A single-gully debris flow susceptibility assessment method, system, device, medium and product
By acquiring remote sensing images and LiDAR data to identify and interpret debris sources, calculating their contribution and inputting them into the model, the problems of refinement and classification in debris flow susceptibility assessment have been solved, achieving accuracy and transparency in debris flow susceptibility assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-10
AI Technical Summary
Existing methods for assessing debris flow susceptibility suffer from problems such as coarse quantification of debris flow susceptibility probability by material source and decoupling of assessment models. They lack comprehensive parameters that characterize the intrinsic ratio between terrain convergence capacity and material source supply, resulting in unclear physical mechanisms of the models, limited accuracy, and inability to achieve refined and categorized assessments.
By acquiring remote sensing images and LiDAR data of the target watershed, we identify and interpret the sediment sources in gullies and on slopes, extract feature parameters, calculate the contribution of sediment sources from slopes and gullies, and substitute them into the debris flow susceptibility probability model to calculate the debris flow susceptibility probability value and classify different susceptibility levels.
It achieves refined and categorized debris flow susceptibility assessment, and provides a training-free, expression-transparent debris flow susceptibility probability calculation model that can objectively quantify debris flow susceptibility.
Smart Images
Figure CN122365137A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geological disaster prevention and remote sensing application technology, and in particular to a method, system, equipment, medium and product for assessing the susceptibility of debris flows in a single gully. Background Technology
[0002] In the mountainous regions of southwestern China, complex terrain and geological conditions, frequent extreme weather events, and particularly severe debris flow disasters, are prevalent. The greater the sediment source within a watershed, the higher the probability of debris flows and the more severe the damage, posing a serious challenge to the lives and property of local people. As a crucial link in disaster prevention and mitigation, single-gully debris flow susceptibility assessment currently employs two main methods: empirical scoring and machine learning. However, empirical scoring is highly subjective, relying on expert experience and lacking objective and unified standards; machine learning algorithms require extensive training with weights inherently assigned by the algorithm, making explicit expression difficult. Therefore, there is an urgent need for a debris flow susceptibility probability calculation model that requires no training, has transparent expressions, and can be quantified.
[0003] As a crucial link in disaster prevention and mitigation, the assessment of single-gully debris flow susceptibility has two major limitations in existing methods: first, the quantification of debris flow susceptibility probability by material source is coarse; second, the assessment model is decoupled, simply piling up topographic and material source parameters without comprehensive parameters that can characterize the "intrinsic ratio between topographic convergence capacity and material source supply," resulting in unclear physical mechanisms and limited accuracy of the model, making it impossible to conduct refined and categorized debris flow susceptibility assessments. Summary of the Invention
[0004] The purpose of this application is to provide a method, system, equipment, medium, and product for assessing the susceptibility of debris flows in a single ditch, in order to solve the problem of refined and classified debris flow susceptibility assessment.
[0005] To achieve the above objectives, this application provides the following solution.
[0006] In the first aspect, this application provides a method for assessing the susceptibility of debris flows in a single ditch, comprising the following steps.
[0007] Acquire remote sensing images and LiDAR data of the target watershed, and identify and interpret channel and slope sources within the target watershed based on the remote sensing images and LiDAR data.
[0008] For each slope source obtained from the interpretation, a first feature parameter is extracted, and for each gully source obtained from the interpretation, a second feature parameter is extracted.
[0009] The contribution of the slope source is calculated based on the first characteristic parameter of the slope source, and the contribution of the gully source is calculated based on the second characteristic parameter of the gully source.
[0010] Substituting the slope material source contribution and the gully material source contribution into the preset debris flow susceptibility probability calculation model, the debris flow susceptibility probability value is calculated.
[0011] Based on the debris flow susceptibility probability value, the debris flow gullies in the target watershed are divided into different susceptibility levels to assess the susceptibility of debris flows in a single gully.
[0012] Secondly, this application provides a single-ditch debris flow susceptibility assessment system, which includes the following modules.
[0013] The identification and interpretation module is used to acquire remote sensing images and LiDAR data of the target watershed, and to identify and interpret the channel material sources and slope material sources in the target watershed based on the remote sensing images and LiDAR data.
[0014] The extraction module is used to extract a first feature parameter for each slope source obtained from the interpretation, and to extract a second feature parameter for each gully source obtained from the interpretation.
[0015] The contribution calculation module is used to calculate the contribution of the slope material source based on the first characteristic parameter of the slope material source, and to calculate the contribution of the gully material source based on the second characteristic parameter of the gully material source.
[0016] The debris flow susceptibility probability calculation module is used to substitute the slope material source contribution and the gully material source contribution into a preset debris flow susceptibility probability calculation model to calculate the debris flow susceptibility probability value.
[0017] The single-ditch debris flow susceptibility assessment module is used to classify debris flow gullies in the target watershed into different susceptibility levels based on the debris flow susceptibility probability value, so as to assess the single-ditch debris flow susceptibility.
[0018] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for assessing the susceptibility of single-ditch debris flows.
[0019] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for assessing the susceptibility of single-ditch debris flows.
[0020] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for assessing the susceptibility of single-ditch debris flows.
[0021] According to the specific embodiments provided in this application, this application has the following technical effects: This application collects remote sensing images and LiDAR data to identify and interpret gully debris sources and slope debris sources in the target watershed, obtains the characteristic parameters of the two types of debris sources respectively, calculates the contribution of the two types of debris sources to the susceptibility of debris flows, and substitutes them into the established debris flow susceptibility probability model to calculate the debris flow susceptibility probability value. Based on the probability value, it is divided into different susceptibility levels, thereby realizing a refined and classified debris flow susceptibility assessment. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 A flowchart of a method for assessing the susceptibility to debris flows in a single ditch, provided as an embodiment of this application.
[0024] Figure 2 This is a remote sensing image provided in one embodiment of this application.
[0025] Figure 3 A LiDAR data diagram provided for an embodiment of this application.
[0026] Figure 4 This is an interpretation and distribution map of channel and slope material sources provided in an embodiment of this application. Detailed Implementation
[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0028] To make the objectives, features, and advantages of this application more apparent and understandable, this application will be further described in detail, taking the calculation of the probability of debris flow in a debris flow gully in southwestern China as an example, in conjunction with the accompanying drawings and specific embodiments.
[0029] like Figure 1 As shown in the embodiment of this application, a method for assessing the susceptibility of debris flows in a single gully is provided, including: S1: Acquire remote sensing images and LiDAR data of the target watershed, and identify and interpret channel and slope sediment sources within the target watershed based on the remote sensing images and LiDAR data. The remote sensing images include... Figure 2 As shown, LiDAR data is as follows Figure 3 As shown, the interpreted channel and slope sources are as follows: Figure 4 As shown.
[0030] S2: For each slope source obtained from the interpretation, extract the first feature parameter, and for each gully source obtained from the interpretation, extract the second feature parameter.
[0031] S3: Calculate the contribution of the slope source based on the first characteristic parameter of the slope source, and calculate the contribution of the gully source based on the second characteristic parameter of the gully source.
[0032] S4: Substitute the slope material source contribution and the gully material source contribution into the preset debris flow susceptibility probability calculation model to calculate the debris flow susceptibility probability value.
[0033] S5: Based on the debris flow susceptibility probability value, the debris flow gullies in the target watershed are divided into different susceptibility levels to assess the susceptibility of debris flows in a single gully.
[0034] In an exemplary embodiment, the first characteristic parameters of the slope material source include: the area, maximum width, maximum length, distance from the gully, and average slope of each slope material source. The relevant parameters of the slope material source are shown in Table 1.
[0035] Table 1. Slope Material Source Related Parameters
[0036] The second characteristic parameters of the channel material source include: the area of each channel material source, the average width of the channel, the longitudinal gradient, the path distance from the center point to the channel mouth, and the average longitudinal gradient of the path from the center point to the channel mouth. The relevant parameters of the channel material source are shown in Table 2.
[0037] Table 2. Parameters related to sediment sources in the channel
[0038] In an exemplary embodiment, S3 specifically includes: S31: When the average slope of the slope source is greater than the preset slope threshold, the contribution of the slope source is obtained based on the first calculation formula. G p The preset slope threshold is 15°; the first calculation formula is: G p = ;in, A i P For the first i The area of the material source on the slope, W i P For the first i The maximum width of the material source on the slope. L i P For the first i The distance from the center point of the material source on the slope to the gully. l i For the first i The maximum length of a slope source θ i For the first i The average slope of each slope source l i g For the first i The average width of the channel where the material source is located. lg It is a logarithm with base 10.
[0039] S32: When the average slope of the slope material source is less than or equal to the preset slope threshold, the contribution degree of the slope material source is obtained based on the second calculation formula. G p The second calculation formula is: G p = ;in, n This represents the total amount of material sources on the slope.
[0040] The contribution of sediment source in the channel was calculated based on the data in Table 1. G P =12.176.
[0041] S33: The contribution of the channel material source is obtained based on the third calculation formula. G g The third calculation formula is: G g = ;in, m This represents the total amount of material sourced from the channel. l k g For the first k The average width of the channel where the material source is located. A k g For the first k The area of material source in each ditch, L k For the first iThe path distance for the center point of the channel sediment source to migrate to the gully mouth J k is the k longitudinal gradient of the J k avg is the i average longitudinal gradient of the path for the center point of the channel sediment source to migrate to the gully mouth.
[0042] According to the data in Table 2, the contribution degree of the channel sediment source is calculated G g = 7.723.
[0043] In an exemplary embodiment, the debris flow susceptibility probability calculation model is: .
[0044] Substitute G g = 7.723 and G P = 12.176 to calculate the debris flow susceptibility probability value P = 0.804.
[0045] In an exemplary embodiment, according to the debris flow susceptibility probability value, the debris flow gullies in the target basin are divided into different susceptibility levels to evaluate the susceptibility of single-gully debris flow, specifically including: When 0 < P ≤ 0.3, the debris flow gullies in the target basin are divided into low susceptibility levels; P is the debris flow susceptibility probability value; When 0.3 < P ≤ 0.7, the debris flow gullies in the target basin are divided into medium susceptibility levels.
[0046] When 0.7 < P ≤ 0.9, the debris flow gullies in the target basin are divided into high susceptibility levels.
[0047] When 0.9 < P ≤ 1, the debris flow gullies in the target basin are divided into extremely high susceptibility levels.
[0048] According to the calculated debris flow susceptibility probability P = 0.804, this debris flow gully is divided into a high susceptibility level.
[0049] This application provides a single-gully debris flow susceptibility evaluation system, including the following modules.
[0050] The recognition and interpretation module is used to obtain the remote sensing images and LiDAR data of the target basin, and recognize and interpret the channel sediment sources and slope sediment sources in the target basin based on the remote sensing images and LiDAR data.
[0051] The extraction module is used to extract a first feature parameter for each slope source obtained from the interpretation, and to extract a second feature parameter for each gully source obtained from the interpretation.
[0052] The contribution calculation module is used to calculate the contribution of the slope material source based on the first characteristic parameter of the slope material source, and to calculate the contribution of the gully material source based on the second characteristic parameter of the gully material source.
[0053] The debris flow susceptibility probability calculation module is used to substitute the slope material source contribution and the gully material source contribution into a preset debris flow susceptibility probability calculation model to calculate the debris flow susceptibility probability value.
[0054] The single-ditch debris flow susceptibility assessment module is used to classify debris flow gullies in the target watershed into different susceptibility levels based on the debris flow susceptibility probability value, so as to assess the single-ditch debris flow susceptibility.
[0055] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.
[0056] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0057] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0058] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0059] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0060] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0061] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0062] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for assessing the susceptibility to debris flows in a single gully, characterized in that, including: Obtain remote sensing images and LiDAR data of the target watershed, and identify and interpret gully sources and slope sources within the target watershed based on the remote sensing images and LiDAR data; For each slope source obtained by interpretation, extract the first characteristic parameters, and for each gully source obtained by interpretation, extract the second characteristic parameters; Calculate the contribution degree of slope sources based on the first characteristic parameters of the slope sources, and calculate the contribution degree of gully sources based on the second characteristic parameters of the gully sources; Substitute the contribution degree of slope sources and the contribution degree of gully sources into a preset debris flow susceptibility probability calculation model to calculate the debris flow susceptibility probability value; According to the debris flow susceptibility probability value, divide the debris flow gullies in the target watershed into different susceptibility levels to evaluate the susceptibility of single-gully debris flows.
2. The method for assessing the susceptibility to debris flows in a single gully according to claim 1, characterized in that, The first characteristic parameters of the slope sources include: the area, maximum width, maximum length, distance from the gully, and average slope of each slope source; The second characteristic parameters of the gully sources include: the area of each gully source, the average width of the gully where it is located, longitudinal gradient, path distance from the center point to the gully mouth, and average longitudinal gradient of the path from the center point to the gully mouth.
3. The method for assessing the susceptibility to debris flows in a single gully according to claim 2, characterized in that, Calculating the contribution degree of slope sources based on the first characteristic parameters of the slope sources specifically includes: When the average slope of the slope source is greater than the preset slope threshold, the contribution degree Gp of the slope source is obtained based on the first calculation formula; the preset slope threshold is 15°; the first calculation formula is: Gp = Where AiP is the area of the i-th slope source, WiP is the maximum width of the i-th slope source, LiP is the distance from the center point of the i-th slope source to the ditch, li is the maximum length of the i-th slope source, θi is the average slope of the i-th slope source, and lg is the logarithm to the base 10. When the average slope of the slope source is less than or equal to the preset slope threshold, the contribution degree Gp of the slope source is obtained based on the second calculation formula; the second calculation formula is: Gp = ; where n is the total number of slope sources.
4. The method for assessing the susceptibility to debris flows in a single gully according to claim 3, characterized in that, Calculating the contribution degree of gully sources based on the second characteristic parameters of the gully sources specifically includes: The channel material source contribution Gg is obtained based on the third calculation formula; the third calculation formula is: Gg= Where m is the total number of material sources in the channel, lkg is the average width of the channel where the k-th material source is located, Akg is the area of the k-th material source, Lk is the path distance from the center point of the k-th material source to the channel mouth, Jk is the longitudinal slope of the k-th material source, and Jkavg is the average longitudinal slope of the path from the center point of the k-th material source to the channel mouth.
5. The method for assessing the susceptibility to debris flows in a single gully according to claim 4, characterized in that, The debris flow susceptibility probability calculation model is as follows: Where P is the probability value of debris flow susceptibility.
6. The method for assessing the susceptibility to debris flows in a single gully according to any one of claims 1 to 5, characterized in that, According to the debris flow susceptibility probability value, dividing the debris flow gullies in the target watershed into different susceptibility levels to evaluate the susceptibility of single-gully debris flows specifically includes: When 0 < P ≤ 0.3, divide the debris flow gullies in the target watershed into low susceptibility levels; P is the debris flow susceptibility probability value; When 0.3 < P ≤ 0.7, divide the debris flow gullies in the target watershed into medium susceptibility levels; When 0.7 < P ≤ 0.9, divide the debris flow gullies in the target watershed into high susceptibility levels; When 0.9 < P ≤ 1, divide the debris flow gullies in the target watershed into extremely high susceptibility levels.
7. A single-ditch debris flow susceptibility assessment system, characterized in that, The single-gully debris flow susceptibility evaluation system executes the single-gully debris flow susceptibility evaluation method according to any one of claims 1-6. The single-gully debris flow susceptibility evaluation system includes: An identification and interpretation module for obtaining remote sensing images and LiDAR data of the target watershed, and identifying and interpreting gully sources and slope sources within the target watershed based on the remote sensing images and LiDAR data; An extraction module for extracting the first characteristic parameters for each slope source obtained by interpretation, and extracting the second characteristic parameters for each gully source obtained by interpretation; A contribution degree calculation module for calculating the contribution degree of slope sources based on the first characteristic parameters of the slope sources, and calculating the contribution degree of gully sources based on the second characteristic parameters of the gully sources; A debris flow susceptibility probability value calculation module for substituting the contribution degree of slope sources and the contribution degree of gully sources into a preset debris flow susceptibility probability calculation model to calculate the debris flow susceptibility probability value; The single-ditch debris flow susceptibility assessment module is used to classify debris flow gullies in the target watershed into different susceptibility levels based on the debris flow susceptibility probability value, so as to assess the single-ditch debris flow susceptibility.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the single-ditch debris flow susceptibility assessment method according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the single-channel debris flow susceptibility assessment method according to any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the single-channel debris flow susceptibility assessment method according to any one of claims 1-6.