Method for evaluating probability of motion danger of debris flow, electronic device and storage medium

Abstract

The embodiment of the application provides a debris flow movement danger probability evaluation method, an electronic device and a storage medium, relates to the technical field of debris flow disaster danger prediction. In the embodiment of the application, a probability method for predicting the movement danger of debris flow disasters by using a reliability method and a dynamic numerical model is provided. The method aims at i) explaining the inherent uncertainty in the input parameters of the FLO-2D model, ii) propagating these uncertainties to the model output (maximum flow depth), iii) quantitatively evaluating the probability of the maximum flow depth exceeding a specific threshold, and iv) generating a probability danger zoning map to facilitate debris flow risk assessment and mitigation. Through case studies of Longdegou and Chengwangmiao gully, the feasibility and effectiveness of the method are verified.

Description

Technical Field

[0001] This invention relates to the field of debris flow hazard prediction technology, and in particular to a method, device, electronic equipment, storage medium and computer program product for evaluating the probability of debris flow movement hazard. Background Technology

[0002] Debris flows triggered by rainfall are a common geological hazard in mountainous areas. Hazard assessment is an effective tool for managing debris flow disasters, crucial not only for disaster prevention and control but also for providing important guidance for land planning and utilization in debris flow-prone areas. Currently, debris flow hazard assessment methods can be broadly classified into two categories: multi-factor assessment methods and numerical simulation methods.

[0003] Multi-factor evaluation methods primarily involve collecting watershed characteristics (such as average slope, watershed area, vegetation condition, and loose material mass) and then processing this data using various mathematical models, including fuzzy mathematics, analytic hierarchy process (AHP), grey relational analysis, and artificial neural networks. This method is simple and practical for determining the hazard of debris flows. However, because the selection of evaluation factors is often subjective, the choices vary from person to person, potentially leading to significant differences in evaluation results. These differences can be attributed to differing perceptions of the disaster event by researchers or engineers. Furthermore, this method struggles to account for the influence of actual terrain and the debris flow process itself.

[0004] Numerical simulation, by simulating the movement of debris flows, can provide valuable information such as flow velocity, depth, and impact range. Therefore, this method offers more intuitive data for quantitative hazard assessment of debris flows and overcomes some limitations of multi-factor evaluation methods. However, in debris flow disaster prediction, the input parameters of numerical models are often difficult to determine directly and accurately.

[0005] Currently, the input parameters of numerical models are mainly determined through experiments or trial and error. However, obtaining input parameters experimentally is time-consuming and expensive. Furthermore, obtaining undisturbed debris flow samples for experiments is practically impossible (it's difficult to obtain pristine samples), leading to discrepancies between experimental results and actual conditions. Therefore, the simpler trial-and-error method, which only incurs computational costs, is currently the primary method for determining input parameters. However, this technique is mainly used for post-event analysis, and the calibrated parameters are rarely used for predicting the hazard of future disasters.

[0006] Therefore, it is necessary to develop a feasible method to provide reliable input parameters for the positive prediction of debris flow disasters. Considering the complexity of the geological environment and debris flow process, as well as the significant uncertainties in the debris flow process, related studies have conducted exploratory research combining reliable methods with dynamic numerical models. These studies employed Monte Carlo Simulation (MCS) combined with MassMov2D to obtain the probability density functions and cumulative density functions of the maximum height and velocity at specific points along the movement path. MCS was also used to evaluate the probability density function of impact force on alluvial fans. Furthermore, 3876 simulations were performed using FLO-2D, and sensitivity and uncertainty analyses were conducted on the model parameters. In these studies, the statistical information of random variables was typically derived from expert experience or statistical analysis based on a globally collected mixed parameter database. However, expert experience often lacks theoretical support and validation, and therefore, it is not always applicable to practical engineering applications. Global databases, on the other hand, contain various types of disasters (such as debris flows, landslides, rockfalls, and avalanches). Such a wide range of data sources significantly increases the standard deviation of random variables, potentially leading to an overestimation of the uncertainties in debris flow processes. Furthermore, debris flow simulation models are typically computationally intensive. Traditional probabilistic methods, such as MCS and its improvements, are impractical in real-world engineering due to their enormous computational costs.

[0007] Furthermore, the probability predictions from related studies are rarely compared with field observations, which makes the accuracy and reliability of these methods unclear.

[0008] Therefore, a new assessment scheme for the hazard of debris flow movement is urgently needed. Summary of the Invention

[0009] To overcome the shortcomings of traditional prediction methods, this invention combines the FLO-2D model with the Probability Density Evolution Method (PDEM) to provide a method, device, electronic equipment, storage medium, and computer program product for assessing the probability of debris flow movement hazards, so as to obtain scientific and reasonable debris flow movement hazard prediction results.

[0010] The first aspect of this invention provides a method for assessing the probability of debris flow movement hazard, the method comprising: Obtain current rainfall monitoring data for the region; Determine the surface runoff data for the current area based on the rainfall monitoring data; The water catchment point is determined based on the current terrain and landforms of the area, combined with satellite images of the area. Based on the surface runoff data and the catchment points, a debris flow simulation model is constructed for the current region. The debris flow simulation model is composed of... M It consists of a continuous grid of cells; Determine the random variable θ of the debris flow simulation model. N Group representative point θ rps , ( rps = 1, 2,…, N ); Will N Group representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ), m = 1, 2, 3, …, M ; According to the θ rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ). Based on the maximum flow depth of each grid cell h m,max (θ rps The probability density function of ) determines the transcendence probability of each grid cell; The debris flow hazard zoning map is determined based on the exceedance probability classification table and the exceedance probability of each grid cell.

[0011] Optionally, the debris flow simulation model is an FLO-2D model; the FLO-2D model uses a central difference numerical strategy to solve the fluid continuity equation and the fluid momentum equation to obtain the flow depth and velocity of each grid cell:

[0012]

[0013]

[0014] in, h Indicates the flow depth; i Indicates rainfall intensity; v x and v y They represent x and y The depth-averaged velocity component in the direction; Sfx and S fy They represent in x and y Friction gradient in the direction of movement; S ox and S oy They represent in x and y bed slope in direction; g Represents gravitational acceleration; The FLO-2D model uses a second-order rheological model to simulate the rheological behavior of debris flows:

[0015] in, S f Indicates the friction gradient; τ y and η These represent yield stress and viscosity, respectively. γ m Indicates the specific gravity of debris flow material; K Indicates the laminar flow resistance coefficient; v Indicates depth-average velocity; n td The equivalent Manning coefficient, which takes into account both turbulent drag and drag generated by sediment particle contact, is expressed as: ,in, e Represents the natural constant. n This represents the Manning coefficient. C v The volume concentration of the fluid is expressed as the sediment volume. V S Divide by the total volume of water and sediment V W+S , C v = V S / V W+S Yield stress τ y and viscosity η Mainly follow C v Changes are represented as: ; ,in, α 1. α 2. β 1 and β 2 represents the empirical coefficient to be determined; The random variable θ = [ α 1, β 1,α 2, β 2, C v , K , n ] T .

[0016] Optionally, the maximum flow depth for each grid cell is determined based on the following formula. h m,max (θ rps The probability density function of )

[0017]

[0018] in, Z ( t () represents the physical response of the system as it changes over time. t This represents virtual time, assumed to be 0–1. D (θ) represents the response of the debris flow simulation model with respect to θ. p Zθ ( z ,θ, t )express( Z ( t The joint probability density function of ),θ), augmented system ( Z ( t ),θ) is a probabilistically conservative system that includes all random variables. p Zθ ( z ,θ rps , t ) represents the discrete probability density of the target physical quantity. p Z ( z , t ) represents the probability density function of the target physical quantity.

[0019] Optionally, the transcendence probability of each grid cell is determined based on the following formula:

[0020]

[0021] in, For functional purposes, Represents a grid m Being affected by mudslides is dangerous. The mesh remains unaffected and is safe. f (θ) denotes the joint probability density function of θ, with the integration domain being such that The range of θ Represents a grid m The probability of exceeding 0 m is the probability that the maximum depth exceeds 0 m.

[0022] Optionally, determine the random variable θ N Representative point θ rps ,include: Based on historical debris flow simulation data, four empirical coefficients were determined. α 1. α 2. β 1. β 2. Statistical information, optimal probability distribution of the four empirical coefficients, and correlation coefficient matrix among the four random variables; Determine the remaining three random variables C v , K and n Following a uniform distribution: C v ~ U(0.45, 0.55), K ~ U(1000,4000) and n ~ U(0.05, 0.13); Based on the determination of four empirical coefficients α 1. α 2. β 1. β Statistical information of 2, optimal probability distribution, correlation coefficient matrix, and the other three random variables. C v , K and n Following a uniform distribution, determine the random variable θ. N Representative point θ rps .

[0023] Optionally, a debris flow hazard zoning map is determined based on the exceedance probability grading table and the exceedance probability of each grid cell, including: Grid cells with an exceedance probability greater than or equal to 50% are defined as extremely dangerous zones; Grid cells with an exceedance probability greater than or equal to 10% and less than 50% are identified as high-risk areas; Grid cells with an exceedance probability greater than or equal to 1% and less than 10% are identified as medium-risk areas; Grid cells with an exceedance probability greater than or equal to 0.1% and less than 1% are identified as low-risk zones; Grid cells with an exceedance probability of less than 0.1% are identified as extremely low-risk zones.

[0024] A second aspect of the present invention provides a device for assessing the probability of debris flow movement hazards, the device comprising: The data acquisition module is used to acquire rainfall monitoring data for the current region. The flow data determination module is used to determine the surface runoff flow data of the current area based on the rainfall monitoring data. The water collection point determination module is used to determine the water collection point based on the current terrain and landforms of the area, combined with satellite images of the current area; The model building module is used to construct a debris flow simulation model for the current region based on the surface runoff data and the catchment points. The debris flow simulation model is composed of... M It consists of a continuous grid of cells; The representative point determination module is used to determine the random variable θ of the debris flow simulation model. N Representative point θ rps , ( rps = 1, 2,…, N ); The maximum flow depth determination module is used to determine the maximum flow depth. N Representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ); The probability density function determination module is used to determine the probability density function based on the θ. rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ). The exceedance probability determination module is used to determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ) determines the transcendence probability of each grid cell; The debris flow hazard zoning determination module is used to determine the debris flow hazard zoning map based on the exceedance probability classification table and the exceedance probability of each grid cell.

[0025] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the debris flow movement hazard probability assessment method as described in the first aspect of the present invention.

[0026] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the debris flow movement hazard probability assessment method as described in the first aspect of the present invention.

[0027] A fifth aspect of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the debris flow movement hazard probability assessment method described in the first aspect of the present invention.

[0028] In this embodiment of the invention, a probabilistic method for predicting the movement hazard of debris flow disasters using a reliability approach and a dynamic numerical model is proposed. This method aims to i) explain the inherent uncertainties in the input parameters of the FLO-2D model, ii) propagate these uncertainties to the model output (maximum flow depth), iii) quantitatively assess the probability that the maximum flow depth exceeds a specific threshold, and iv) generate a probabilistic hazard zoning map to facilitate debris flow risk assessment and mitigation. The feasibility and effectiveness of this method are verified through case studies of Longdegou and Chenghuangmiaogou. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of a method for evaluating the probability of debris flow movement hazard according to an embodiment of the present invention; Figure 2 These are four empirical coefficients (in the database compiled in a debris flow movement hazard probability assessment method according to an embodiment of the present invention) α 1 、α 2 、β 1 and β 2) Statistical information; Figure 3 This is a flowchart of a method for evaluating the probability of debris flow movement hazard provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of precipitation data provided in an example embodiment of the present invention; Figure 5 This is an example embodiment of the Longde debris flow probabilistic hazard zoning map provided in this invention. Figure 6 This is a schematic diagram of precipitation data from another example embodiment of the present invention. Figure 7A probability hazard zoning map of debris flow in Chenghuangmiao Gully, provided as another example embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a debris flow movement hazard probability assessment device according to an embodiment of the present invention. Detailed Implementation

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Debris flows, as a common natural disaster, often cause severe casualties and property damage. Therefore, effectively predicting the potential impact range of debris flows is crucial. Numerical modeling methods are considered an effective way to predict the impact range of debris flows; however, current research rarely considers the uncertainties of the numerical model input parameters and their impact on the prediction results.

[0033] Based on this, this embodiment of the invention proposes, firstly, to use a targeted database to characterize the uncertainty of the input parameters, and secondly, to develop a probabilistic method that can more efficiently predict the hazard of debris flow movement.

[0034] This invention combines a debris flow simulation model with a PDEM (Plasma Flow Model). Specifically, the FLO-2D model can be used to simulate the debris flow process, determine the maximum flow depth, establish a dedicated input parameter database for FLO-2D to obtain statistical information on random variables, and then determine the random variables. N Taking into account the uncertainty of input parameters, PDEM was used to efficiently calculate the probability density function of the maximum flow depth for all grid cells and to calculate the exceedance probability. The calculated exceedance probability was then used to create a probabilistic hazard zoning map of debris flows. Specifically, refer to... Figure 1 The diagram illustrates a flowchart of a debris flow movement hazard probability assessment method according to an embodiment of the present invention. The method can be executed by a server and includes the following steps: S101, obtain rainfall monitoring data for the current area.

[0035] S102, determine the surface runoff flow data of the current area based on the rainfall monitoring data.

[0036] S103, determine the water collection point based on the current terrain and landforms of the area and in conjunction with satellite images of the area.

[0037] S104, Based on the surface runoff data and the catchment points, construct a debris flow simulation model for the current region. The debris flow simulation model consists of... M It consists of a series of continuous grid cells.

[0038] S105, determine the random variable θ of the debris flow simulation model. N Representative point θ rps， ( rps = 1, 2,…, N ).

[0039] S106, N Representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ), m = 1, 2, 3, …, M .

[0040] S107, according to the θ rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ).

[0041] S108, based on the maximum flow depth per grid cell h m,max (θ rps The probability density function determines the transcendence probability of each grid cell.

[0042] S109, Determine the debris flow hazard zoning map based on the exceedance probability classification table and the exceedance probability of each grid cell.

[0043] In step S101, the embodiments of the present invention typically obtain the rainfall data monitored by the nearest rain gauge station in the current area as rainfall monitoring data.

[0044] In step S102, this embodiment of the invention uses a widely adopted hydrological analysis model (Hydrological Modeling System of the Hydrological Engineering Center (HEC-HMS)) to calculate the surface runoff data (flow process line corresponding to debris flow outbreak) of the current area based on the rainfall data monitored by the recent rain gauge.

[0045] Given that specific records of debris flow durations are typically unavailable, this embodiment of the invention, referencing previous research, uses the duration of rainfall events as an alternative. The end of a rainfall event is marked by six consecutive hours of rainfall less than 1 mm. Using this definition, this embodiment combines actual rainfall data during a debris flow event with the HEC-HMS model to calculate the clear water flow process curve of the channel. Subsequently, the debris flow process curve is estimated by multiplying the clear water flow process curve by the expansion factor (BF). Specifically, BF is represented as follows: ,in, C v This indicates the volume concentration of the fluid.

[0046] In step S103, the selection of the catchment point is a crucial preparatory step for the FLO-2D simulation, significantly impacting the simulation results. Currently, there is no quantitative method for selecting the catchment point. Related methods include: i) establishing the catchment area in the potential initiation zone of debris flow by investigating the topography and geomorphology, supplemented by satellite imagery; ii) placing the catchment point at the top of the alluvial fan; iii) designating the catchment point as a loose material accumulation area; and iv) placing the catchment point at the boundary between erosion and deposition areas. In this embodiment of the invention, methods i) and iii) can be used to determine the catchment point. Specifically, by consulting relevant literature and combining it with field investigations to determine the topography and geomorphology of the current area, and then interpreting satellite imagery, the catchment point is determined at the intersection of the source area and the flow area.

[0047] In this embodiment of the invention, the debris flow simulation model is a dynamic numerical model used to simulate the movement process of debris flow in order to determine the scope of influence of debris flow.

[0048] The dynamic numerical model can be the FLO-2D model: FLO-2D is a two-dimensional finite difference model developed and verified by O'Brien et al., which can be applied to various practical aspects, such as flood, debris flow, dam failure and urban inundation analysis. In this embodiment of the invention, the motion characteristics of debris flow (e.g., maximum flow depth and influence range) are simulated. The FLO-2D model uses a central difference numerical strategy to solve the fluid continuity (Equation (1)) and fluid momentum equations (Equations (2) and (3)) to obtain the depth and velocity of each grid cell. Then, the influence range of the debris flow is determined according to the maximum flow depth of each grid cell.

[0049] (1) (2) (3) in, h Indicates the flow depth; i Indicates rainfall intensity;v x and v y They represent x and y The depth-averaged velocity component in the direction; S fx and S fy They represent in x and y Friction gradient in the direction of movement; S ox and S oy They represent in x and y bed slope in direction; g Represents gravitational acceleration; The FLO-2D model uses a second-order rheological model to simulate the rheological behavior of debris flows: (4) in, S f Indicates the friction gradient; τ y and η These represent yield stress and viscosity, respectively. γ m Indicates the specific gravity of debris flow material; K Indicates the laminar flow resistance coefficient; v Indicates depth-average velocity; n td The equivalent Manning coefficient, which takes into account both turbulent drag and drag generated by sediment particle contact, is expressed as: (5) in, e Represents the natural constant. n This represents the Manning coefficient. C v The volume concentration of the fluid is expressed as the sediment volume. V S Divide by the total volume of water and sediment V W+S As shown below: C v = V S / V W+S (6) Yield stress τ y and viscosity η Mainly follow C vChanges are represented as: (7) (8) in, α 1. α 2. β 1 and β 2 represents the empirical coefficient to be determined; The random variable θ = [ α 1, β 1, α 2, β 2, C v , K , n ] T .

[0050] In the FLO-2D model, rheological parameters and terrain parameters are key factors affecting the simulation results. Rheological parameters τ and η Mainly influenced by empirical coefficients α 1, β 1, α 2, β 2 and volume concentration C v The influence of surface roughness in topographic parameters on laminar flow retardation coefficient. K and Manning coefficient n Therefore, in this embodiment of the invention, the seven input parameters (i.e. α 1 、α 2 、β 1 、β 2 、C v 、 K, n ) is considered a random variable.

[0051] To determine the four empirical coefficients (i.e. α 1 、α 2 、β 1 and β 2) Regarding the statistical information, this embodiment of the invention compiled a database. This database incorporates historical debris flow simulation research data. The database includes datasets from 14 debris flow simulations and 17 experiments. The mean and standard deviation of four random variables were further calculated using the data included in the database. For example... Figure 2 As shown, Figure 2 The following diagram illustrates four empirical coefficients in the database compiled according to an embodiment of the present invention. α 1 、α 2 、β 1 and β2) Statistical information. In this embodiment of the invention, the Kolmogorov–Smirnov (K–S) test is also used to determine the optimal probability distribution of the four random variables, and finally determine... α 1 、α 2 、β 1 and β 2. They respectively follow the Weibull distribution, the generalized extreme value distribution, the log-normal distribution, and the Weibull distribution.

[0052] In addition, the Pearson correlation coefficient matrix among the four random variables was calculated, as shown in Table 1.

[0053] Table 1 α 1 、α 2 、β 1 and β Pearson correlation coefficient matrix of 2:

[0054] Some of these variables are positively correlated (e.g., β 1 and β 2, α 2 and β 2), while other variables show a negative correlation (e.g.: α 1 and β 1, α 1 and α 2, α 1 and β 2, β 1 and α 2) No single variable showed a strong correlation (i.e., all absolute correlation coefficients were less than 0.6); only relatively moderate or weak correlations were observed among the four random variables. Based on these statistical results, a more reasonable combination of parameters can be generated.

[0055] For the other three random variables C v , K and n The FLO-2D reference manual provides an approximate estimate of its potential extent. Furthermore, based on field investigations of two actual debris flow case studies (see below), it is assumed that the three random variables follow a uniform distribution: C v ~ U(0.45, 0.55), K ~ U(1000, 4000) and n ~ U(0.05, 0.13). Due to the lack of supporting data, these variables are assumed to be independent in this embodiment of the invention. Table 2 provides a summary of statistical information for the seven random variables considered in this invention: Table 2. Summary of statistical information for seven random variables:

[0056] Based on the above research, in step S105, the random variable θ is determined. N The specific steps for each representative point include: S1051, based on historical debris flow simulation data, determines four empirical coefficients. α 1. α 2. β 1. β The statistical information of 2, the optimal probability distribution of the four empirical coefficients, and the correlation coefficient matrix between the four random variables.

[0057] S1052, determine the remaining three random variables. C v , K and n Following a uniform distribution: C v ~ U(0.45, 0.55), K ~ U(1000, 4000) and n ~ U(0.05, 0.13).

[0058] S1053, based on the determination of four empirical coefficients α 1. α 2. β 1. β Statistical information of 2, optimal probability distribution, correlation coefficient matrix, and the other three random variables. C v , K and n Following a uniform distribution, determine the random variable θ. N Representative point θ rps .

[0059] In this embodiment of the invention, PDEM is used to propagate uncertainties associated with random variables into the simulation results, obtaining the probability density function of the maximum flow depth in each grid cell, thus providing a probabilistic basis for hazard zoning. For a comprehensive understanding, a brief introduction to PDEM is provided below: First, the system response Z(t) of the debris flow simulation model can be expressed as: (9) Where θ represents the vector of random variables involved in FLO-2D, θ = [ α 1, β 1, α 2, β 2, Cv , K , n ] T T represents the transpose operator; D (θ) represents the FLO-2D response to θ, such as the maximum flow depth; t The virtual time can be assumed to be 0–1. Based on a series of derivations in related technologies using the principle of probability conservation, the generalized density evolution equation is obtained as follows: (10) in express( Z ( t The joint probability density function of ),θ), augmented system ( Z ( t ),θ) is a probabilistically conservative system that includes all random variables; j (θ, t ) express D j (θ, t ) ( j = 1, 2, …, J , in j The rate of evolution (representing the number of system responses).

[0060] The boundary conditions and initial conditions in equation (10) are expressed as follows: (11) (12) Where δ(·) represents the Dirac function, and z0 represents the deterministic initial value. Let θ represent the probability density function of the random variable.

[0061] If there is only one system response of interest ( J If =1), then the generalized density evolution equation is simplified to a one-dimensional partial differential equation, as shown below: (13) Furthermore, a reasonable strategy for selecting representative points plays a crucial role in the efficiency and accuracy of PDEM. The Generalized F-discrepancy (GFD) proposed in related technologies has proven to be an effective strategy for selecting representative points. Therefore, in the embodiments of the invention, representative points θ can be generated based on the GFD strategy. rps ( rps =1, 2, ..., N ,in N (representing the total number of representative points), and the Nataf transform was used to account for...α 1. β 1. α 2 and β The correlation between 2. Then, for each representative point θ rps and its corresponding system response D(θ) rps Substitute into equation (13). Solve equation (13) using the finite difference method to obtain the discrete probability density of the physical quantity of interest. Next, obtain the probability density function of the physical quantity of interest by summing all the discretized probabilities: (14) In this embodiment of the invention, the result obtained is in t The calculation is performed when =1, because when t When =1, Z= D (θ)(Equation (9)), D (θ) is the output of FLO-2D.

[0062] In debris flow hazard assessment, it is necessary to intuitively determine whether the flowing material will reach the grid cell of interest and the location of grid cells in danger. This can be mathematically expressed using a performance function that considers whether the maximum flow depth exceeds 0 m, as shown below: (15) in M This indicates the number of grid cells in the debris flow simulation model. Indicates the first m The maximum flow depth at each grid cell is calculated using θ in FLO-2D. Represents a grid m It is unsafe due to the impact of mudslides, and Represents a grid m Unaffected by debris flows, it is therefore safe. Therefore, the probability of the maximum flow depth exceeding 0 m (i.e., the exceedance probability) can be estimated by solving the following probability integral: (16) in f (θ) denotes the joint probability density function of θ, which satisfies the following in the integration domain: The parameter space of θ.

[0063] Equation (16) is solved efficiently using PDEM (i.e., in Within the range of >0 By integrating the probability density function, the transcendence probability of any point of interest can be obtained. Then, by projecting all the calculation results onto the satellite image map, the areas affected by debris flows with different exceedance probabilities can be displayed intuitively.

[0064] In this embodiment of the invention, the exceedance probability was also classified according to the event probability classification table, and the debris flow was divided into five risk levels of probability hazard zoning to obtain the exceedance probability classification table.

[0065] The exceedance probability classification table specifically includes: classifying grid cells with exceedance probabilities greater than or equal to 50% as extremely dangerous zones; classifying grid cells with exceedance probabilities greater than or equal to 10% and less than 50% as high dangerous zones; classifying grid cells with exceedance probabilities greater than or equal to 1% and less than 10% as medium dangerous zones; classifying grid cells with exceedance probabilities greater than or equal to 0.1% and less than 1% as low dangerous zones; and classifying grid cells with exceedance probabilities less than 0.1% as extremely low dangerous zones.

[0066] Specifically, in step S109 above, a debris flow hazard zoning map can be determined based on the exceedance probability classification table and the exceedance probability of each grid cell.

[0067] Figure 3 A flowchart of a debris flow movement hazard probability assessment method provided by an embodiment of the present invention is shown, as follows: Figure 3 As shown, in order to realize the method for assessing the probability of debris flow movement hazard in this embodiment of the invention, the following is specifically proposed: (1) Select a suitable dynamic numerical model to simulate the movement process of debris flow. Specifically, the FLO-2D numerical model, widely used in engineering practice for debris flow simulation, can be adopted. Understandably, other numerical models (e.g., MassFlow and MassMov2D) can also be used. Random variables are determined based on sensitivity analysis or expert experience. In this embodiment of the invention, seven random variables (θ = [...]) involved in the FLO-2D model are considered. α 1, β 1, α 2, β 2, C v , K , n ] T The system obtains a parameter database based on collected historical messages, and then obtains statistical information (such as mean, standard deviation and distribution type) of these parameters included in the random variables.

[0068] (2) Obtain monitoring rainfall data on the day of debris flow outbreak, and use the hydrological model (i.e. HEC-HMS) to calculate surface runoff to obtain the required flow data; the digital elevation model of the target watershed was obtained by satellite or UAV measurement to determine the catchment point; (3) Based on surface runoff data and water collection points, an FLO-2D calculation model was established.

[0069] (4) Generate N representative points θ of random variables based on the GF difference point selection strategy. rps ( rps =1, 2, ..., N ); and apply the Nataf transform to consider the correlation between different random variables.

[0070] (5) N Representative point θ rps Substitute the values ​​into the FLO-2D model sequentially and run the model to obtain each mesh cell. m Corresponding maximum flow depth h m,max (θ rps ).

[0071] (6) Represent the point θ rps and h m,max (θ rps Substitute equations (13) and (14) to solve. h m,max (θ rps The probability density function of ) is then used to estimate the transcendence probability of each grid cell using equation (16).

[0072] (7) Finally, a debris flow hazard zoning map was generated based on the exceedance probability classification table.

[0073] To facilitate understanding, the following two specific embodiments further illustrate the debris flow movement hazard probability assessment method provided by the present invention. It should be understood that these embodiments are merely examples: Longdegou mudslide Longdegou is located in Zengda Township, Jinchuan County, Sichuan Province, China, with its mouth at coordinates of 102°01′25.90″E, 31°11′21.84″N. The Longdegou drainage area is approximately 3.52 km². Zengda Township has a plateau monsoon climate with four distinct seasons, abundant sunshine, and a long frost-free period. Meteorological data shows an average annual temperature of 16°C and an average annual precipitation of 360 mm. The main exposed rock strata in the study area include quartz sandstone and slate of the Zagunao Formation. In 2020, Longdegou experienced three consecutive debris flow events on July 25, August 17, and September 15, resulting in damage to some farmland and five buildings. This case study focuses on the second debris flow event that occurred on August 17, 2020.

[0074] According to rainfall monitoring data from the Jinchuan meteorological station near Zengda Township, Jinchuan County experienced continuous rainfall from August 14th to 17th, 2020 (e.g., ...). Figure 4As shown in section (a), which illustrates the daily rainfall from August 14 to 17, 2020. Meteorological data from August 17 shows that Jinchuan County received 21.1 mm of cumulative rainfall in 24 hours, with a maximum hourly rainfall of 5.6 mm (as shown in section (a)). Figure 4 As shown in section (b), which displays 24-hour rainfall data for August 17, 2020, the Longdegou debris flow was triggered. Using the rainfall data, the clear water flow process curve of Longdegou was derived using HEC-HMS (e.g., Figure 4 As shown in section (c), which illustrates the clear water flow process line of Longde Ditch.

[0075] According to the investigation, a recent small landslide in the gully provided material for the debris flow, creating favorable conditions for its outbreak. Therefore, the catchment point was selected at the accumulation site of the landslide material. Subsequently, a computational model of the Longde Gully was constructed using FLO-2D software, with a grid size of 15 m × 15 m. In this embodiment of the invention, the debris flow motion simulation was initiated by inputting the flow process curve of the catchment point, along with necessary rheological and topographic parameters. Based on the statistical information provided in Table 2, 500 representative points for random variables were generated using the GF differential point selection strategy.

[0076] In this embodiment of the invention, the process involves 500 repeated deterministic motion analyses using an FLO-2D model. The number of sampling points is determined through trial and error to strike a balance between computational accuracy and efficiency. Subsequently, a PDEM is used to estimate the probability density function of the maximum flow depth for each grid cell. Furthermore, the probability that the maximum flow depth in each grid cell exceeds 0 m is calculated, indicating the likelihood of debris flow material reaching a specific grid cell. In the final stage, the potential impact range is divided into five regions (from extremely high to extremely low) using the aforementioned exceedance probability classification criteria, such as... Figure 5 As shown, Figure 5 The diagram shows the probabilistic hazard zoning map of the Longde debris flow. Theoretically, if the probability of an event occurring exceeds 50%, the likelihood of that event occurring is greater than the likelihood of it not occurring. Therefore, areas with a probability exceeding 50% can be considered most likely to be affected by debris flows. Figure 5 The study also revealed that areas of extremely high risk are primarily located in the flow and deposition zones of debris flows. The predicted range of extremely high-risk areas closely matches the observed deposition range, thus confirming the accuracy of the probabilistic hazard assessment method proposed in this invention.

[0077] City God Temple mudslide To further validate the proposed method, this embodiment of the invention also selected another case study involving a debris flow event that occurred on July 6, 2020, in Chenghuangmiao Gully. Chenghuangmiao Gully is located in Zhailong Township, Xiaojin County, Sichuan Province, China. The gully mouth coordinates are 102°10′23.39″E, 31°02′07.24″N. The area has a subtropical monsoon climate with an average annual rainfall of 613.9 mm. The drainage area is approximately 6 square kilometers, and vegetation within the gully is sparse. The main lithologies in the area include slate, quartzite, and interbedded sandstone.

[0078] The total volume of debris flow in the depositional area is approximately 16 × 10⁻⁶. 4 m 3 This resulted in over 200 meters of G350 highway being damaged.

[0079] According to rainfall monitoring data from Xiaojin Meteorological Observatory, from July 3rd to 6th, 2020, Xiaojin County experienced continuous rainfall (such as...). Figure 6 As shown in section (a), which illustrates the daily rainfall from July 3 to 6, 2020. From 5 PM on July 5 to the early morning of July 6, there was a brief but intense period of rainfall, with a maximum hourly rainfall of 10.5 mm (as shown in the image). Figure 6 As shown in section (b), which illustrates hourly rainfall data for July 5, 2020, this triggered a debris flow in Chenghuangmiao Ditch. The clear water flow process curve of Chenghuangmiao Ditch was simulated using the HEC-HMS model based on this rainfall data (e.g., Figure 6 As shown in section (c), which illustrates the clear water flow process line.

[0080] A similar method was used to determine that the catchment point in Chenghuangmiao Ditch is located at the intersection of the source area and the flow area. An FLO-2D model of Chenghuangmiao Ditch was constructed using a 15m × 15m resampling grid. Using the same input information (i.e., statistical information of random variables and the number of representative points) used in the Longde Ditch debris flow case, PDEM was used to estimate the probability that the maximum flow depth of each grid cell would exceed 0 m. This led to the probabilistic hazard zoning of the Chenghuangmiao debris flow, as follows: Figure 7 As shown, it displays a probability zoning map of the debris flow hazard in Chenghuangmiao Gully.

[0081] This invention proposes a probabilistic method for predicting debris flow hazard using a reliability approach and a dynamic numerical model. The method aims to i) explain the inherent uncertainties in the input parameters of the FLO-2D model, ii) propagate these uncertainties to calculate the maximum flow depth, iii) quantitatively assess the probability that the maximum flow depth exceeds a specific threshold, and iv) generate a probabilistic hazard zoning map to facilitate debris flow risk assessment and mitigation. Case studies of Longdegou and Chenghuangmiaogou validate the feasibility and effectiveness of this method. The specific details of the debris flow hazard probabilistic assessment method proposed in this invention are as follows: 1) A database was established using the input parameters of the FLO-2D model, providing fundamental information for representing random variables. The collected statistical data can be used for future debris flow hazard assessments by applying probabilistic methods.

[0082] 2) A probabilistic framework is introduced to calculate the exceedance probability of the maximum flow depth and generate a probabilistic hazard zoning map, thereby improving the traditional deterministic debris flow hazard zoning method.

[0083] 3) The PDEM used in the embodiments of the present invention greatly improves the calculation efficiency of probabilistic debris flow hazard assessment, thereby promoting its application in engineering practice.

[0084] 4) The calculation results of the case studies of Longdegou and Chenghuangmiaogou confirm the reliability and effectiveness of the proposed method, demonstrating its potential to make a significant contribution to the quantitative risk assessment and mitigation of debris flows.

[0085] Based on the same inventive concept, embodiments of the present invention provide a device for assessing the probability of debris flow movement hazards. (Reference) Figure 8 , Figure 8 This is a schematic diagram of the debris flow movement hazard probability assessment device provided in an embodiment of the present invention. Figure 8 As shown, the device includes: Data acquisition module 801 is used to acquire rainfall monitoring data for the current region; The flow data determination module 802 is used to determine the surface runoff flow data of the current area based on the rainfall monitoring data. The water collection point determination module 803 is used to determine the water collection point based on the terrain and landforms of the current area and in conjunction with satellite images of the current area; Model building module 804 is used to build a debris flow simulation model for the current region based on the surface runoff data and the catchment point. The debris flow simulation model is composed of... M It consists of a continuous grid of cells; Representative point determination module 805 is used to determine the random variable θ of the debris flow simulation model. N Representative point θ rps， ( rps = 1, 2,…, N ); Maximum flow depth determination module 806 is used to determine the maximum flow depth. N Representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ); The probability density function determination module 807 is used to determine the probability density function based on the θ. rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ). Exceedance probability determination module 808, used for determining the maximum flow depth per grid cell. h m,max (θ rps The probability density function of ) determines the transcendence probability of each grid cell; The debris flow hazard zoning determination module 809 is used to determine the debris flow hazard zoning map based on the exceedance probability classification table and the exceedance probability of each grid cell.

[0086] Optionally, the debris flow simulation model is an FLO-2D model; the FLO-2D model uses a central difference numerical strategy to solve the fluid continuity equation and the fluid momentum equation to obtain the flow depth and velocity of each grid cell:

[0087]

[0088]

[0089] in, h Indicates the flow depth; i Indicates rainfall intensity; v x and v y They represent x and y The depth-averaged velocity component in the direction; S fx and S fy They represent in x and y Friction gradient in the direction of movement; S ox and S oy They represent in x and y bed slope in direction; g Represents gravitational acceleration; The FLO-2D model uses a second-order rheological model to simulate the rheological behavior of debris flows:

[0090] in,S f Indicates the friction gradient; τ y and η These represent yield stress and viscosity, respectively. γ m Indicates the specific gravity of debris flow material; K Indicates the laminar flow resistance coefficient; v Indicates depth-average velocity; n td The equivalent Manning coefficient, which takes into account both turbulent drag and drag generated by sediment particle contact, is expressed as: ,in, e Represents the natural constant. n This represents the Manning coefficient. C v The volume concentration of the fluid is expressed as the sediment volume. V S Divide by the total volume of water and sediment V W+S , C v = V S / V W+S Yield stress τ y and viscosity η Mainly follow C v Changes are represented as: ; ,in, α 1. α 2. β 1 and β 2 represents the empirical coefficient to be determined; The random variable θ = [ α 1, β 1, α 2, β 2, C v , K , n ] T .

[0091] Optionally, the maximum flow depth for each grid cell is determined based on the following formula. h m,max (θ rps The probability density function of )

[0092]

[0093] in,Z ( t () represents the physical response of the system as it changes over time. t This represents virtual time, assumed to be 0–1. D (θ) represents the response of the debris flow simulation model with respect to θ. p Zθ ( z ,θ, t )express( Z ( t The joint probability density function of ),θ), augmented system ( Z ( t ),θ) is a probabilistically conservative system that includes all random variables. p Zθ ( z ,θ rps , t ) represents the discrete probability density of the target physical quantity. p Z ( z , t ) represents the probability density function of the target physical quantity.

[0094] Optionally, the transcendence probability of each grid cell is determined based on the following formula:

[0095]

[0096] in, For functional purposes, Represents a grid m The grid is affected by debris flows, but conversely, if it is not affected by debris flows, the grid is unaffected and safe. f (θ) denotes the joint probability density function of θ, with the integration domain being such that The range of θ Represents a grid m The probability of exceeding 0 m is the probability that the maximum depth exceeds 0 m.

[0097] Optionally, the representative point determination module is specifically used for: Based on historical debris flow simulation data, four empirical coefficients were determined. α 1. α 2. β 1. β 2. Statistical information, optimal probability distribution of the four empirical coefficients, and correlation coefficient matrix among the four random variables; Determine the remaining three random variables C v , K and n Following a uniform distribution: Cv ~ U(0.45, 0.55), K ~ U(1000,4000) and n ~ U(0.05, 0.13); Based on the determination of four empirical coefficients α 1. α 2. β 1. β Statistical information of 2, optimal probability distribution, correlation coefficient matrix, and the other three random variables. C v , K and n Following a uniform distribution, determine the random variable θ. N Representative point θ rps .

[0098] Optionally, the debris flow hazard zoning map determination module is specifically used for: Grid cells with an exceedance probability greater than or equal to 50% are defined as extremely dangerous zones; Grid cells with an exceedance probability greater than or equal to 10% and less than 50% are identified as high-risk areas; Grid cells with an exceedance probability greater than or equal to 1% and less than 10% are identified as medium-risk areas; Grid cells with an exceedance probability greater than or equal to 0.1% and less than 1% are identified as low-risk zones; Grid cells with an exceedance probability of less than 0.1% are identified as extremely low-risk zones.

[0099] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0100] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the steps in the debris flow movement hazard probability assessment method described in any of the above embodiments.

[0101] This invention also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps in the debris flow movement hazard probability assessment method described in any of the above embodiments.

[0102] This invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the debris flow movement hazard probability assessment method described in any of the above embodiments.

[0103] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0109] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0110] The above provides a detailed description of the method, apparatus, electronic device, storage medium, and computer program product for assessing the probability of debris flow movement hazards provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, those skilled in the art will recognize that, based on the ideas of this invention, 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 invention.

Claims

1. A method for assessing the probability of debris flow movement hazard, characterized in that, The method includes: Obtain current rainfall monitoring data for the region; Determine the surface runoff data for the current area based on the rainfall monitoring data; The water catchment point is determined based on the current terrain and landforms of the area, combined with satellite images of the area. Based on the surface runoff data and the catchment points, a debris flow simulation model is constructed for the current region. The debris flow simulation model is composed of... M It consists of a continuous grid of cells; Determine the random variable θ of the debris flow simulation model. N Group representative point θ rps , ( rps = 1, 2,…, N The random variable θ = [ α 1, β 1, α 2, β 2, C v , K , n ] T , n This represents the Manning coefficient. C v The volume concentration of the fluid is expressed as the sediment volume. V S Divide by the total volume of water and sediment V W+S , C v = V S / V W+S Yield stress τ y and viscosity η Mainly follow C v Changes are represented as: ; ,in, α 1. α 2. β 1 and β 2 represents the empirical coefficient to be determined; Will N Group representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ), m = 1, 2, 3, …, M ; According to the θ rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ). Based on the maximum flow depth of each grid cell h m,max (θ rps The probability density function of ) determines the transcendence probability of each grid cell; The debris flow hazard zoning map is determined based on the exceedance probability classification table and the exceedance probability of each grid cell. The transcendence probability of each grid cell is determined based on the following formula: in, For functional purposes, Represents a grid m The grid is affected by debris flows, but conversely, if it is not affected by debris flows, the grid is unaffected and safe. f (θ) denotes the joint probability density function of θ, with the integration domain being such that The range of θ Represents a grid m The probability of exceeding 0 m is the probability that the maximum depth exceeds 0 m.

2. The method for assessing the probability of debris flow movement hazard according to claim 1, characterized in that, The debris flow simulation model is the FLO-2D model; the FLO-2D model uses a central difference numerical strategy to solve the fluid continuity equation and the fluid momentum equation to obtain the flow depth and velocity of each grid cell: in, h Indicates the flow depth; i Indicates rainfall intensity; v x and v y They represent x and y The depth-average velocity component in the direction; S fx and S fy They represent in x and y Friction gradient in the direction of movement; S ox and S oy They represent in x and y bed slope in direction; g Represents gravitational acceleration; The FLO-2D model uses a second-order rheological model to simulate the rheological behavior of debris flows: in, S f Indicates the friction gradient; τ y and η These represent yield stress and viscosity, respectively. γ m Indicates the specific gravity of debris flow material; v Indicates depth-average velocity; n td The equivalent Manning coefficient, which takes into account both turbulent drag and drag generated by sediment particle contact, is expressed as: ,in, e Represents the natural constant.

3. The method for assessing the probability of debris flow movement hazard according to claim 2, characterized in that, The maximum flow depth for each grid cell is determined based on the following formula. h m,max (θ rps The probability density function of ) in, Z ( t () represents the physical response of the system as it changes over time. t This represents virtual time, assumed to be 0–1. D (θ) represents the response of the debris flow simulation model with respect to θ. p Zθ ( z ,θ, t )express( Z ( t The joint probability density function of ),θ), augmented system ( Z ( t ),θ) is a probabilistically conservative system that includes all random variables. p Zθ ( z ,θ rps , t ) represents the discrete probability density of the target physical quantity. p Z ( z , t ) represents the probability density function of the target physical quantity.

4. The method for assessing the probability of debris flow movement hazard according to claim 2, characterized in that, Determine the random variable θ N Representative point θ rps ,include: Based on historical debris flow simulation data, four empirical coefficients were determined. α 1. α 2. β 1. β 2. Statistical information, optimal probability distribution of the four empirical coefficients, and correlation coefficient matrix among the four random variables; Determine the remaining three random variables C v , K and n Following a uniform distribution: C v ~ U(0.45, 0.55), K ~ U(1000,4000) and n ~ U(0.05, 0.13); Based on the determination of four empirical coefficients α 1. α 2. β 1. β Statistical information of 2, optimal probability distribution, correlation coefficient matrix, and the other three random variables. C v , K and n Following a uniform distribution, determine the random variable θ. N Representative point θ rps .

5. The method for assessing the probability of debris flow movement hazard according to claim 1, characterized in that, The debris flow hazard zoning map is determined based on the exceedance probability classification table and the exceedance probability of each grid cell, including: Grid cells with an exceedance probability greater than or equal to 50% are identified as extremely dangerous zones; Grid cells with an exceedance probability greater than or equal to 10% and less than 50% are identified as high-risk areas; Grid cells with an exceedance probability greater than or equal to 1% and less than 10% are identified as medium-risk areas; Grid cells with an exceedance probability greater than or equal to 0.1% and less than 1% are identified as low-risk zones; Grid cells with an exceedance probability of less than 0.1% are identified as extremely low-risk zones.

6. A device for assessing the probability of debris flow movement hazards, characterized in that, The device includes: The data acquisition module is used to acquire rainfall monitoring data for the current region. The flow data determination module is used to determine the surface runoff flow data of the current area based on the rainfall monitoring data. The water collection point determination module is used to determine the water collection point based on the current terrain and landforms of the area, combined with satellite images of the current area; The model building module is used to construct a debris flow simulation model for the current region based on the surface runoff data and the catchment points. The debris flow simulation model is composed of... M It consists of a continuous grid of cells; The representative point determination module is used to determine the random variable θ of the debris flow simulation model. N Representative point θ rps , ( rps =1, 2,…, N The random variable θ = [ α 1, β 1, α 2, β 2, C v , K , n ] T , n This represents the Manning coefficient. C v The volume concentration of the fluid is expressed as the sediment volume. V S Divide by the total volume of water and sediment V W+S , C v = V S / V W+S Yield stress τ y and viscosity η Mainly follow C v Changes are represented as: ; ,in, α 1. α 2. β 1 and β 2 represents the empirical coefficient to be determined; The maximum flow depth determination module is used to determine the maximum flow depth. N Representative point θ rps By substituting the values ​​into the debris flow simulation model, the maximum flow depth corresponding to each grid cell can be obtained. h m,max (θ rps ); The probability density function determination module is used to determine the probability density function based on the θ. rps and h m,max (θ rps Determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ). The exceedance probability determination module is used to determine the maximum flow depth for each grid cell. h m,max (θ rps The probability density function of ) determines the transcendence probability of each grid cell; The debris flow hazard zoning map determination module is used to determine the debris flow hazard zoning map based on the exceedance probability classification table and the exceedance probability of each grid cell. The transcendence probability of each grid cell is determined based on the following formula: in, For functional purposes, Represents a grid m The grid is affected by debris flows, but conversely, if it is not affected by debris flows, the grid is unaffected and safe. f (θ) denotes the joint probability density function of θ, with the integration domain being such that The range of θ Represents a grid m The probability of exceeding 0 m is the probability that the maximum depth exceeds 0 m.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the debris flow movement hazard probability assessment method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the debris flow movement hazard probability assessment method according to any one of claims 1-5.

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