefficient of each preliminary flooding sub-area are obtained, and the vegetation density, groundwater level and soil permeability coefficient are used as data points of each preliminary flooding sub-area to obtain a data set, and the data set is clustered to determine the water storage capacity impact index of each preliminary flooding sub-area;

[0010] The second flood water storage of each preliminary flooded sub-area is obtained according to the water storage impact index and the first flood water storage, and the final flooded area is determined according to the second flood water storage.

[0011] Optionally, the dam breach risk factor obtained based on the water level, maintenance time and dam cracks includes:

[0012] Obtain the water level rise height of the target dam within a future preset period, and add the water level rise height to the current water level of the target dam to obtain the final water level within the future preset period;

[0013] Obtain the time interval from the last maintenance of the target dam to the current time as the actual time interval, and divide the actual time interval by the preset maintenance time interval to obtain the dam maintenance time index;

[0014] Obtain the crack length and average crack depth of the target dam, and multiply the crack length by the average crack depth to obtain the dam crack index;

[0015] The dam breach risk coefficient is obtained based on the normalized final water level, dam maintenance time index, and dam crack index.

[0016] Optionally, the dam breach risk coefficient obtained according to the normalized final water level, dam repair time index, and dam crack index includes: ; In the formula, is the dam breach risk factor, , , are the normalized final water level, dam repair time index, and dam crack index, respectively. are the preset proportional coefficients of the normalized final water level, dam maintenance time index, and dam crack index, respectively, and Both are greater than 0.

[0017] Optionally, vegetation density, groundwater level and soil permeability coefficient are used as data points for each preliminary flooded sub-area, and the resulting data set includes:

[0018] Obtain the vegetation type of each preliminary flooded sub-area, obtain the area of ​​each vegetation type, and combine it with the corresponding preset water absorption coefficient to obtain the vegetation coefficient. The calculation formula is: In the formula, is the vegetation coefficient, For the Area of ​​vegetation type, For the The vegetation type corresponds to the preset water absorption coefficient. Indicates the total number of vegetation types;

[0019] Obtain the average height of the groundwater level in each preliminary flooded sub-area, and calculate the difference between the average height of the groundwater level and the surface height as the groundwater level height coefficient;

[0020] The vegetation coefficient, groundwater level coefficient and soil permeability coefficient were used as data points for each preliminary flooded sub-area to obtain the data set.

[0021] Optionally, clustering the data set to determine the water storage impact index of each preliminary flooded sub-area includes:

[0022] The data set is clustered to obtain the final clusters, which are then mapped to three-dimensional coordinates to obtain the water storage impact index of the preliminary flooded sub-area.

[0023] Optionally, the final clusters are mapped to three-dimensional coordinates to obtain the water storage impact index of the preliminary flooded sub-area, including:

[0024] Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters;

[0025] Calculate the distance from each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the corresponding data point, and multiply the preliminary coordinate distance of the corresponding data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the corresponding data point;

[0026] The actual coordinate distance of each data point is taken as the water storage impact index of the corresponding preliminary flooded sub-area.

[0027] Optionally, the first flood water storage capacity includes:

[0028] Obtain the average depth and average area of ​​the low-lying terrain in each preliminary flooded sub-region, multiply the average depth by the average area to obtain the water storage capacity of each preliminary flooded sub-region as the first flood water storage capacity. The calculation formula is: , where is the first flood storage capacity, and It is the average depth and average area of ​​low-lying areas.

[0029] Optionally, obtaining the second flood water storage of each preliminary flooded sub-area according to the water storage impact index and the first flood water storage, and determining the final flooded area according to the second flood water storage includes: In the formula, The second flood water storage capacity, is the first flood storage capacity, is the water storage impact index;

[0030] The second flood water storage capacity of each preliminary flooded sub-area is compared with the corresponding preset flood inundation benchmark water volume. If the second flood water storage capacity is not less than the corresponding preset flood inundation benchmark water volume, the corresponding preliminary flooded sub-area is recorded as a flood inundation area, and all flood inundation areas are recorded as final flood inundation areas.

[0031] Beneficial effects of the present invention:

[0032] The present invention proposes a flood inundation area prediction method based on land survey data, and obtains the dam breach risk coefficient by obtaining the water level, maintenance time and dam cracks of the target dam; when the dam breach risk coefficient is not less than the preset dam breach risk coefficient, simulates a number of preliminary inundation sub-areas according to the flood water flow, and obtains the flood water storage capacity of each preliminary inundation sub-area as the first flood water storage capacity; obtains the current vegetation density, groundwater level and soil permeability of each preliminary inundation sub-area as the data points of each preliminary inundation sub-area to obtain a data set, clusters the data set to determine the water storage capacity impact index of each preliminary inundation sub-area; obtains the second flood water storage capacity of each preliminary inundation sub-area according to the water storage capacity impact index and the first flood water storage capacity, and determines the final flood inundation area according to the second flood water storage capacity, so that the flood inundation area of ​​the future cycle can be predicted, and the normal flood control planning and emergency plan can be ensured, so that effective prevention and evacuation can be carried out before the flood comes, and casualties and property losses can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 A flow chart of a method for predicting flood submerged areas based on land survey data is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0037] The embodiment of the present invention provides a method for predicting flood submerged areas based on land survey data. Figure 1 , Figure 1A flow chart of a method for predicting flood submerged areas based on land survey data provided by an embodiment of the present invention. The method comprises the following steps:

[0038] Obtain the water level, maintenance time and dam cracks of the target dam, and obtain the dam breach risk coefficient based on the water level, maintenance time and dam cracks;

[0039] When the dam breach risk coefficient is not less than the preset dam breach risk coefficient, a first flood area is simulated according to the flood water flow, and the first flood area is divided into a number of preliminary flood sub-areas, and the flood water storage capacity of each preliminary flood sub-area is obtained as the first flood water storage capacity;

[0040] The current vegetation density, groundwater level and soil permeability coefficient of each preliminary flooding sub-area are obtained, and the vegetation density, groundwater level and soil permeability coefficient are used as data points of each preliminary flooding sub-area to obtain a data set, and the data set is clustered to determine the water storage capacity impact index of each preliminary flooding sub-area;

[0041] The second flood water storage of each preliminary flooded sub-area is obtained according to the water storage impact index and the first flood water storage, and the final flooded area is determined according to the second flood water storage.

[0042] It should be noted that the future preset period is determined by professionals based on actual conditions and is not limited or elaborated on; for example, it is necessary to predict the flood-inundated area in one month, or the flood-inundated area in half a month, etc. The specific preset period is determined based on actual conditions and is not limited.

[0043] Based on the flood inundation area prediction method based on land survey data provided by the embodiment of the present invention, the flood inundation area in the future period can be predicted through the above method, ensuring the normalization of flood control planning and emergency plans, so that effective prevention and evacuation can be carried out before the flood comes, reducing casualties and property losses.

[0044] In one embodiment, the risk factor of dam breach is obtained according to the water level, maintenance time and dam cracks, including:

[0045] Obtain the water level rise height of the target dam within a future preset period, and add the water level rise height to the current water level of the target dam to obtain the final water level within the future preset period;

[0046] Obtain the time interval from the last maintenance of the target dam to the current time as the actual time interval, and divide the actual time interval by the preset maintenance time interval to obtain the dam maintenance time index;

[0047] Obtain the crack length and average crack depth of the target dam, and multiply the crack length by the average crack depth to obtain the dam crack index;

[0048] The dam breach risk coefficient is obtained based on the normalized final water level, dam maintenance time index, and dam crack index.

[0049] It should be noted that the method for obtaining the water level rise height of the target dam within the future preset period is to obtain the rainfall height within the future preset period through the meteorological relevant departments, and use the rainfall height as the water level rise height; in addition, the current water level of the target dam can obtain the current water level data in real time through the water level monitoring equipment installed on the target dam (such as water level sensor, measuring ruler, etc.); in addition, the time interval from the last maintenance of the target dam to the current time can be obtained from the dam management department, and the dam management department will record the maintenance and repair time of each dam; the preset maintenance time interval is determined by professionals according to the actual situation, and no specific restrictions or elaborations are made; the crack length and average crack depth of the target dam can be obtained by using drones to perform high-precision imaging of the dam surface, combining computer image processing technology to measure the length and depth of the crack, or by using crack measurement equipment (such as crack meters, acoustic wave tests) to obtain the length and depth of the crack, and no specific restrictions or elaborations are made.

[0050] In one embodiment, the dam breach risk coefficient is obtained according to the normalized final water level, the dam repair time index, and the dam crack index, including: ; In the formula, is the dam breach risk factor, , , are the normalized final water level, dam repair time index, and dam crack index, respectively. are the preset proportional coefficients of the normalized final water level, dam maintenance time index, and dam crack index, respectively, and Both are greater than 0.

[0051] It should be noted that It is set by professionals according to the actual situation. Generally, The sum of is 1, for example They can be 0.4, 0.2, 0.4, or other numbers, without limitation.

[0052] It should be noted that the higher the final water level of the target dam after the preset period in the future, the higher the dam maintenance time index, and the higher the dam crack index, the greater the risk of the target dam bursting, and the more likely it is that the downstream will suffer from flood disasters, because the excessively high water level will not only exert greater water pressure on the dam, but may also increase the permeability of the dam soil or foundation materials, further reducing the stability of the dam structure and increasing the possibility of bursting; the larger the maintenance time index, the longer the dam has not undergone necessary maintenance, and the various parts of the dam (such as soil, concrete, metal components, etc.) may fail, especially during heavy rain or flood periods, the unrepaired dam structure may be difficult to resist water erosion, thereby accelerating the bursting of the dam; the length and depth of the cracks are important indicators for measuring the integrity of the dam structure. The larger the dam crack index, the worse the bearing capacity of the dam, especially under extreme conditions such as high water levels and heavy rainfall, water will penetrate into the dam along the cracks, further eroding the dam material and weakening its bearing capacity. When the cracks intensify to a certain extent, the dam is more likely to burst, causing flood disasters downstream.

[0053] In one implementation method, the advantage of obtaining the dam breach risk coefficient based on the final water level, dam maintenance time index, and dam crack index through the above method is that the water level reflects the external environmental pressure, the maintenance time reflects the maintenance status of the internal dam, and the crack index reflects the structural damage, making the final calculated dam breach risk coefficient more accurate and more representative of the risk of possible dam breach.

[0054] In one embodiment, when the dam breach risk coefficient is not less than a preset dam breach risk coefficient, determining that the target dam will breach includes:

[0055] The dam breach risk coefficient is compared with the preset dam breach risk coefficient threshold. When the dam breach risk coefficient is not less than the preset dam breach risk coefficient, it means that the target dam will be at risk of breaching, and the water stored in the dam will overflow, causing flood disasters downstream.

[0056] When the dam breach risk factor is less than the preset dam breach risk factor, it indicates the risk of the target dam breaching and will not cause flood disasters downstream.

[0057] It should be noted that the preset dam breach risk factor threshold is set by professionals based on actual conditions and is not limited or elaborated on in detail.

[0058] In one embodiment, the flood water flow can be estimated by simulation using a model, for example: selecting a suitable hydrological and hydrodynamic model, such as HEC-RAS, MIKE11, etc. These models can simulate the flow dynamics of rivers and breaches;

[0059] Collect input data; Water level data: current and predicted water level data, including water levels around dams and future water level rise;

[0060] Dam structure: the geometric shape of the dam, the location and size of the breach;

[0061] Topographic features: topographic data around the breach,

[0062] Rainfall data: Predicted rainfall data within a preset period in the future;

[0063] Set model parameters: Input the above data into the model and set relevant parameters, such as flow distribution, erosion coefficient, etc.

[0064] Run simulation: Run the model to simulate the flow at the breach. The model will generate a curve or data set showing the flow at the breach over time.

[0065] Obtain the flow change curve of the breach port within a preset period in the future from the model, usually with time as the horizontal axis and flow as the vertical axis;

[0066] Use numerical integration methods (such as trapezoidal method, Simpson method) to integrate the flow curve to calculate the total water flow; the integral calculation formula is: ,in, is the total water flow, is the flow rate that varies with time, and is the start and end time of the future preset period;

[0067] The rainfall data for the next month is input into the model to consider its impact on flow. Rainfall affects the rate of increase of water flow and the total amount of water.

[0068] Basin convergence and distribution: Consider the convergence effect and distribution characteristics of water flow in the basin; ensure that the model can simulate the process of water flowing in and out of the breach, and consider the convergence and diffusion of water flow.

[0069] Model calibration: Calibrate the model to ensure that the simulation results are accurate, which includes verification with historical data and adjusting model parameters to match the actual situation;

[0070] The total water flow within a preset future period is finally estimated based on the model output and is used as the flood flow.

[0071] In one implementation method, the total water flow at the breach can be accurately calculated by using hydrological and hydrodynamic models (such as HEC-RAS and MIKE 11) to simulate the flow at the breach and combining it with the flow change data within a preset period in the future.

[0072] In one embodiment, the area through which the flood flows is simulated based on the flood water flow. As the flow area, the flow path of the flood can be determined using a hydrodynamic model and terrain data. The model will calculate the direction and speed of the flood flow to form a preliminary outline of the area through which the flood flows; and based on the simulation results, the low-lying areas in the flow area are identified as the first flooded area.

[0073] The first flooded area is divided into several sub-areas, and the flood water storage capacity of each preliminary flooded sub-area is obtained. The first flood water storage capacity can be:

[0074] Obtain the average depth and average area of ​​the low-lying terrain in each preliminary flooded sub-region, multiply the average depth by the average area to obtain the water storage capacity of each preliminary flooded sub-region as the first flood water storage capacity. The calculation formula is: , where is the first flood storage capacity, and It is the average depth and average area of ​​low-lying areas.

[0075] It should be noted that the first flooded area is divided into several sub-areas. This can be achieved based on terrain features, watershed division or through gridding methods to ensure that the characteristics and environmental conditions in each sub-area are as uniform as possible; collect detailed terrain data for each sub-area, including terrain elevation, slope and surface features, which can be obtained using high-resolution digital elevation models (DEM) or remote sensing data.

[0076] In one embodiment, the vegetation density, groundwater level and soil permeability coefficient are used as data points of each preliminary flooded sub-area, and the obtained data set includes:

[0077] Obtain the vegetation type of each preliminary flooded sub-area, obtain the area of ​​each vegetation type, and combine it with the corresponding preset water absorption coefficient to obtain the vegetation coefficient. The calculation formula is: In the formula, is the vegetation coefficient, For the Area of ​​vegetation type, For the The vegetation type corresponds to the preset water absorption coefficient. Indicates the total number of vegetation types;

[0078] Obtain the average height of the groundwater level in each preliminary flooded sub-area, and calculate the difference between the average height of the groundwater level and the surface height as the groundwater level height coefficient;

[0079] The vegetation coefficient, groundwater level coefficient and soil permeability coefficient were used as data points for each preliminary flooded sub-area to obtain the data set.

[0080] It should be noted that the area of ​​each type of vegetation can be obtained through remote sensing images, GIS data, or field surveys;

[0081] It should be noted that different vegetation types have different effects on their water absorption capacity during floods. For example, grassland vegetation usually has a higher water absorption capacity and can quickly absorb and store water, while forest vegetation has a deeper root system and may be more effective in absorbing water for a long time. The root structure, leaf area and growth habits of different vegetation types will affect their ability to regulate floods. Therefore, when calculating the vegetation coefficient, these differences need to be considered to accurately assess the impact of each vegetation on flood storage and inundation areas. In addition, the preset water absorption coefficients corresponding to different vegetation types can be set by professionals according to actual conditions, and the specific details will not be repeated or limited.

[0082] It should be noted that the groundwater level height coefficient refers to the difference between the groundwater level and the ground surface. It indicates the height of the groundwater surface from the ground surface. When the groundwater level height coefficient is larger, it means that the soil has more water storage space and can accommodate more water, which helps to disperse and remove floods and reduce the flooded area and depth; the soil permeability coefficient is the soil's ability to penetrate water and measures the rate at which water passes through the soil. It reflects the soil's drainage capacity; soils with high soil permeability coefficients (such as sandy soils) can absorb and remove floods faster, resulting in less flood water accumulation and a relatively small flooded area. This helps to reduce the depth and scope of flooding. It should be noted that the coefficient can be obtained through soil permeability tests, which include laboratory permeability tests (such as conventional permeameter tests) and field permeability tests (such as well tests); the permeability coefficient can be determined by collecting soil samples from each preliminary flooded sub-area and measuring the water flow rate under controlled conditions, or by other methods, which are not specifically limited.

[0083] In one embodiment, clustering the data set to determine the water storage capacity impact index of each preliminary flooded sub-area includes:

[0084] The data set is clustered to obtain the final clusters and final cluster centers, and the final clusters and final cluster centers are mapped to three-dimensional coordinates to obtain the water storage impact index of the preliminary flooded sub-area.

[0085] The final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain the water storage impact index of the preliminary flooded sub-area.

[0086] It should be noted that the specific steps are:

[0087] Data collection and preprocessing: The vegetation coefficient, groundwater level height coefficient and soil permeability coefficient of each preliminary flooded sub-area were collected as data.

[0088] The data is standardized to ensure that different features have the same scale to avoid the influence of data scale on clustering results.

[0089] Select a clustering algorithm: Choose a suitable clustering algorithm, such as K-means, DBSCAN, or hierarchical clustering. K-means is a common choice, but the specific choice depends on the data characteristics and requirements.

[0090] Determine the number of clusters (K value) (for K-means): Use methods such as the Elbow Method or the Silhouette Score to determine the best K value (number of clusters).

[0091] Perform clustering: Perform clustering calculations on the data. Taking K-means as an example, the algorithm will continuously adjust the location of the cluster and cluster center of each data point through an iterative process until convergence.

[0092] In each iteration, the data point is assigned to the nearest cluster center, which is calculated as the mean of all data points belonging to that cluster.

[0093] Obtain the final clustering cluster: After the clustering calculation is completed, the data points will be assigned to the final clusters, and the data points in each cluster have similar characteristics.

[0094] Final cluster center: The center of each cluster is the mean of all data points in the cluster, representing the center position of the cluster.

[0095] It should be noted that the step of assigning a data point to the cluster to which the nearest cluster center belongs is to sort the distances between the data point and each cluster center from large to small, and select the smallest distance to assign the data point to the cluster to which it belongs.

[0096] In one implementation, cluster analysis can help evaluate the water absorption capacity of each preliminary flooded sub-area when it is flooded, thereby making the subsequent flood inundation area prediction results more accurate.

[0097] In one embodiment, the final cluster is mapped to a three-dimensional coordinate to obtain the water storage impact index of the preliminary sub-area, including:

[0098] Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters;

[0099] Calculate the distance from each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the corresponding data point, and multiply the preliminary coordinate distance of the corresponding data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the corresponding data point;

[0100] The actual coordinate distance of each data point is taken as the water storage impact index of the corresponding preliminary flooded sub-area.

[0101] It should be noted that because the vegetation coefficient, groundwater level coefficient and soil permeability coefficient are selected as data points for clustering, these three data points can now be used as coordinates and mapped to the three-dimensional coordinate axis. Each preliminary flooded sub-area will be mapped to a point on the three-dimensional coordinate axis. After the clustering is completed, all data points will be distributed in different final clustering clusters on the three-dimensional coordinate axis. The closer the data point is to the original center, the weaker the water absorption capacity of the corresponding preliminary flooded sub-area and the less water storage capacity; conversely, the larger the water absorption capacity is.

[0102] In one embodiment, obtaining the second flood water storage of each preliminary flooded sub-area according to the water storage impact index and the first flood water storage, and determining the final flooded area according to the second flood water storage includes: In the formula, The second flood water storage capacity, is the first flood water storage capacity, is the water storage impact index;

[0103] The second flood water storage capacity of each preliminary flooded sub-area is compared with the corresponding preset flood inundation benchmark water volume. If the second flood water storage capacity is not less than the corresponding preset flood inundation benchmark water volume, the corresponding preliminary flooded sub-area is recorded as a flood inundation area, and all flood inundation areas are recorded as final flood inundation areas.

[0104] It should be noted that the preset flood inundation benchmark water volume is set by professionals based on actual conditions and is not limited or elaborated on in detail.

[0105] It should be noted that the above combination of the impact of preliminary flood water storage and water storage impact index provides a flood inundation assessment method that takes various factors into consideration. By comparing the second flood water storage with the baseline water volume, it can be more accurately determined which areas will indeed be severely flooded, thereby optimizing flood warning and emergency response measures and improving the efficiency of disaster prevention and mitigation; at the same time, it also prevents areas that may be less affected by floods from being misjudged as high-risk areas, avoiding unnecessary waste of resources and evacuation measures, thereby making flood management more accurate and efficient.

[0106] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A flood inundation area prediction method based on land survey data, characterized in that: The following steps are involved: Obtain the water level, maintenance time and dam cracks of the target dam, and obtain the dam breach risk coefficient based on the water level, maintenance time and dam cracks; When the dam breach risk coefficient is not less than the preset dam breach risk coefficient, a first flood area is simulated according to the flood water flow, and the first flood area is divided into a number of preliminary flood sub-areas, and the flood water storage capacity of each preliminary flood sub-area is obtained as the first flood water storage capacity; The current vegetation density, groundwater level and soil permeability coefficient of each preliminary flooding sub-area are obtained, and the vegetation density, groundwater level and soil permeability coefficient are used as data points of each preliminary flooding sub-area to obtain a data set, and the data set is clustered to determine the water storage capacity impact index of each preliminary flooding sub-area; The second flood water storage of each preliminary flooded sub-area is obtained according to the water storage impact index and the first flood water storage, and the final flooded area is determined according to the second flood water storage; The risk factors of dam failure obtained based on water level, maintenance time and dam cracks include: Obtain the water level rise height of the target dam within a future preset period, and add the water level rise height to the current water level of the target dam to obtain the final water level within the future preset period; Obtain the time interval from the last maintenance of the target dam to the current time as the actual time interval, and divide the actual time interval by the preset maintenance time interval to obtain the dam maintenance time index; Obtain the crack length and average crack depth of the target dam, and multiply the crack length by the average crack depth to obtain the dam crack index; The dam breach risk coefficient is obtained based on the normalized final water level, dam maintenance time index, and dam crack index.

2. The flood inundation area prediction method based on land survey data according to claim 1 is characterized in that: The dam breach risk coefficient obtained according to the normalized final water level, dam maintenance time index and dam crack index includes: Bsx=ln(f1×hs+f2×nt+f3×rf+1); where Bsx is the dam breach risk coefficient, hs, nt and rf are the normalized final water level, dam maintenance time index and dam crack index, respectively, and f1, f2 and f3 are the preset proportional coefficients of the normalized final water level, dam maintenance time index and dam crack index, respectively, and f1, f2 and f3 are all greater than 0.

3. The flood inundation area prediction method based on land survey data according to claim 1 is characterized in that: The vegetation density, groundwater level and soil permeability coefficient are used as data points for each preliminary flooded sub-area, and the resulting data set includes: Obtain the vegetation type of each preliminary flooded sub-area, obtain the area of ​​each vegetation type, and combine it with the corresponding preset water absorption coefficient to obtain the vegetation coefficient. The calculation formula is: In the formula, RF is the vegetation coefficient, TG k is the area of ​​the kth vegetation type, QS k is the preset water absorption coefficient corresponding to the kth vegetation type, and w represents the total number of vegetation types; Obtain the average height of the groundwater level in each preliminary flooded sub-area, and calculate the difference between the average height of the groundwater level and the surface height as the groundwater level height coefficient; The vegetation coefficient, groundwater level coefficient and soil permeability coefficient were used as data points for each preliminary flooded sub-area to obtain the data set.

4. The flood inundation area prediction method based on land survey data according to claim 1 is characterized in that: Clustering the data set to determine the water storage impact index for each initial flooding sub-area includes: The data set is clustered to obtain the final clusters, which are then mapped to three-dimensional coordinates to obtain the water storage impact index of the preliminary flooded sub-area.

5. The flood inundation area prediction method based on land survey data according to claim 4 is characterized in that: The final clusters are mapped to three-dimensional coordinates, and the water storage impact index of the preliminary flooded sub-area is obtained, including: Calculate the weight coefficient of each final cluster, the calculation formula is: Among them YL j It is expressed as the weight coefficient of the jth final cluster, H 总 Represented as the number of all data points in the data set, H j Represents the number of data points in the jth final cluster; Calculate the distance from each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the corresponding data point, and multiply the preliminary coordinate distance of the corresponding data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the corresponding data point; The actual coordinate distance of each data point is taken as the water storage impact index of the corresponding preliminary flooded sub-area.

6. The flood inundation area prediction method based on land survey data according to claim 1 is characterized in that: The first flood storage volume includes: Obtain the average depth and average area of ​​the low-lying terrain area in each preliminary inundation sub-area, multiply the average depth by the average area, and obtain the water storage capacity of each preliminary inundation sub-area as the first flood water storage capacity. The calculation formula is: PH=PL×PK, where PH is the first flood water storage capacity, and PL and PK are the average depth and average area of ​​the low-lying terrain area.

7. The flood inundation area prediction method based on land survey data according to claim 1 is characterized in that: The second flood storage capacity of each preliminary flooded sub-area is obtained based on the water storage impact index and the first flood storage capacity, and the final flooded area is determined based on the second flood storage capacity, including: WFG=PH×(1-UY) Where WFG is the second flood water storage, PH is the first flood water storage, and UT is the water storage impact index; The second flood water storage capacity of each preliminary flooded sub-area is compared with the corresponding preset flood inundation benchmark water volume. If the second flood water storage capacity is not less than the corresponding preset flood inundation benchmark water volume, the corresponding preliminary flooded sub-area is recorded as a flood inundation area, and all flood inundation areas are recorded as final flood inundation areas.

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