Debris flow disaster chain analysis method combining numerical simulation and complex network

Abstract

The invention relates to the technical field of hydrological disaster prevention and risk assessment, in particular to a debris flow disaster chain analysis method combining numerical simulation and a complex network. The method comprises the following steps: collecting and analyzing historical data of rainstorm, flood and debris flow in a research area, extracting disaster chain associated information, establishing a debris flow numerical model, and constructing a debris flow disaster chain network; network vulnerability and efficiency are evaluated, a chain breaking strategy is made by deleting key nodes or edges, network density change is calculated to verify the disaster reduction effect, and the chain breaking strategy and the disaster reduction effect thereof are quantitatively analyzed and discussed. According to the method, high-risk areas and fragile nodes can be identified, and the fragility and network efficiency of a disaster chain can be quantified, so that targeted disaster reduction measures are formulated for different rainfall intensities, and a scientific basis is provided for disaster prevention and reduction and emergency management.

Description

Technical Field

[0001] The present invention relates to the technical field of prevention and control of hydrological disasters and risk assessment, and particularly relates to a method for analyzing debris flow disaster chains by combining numerical simulation and complex networks. Background Art

[0002] Rainstorm disasters caused by global climate change have led to frequent occurrence of secondary disasters such as debris flows, posing a serious threat to the ecological environment and human safety. The formation of debris flows is affected by various factors, including rainfall intensity, topographical features, and soil conditions. Moreover, its occurrence is usually accompanied by the superposition effect of disasters such as rainstorm floods, increasing the complexity of the disaster and the difficulty of emergency response. Accurately simulating and evaluating the debris flow disaster chain triggered by rainstorms and its propagation process not only helps to identify vulnerable areas in advance but also provides a theoretical basis and technical support for disaster risk assessment and the construction of early warning systems. Currently, most existing studies focus on the analysis of single disasters or simple disaster chains, lacking a systematic assessment of multiple disaster chains triggered by rainstorm floods, and unable to comprehensively reveal the interaction and superposition effect between different disaster nodes, resulting in possible limitations in the practical application of disaster prevention and mitigation strategies.

[0003] Currently, in order to evaluate the propagation characteristics and disaster risks of debris flow disaster chains, commonly used methods include statistical analysis based on historical data and assessment methods of geological environment characteristic factors. Although these traditional methods are effective in certain specific environments, they cannot comprehensively and dynamically simulate the process of debris flow induced by rainstorm floods and its disaster chain response, especially it is difficult to consider the coupling effect of multiple factors and the spatial dynamic changes of disaster propagation. In addition, existing disaster chain analysis methods usually can only conduct risk assessment for a single disaster, lacking the analysis of the interaction between multiple disaster nodes and the quantitative assessment of the vulnerability and network efficiency of the disaster chain.

[0004] CN202411473213.6. This invention provides a method for evaluating the susceptibility of typhoon rainstorm-induced debris flows based on an ensemble learning algorithm. For the study area, influencing factors are selected to establish an evaluation index system, and relevant data are obtained according to the index requirements. Then, ArcGIS is used to extract and preprocess the factor data. Feature factors with no correlation are screened through the factor correlation analysis method. The multi-collinearity among multiple factors is quantitatively measured using the multi-collinearity analysis method, and the correlation between factors is eliminated using the KPCA method. Then, the SMOTE algorithm is used to oversample the debris flow occurrence samples. Next, through an ensemble optimization algorithm based on the WOA, ISO, CSA, and IGA optimization models, the parameters of the CatBoost regression model are optimized. The stability of the model is evaluated through k-fold cross-validation using Sklearn to identify the best model. Finally, the best model is used to draw the debris flow susceptibility map. This patent document mainly analyzes the impact of rainfall data in different time ranges on the prediction accuracy of debris flow susceptibility, and is limited to debris flows caused by short-duration heavy rainfall brought by typhoon rainstorms.

[0005] Therefore, in order to more accurately identify and evaluate the propagation characteristics and impacts of debris flow disaster chains, it is necessary to adopt numerical simulation and complex network analysis methods, integrate multiple factors of disaster occurrence, propagation, and interaction, and construct a comprehensive evaluation model for debris flow disaster chains. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a method for analyzing debris flow disaster chains combining numerical simulation and complex networks. By combining numerical simulation and complex network analysis, a debris flow disaster chain model is constructed. Through simulating the process of debris flows induced by rainstorm floods, combined with the analysis of disaster propagation paths and key nodes, the dynamic evolution process of the disaster chain is comprehensively evaluated. This method can not only identify high-risk areas and vulnerable nodes, but also quantify the vulnerability and network efficiency of the disaster chain, and then formulate targeted disaster reduction measures for different rainfall intensities, providing a scientific basis for disaster prevention, mitigation, and emergency management.

[0007] The technical solution of the present invention to solve the above problems is as follows:

[0008] A method for analyzing debris flow disaster chains combining numerical simulation and complex networks, comprising the following steps:

[0009] Step (1), collect historical data of rainstorms, floods, and debris flows in the study area, extract the associated information of the disaster chain, and preliminarily determine the propagation path of the disaster chain;

[0010] Step (2), based on DEM, rainfall, and underlying surface data, establish a debris flow numerical model, simulate the debris flow discharge, mud depth, and influence range under different rainfall frequencies, and divide the danger levels in combination with the mud depth and flow velocity;

[0011] Step (3): Based on the data collection and summary results in step (1) and the numerical simulation in step (2), abstract the disaster events as nodes and the propagation relationships as connecting edges between nodes to construct a debris flow disaster chain network;

[0012] Step (4): Evaluate the importance of different nodes in the disaster chain network by calculating the degree centrality, betweenness centrality, and closeness centrality of the nodes;

[0013] Step (5): For the network connecting edges, identify the critical edges by analyzing the network vulnerability and network efficiency;

[0014] Step (6): Based on the analysis results of the disaster chain network structure in steps (4) and (5), evaluate the network vulnerability and efficiency, formulate a chain-breaking strategy by deleting critical nodes or edges, calculate the change in network density to verify the disaster reduction effect, and quantitatively analyze and discuss the chain-breaking strategy and its disaster reduction effect.

[0015] Preferably, the relevant data obtained in step (1) includes meteorological data, topographic and geomorphic data, hydrogeological data, and historical disaster data.

[0016] Preferably, step (2) includes analyzing the water flow direction and catchment area of the DEM data to determine the location of the debris flow catchment point.

[0017] Preferably, the specific steps for determining the location of the debris flow catchment point include filling in the sunken areas in the DEM, calculating the flow direction, generating a flow path map and a cumulative flow map, and then analyzing the location of the catchment area in combination with the spatial distribution of the debris flow source.

[0018] Preferably, in step (2), the debris flow numerical model uses the continuity equation and the momentum equation, and the parameters mainly include the volume concentration Cv, Bingham yield stress (τ), Bingham viscosity coefficient (η), Manning coefficient (n), laminar flow retardation coefficient (K), and the soil-rock ratio (Gs) of the debris flow.

[0019] Preferably, the hazard level of the simulation results in step (2) is divided according to the relationship between the mud depth and flow velocity of the debris flow and the recurrence period.

[0020] Preferably, for the debris flow disaster chain network constructed in step (3), the weight value of the edge is dynamically assigned according to the hazard level: high hazard area = 2, medium hazard area = 1, low hazard area = 0.5.

[0021] Preferably, the chain-breaking strategy in step (6) is achieved by removing critical nodes or edges in the network model. By deleting different critical nodes and critical edges, calculate the network density of the newly generated network, and compare it with the original network density. A decrease in network density indicates a reduction in the propagation path, that is, the chain-breaking effect is significant;

[0022] The network density calculation formula is as follows: In the formula: M is the actual number of edges, and N is the total number of nodes.

[0023] The present invention has the following beneficial effects:

[0024] By combining numerical simulation with complex network analysis, the present invention provides a more comprehensive and dynamic assessment method for debris flow disaster chains induced by rainstorm floods, breaking through the limitations of single disaster chain analysis and static assessment existing in the prior art. Compared with traditional methods, the present invention can comprehensively consider the propagation process, interaction and superposition effect of multiple disaster chains, overcoming the deficiencies of the prior art in dealing with complex disaster chains. Through numerical simulation, the formation and propagation process of debris flow can be accurately simulated, and further the diffusion range and influence degree of the disaster chain can be determined. In addition, the present invention adopts the analysis method of complex network, which can identify the key nodes and vulnerable edges in the disaster chain, so as to provide a quantitative basis for disaster prevention and control and emergency management. It can not only identify high-risk areas and vulnerable nodes, but also evaluate the vulnerability and network efficiency of the disaster chain network, comprehensively analyze the propagation path, node importance and influence degree of the disaster chain, and adopt targeted disaster chain breaking strategies for different rainfall intensities, which can effectively reduce the network density and control the propagation of chain disasters. This dynamic assessment and monitoring ability makes the disaster prevention and mitigation strategies more scientific and reasonable, and can effectively guide disaster prevention decision-making and emergency response. In summary, the technical solution of the present invention has significant advantages and broad application prospects in improving the assessment accuracy of disaster chains, enhancing the emergency response ability and providing more accurate disaster prevention and mitigation strategies, and can provide strong technical support for the comprehensive prevention and control of various disasters. Brief Description of the Drawings

[0025] Figure 1 It is the overall flowchart of a debris flow disaster chain analysis method combining numerical simulation with complex network according to an embodiment of the present invention;

[0026] Figure 2 It is the comparison chart of mud depth and danger level classification according to an embodiment of the present invention;

[0027] Figure 3 It is the flowchart of network construction and connection edge assignment according to an embodiment of the present invention;

[0028] Figure 4 It is the schematic diagram of the disaster chain triggered by debris flow in the present invention;

[0029] Figure 5 It is the flowchart of weighted complex network construction according to an embodiment of the present invention;

[0030] Figure 6This is a schematic diagram of the complex network of debris flow disaster chains in the present invention. Among them, (a) is an unweighted complex network, and (b), (c), and (d) are the weighted complex networks corresponding to rainfall frequencies of 2%, 1%, and 0.5% respectively;

[0031] Figure 7 This is the in-degree, out-degree, and three centrality indices of the network nodes of the debris flow disaster chain in an embodiment of the present invention under rainfall frequencies of 2%, 1%, and 0.5%;

[0032] Figure 8 Among them, (a), (b), and (c) are the network vulnerabilities of the debris flow disaster chain complex networks corresponding to rainfall frequencies of 2%, 1%, and 0.5% respectively, and (d), (e), and (f) are the network efficiencies of the debris flow disaster chain complex networks corresponding to rainfall frequencies of 2%, 1%, and 0.5% respectively;

[0033] Figure 9 This is the network density value under different strategies in an embodiment of the present invention. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] Embodiment

[0036] A method for analyzing debris flow disaster chains combining numerical simulation and complex networks is provided, taking Shishugou in Luanchuan County, Henan Province as the research area. Luanchuan is located in the mountainous area of western Henan. The county has distinct rainy and dry seasons, and heavy rains are concentrated, creating a favorable environment for debris flow breeding in the whole county. Shishugou is located in Shimiao Town, Luanchuan County, Henan Province. The widest part of its downstream gully is about 15 m, and a simple retaining wall is built at the gully entrance to form terraced fields. The Shishugou watershed is in the mid-low mountain landform area between 1000 - 2000 m, with a watershed area of 1.17 km 2 , belonging to the area prone to debris flow, with a gully length of about 2.4 km and an average longitudinal slope drop of 206.46‰. The rainfall data is selected as the hourly rainfall from July 23 to 25, 2010 obtained from the Luanchuan meteorological station. The land use data is interpreted using landsat8 remote sensing images, and the elevation data is selected as the 12.5 m DEM collected by the ALOS satellite. The data is processed in each step in combination with GIS software, Pajek software, and programming software. Now, in combination with Figures 1 to 9 As shown, taking Shishugou as the object, the method for analyzing debris flow disaster chains combining numerical simulation and complex networks of the present application will be described.

[0037] An embodiment of a debris flow disaster chain analysis method combining numerical simulation and complex network in an embodiment of the present application includes:

[0038] Step (1): Collect historical data of rainstorms, floods, and debris flows in the study area, extract disaster chain correlation information, and preliminarily determine the propagation path of the disaster chain.

[0039] In a specific embodiment, the relevant data obtained in step (1) includes meteorological data, topographic and geomorphic data, hydrogeological data, and historical disaster data.

[0040] Specifically, collect historical disaster data in the study area, and search for relevant literature materials such as theories, processes, and accident experiences of disaster chain disaster risk assessment at home and abroad using keywords such as "debris flow", "debris flow risk", "debris flow disaster", "disaster chain disaster", and "Luanchuan". Analyze the latest safety evaluation report and historical disaster situation report of debris flow, and adjust the disaster event set of Shishugou in Luanchuan. Combine event tree analysis and expert investigation method to add, delete, and modify disaster events to form the identification result of disaster events. Analyze the mutual relationship and spatio-temporal distribution characteristics between various disaster events, identify the key disaster events and potential propagation paths involved in the disaster chain, and roughly determine the propagation path of the disaster chain.

[0041] Step (2): Based on DEM, rainfall, and underlying surface data, establish a debris flow numerical model, simulate the debris flow discharge, mud depth, and influence range under different rainfall frequencies, and divide the danger level by combining the mud depth and flow velocity.

[0042] In a specific embodiment, step (2) includes analyzing the water flow direction and catchment area of the DEM data to determine the location of the debris flow catchment point.

[0043] In a specific embodiment, to determine the location of the debris flow catchment point, the specific steps include filling in the sunken areas in the DEM, calculating the flow direction, generating a flow path map and a cumulative flow map, and then analyzing the location of the catchment area in combination with the spatial distribution of the debris flow source.

[0044] Specifically, rainfall data, underlying surface data (such as soil type, land use, vegetation cover, etc.), and high-resolution digital elevation model (DEM) data during debris flow disasters in the study area are collected. Using the hydrological analysis tools in geographic information systems, the DEM data is analyzed for water flow direction and catchment area to determine the location of debris flow catchment points. The specific steps include filling in the depressions in the DEM, calculating the flow direction, generating a flow path map and an accumulated flow map. On this basis, combined with the spatial distribution of debris flow sources, the location of the catchment area is analyzed. The catchment points are usually located in the concentrated areas of debris flow sources and are also the zones where rainfall converges intensively, providing model inputs as the starting points of debris flows.

[0045] Step (3): According to the data collection and summary results in step (1) and the numerical simulation in step (2), abstract the disaster events as nodes and the propagation relationships as the connecting edges between nodes to construct a debris flow disaster chain network.

[0046] Specifically, the debris flow numerical simulation model uses the continuity equation and the momentum equation to obtain the flow velocity and mud depth of the debris flow fluid.

[0047]

[0048] In the formula: i is the rainfall intensity, mm / h; h is the fluid depth, m; t is the rainfall duration in the debris flow basin, h; λ and θ are the average flow velocities in the x-axis and y-axis directions respectively, m / s; Sax and Say are the debris flow gully bed slopes in the x and y directions respectively, %; Sfx and Sfy are the friction slopes in the x and y directions of the debris flow gully respectively, %. The results of Sax - Sfx and Say - Sfy respectively represent the net driving forces of the debris flow in the x and y directions.

[0049] Rheological equation:

[0050]

[0051] In the formula: Sf is the friction slope (the relationship between Sf and Sfx, Sfy), %; Sy is the yield slope, %; Sv is the viscous slope, %; Std is the turbulent dispersion slope, %; τy is the yield stress, MPa; ym is the fluid specific gravity, t / m 3 ; k is the laminar resistance coefficient; h is the mud depth; η is the fluid viscosity coefficient; n is the Manning coefficient; v is the fluid velocity, m / s, where

[0052]

[0053] In the formula: β1 and β2 are rheological parameters.

[0054] In a specific embodiment, in step (2), the debris flow numerical model adopts the continuity equation and the momentum equation, and the parameters mainly include the volume concentration Cv, Bingham yield stress (τ), Bingham viscosity coefficient (η), Manning coefficient (n), laminar flow retardation coefficient (K), and the ratio of soil to rock of the debris flow (Gs).

[0055] Specifically, various parameters required in the numerical simulation are determined. According to the field survey data and combined with the user manual of the simulation software, values of different parameters that conform to the actual situation of the study area are selected, mainly including the volume concentration Cv, Bingham yield stress (τ), Bingham viscosity coefficient (η), Manning coefficient (n), laminar flow retardation coefficient (K), and the ratio of soil to rock of the debris flow (Gs), etc. According to the isohyet maps of rainstorms with different time periods and frequencies in Henan, the runoff hydrograph of Shishugou clear water with rainfall frequencies of 2%, 1%, and 0.5% is calculated by the rain-flood method. The debris flow discharge is calculated by multiplying the clear water discharge by the amplification factor BF, where BF = 1 / (1 - Cv). The one-time outwash volume QT of the debris flow under different rainfall frequencies is 0.264TQc, where T is the duration of the debris flow in seconds (s), and Qc is the debris flow discharge in cubic meters per second (m 3 / s). The generalized hydrograph of the debris flow under different rainfall frequencies is obtained by using the pentagon generalization model. The specific values of the parameters are shown in Table 1.

[0056] Table 1 Debris flow parameter values

[0057]

[0058] In a specific embodiment, in step (2), the danger level of the simulation result is divided according to the relationship between the mud depth and flow velocity of the debris flow and the recurrence period.

[0059] Specifically, the intensity classification is carried out according to the mud depth and the relationship between the product of the mud depth and velocity of the debris flow with a recurrence period of 50-year return period, 100-year return period, and 200-year return period. The debris flow intensity and the recurrence period are combined to carry out the risk division. Referring to the standard of the influence of debris flow intensity and the risk zoning as shown in Table 2, in the geographic information system software, the raster calculation tool is used to make the relevant data operate to obtain the superimposed raster data, and then through the reclassification tool, the attributes of low risk, medium risk, and high risk are respectively assigned values, and the risk zoning of the Shishugou watershed can be obtained.

[0060] Table 2 Debris flow intensity classification table

[0061]

[0062] In a specific embodiment, in step (3), for the debris flow disaster chain network constructed, the weight value of the edge is dynamically assigned according to the danger level: high danger area = 2, medium danger area = 1, low danger area = 0.5.

[0063] Specifically, according to the risk assessment results simulated by FLO-2D, corresponding to the characteristic values of vulnerability levels (high vulnerability, medium vulnerability, low vulnerability), the weight values of all edges are converted into numerical values between 0 and 2, where "high vulnerability" corresponds to 2, "medium vulnerability" corresponds to 1, and "low vulnerability" corresponds to 0.5. According to the numerical simulation results and combined with the previous data collection, the disaster events in the model are abstracted as nodes, and the connection relationships between disaster events are abstracted as connecting edges between nodes. The disaster chain system is networked to establish a debris flow disaster network model.

[0064] Step (4): Evaluate the importance of different nodes in the disaster chain network by calculating the degree centrality, betweenness centrality, and closeness centrality of the nodes.

[0065] Specifically, analyze the disaster chain network structure. First, identify the key nodes in the disaster chain.

[0066] Degree centrality is the most direct indicator reflecting the centrality of nodes in network analysis. The calculation formula is:

[0067]

[0068] Among them, C D (i) is the degree centrality value of disaster event i, N is the number of disaster events in the debris flow disaster chain, d in (i) and d out (i) are the in-degree and out-degree of disaster event i respectively. Degree centrality combines the in-degree and out-degree. A disaster event with a high degree centrality value indicates that it plays an important role in the debris flow disaster chain.

[0069] Betweenness centrality is a metric used to measure the control and restriction of a node on other nodes in the network. The betweenness centrality calculation formula is as follows:

[0070]

[0071] Among them, C B (i) is the betweenness centrality value of i, n(s, t) is the number of shortest paths between disaster event nodes s and t, n(s, t|i) is the number of shortest paths between disaster event nodes s and t passing through i, and i is the set of disaster events in the debris flow disaster chain. A disaster event with a relatively high betweenness centrality value means that it is more important as a bridge in the network. If a disaster event can be effectively controlled, the connections between disaster events will be quickly suppressed or even blocked.

[0072] Closeness centrality is an indicator to measure the ability of a node to influence other nodes in the network through the network, indicating the closeness between this node and other nodes. The closeness centrality calculation formula is as follows:

[0073]

[0074] Among them, N in (i) is the in-degree of node i, and N out (i) refers to the out-degree of node i. L(j, i) is the distance from other nodes to node i, and L(i, j) is the distance from node i to the connected nodes. The larger the closeness centrality value, the shorter the average shortest distance between the node and other nodes. Similar to degree centrality, both inward and outward distances should be considered. A disaster event with a high closeness centrality value indicates that it plays an important role in risk propagation.

[0075] Calculate the three centrality indicators of the debris flow disaster chain network nodes at rainfall frequencies of 2%, 1%, and 0.5% respectively, identify important disaster events from different perspectives, and evaluate the importance of different nodes in the disaster chain network.

[0076] Secondly, identify the key edges. Using betweenness as the evaluation index, the larger the betweenness, the wider the influence range caused by the causal relationship between the two disaster events represented by this edge, and the greater the vulnerability of this edge.

[0077] Step (5): For the network connection edges, identify the key edges by analyzing the network vulnerability and network efficiency.

[0078] Specifically, analyze the network vulnerability and network efficiency. The network vulnerability represents the degree of decline in the overall performance of the network when facing external interference, node or edge loss. The network vulnerability index needs to comprehensively consider the betweenness, average path length, and connectivity of the complex network. Network efficiency refers to the effective degree of information transfer between nodes in the network, reflecting the overall transmission speed of the network, and it reflects the ability of the network to respond quickly when suffering from disturbances or damages.

[0079] The calculation formula for the network vulnerability is as follows,

[0080]

[0081] In the formula: V j→i is the vulnerability and betweenness of the network after removing the connection edge between disaster j and disaster i, B j→i is the betweenness of the edge j→i, L and H are the average path length and network connectivity after removing this edge respectively, d ij is the shortest path distance between node i and node j, N j→i is the number of nodes that can be connected starting from the starting node after removing the edge j→i, and N is the total number of nodes in the network.

[0082] Network efficiency reflects the ease of connection between nodes in the entire network, and the calculation formula is:

[0083]

[0084] Where: N is the total number of initial network nodes; is the shortest path length from node i to node j considering the edge weights. If there is no shortest path between two nodes, the length is expressed as infinity, and the reciprocal of the shortest path between corresponding node pairs is 0. The greater the efficiency value of the disaster chain network, the faster the disaster event spreads in the network.

[0085] Step (6): Based on the analysis results of the disaster chain network structure in steps (4) and (5), evaluate the network vulnerability and efficiency. Develop a chain-breaking strategy by deleting key nodes or edges, calculate the change in network density to verify the disaster reduction effect, and quantitatively analyze and discuss the chain-breaking strategy and its disaster reduction effect.

[0086] Specifically, find out the events with greater influence through the above analysis of nodes and edges, and accordingly develop a chain-breaking disaster reduction strategy to mitigate the spread of the disaster chain and its disaster impact. By deleting different key nodes and key edges, calculate the network density of the newly generated complex network, and compare it with the original network density. A decrease in network density indicates a reduction in the propagation path, that is, the chain-breaking effect is more significant. Network density reflects the ratio between the actual number of edges in the network and all possible numbers of edges.

[0087]

[0088] Where: NetD represents the network density, M represents the actual number of edges in the complex network, and N represents the total number of network nodes. N(N - 1) represents the number of edges in a directed complete graph with N nodes.

[0089] Analyze the influence of different chain-breaking strategies on network density, and judge which strategy has the best inhibitory effect on disaster propagation under different rainfall frequencies. Find the most effective chain-breaking scheme, provide a scientific basis for disaster prevention and mitigation decision-making, and form an optimization strategy for key nodes and edges of the disaster chain to minimize the impact of the disaster chain on regional security.

[0090] Through the collaborative cooperation of the above-mentioned various components, the present invention provides a more comprehensive and dynamic assessment method for debris flow disaster chains induced by rainstorm floods by combining numerical simulation and complex network analysis, breaking through the limitations of single disaster chain analysis and static assessment existing in the prior art. Compared with traditional methods, the present invention can comprehensively consider the propagation process, interaction and superposition effect of multiple disaster chains, and overcome the deficiencies of the prior art in dealing with complex disaster chains. Through numerical simulation, the formation and propagation process of debris flow can be accurately simulated, and further the diffusion range and influence degree of the disaster chain can be determined. In addition, the present invention adopts the analysis method of complex network, which can identify the key nodes and vulnerable edges in the disaster chain, so as to provide a quantitative basis for disaster prevention and control and emergency management. It can not only identify high-risk areas and vulnerable nodes, but also evaluate the vulnerability and network efficiency of the disaster chain network, comprehensively analyze the propagation path, node importance and influence degree of the disaster chain, and adopt targeted disaster chain breaking strategies for different rainfall intensities, which can effectively reduce the network density and control the spread of chain disasters. This dynamic assessment and monitoring ability makes the disaster prevention and mitigation strategies more scientific and reasonable, and can effectively guide disaster prevention decision-making and emergency response. In summary, the technical solution of the present invention has significant advantages and broad application prospects in improving the accuracy of disaster chain assessment, enhancing the emergency response ability and providing more accurate disaster prevention and mitigation strategies, and can provide strong technical support for the comprehensive prevention and control of various disasters.

[0091] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0092] Although the embodiments of the present application have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing debris flow disaster chains by combining numerical simulation and complex networks, characterized in that, It includes the following steps: Step (1): Collect historical data of rainstorms, floods and debris flows in the research area, extract the associated information of the disaster chain, and preliminarily determine the propagation path of the disaster chain; Step (2): Based on DEM, rainfall and underlying surface data, establish a debris flow numerical model, simulate the debris flow discharge, mud depth and influence range under different rainfall frequencies, and divide the danger levels by combining the mud depth and flow velocity; Step (3): According to the data collection and summary results of Step (1) and the numerical simulation of Step (2), abstract the disaster events as nodes and the propagation relationships as the connecting edges between nodes to construct a debris flow disaster chain network; Step (4): Evaluate the importance of different nodes in the disaster chain network by calculating the degree centrality, betweenness centrality and closeness centrality of the nodes; Step (5): For the network connecting edges, identify the key edges by analyzing the network vulnerability and network efficiency; Step (6): Through the analysis results of the disaster chain network structure in Step (4) and Step (5), evaluate the network vulnerability and efficiency, formulate a chain-breaking strategy by deleting key nodes or edges, calculate the change in network density to verify the disaster reduction effect, and quantitatively analyze and discuss the chain-breaking strategy and its disaster reduction effect.

2. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that The relevant data obtained in Step (1) include meteorological data, topographic and geomorphic data, hydrogeological data and historical disaster data.

3. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that Step (2) includes analyzing the water flow direction and catchment area of the DEM data to determine the location of the debris flow catchment point.

4. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 3, characterized in that The specific steps for determining the location of the debris flow catchment point include filling the depression area in the DEM, calculating the flow direction, generating a flow path map and a cumulative flow map, and then analyzing the location of the catchment area in combination with the spatial distribution of the debris flow source materials.

5. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that The debris flow numerical model in Step (2) adopts the continuity equation and the momentum equation, and the parameters mainly include the volume concentration Cv, Bingham yield stress (τ), Bingham viscosity coefficient (η), Manning coefficient (n), laminar flow retardation coefficient (K), and the soil-rock ratio (Gs) of the debris flow.

6. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that The danger levels of the simulation results in Step (2) are divided according to the relationship between the mud depth and flow velocity of the debris flow and the recurrence period.

7. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that For the debris flow disaster chain network constructed in Step (3), the weights of the edges are dynamically assigned according to the danger levels: high danger area = 2, medium danger area = 1, low danger area = 0.

5.

8. The debris flow disaster chain analysis method combining numerical simulation and complex network according to claim 1, characterized in that The chain-breaking strategy in Step (6) is realized by removing key nodes or edges in the network model. By deleting different key nodes and key edges, calculate the density of the newly generated network, and compare it with the original network density. A decrease in network density indicates a reduction in the propagation path, that is, the chain-breaking effect is significant; The network density calculation formula is as follows: Where: M is the actual number of edges, and N is the total number of nodes.

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