Dammed lake disaster damage information grading rapid assessment method and computer equipment

Abstract

The application discloses a dammed lake disaster damage information grading and rapid evaluation method and computer equipment, wherein regional grading is performed through prior flood risk simulation, limited manpower, time and computing resources are accurately focused on a core region with high dam-break probability and serious consequences, and a highest-precision data source is adopted to ensure that key information is reliable; a data source with wide coverage and high acquisition efficiency is adopted for a low-risk region, so that the efficiency of emergency investigation is greatly improved as a whole, and the investigation accuracy of the core region is ensured.

Description

Technical Field

[0001] This invention belongs to the field of natural disaster emergency management and geographic information technology, specifically relating to a method for rapid investigation and assessment of flood damage information caused by landslide dam breaches, and in particular a method for rapid assessment of landslide dam damage information by classification and computer equipment. Background Technology

[0002] A landslide-dammed lake is a lake formed by natural disasters such as earthquakes, landslides, and collapses blocking river channels. Its dam structure (dam body) is loose and unstable, making it highly susceptible to sudden breaches under the influence of heavy rainfall or aftershocks. Breach floods are characterized by their suddenness, large peak flow, wide destructive range, and complex hazard chains, seriously threatening the lives and property of people downstream and the safety of critical infrastructure. During the golden window of emergency response, rapidly and accurately obtaining detailed information on various disaster-bearing entities (such as houses, populations, farmland, roads, and bridges) in the downstream potentially inundated area is crucial for conducting disaster pre-assessment, developing scientific personnel evacuation plans, and implementing engineering rescue measures.

[0003] Traditional methods for acquiring disaster damage information face numerous challenges in responding to sudden disasters like landslide dams. The main methods rely on manual field surveys and high-resolution remote sensing image interpretation. However, field surveys, under emergency conditions of tight deadlines, complex terrain, and disrupted transportation, struggle to provide rapid and comprehensive coverage of large areas, resulting in low efficiency. While relying solely on high-resolution satellite or aerial remote sensing imagery offers broad coverage, uniformly interpreting the entire potentially flooded area with high precision requires processing large amounts of data and is time-consuming, failing to meet the demands of rapid response. More importantly, the disaster risk varies significantly across different regions. A one-size-fits-all approach to survey precision often leads to insufficient information accuracy in extremely high-risk core areas, while excessive resources are invested in lower-risk areas, resulting in wasted emergency resources. Furthermore, the lack of clear and quantifiable data priorities and fusion rules when integrating multi-source data (such as land survey vector data, UAV aerial photography data, and satellite imagery from different time periods) easily leads to data redundancy and information conflicts, affecting the consistency and reliability of the final disaster damage information database.

[0004] Therefore, there is an urgent need for a method to rapidly acquire information on landslide dam disaster damage that can balance the timeliness of emergency response with the accuracy of assessment results. By optimizing resource allocation through a scientific hierarchical strategy, we can achieve structured and efficient collection and management of disaster damage information, thus providing support for precise emergency decision-making. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and computer equipment for rapid assessment of landslide dammed lake disaster damage information classification. This method achieves classified, rapid, and reliable acquisition of disaster damage information by constructing a complete technical system encompassing risk simulation, regional classification, data adaptation, and integrated application, effectively balancing the dual requirements of information accuracy and acquisition efficiency in emergency response.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A rapid assessment method for classifying and evaluating damage information from landslide-dammed lakes includes the following steps: S1: Based on the three set outflow modes, simulate the evolution of outflow flood and obtain the inundation range area vector data under each outflow mode; Basic data such as topography, reservoir capacity, and dam geometry of the target landslide dammed lake were collected. Different breach modes were set, typically including complete breach, 1 / 2 breach, and 1 / 3 breach modes. Mature hydrodynamic models (such as HEC-RAS and MIKE) were used to simulate the evolution of the breach flood, calculating the downstream inundation extent under the three breach scenarios and obtaining the corresponding areal vector data of the first inundation extent. A 1. Second flooded area planar vector data A 2 and third flood extent area vector data A 3. Among them, A 3 corresponds to the 1 / 3 collapse mode, which has the smallest scope but the highest risk (the flood will inevitably reach it). A 1 corresponds to the total collapse mode, which has the largest range and represents the outermost edge that the flood may affect. A 2 between A 1 and A Between 3.

[0007] S2: Divide the three types of flood inundation area vector data obtained in step S1 into three levels of risk areas. The smallest flood inundation area vector data is the level 1 core risk area. The spatial difference between the second smallest flood inundation area vector data and the smallest flood inundation area vector data is the level 2 secondary core risk area. The spatial difference between the largest flood inundation area vector data and the second smallest flood inundation area vector data is the level 3 general risk area. The calculation formula is as follows: Equation (1) In the formula, the Level 1 core risk area corresponds to the areal vector data of the third inundation range. A 3, namely, the area with the highest probability of being inundated by a breach flood; Level 2 secondary core risk area, corresponding to the areal vector data of the inundation range. A 2- A 3 represents the area most likely to be inundated by a breach flood; Level 3 is a general risk area, corresponding to the areal vector data of the inundation range.A 1- A 2, namely, areas with a low probability of being flooded by a breach flood; "-" indicates spatial difference calculation of the inundation range area vector data, which can be achieved through spatial analysis in GIS software.

[0008] The probabilities of three collapse scenarios—full collapse, half collapse, and one-third collapse—are defined to characterize the uncertainty of the collapse scenario. It is generally believed that the probability of a smaller-scale collapse (such as a one-third collapse) is relatively complex, and its inundation range... A 3 represents the smallest area affected by the flood under various potentially adverse scenarios; while the most extreme scenario of total collapse, although having the most severe consequences, requires much more stringent conditions to occur. Therefore, A 3. The entire area is classified as a Level 1 core risk area, which means that the disaster-bearing bodies in this area need to be investigated according to the highest standards in order to deal with all possible risks. A 3 and A The area between 2 ( R 2) These are areas that will only be flooded if a medium-sized or larger breach occurs, and are of secondary risk. A 2 and A The area between 1 ( R 3) These are areas that will only be flooded in the most extreme event of total collapse, and therefore have the lowest risk. This classification based on flood evolution simulation scenarios ensures that the survey level matches the probability and severity of flood threats to different spatial locations. Logically, this guarantees that high-risk areas correspond to high survey accuracy, while low-risk areas use efficient methods, thus avoiding resource misallocation from the design stage.

[0009] This tiered model ensures that areas with higher risks receive higher survey levels, while avoiding excessive resource allocation to areas with low survey levels.

[0010] S3: For the Level 1 core risk area, the most up-to-date preset precision vector geographic information database is used as the first vector database D1; for the Level 2 secondary core risk area, vector data generated based on UAV aerial image interpretation is used as the second vector database D2; for the Level 3 general risk area, vector data generated based on satellite remote sensing image interpretation is used as the third vector database D3. For the Level 1 core risk areas with the highest risk, the highest resolution accuracy of data sources is required (for example, P1 ≤ 0.1 m), and existing authoritative vector data such as the Third National Land Survey, real estate registration, and high-precision basic surveying and mapping are preferentially adopted to ensure the currentness and highest accuracy of information, and a first vector database D1 is formed after processing. For the Level 2 sub-core risk areas, data sources with relatively high resolution accuracy (for example, 0.1m < P2 ≤ 0.2m) are adopted, orthophotos are mainly obtained through UAV aerial photography, and vector information of ground features such as houses and roads is extracted therefrom using remote sensing interpretation technology, so as to form a second vector database D2. For the Level 3 general risk areas, data sources with relatively loose resolution accuracy requirements (for example, 0.2m < P3 ≤ 0.5m) are adopted, such as commercial satellite images, which are preprocessed and interpreted to form a third vector database D3. This model realizes the optimal resource allocation of high-precision survey in high-risk areas and high-efficiency survey in low-risk areas.

[0011] Through the above data source configuration rules strictly matching the risk levels, the traditional one-size-fits-all survey mode is changed, and the optimal allocation of emergency resources is realized.

[0012] S4: When two or more of the first vector database D1, the second vector database D2 and the third vector database D3 cover the same position, the finally adopted data is selected according to the priority order to generate a vector base map for disaster loss survey; The vector databases (D1, D2, D3) with different accuracies generated in step S3 are imported into a unified geographic information platform. Since different data sources may have spatial overlap, in order to eliminate data redundancy and conflicts, clear priority fusion rules are established: when the same position is covered by multiple databases, according to the accuracy levels P1, P2, P3 of the data source resolution, that is, the priority of D1>D2>D3, the data with the highest accuracy is selected as the final valid data of the position. Through this rule, a set of seamlessly connected, logically consistent and uniformly accurate vector base maps for disaster loss survey can be generated.

[0013] This rule clearly solves the problems of information conflict and difficult selection when multi-source data overlaps mentioned in the background art, and ensures the uniqueness and highest accuracy of the disaster loss information base map.

[0014] S5: Based on the disaster loss survey vector base map obtained in step S4 and the three levels of risk areas obtained in step S2, a general disaster loss information database for comprehensive risk assessment and a core disaster loss information database for supporting emergency measure decision-making are dynamically constructed through spatial analysis; First, construct the general disaster loss information database L applicable to different decision-making objectives total As shown in formula (2): Formula (2) In the formula, ∪ represents vector merging of data, and GIS_Intersect represents spatial intersection analysis, realizing the areal vector data of land types, buildings, infrastructure, highways and railways within the first inundation area. A Rapid acquisition of disaster damage information within 1 hour, applied to landslide dam risk assessment; L, a core disaster loss information database used to support emergency response decisions core As in equation (3): Equation (3) Realize the areal vector data of land types, houses, infrastructure, highways and railways in the second inundation area. A Disaster damage information can be quickly obtained within 2 hours to support decision-making on emergency response measures for the relocation of at-risk populations.

[0015] A computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method described above.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0017] The beneficial effects of this invention are as follows: 1. Achieving a balance between efficiency and accuracy: By using prior flood risk simulation for regional classification, limited human, time, and computing resources are precisely focused on core areas with high probability of breach and severe consequences, and the highest accuracy data sources are used to ensure the reliability of key information; for low-risk areas, data sources with wide coverage and high acquisition efficiency are used, thereby significantly improving the overall efficiency of emergency investigations while ensuring the accuracy of investigations in core areas.

[0018] 2. Ensured the scientific nature and consistency of multi-source data fusion: Established quantitative and hierarchical data source priority fusion rules, effectively solved the selection conflict problem when multiple source data cover the same area, ensured the uniqueness and highest accuracy of the final data entered into the database, and improved the reliability of the disaster damage information database.

[0019] 3. Enhanced the targeted nature of decision support: By constructing two application models, the "Overall Disaster Loss Information Database" and the "Core Disaster Loss Information Database," the data results can serve both comprehensive risk assessment and urgent emergency decision-making, thereby improving the scientific nature and timeliness of emergency response.

[0020] 4. It has good versatility and operability: The method is supported by geographic information system (GIS) and remote sensing (RS) technologies. It does not rely on data from specific regions and can be quickly applied to emergency scenarios of landslide dams or other similar sudden flood disasters in different regions and of different sizes. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The method for rapid assessment of landslide dammed lake disaster damage information classification provided in this embodiment includes the following specific steps: S1. Data Preparation and Flood Evolution Simulation: Obtaining Vector Data of the Inundation Range from the Breach-Damaged Flood. Collect basic data such as the digital elevation model (DEM), dam geometry, reservoir capacity curve, and river cross-section of the area to be assessed. Define three standard breach-damming modes: full breach, 1 / 2 breach, and 1 / 3 breach, and define the corresponding breach development process. Use the HEC-RAS two-dimensional hydrodynamic model to simulate the flood evolution under each of the three modes. Simulation outputs include flood velocity, water depth, arrival time, and inundation range. Extract the inundated areas with a water depth greater than 0, and generate vector surface files for each, denoted as [file name missing]. A 1 (complete collapse) A 2 (1 / 2 collapse) A 3 (1 / 3 collapsed).

[0023] S2, Risk Zone Delineation and Classification. In ArcGIS Pro software, load the data generated in step S1. A 1. A 2. A 3. Three vector layers. Using the erase function in the spatial analysis tool, first, calculate... A 2 pairs A Erasing 3 yields the area. R 2= A 2- A 3; Then, calculate A 1 pair A Erasing 2 yields the area. R 3= A 1- A 2. Region R 1 is A 3 itself. For R 1. R 2. R 3. Add risk level attribute fields and assign values ​​of 1, 2, and 3 respectively. This completes the division of the survey area from the highest risk (Level 1) to the lowest risk (Level 3).

[0024] S3, hierarchical data acquisition and vectorization processing.

[0025] For Level 1 core risk areas ( R1) Obtain the latest land survey vector data, real estate registration data, and transportation network vector data from the natural resources department. Perform coordinate system conversion, format conversion, and topology checks on these data to ensure their positional accuracy and attribute integrity, forming database D1. Its equivalent resolution meets the requirement of ≤0.1 meters.

[0026] For Level 2 secondary core risk areas ( R 2) Plan the UAV aerial photography route, ensuring that the overlap between the flight path and lateral directions meets the modeling requirements, and execute the aerial photography mission to obtain original images with a resolution better than 0.2 meters. Use software such as Pix4D or ContextCapture for aerial triangulation and dense matching to generate digital orthophotos (DOM). Interpret the DOM using object-oriented classification methods or deep learning models (such as U-Net), automatically extracting vector layers such as building outlines and main roads. After manual spot checks and corrections, the data is compiled into database D2.

[0027] For Level 3 general risk areas ( R 3) Order or retrieve archived sub-meter (e.g., 0.5 meter) satellite imagery from commercial satellite data platforms (e.g., WorldView, GeoEye). Perform preprocessing on the imagery, including radiometric calibration, atmospheric correction, and orthorectification. Using a remote sensing interpretation method similar to that used for Level 2 regions (the classification threshold can be appropriately relaxed to improve efficiency), extract vector information such as land use / cover and large structures to form database D3.

[0028] By using prior flood risk simulation to classify regions, limited human, time, and computing resources can be precisely focused on core areas with high probability of breach and severe consequences, and the highest precision data sources can be used to ensure the reliability of key information. For low-risk areas, data sources with wide coverage and high acquisition efficiency are used, thereby significantly improving the overall efficiency of emergency investigations while ensuring the accuracy of investigations in core areas.

[0029] S4, Data Fusion Based on Priority Rules. In the ArcGIS geodatabase, create a new feature dataset and import similar feature classes (such as houses and roads) from D1, D2, and D3. Use ModelBuilder to build a fusion model: using D1 as the baseline, append the non-overlapping parts of D2 to D1 to generate a temporary layer, then append the non-overlapping parts of D3 to this temporary layer, finally generating the fused feature classes such as "Final Houses" and "Final Roads". This process essentially performs a spatial logical union operation prioritizing D1, followed by D2, and then supplementing with D3, resulting in a seamless disaster damage survey vector base map for the entire area.

[0030] A quantitative and hierarchical data source priority fusion rule was established, which effectively solved the selection conflict problem when multiple data sources cover the same area, ensured the uniqueness and highest accuracy of the final data entered into the database, and improved the reliability of the disaster loss information database.

[0031] S5, Construction and output of disaster damage information database.

[0032] Constructing a comprehensive disaster damage information database: Using ArcGIS's analysis tools—Overlay Analysis—Intersect tool, the merged vector base map obtained in S1 was compared with the maximum inundation area. A 1. Perform intersection operations. For the resulting attribute table, use summary statistical tools to perform statistics such as counting and area summation according to the land cover type field, generating the "Statistical Report of Disaster-Bearing Bodies within A1 Scope". This constitutes the total disaster loss information database L. total It can be used to assess the total potential loss in the worst-case scenario.

[0033] Constructing a core disaster loss information database: First, merge the corresponding feature classes of D1 and D2. Then, compare this merged high-precision data with the high-risk inundation area. A 2 (i.e.) R 1∪ R 2) Perform intersection operations. Also perform statistical analysis to generate the <<...>...> A 2. Detailed Statistical Report of Disaster-Bearing Bodies in High / Medium Risk Areas. This is the core disaster loss information database L. core It can quickly locate the location and number of people and important facilities that need to be urgently transferred in high-risk areas.

[0034] Finally, the generated statistical reports and corresponding risk classification maps, disaster-bearing body distribution maps, etc., are output in the form of thematic maps, Excel spreadsheets, etc., and submitted to the emergency command department as a basis for decision-making.

[0035] Through the above steps, this invention achieves fully automated and semi-automated processing from rapid risk identification and optimized allocation of investigation resources to multi-source information fusion and the generation of thematic products. It can provide a scientific, reliable, and tiered baseline disaster damage data support for emergency response within a very short time after a landslide dammed lake incident. Furthermore, the specific tools and parameters mentioned above are merely examples and not limitations of this invention.

[0036] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the evaluation method described above. This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the evaluation method described above.

[0037] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0038] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid assessment of landslide dammed lake disaster damage information classification, characterized in that, comprising the following steps: S1: Performing dam-break flood routing simulation based on three set dam-break modes, and acquiring the surface vector data of the inundation range under each dam-break mode; S2: Dividing the three pieces of inundation range surface vector data of dam-break floods obtained in step S1 into three levels of risk areas, where the one with the smallest inundation range surface vector data is defined as a level-1 core risk area, the spatial difference between the inundation range surface vector data with the second smallest value and the inundation range surface vector data with the smallest value is defined as a level-2 sub-core risk area, and the spatial difference between the inundation range surface vector data with the largest value and the inundation range surface vector data with the second smallest value is defined as a level-3 general risk area; S3: For the level-1 core risk area, calling a preset-precision vector geographic information database with the highest currentness as a first vector database D1; for the level-2 sub-core risk area, adopting vector data generated by interpreting UAV aerial images as a second vector database D2; for the level-3 general risk area, adopting vector data generated by interpreting satellite remote sensing images as a third vector database D3; S4: When two or more of the first vector database D1, the second vector database D2 and the third vector database D3 cover the same position, selecting the finally adopted data according to a priority order, and generating a vector base map for disaster loss investigation; S5: Based on the vector base map for disaster loss investigation obtained in step S4 and the three levels of risk areas obtained in step S2, dynamically constructing a total disaster loss information database for comprehensive risk assessment and a core disaster loss information database for supporting emergency measure decision-making through spatial analysis.

2. The rapid assessment method for classifying and evaluating landslide dammed lake disaster damage information according to claim 1, characterized in that, In step S1, the three breach modes are the full breach mode, the 1 / 2 breach mode, and the 1 / 3 breach mode. Through flood evolution simulation, the corresponding areal vector data of the first inundation range are obtained for each mode. A 1. Second flooded area planar vector data A 2 and third flood extent area vector data A 3.

3. The method for rapid assessment of landslide dammed lake disaster damage information according to claim 2, characterized in that, The calculation formula for dividing the three pieces of inundation range surface vector data of dam-break floods into three levels of risk areas in step S2 is as follows: Equation (1) In the formula, the Level 1 core risk area corresponds to the areal vector data of the third inundation range. A 3; Level 2 secondary core risk area, corresponding to the areal vector data of the inundation range. A 2- A 3; Level 3 general risk area, corresponding to the areal vector data of the inundation range. A 1- A 2; "-" indicates spatial difference operation of the flooded area vector data.

4. The rapid assessment method for classifying and evaluating landslide dammed lake disaster damage according to claim 3, characterized in that, Step S3 is specifically: Level-1 core risk area: the resolution accuracy P1 of the data source is ≤0.1m, and the data source includes the data from the third national land survey and real estate registration data, which are used as the first vector database D1; Level-2 sub-core risk area: the resolution accuracy P2 of the data source is 0.1m<P2≤0.2m, and vector data is obtained through UAV aerial photography and interpretation as the second vector database D2; Level-3 general risk area: the resolution accuracy P3 of the data source is 0.2m<P3≤0.5m, and vector data is obtained through satellite image interpretation as the third vector database D3; For UAV aerial photos and satellite image data, vector information of land types, houses and infrastructure is extracted by means of remote sensing interpretation.

5. The rapid assessment method for classifying and evaluating landslide dammed lake disaster damage information according to claim 4, characterized in that, The priority order in step S4 is: first vector database D1 > second vector database D2 > third vector database D3.

6. The rapid assessment method for classifying and evaluating landslide dammed lake disaster damage information according to claim 5, characterized in that, In step S5, a total disaster loss information database L suitable for different decision-making objectives is constructed. total As shown in equation (2): Equation (2) In the formula, ∪ represents vector merging of data, and GIS_Intersect represents spatial intersection analysis, realizing the areal vector data of land types, buildings, infrastructure, highways and railways within the first inundation area. A Rapid acquisition of disaster damage information within 1 hour, applied to landslide dam risk assessment; L, a core disaster loss information database used to support emergency response decisions core As in equation (3): Equation (3) Realize the areal vector data of land types, houses, infrastructure, highways and railways in the second inundation area. A Disaster damage information can be quickly obtained within 2 hours to support decision-making on emergency response measures for the relocation of at-risk populations.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the method for rapid hierarchical assessment of barrier lake disaster loss information according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method for rapid hierarchical assessment of barrier lake disaster loss information according to any one of claims 1 to 6 are implemented.

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