Extreme rainfall flood risk propagation path identification method based on hydrological connectivity

Abstract

The invention discloses an extreme rainfall flood risk propagation path identification method based on hydrological connectivity. The method comprises the following steps: constructing a hydrological topology network; calculating the runoff yield and hydrological potential energy of each grid unit; constructing a connectivity weight matrix according to the potential energy difference and the Manning roughness coefficient; a dynamic connectivity adjacency matrix is generated through a ponding depth triggering threshold value; constructing a risk propagation probability transfer matrix; and identifying a risk propagation main path and a key conduction node. According to the method, hydrological connectivity changes under extreme rainfall conditions can be dynamically represented, flood risk propagation paths and key nodes are effectively identified, and technical support is provided for urban flood disaster risk early warning and emergency management.

Description

Technical Field

[0001] This invention relates to the field of urban flood disaster risk assessment and management technology, and in particular to a method for identifying the propagation path of extreme rainfall flood risks based on hydrological connectivity. Background Technology

[0002] Traditional flood risk assessment methods primarily rely on hydrological and hydrodynamic models to simulate inundation extent. However, these methods are computationally inefficient and struggle to respond in real-time to extreme rainfall events. Furthermore, existing methods often focus on the inundation depth and extent at single points, lacking a systematic analysis of the spatial propagation paths of flood risk and failing to identify key nodes in risk transmission. While network analysis-based methods can describe the connectivity between spatial units, existing studies mostly employ static connectivity representations, failing to adequately consider the dynamic changes in water accumulation during rainfall on connectivity. In addition, the hindering effect of underlying surface features such as surface roughness on water flow propagation is not fully reflected.

[0003] Therefore, there is an urgent need to develop a method that can dynamically characterize hydrological connectivity and quickly identify risk propagation paths and key transmission nodes, so as to provide technical support for risk early warning and emergency management of urban flood disasters. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying the propagation path of extreme rainfall and flood risks based on hydrological connectivity, so as to solve the technical problems in the existing flood risk assessment methods that are difficult to dynamically characterize hydrological connectivity and cannot effectively identify risk propagation paths and key transmission nodes.

[0005] To achieve the above objectives, this invention provides a method for identifying the propagation path of extreme rainfall-flood risks based on hydrological connectivity, comprising the following steps: Step S1: Obtain topographic data and water system distribution data of the study area, divide the study area into regular grid units, and construct a hydrological topology network with each grid unit as a node and the hydraulic connection between adjacent grid units as an edge.

[0006] Step S2: Calculate the runoff of each grid cell based on the extreme rainfall scenario, and calculate the hydrological potential energy of the grid cell based on the runoff. The hydrological potential energy is the sum of the topographic elevation and the water depth.

[0007] Step S3: Calculate the hydrological potential energy difference between adjacent grid cells, and construct a potential energy-driven connectivity weight matrix based on the potential energy difference and Manning roughness coefficient.

[0008] Step S4: Set a water depth trigger threshold. When the water depth of a grid cell exceeds the trigger threshold, activate the external connectivity edge of that cell and generate a dynamic connectivity adjacency matrix.

[0009] Step S5: Calculate the risk propagation probability between adjacent grid cells based on the dynamic connectivity adjacency matrix, and construct the risk propagation probability transition matrix based on the risk propagation probability.

[0010] Step S6: Traverse the risk propagation probability transition matrix to identify the main risk propagation path from high-risk source points to low-lying convergence areas, and calculate the path betweenness of each grid cell to identify key risk transmission nodes.

[0011] Furthermore, the method for calculating the hydrological potential energy is as follows: for grid cells... Its output flow The calculation is as follows: ;in, Extreme rainfall intensity, For grid cells The overall infiltration rate For grid cells area, This is the calculation period.

[0012] Grid cells Rainfall duration Water volume at any time Water balance calculation: ;in, This represents the water volume at the previous moment. For outflow, This refers to the inflow.

[0013] Then the grid cell hydrological potential energy for: ;in, For grid cells Surface elevation, This refers to the depth of the accumulated water.

[0014] Furthermore, grid cells Comprehensive infiltration rate The calculation method is as follows: based on the mesh element Determining the initial infiltration rate based on land use type and stable infiltration rate The Horton infiltration model was used to calculate the rainfall duration. Grid cell at time Time-varying infiltration rate : ;in, The infiltration attenuation coefficient; the overall infiltration rate. Take the average time-varying infiltration rate within the calculation period: .

[0015] Furthermore, the connectivity weight matrix is ​​constructed by calculating the values ​​of adjacent grid cells. and The difference in hydrological potential energy between them: ;when When, it indicates the existence of a unit. To unit The driving force of water flow.

[0016] Constructing the connectivity weight matrix Computing unit With unit Connectivity weights between ,when hour, ;in, For grid cells and The distance between the center points For unit and The overall roughness coefficient between; when When, take the calculated value above. .

[0017] Furthermore, the comprehensive roughness coefficient The calculation method is as follows: based on the mesh element and The Manning roughness coefficient was determined for each land use type. and The roughness coefficients for different land use types are as follows: water bodies 0.025~0.035, roads 0.011~0.020, bare land 0.020~0.035, grassland 0.030~0.050, forest land 0.100~0.200, and building areas 0.012~0.025.

[0018] The comprehensive roughness coefficient Take grid cells and The average value of the Manning roughness coefficient is used to characterize the hindering effect of surface resistance characteristics on water flow propagation.

[0019] Furthermore, the method for generating the dynamic connectivity adjacency matrix is ​​as follows: define grid cells. Activation state function : ,in, Set a water depth threshold; generate a dynamic connectivity adjacency matrix. Its elements for: The dynamic connectivity adjacency matrix It represents the effective connectivity between grid cells under the current water accumulation state.

[0020] Furthermore, the water depth trigger threshold The method for determining the threshold is as follows: set the water depth trigger threshold based on the topographic features and drainage capacity of the study area. The value ranges from 0.05 to 0.30m.

[0021] For urban built-up areas The value is 0.05~0.15m; for suburban and agricultural land, The value is 0.10~0.20m; for mountainous and hilly areas, The value ranges from 0.15 to 0.30 m.

[0022] Furthermore, the method for constructing the risk propagation probability transition matrix is ​​as follows: for the grid cells in the active state... Calculate its direction to adjacent units Risk transmission probability : ;in, To be compatible with grid cells The set of all adjacent grid cells.

[0023] Probability of all risk propagation Constructing a risk propagation probability transition matrix ,matrix Satisfy row normalization conditions: .

[0024] Furthermore, the method for identifying the main risk transmission path is as follows: [The text abruptly shifts to a different topic] ...the risk transmission probability transition matrix... Convert to path cost matrix Its elements are: For a given source point Hehuidian The shortest path algorithm is used to find the path with the minimum cumulative cost. : ; This represents all possible paths from the source node s to the sink node d.

[0025] The minimum cumulative cost path That is, from the source point to the remittance point The main risk transmission path, and its corresponding transmission probability are: .

[0026] Furthermore, the method for identifying the key risk transmission nodes is as follows: for grid cells Calculate its path betweenness : ;in, To start from the source to the remittance point The number of all main risk transmission paths, For passing through grid cells The number of main pathways for risk transmission.

[0027] The criticality index is obtained by normalizing the path betweenness. : ,in, and These are the maximum and minimum values ​​of the path betweenness of all grid cells, respectively.

[0028] when Greater than the preset threshold At that time, determine the grid cell As a key risk transmission node, the preset threshold The value range is 0.6 to 0.9.

[0029] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a dynamic connectivity network based on hydrological potential energy, which can reflect the changes in hydraulic connections between grid cells in real time under extreme rainfall conditions. It employs a water depth trigger threshold mechanism to dynamically activate connected edges, avoiding the oversimplification of the actual water flow propagation process by static connectivity methods. It utilizes probability transition matrices and shortest path algorithms to identify the main risk propagation paths, providing a quantitative tool for analyzing the spatial transmission mechanism of flood risk. Furthermore, it identifies key risk transmission nodes through path betweenness calculations, providing a scientific basis for flood control engineering layout and emergency resource allocation. This method boasts high computational efficiency and a clear physical mechanism, offering effective technical support for urban flood disaster risk early warning and emergency management. Attached Figure Description

[0030] Figure 1 This is a flowchart of a method for identifying the propagation path of extreme rainfall and flood risks based on hydrological connectivity, according to the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of this invention, not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0032] It should be noted that the method described in this invention is applicable to research areas of different scales, including flood risk analysis at the urban built-up area, watershed scale, and regional scale.

[0033] The grid cell division accuracy can be adjusted according to the characteristics of the study area and computing resources. Generally, urban areas use a grid accuracy of 10m×10m to 50m×50m, while watershed scales can use a grid accuracy of 100m×100m to 500m×500m. The extreme rainfall scenarios in this invention can be determined based on statistical analysis of historical rainfall data, or future extreme rainfall scenarios predicted by climate models can be used.

[0034] In this invention, the setting of extreme rainfall scenarios is determined by the return period method. Common design return periods include 5 years, 10 years, 20 years, 50 years and 100 years. The longer the return period, the greater the corresponding extreme rainfall intensity. For example, the design rainfall intensity of a city during a 50-year return period is 85 mm / h, and the design rainfall intensity of a city during a 100-year return period is 105 mm / h.

[0035] Example like Figure 1 The diagram shown is a flowchart of a method for identifying the propagation path of extreme rainfall and flood risks based on hydrological connectivity, according to the present invention, which includes the following steps: Step S1: Obtain topographic data and water system distribution data of the study area, divide the study area into regular grid units, and construct a hydrological topology network with each grid unit as a node and the hydraulic connection between adjacent grid units as an edge.

[0036] The study area, approximately 25 km², is the central urban area of ​​a city. Digital elevation model (DEM) data with a spatial resolution of 30 m is used. The study area is divided into regular 30 m × 30 m grid cells, totaling approximately 27,778 grid cells. For any grid cell i, its adjacent cells include those in the top, bottom, left, and right directions, as well as those in the four diagonal directions, for a maximum of eight adjacent cells. When a grid cell is located at the boundary of the study area, the number of adjacent cells decreases accordingly. In the hydrological topology network, there are a total of 27,778 nodes. The number of edges is determined based on adjacency relationships; internal grid cells have eight connecting edges, while boundary grid cells have three to five connecting edges.

[0037] Step S2: Calculate the runoff of each grid cell based on the extreme rainfall scenario, and calculate the hydrological potential energy of the grid cell based on the runoff. The hydrological potential energy is the sum of the topographic elevation and the water depth.

[0038] The method for calculating the hydrological potential energy is as follows: for grid cells Its output flow The calculation is as follows: ;in, Extreme rainfall intensity, For grid cells The overall infiltration rate For grid cells area, This is the calculation period.

[0039] Grid cells Rainfall duration Water volume at any time Water balance calculation: ;in, This represents the water volume at the previous moment. For outflow, This refers to the inflow.

[0040] Then the grid cell hydrological potential energy for: ;in, For grid cells Surface elevation, This refers to the depth of the accumulated water.

[0041] Assume the land use type of the grid cell is an urban building area, with an area of ​​A. i =900m² (i.e., 30m × 30m). Extreme rainfall intensity h, overall infiltration rate f i =5mm / h, calculation period Δt=0.5h, then the flow rate of this grid cell is: R i =(80-5)×900×0.5=33,750L=33.75m³.

[0042] Grid cells Comprehensive infiltration rate The calculation method is as follows: based on the mesh element Determining the initial infiltration rate based on land use type and stable infiltration rate The Horton infiltration model was used to calculate the rainfall duration. Grid cell at time Time-varying infiltration rate : ;in, The infiltration attenuation coefficient; the overall infiltration rate. Take the average time-varying infiltration rate within the calculation period: .

[0043] Taking urban built-up areas as an example, the initial infiltration rate is taken as 15 mm / h, the stable infiltration rate is taken as 3 mm / h, and the infiltration attenuation coefficient is taken as 0.5 h. -1 When the rainfall duration is t=1h, the time-varying infiltration rate f i(1) = 10.28 mm / h. When the rainfall duration t = 2 h, the time-varying infiltration rate f i (2) = 7.42 mm / h; As rainfall continues, the infiltration rate gradually approaches the stable infiltration rate.

[0044] Step S3: Calculate the hydrological potential energy difference between adjacent grid cells, and construct a potential energy-driven connectivity weight matrix based on the potential energy difference and Manning roughness coefficient.

[0045] The connectivity weight matrix is ​​constructed by calculating the values ​​of adjacent grid cells. and The difference in hydrological potential energy between them: ;when When, it indicates the existence of a unit. To unit The driving force of water flow.

[0046] Constructing the connectivity weight matrix Computing unit With unit Connectivity weights between ,when hour, ;in, For grid cells and The distance between the center points For unit and The overall roughness coefficient between; when When, take the calculated value above. .

[0047] Assume the surface elevation E of a certain grid cell i =15.6m, current water volume V i If (t) = 180 m³, then the depth of the accumulated water is V. i (t) / A i =180 / 900=0.2m, hydrological potential energy is Φ i =15.6+0.2=15.8m.

[0048] If the surface elevation E of adjacent grid cell j j =15.2m, the water depth is 0.15m, then its hydrological potential energy Φ j =15.2+0.15=15.35m; the hydrological potential energy difference ΔΦ between the two grid cells ij =15.8-15.35=0.45m, indicating that there is a water flow driving force from unit i to unit j.

[0049] The comprehensive roughness coefficient The calculation method is as follows: based on the mesh element and The Manning roughness coefficient was determined for each land use type. and The roughness coefficients for different land use types are as follows: water bodies 0.025~0.035, roads 0.011~0.020, bare land 0.020~0.035, grassland 0.030~0.050, forest land 0.100~0.200, and building areas 0.012~0.025.

[0050] The comprehensive roughness coefficient Take grid cells and The average roughness coefficient of Manning is used to characterize the hindering effect of surface resistance characteristics on water flow propagation; an example of the comprehensive roughness coefficient for different combinations of land use types is given: when roads are adjacent to each other, The value is approximately 0.015; when the building area is adjacent to the road, n ij The value is approximately 0.017; when grassland and woodland are adjacent, n ij The value is approximately 0.115; when a water body is adjacent to bare land, The value is approximately 0.028; the larger the roughness coefficient, the stronger the obstruction effect of the ground surface on water flow, and the slower the water flow propagation speed.

[0051] If the distance d between the center points of the two units ij =30m (horizontally or vertically adjacent) or d ij =42.43m (diagonally adjacent); Assuming unit For the building area, the Manning roughness coefficient n i =0.015, unit For roads, the Manning roughness coefficient n j =0.013, then the comprehensive roughness coefficient =(0.015+0.013) / 2=0.014. For the case of horizontal adjacency, the connectivity weight W... ij =0.45 / (30×0.014)=1.071m -1 .

[0052] Step S4: Set a water depth trigger threshold. When the water depth of a grid cell exceeds the trigger threshold, activate the external connectivity edge of that cell and generate a dynamic connectivity adjacency matrix.

[0053] The method for generating the dynamic connectivity adjacency matrix is ​​as follows: Define the grid cell. Activation state function : ,in, Set a water depth threshold; generate a dynamic connectivity adjacency matrix. Its elements for: The dynamic connectivity adjacency matrix It represents the effective connectivity between grid cells under the current water accumulation state.

[0054] The water depth trigger threshold The method for determining the threshold is as follows: set the water depth trigger threshold based on the topographic features and drainage capacity of the study area. The value ranges from 0.05 to 0.30m.

[0055] For urban built-up areas The threshold value is set between 0.05 and 0.15 m. For newly developed urban areas with strong drainage capacity, a lower threshold (e.g., 0.05 m) can be used to improve the model's response sensitivity to initial water accumulation. For older urban areas with weak drainage capacity, a higher threshold (e.g., 0.15 m) can be used to avoid frequent false positive activations. For suburban areas and agricultural land, The value is 0.10~0.20m; for mountainous and hilly areas, The value ranges from 0.15 to 0.30 m.

[0056] Set the water depth trigger threshold H th =0.10m, when the water depth V of grid cell i is... i (t) / A i When =0.20m≥0.10m, activate the state function. =1, all externally connected edges of this cell are activated; when adjacent mesh cells... water depth When =0.08m < 0.10m, activate the state function. All external connected edges of this cell are inactive; therefore, the dynamic connectivity adjacency matrix elements are... ,and This reflects the directionality of risk transmission.

[0057] Step S5: Calculate the risk propagation probability between adjacent grid cells based on the dynamic connectivity adjacency matrix, and construct the risk propagation probability transition matrix based on the risk propagation probability.

[0058] The method for constructing the risk propagation probability transition matrix is ​​as follows: for the grid cells in the active state... Calculate its direction to adjacent units Risk transmission probability : ;in, To be compatible with grid cells The set of all adjacent grid cells.

[0059] Assuming unit i is in an active state, the dynamic connectivity weights of its four adjacent units (up, down, left, and right directions) are G respectively. i1 =1.2, G i2 =0.8, G i3 =0.6, G i4 =0.4, then the sum of weights ΣG ik =1.2 + 0.8 + 0.6 + 0.4 = 3.0. The probabilities of risk propagation in each direction are: T i1 =1.2 / 3.0=0.40, T i2 =0.8 / 3.0=0.267, T i3 =0.6 / 3.0=0.20, T i4 =0.4 / 3.0=0.133. This shows that flood risk is more likely to propagate in directions with larger potential energy differences and lower roughness.

[0060] Probability of all risk propagation Constructing a risk propagation probability transition matrix ,matrix Satisfy row normalization conditions: The physical meaning of the risk propagation probability transition matrix T is to describe the probability distribution of flood risk propagating from one grid cell to its neighboring cells. The row normalization condition ensures that the sum of the risk propagation probabilities starting from any active cell is 1, which meets the basic requirements of probability theory. For inactive grid cells, all elements in their corresponding rows are 0, indicating that the cell does not propagate risk outward.

[0061] Step S6: Traverse the risk propagation probability transition matrix to identify the main risk propagation path from high-risk source points to low-lying convergence areas, and calculate the path betweenness of each grid cell to identify key risk transmission nodes.

[0062] The method for identifying the main risk propagation path is as follows: [This involves] analyzing the risk propagation probability transition matrix. Convert to path cost matrix Its elements are: For a given source point Hehuidian The shortest path algorithm is used to find the path with the minimum cumulative cost. : ; This represents all possible paths from the source node s to the sink node d.

[0063] The path cost matrix is ​​constructed using a negative logarithmic transformation, converting the probability product problem into a cost summation problem; for example, if T ij =0.4, then C ij =-ln(0.4)=0.916; if Tjk =0.5, then C jk =-ln(0.5)=0.693; The cumulative cost from unit i to unit k via unit j is C. ij +C jk =0.916 + 0.693 = 1.609, and the corresponding propagation probability is exp(-1.609) = 0.4 × 0.5 = 0.2. This transformation allows for the efficient solution of the main path of risk propagation using classic shortest path algorithms such as Dijkstra's algorithm.

[0064] The minimum cumulative cost path That is, from the source point to the remittance point The main risk transmission path, and its corresponding transmission probability are: The process of identifying the main risk propagation path is illustrated using a simplified 5×5 grid area as an example. Assume the source point *s* is located on high ground in the northwest corner of the area, and the sink point *d* is located in a low-lying area in the southeast corner. By calculating the cumulative cost of all possible paths, the grid sequence traversed by the path with the minimum cumulative cost is identified as s→A→B→C→D→d, with a corresponding cumulative cost of 2.35 and a propagation probability of exp(-2.35) = 0.095. This path represents the main channel through which flood risk propagates from high ground to low-lying areas.

[0065] The method for identifying the key risk transmission nodes is as follows: for grid cells Calculate its path betweenness : ;in, To start from the source to the remittance point The number of all main risk transmission paths, For passing through grid cells The number of main pathways for risk transmission.

[0066] The criticality index is obtained by normalizing the path betweenness. : ,in, and These are the maximum and minimum values ​​of the path betweenness of all grid cells, respectively.

[0067] when Greater than the preset threshold At that time, determine the grid cell As a key risk transmission node, the preset threshold The value range is 0.6 to 0.9.

[0068] Path betweenness reflects the pivotal role of a grid cell in a risk propagation network. Taking a grid cell i in the study area as an example, assuming the study area has 10 source points and 5 sink points, forming 50 source-sink pairs; calculations show that 35 main risk propagation paths pass through cell i, then the path betweenness B of this cell is... i =35 / 50=0.7. If the maximum value of the path betweenness in all grid cells is B... max =0.85, minimum value B min =0, then the keyness index K of unit i is 0. i =(0.7-0) / (0.85-0)=0.824. When the preset threshold K... th When K = 0.7, because K i =0.824>0.7, therefore, unit i is a key risk transmission node.

[0069] When K th When a lower value is taken (e.g., 0.6), a larger number of key nodes are identified, which is suitable for comprehensively investigating risk transmission channels; when K th When a higher value (such as 0.9) is used, fewer key nodes are identified, but their importance is higher, which is suitable for key prevention and control under conditions of limited resources.

[0070] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the propagation path of extreme rainfall flood risk based on hydrological connectivity, characterized in that, The method includes the following steps: Step S1: Obtain topographic data and water system distribution data of the study area, divide the study area into regular grid units, and construct a hydrological topology network with each grid unit as a node and the hydraulic connection between adjacent grid units as an edge. Step S2: Calculate the runoff of each grid cell based on the extreme rainfall scenario, and calculate the hydrological potential energy of the grid cell based on the runoff. The hydrological potential energy is the sum of the topographic elevation and the water depth. Step S3: Calculate the hydrological potential energy difference between adjacent grid cells, and construct a potential energy-driven connectivity weight matrix based on the potential energy difference and Manning roughness coefficient. Step S4: Set a water depth trigger threshold. When the water depth of a grid cell exceeds the trigger threshold, activate the external connectivity edge of that cell and generate a dynamic connectivity adjacency matrix. Step S5: Calculate the risk propagation probability between adjacent grid cells based on the dynamic connectivity adjacency matrix, and construct the risk propagation probability transition matrix based on the risk propagation probability. Step S6: Traverse the risk propagation probability transition matrix to identify the main risk propagation path from high-risk source points to low-lying convergence areas, and calculate the path betweenness of each grid cell to identify key risk transmission nodes.

2. The method according to claim 1, characterized in that, The method for calculating the hydrological potential energy is as follows: for grid cells Its output flow The calculation is as follows: ;in, Extreme rainfall intensity, For grid cells The overall infiltration rate For grid cells area, For the calculation period; Grid cells Rainfall duration Water volume at any time Water balance calculation: ;in, This represents the water volume at the previous moment. For outflow, Inflow; Then the grid cell hydrological potential energy for: ;in, For grid cells Surface elevation, This refers to the depth of the accumulated water.

3. The method according to claim 2, characterized in that, Grid cells Comprehensive infiltration rate The calculation method is as follows: based on the mesh element Determining the initial infiltration rate based on land use type and stable infiltration rate The Horton infiltration model was used to calculate the rainfall duration. Grid cell at time Time-varying infiltration rate : ;in, The infiltration attenuation coefficient; the overall infiltration rate. Take the average time-varying infiltration rate within the calculation period: .

4. The method according to claim 3, characterized in that, The connectivity weight matrix is ​​constructed by calculating the values ​​of adjacent grid cells. and The difference in hydrological potential energy between them: ;when When, it indicates the existence of a unit. To unit The driving force of water flow; Constructing the connectivity weight matrix Computing unit With unit Connectivity weights between ,when hour, ;in, For grid cells and The distance between the center points For unit and The overall roughness coefficient between; when When, take the calculated value above. .

5. The method according to claim 4, characterized in that, According to grid cells and The Manning roughness coefficient was determined for each land use type. and The roughness coefficients for different land use types are as follows: water bodies 0.025~0.035, roads 0.011~0.020, bare land 0.020~0.035, grassland 0.030~0.050, forest land 0.100~0.200, and built-up areas 0.012~0.

025. The comprehensive roughness coefficient Take grid cells and The average value of the Manning roughness coefficient is used to characterize the hindering effect of surface resistance characteristics on water flow propagation.

6. The method according to claim 5, characterized in that, The method for generating the dynamic connectivity adjacency matrix is ​​as follows: Define the grid cell. Activation state function : ,in, The threshold for triggering the water depth setting; Generate dynamic connectivity adjacency matrix Its elements for: The dynamic connectivity adjacency matrix It represents the effective connectivity between grid cells under the current water accumulation state.

7. The method according to claim 6, characterized in that, The water depth trigger threshold was set based on the topographic features and drainage capacity of the study area. The value ranges from 0.05 to 0.30 m; For urban built-up areas The value is 0.05~0.15m; for suburban and agricultural land, The value is 0.10~0.20m; for mountainous and hilly areas, The value ranges from 0.15 to 0.30 m.

8. The method according to claim 7, characterized in that, The method for constructing the risk propagation probability transition matrix is ​​as follows: for the grid cells in the active state... Calculate its direction to adjacent units Risk transmission probability : ;in, To be compatible with grid cells The set of all adjacent grid cells; Probability of all risk propagation Constructing a risk propagation probability transition matrix ,matrix Satisfy row normalization conditions: .

9. The method according to claim 8, characterized in that, The method for identifying the main risk propagation path is as follows: [This involves] analyzing the risk propagation probability transition matrix. Convert to path cost matrix Its elements are: For a given source point Hehuidian The shortest path algorithm is used to find the path with the minimum cumulative cost. : ; For all possible paths from source s to sink d; The minimum cumulative cost path That is, from the source point to the remittance point The main risk transmission path, and its corresponding transmission probability are: .

10. The method according to claim 9, characterized in that, The method for identifying the key risk transmission nodes is as follows: for grid cells Calculate its path betweenness : ;in, To start from the source to the remittance point The number of all main risk transmission paths, For passing through grid cells The number of main risk transmission pathways; The criticality index is obtained by normalizing the path betweenness. : ,in, and These are the maximum and minimum values ​​of the path betweenness of all grid cells, respectively; when Greater than the preset threshold At that time, determine the grid cell As a key risk transmission node, the preset threshold The value range is 0.6 to 0.9.

Images