A method for evaluating robustness of river network based on complex network theory

Abstract

The application discloses a river network robustness evaluation method based on a complex network theory, and collects and pre-processes terrain features, river network function indexes and river network vector data; calculates all function indexes of each river section in the river network, including flood control capacity indexes, water supply capacity indexes and ecological capacity indexes; constructs a river network complex network model G=(V, E) of a to-be-evaluated river basin; describes and quantifies topological structure features of the river network based on the river network complex network model; carries out node removal attack simulation on the river network complex network model, and observes the change trend of the overall robustness level of the river network complex network model subjected to the node removal attack, so that the evaluation indexes most obviously affecting the global robustness of the network are determined. The application is suitable for evaluating the structural stability, function evolution and resistance to various risk events of the river network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of river network evaluation and management, and particularly relates to a river network robustness evaluation method. BACKGROUND

[0002] Early river network research mainly relied on qualitative description. With the rise of quantitative analysis methods, Horton-Strahler system, Shreve-Smart random topology model, fractal theory and self-similarity theory were proposed in succession, providing quantitative tools for river network research. In recent years, the quantitative description of river network morphological characteristics has made continuous progress, trying to link river network characteristic parameters with geological structure, climate conditions and watershed factors. However, traditional researches mostly focus on river network in a specific period or digital river network extracted based on DEM, which has significant differences with the actual "natural + artificial" coupled river network, especially in plain areas, the accuracy of extracted river network is difficult to guarantee. The actual river network is influenced by multiple factors and presents a complex network structure rather than an ideal tree structure, which makes it difficult to accurately describe the quantitative relationship between large watershed topography and actual network structure. In addition, traditional indicators are usually based on static network topology, which cannot fully capture the dynamic changes and heterogeneity of the network, and the study of the dynamic response mechanism of nodes and river sections under external disturbance is also not mature.

[0003] In recent years, complex network theory has been widely applied to the study of various complex systems, providing a new perspective for understanding and analyzing river network systems. Complex network theory abstracts river network as a network composed of nodes (river junctions, reservoirs, water gates, etc.) and edges (river sections), and uses network topology indicators to quantify the structural characteristics and functions of river network. Some studies have applied complex network theory to river network analysis, such as studying the connectivity, hierarchy and small-world characteristics of river network. However, these studies mostly focus on static structural analysis, which can only reflect the topological characteristics of river network at a specific moment, and lack in-depth research on the functional evolution and dynamic robustness of river network. Existing studies on river network robustness also have some limitations: traditional robustness indicators are usually based on static network topology, ignoring the differences in river section functions, and cannot fully capture the dynamic changes and heterogeneity of the network, making it difficult to reflect the dynamic response mechanism of nodes and river sections under external disturbance; many studies only focus on the impact of a single risk event on river network, while in actual situations, river network often faces the superimposed influence of multiple risks, leading to the occurrence of cascading effects; there is a lack of systematic evaluation of the changes in river network robustness under artificial modification, and few studies have given the functions of river network to the edges of complex network, and then analyzed the contribution of different functional river sections to network robustness. SUMMARY

[0004] In order to make up for the deficiency of the prior art, the present application aims to provide a river network robustness evaluation method and system based on complex network theory, which uses a graph theory represented river network complex network model to simulate a variety of node removal attack strategies combining the design static attack and dynamic attack of the nodes in the dynamic / static river network complex network model, and realizes the evaluation of the river network robustness change under the natural and artificial transformation.

[0005] The present application realizes the following technical solutions:

[0006] The present application proposes a river network robustness evaluation method based on complex network theory, which comprises:

[0007] S1, collecting and preprocessing the terrain features, river network function indexes and river network vector data;

[0008] S2, performing river network function analysis based on the terrain features, river network function indexes and river network vector data, and calculating all the function indexes of each river section in the river network, including the flood control capacity index, water supply capacity index and ecological capacity index;

[0009] S3, constructing a river network complex network model G=(V,E) of the to-be-evaluated river basin based on the preprocessed river network vector data, wherein the node set V represents the source, intersection point and control engineering key position in the river section; the edge set E represents the river section as the node connection edge in the network; the input of the model is the river network vector data, the attribute data of the nodes and the flood control capacity index, water supply capacity index and ecological capacity index of each river section; and the output of the model is the abstract network topology, network calculation index and edge weight attribute of the river network represented by graph theory;

[0010] S4, describing and quantifying the topological structure characteristics of the river network based on the river network complex network model, specifically comprising: calculating the node and edge centrality indexes of the river network, such as the degree centrality, betweenness centrality and closeness centrality;

[0011] S5, performing node removal attack simulation on the river network complex network model based on the degree centrality, betweenness centrality and closeness centrality indexes, respectively, calculating the maximum connected subgraph proportion and network efficiency of the network after removing the river section, observing the change trend of the overall robustness level of the river network complex network model subjected to the node removal attack, and determining the evaluation index which has the most significant influence on the global robustness of the network.

[0012] In some embodiments, the S2 further comprises:

[0013] The flood control capacity index of each river section is calculated, and the expression is as follows:

[0014]

[0015] FD,i=a1F Ri + a2Fc,i

[0016] wherein Fc,i is the flood control capacity improvement index of the ith river reach, F D i is the flood control capacity index of the ith river reach, step is the path length of the river reach in the graph model from the upstream reservoir, As is the reservoir capacity of the upstream reservoir of the river reach, a1 and a2 are the weights of the flood discharge capacity and the flood control improvement index respectively; wherein the average flood discharge capacity index of each river reach is calculated, and the expression is as follows:

[0017] F R,i = Avg(Q1, Q2, Q3)

[0018] wherein A is the cross-section area, C is the Chezy coefficient, R is the hydraulic radius, J is the hydraulic slope, F R i is the average flood discharge capacity index of the ith river reach, i is the river reach number, Q1, Q2 and Q3 are the flow rates of the three cross-sections at the upstream, middle and downstream of the river reach respectively;

[0019] The water supply capacity index of each river reach is evaluated, and the expression is as follows:

[0020]

[0021] F S,i = a3E G +a4Z i +a5F c,i

[0022] wherein E G is the network efficiency index, e st is the efficiency index of nodes s and t in the network, d st is the path distance of nodes s and t in the network, N is the number of nodes of the graph model, Fc,i is the water supply improvement index of the ith river reach, Zi is the cultivated land area of the river reach of the ith river reach, Fs,i is the water supply capacity index of the ith river reach, i.e. the local water supply capacity index, a3 is the influence degree of the river network efficiency index on the water supply capacity index, a4 represents the influence degree of the cultivated land distribution on the water supply capacity index, and a5 is the influence degree of the water supply improvement index on the water supply capacity index;

[0023] The ecological capacity index of each river reach is represented by the functional connectivity, and the expression is as follows:

[0024]

[0025] wherein BC is the network betweenness centrality index, TWI is the terrain wetness index, TCI is the terrain connectivity index, β is the water network connectivity correction coefficient, F E i is the ecological capacity index of the ith river reach.

[0026] In some embodiments, the S4 further comprises:

[0027] The expression of the degree centrality of each node in the complex network model of river network is as follows:

[0028]

[0029] In the formula, k j is the degree of node j, N is the total number of nodes; node s, node t and node j are any three nodes in the network;

[0030] The expression of the betweenness centrality of each node in the complex network model of river network is as follows:

[0031]

[0032] In the formula, g st is the number of shortest paths from node s to node t, is the number of shortest paths from node s to node t through node j;

[0033] The expression of the closeness centrality of each node in the complex network model of river network is as follows:

[0034]

[0035] In the formula, N is the total number of nodes, d sj is the length of the shortest path between node s and node j.

[0036] In some embodiments, the removing attack in the S5 further comprises static attack and dynamic attack, and further comprises performing static attack analysis, identifying the node that will cause the largest connected subgraph proportion or the global efficiency to decrease significantly after removal by removing different nodes one by one and observing the changes of network indicators; performing dynamic attack analysis, sorting the nodes based on different centrality indicators and removing them step by step, and observing the change trend of the overall robustness level of the network to determine the evaluation indicator that has the most significant impact on the global robustness of the network.

[0037] In some embodiments, the S5 further comprises: based on the edge betweenness centrality and the flood control capacity index, the water supply capacity index and the ecological capacity index calculated in the S2, weighting, sorting the river sections according to the node centrality indicators, respectively simulating the edge removal attack of the sorted river network, and calculating the maximum connected subgraph proportion and the global efficiency of the network after removing the river section.

[0038] In some embodiments, the expression of the maximum connected subgraph proportion σ is as follows:

[0039]

[0040] In the formula, S is the number of nodes contained in the largest connected subgraph, S = max (Nj'), Nj' is the set of the number of nodes in the connected subgraph after the network node j' is destroyed.

[0041] In some embodiments, the expression of network efficiency is as follows:

[0042]

[0043] In the formula: Ej'j = 1 / dj'j, dj'j is the shortest path length between node j' and node j, and N is the total number of network nodes.

[0044] Compared with the existing work, the present application achieves the following positive technical effects:

[0045] 1) The present application is suitable for evaluating the structural stability, functional evolution and resistance to various risk events such as natural disasters and human interference of river networks;

[0046] 2) By constructing a river network complex network model reflecting the topology of natural and current river networks, the topological structure characteristics of the river network can be described and quantified, and the robustness of the nodes and edges of the river network can be dynamically and accurately evaluated;

[0047] 3) Combined with various river network node removal attack simulation strategies and centrality indexes, the robustness of the river network is dynamically evaluated, and a more scientific basis is provided for water network planning and management. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a whole flow chart of a river network robustness evaluation method based on complex network theory of the present application;

[0049] Figure 2 is a flow implementation block diagram of a river network robustness evaluation method based on complex network theory of the present application;

[0050] Figure 3 is a complex network model construction schematic diagram of the river network; (a) is a river network and water conservancy project distribution diagram, and (b) is an example diagram of the complex network model of the constructed example river basin, including taking the river junction, reservoir and sluice as the nodes of the complex network and taking the river channel as the edge of the complex network;

[0051] Figure 4 is a schematic diagram of the robustness of the nodes and edges of the river network using a static attack strategy;

[0052] Figure 5 is a schematic diagram of the robustness of the nodes and edges of the river network using a dynamic attack strategy. DETAILED DESCRIPTION

[0053] The technical scheme of the river network robustness evaluation method based on the complex network theory is described in detail below with reference to the drawings.

[0054] As shown in the drawings, Figure 1 The river network robustness evaluation method based on the complex network theory of the present application comprises the following steps:

[0055] Step 1: Collect and preprocess the terrain features, river network function index and river network vector data, specifically, collect the digital elevation model (DEM) data and river network vector data of the study area, wherein the DEM data is used to extract the terrain features and calculate the river network function index, and the river network vector data is used to construct the topological structure of the river network, and the data is preprocessed, including data cleaning, format conversion, projection transformation, etc., to ensure the accuracy and consistency of the data;

[0056] Step 2: Perform river network function analysis based on the terrain features, river network function index and river network vector data, specifically, calculate the flood control capacity index, water supply capacity index and ecological capacity index of the river network based on the digital elevation model (DEM) data and river network vector data, and analyze the spatial distribution characteristics of different functions and the influence of artificial modification; according to the function evaluation results of a certain watershed, the spatial distribution characteristics include the statistics of flood control function index, water supply function index and ecological function index; artificial modification is the result of human intervention on water resources, which directly changes the flow direction and connectivity of natural water system and directly affects the spatial distribution of different water network functions, for example, the construction of reservoirs will change the flow process of the downstream, thereby affecting the flood control and ecological functions of the downstream; the construction of artificial channels will improve the water supply capacity of a certain area, but may reduce the ecological connectivity of the river; the specific processing includes:

[0057] Step 2-1: The flood carrying capacity of the river and the flood storage capacity of large-scale engineering determine the local river flood control capacity, based on these two factors, the average flood carrying capacity index of each river section is calculated, the expression is as follows:

[0058] F R,i =Avg(Q1,Q2,Q3) (1)

[0059] In the formula, A is the cross-sectional area of water (m 2 ), C is the Chezy coefficient, R is the hydraulic radius, J is the hydraulic slope, F R i is the average flood carrying capacity index of the i-th river section, i is the river section number, Q1, Q2, Q3 are the flow rates of the upper, middle and lower reaches of the river section (m 3 / s), respectively, the flow rate of the cross section follows the formula (m 3 / s) is calculated;

[0060] Since the reservoir has the effect of improving flood control for the downstream river section, the distance between the river section and the upstream reservoir is calculated, and the flood control improvement index is estimated according to the reservoir capacity, and finally the flood control capacity index of each river section is calculated by weighting, and the expression is as follows:

[0061]

[0062] F D ,i=a1F R ,i+a2Fc,i (3)

[0063] In the formula, Fc,i is the flood control capacity improvement index of the i-th river section, F D ,i is the flood control capacity index of the i-th river section, step is the path length of the river section to the upstream reservoir in the graph model, As is the capacity of the upstream reservoir of the river section, a1 and a2 are the weights of the flood discharge capacity and the flood control improvement index of the river section respectively. The flood control function index mainly focuses on the drainage and flood resistance capacity of the water network, and the reservoir and other storage projects have an auxiliary improvement effect on the flood control capacity of the downstream river section, therefore, a1=0.7 and a2=0.3 are taken to estimate the flood control function index of each river section in the Haihe River Basin;

[0064] Step 2-2: The network efficiency index and the cultivated land distribution in the water receiving range are used to evaluate the water supply capacity index of each river section, and the expression is as follows:

[0065]

[0066] F S,i =a3E G +a4Z i +a5F c,i (5)

[0067] In the formula, E G is the network efficiency index, e st is the efficiency index of nodes s and t in the network, d st is the path distance of nodes s and t in the network, N is the number of nodes in the graph model, Fc,i is the water supply improvement index of the i-th river section, Zi is the cultivated land area of the i-th river section, the water receiving range is approximately determined by the river section buffer zone, and 5 kilometers is selected as the buffer zone division range, Fsi is the water supply capacity index of the i-th river section, that is, the local water supply capacity index, a3 is the influence degree of the river network efficiency index on the water supply capacity index, a4 represents the influence degree of the cultivated land distribution on the water supply capacity index, and a5 is the influence degree of the water supply improvement index on the water supply capacity index. Based on the actual situation, in the water supply network of the example shown in the application, a3=0.4, a4=0.3, and a5=0.3;

[0068] Step 2-3: Characterize the ecological capacity index of each river segment using functional connectivity, expressed as follows:

[0069]

[0070] where BC is the network betweenness centrality index, TWI is the topographic wetness index, TCI is the topographic connectivity index, β is the water network connectivity correction coefficient, F E is the ecological capacity index of the ith river segment, that is, the local ecological capacity index. Among them, BC represents the intermediary capacity of the river segment, and the water network connectivity correction coefficient β aims to adjust the difference between the intermediary capacity and the actual flow process, It can be understood as a weight coefficient, which takes into account the influence of topography on the intermediary capacity of the water network.

[0071] Step 3: Based on the preprocessed river network vector data, use the NetworkX and igraph modules of Python to build a river network complex network model G = (V, E) for the basin to be evaluated. The following inputs are required to build the model: 1) river network vector data, including geometric shape information of rivers and geographic location information of key facilities; 2) attribute data of nodes, including node type and name and location information, where the node type is river segment node and engineering node; 3) all function indexes of each river segment calculated in step 2, including flood control capacity index, water supply capacity index and ecological capacity index; Among them, the node set V represents the source, intersection and key positions such as control engineering (reservoir, large sluice) in the river segment, which are generalized as nodes in the network; the edge set E represents the river segments connecting these nodes, which are generalized as edges in the network; V = {v j}, j = 1, 2,..., N, N is the total number of nodes, E = {e i}, i' = 1, 2,..., M, M is the total number of river segments. After the model is built, the following outputs will be obtained: 1) an abstract network topology structure, which is stored in graph theory representation, including the connection relationship of nodes and edges; 2) indexes calculated based on the network, such as degree centrality, betweenness centrality and closeness centrality, which are used for subsequent attack simulation; 3) the weight attribute of the edge, that is, the function index of each river segment calculated in step 2, which will be used when performing edge removal attack simulation. As Figure 2 shown, a schematic diagram of the constructed river network complex network model is shown;

[0072] Step 4: Describe and quantify the topological structure characteristics of the river network based on the river network complex network model, which specifically includes: calculating the node and edge centrality indexes such as degree centrality, betweenness centrality and closeness centrality of the river network;

[0073] The degree centrality index evaluates the number of edges of a node, highlighting nodes with high connectivity, which can have an important influence on network function. The expression of the degree centrality of each node in the river network complex network is as follows:

[0074]

[0075] where k j is the degree of node j, N is the total number of nodes; node s, node t, and node j are any three nodes in the network;

[0076] The betweenness centrality index evaluates the number of times a node or connection is located on the shortest path between other pairs of nodes, which is a key element in identifying information or resource flow in the network. The expression of the betweenness centrality of each node in the river network complex network is as follows:

[0077]

[0078] where g st is the number of shortest paths from node s to node t, is the number of shortest paths from node s to node t through node j;

[0079] The closeness centrality index measures the proximity of the node to other nodes in the network. This index reflects the efficiency of a node to reach all other nodes in the network, which can be critical to network communication or efficiency. The expression of the closeness centrality of each node in the river network complex network is as follows:

[0080]

[0081] where N is the total number of nodes, d sj is the shortest path length between node s and node j;

[0082] Step 5: Based on the degree centrality, betweenness centrality, and closeness centrality indexes, respectively, the river network complex network model is simulated for node removal attacks. The trend of the overall robustness level of the river network complex network model under node removal attacks is observed to determine the evaluation index that has the most significant impact on the global robustness of the network. The types of attacks involved include:

[0083] (1) Static attack: According to the node centrality index, the highest ranked node is removed one by one. After each removal, the maximum connected subgraph proportion and global efficiency of the network are calculated, and then the network is restored to the initial state for the next node removal. This strategy can help identify key nodes and edges in the network and assess the vulnerability of the network to single risk events.

[0084] (2) Dynamic attack: According to the node centrality index ranking, the highest ranked node is removed one by one, and after removing one node each time, the maximum connected subgraph proportion and global efficiency of the network are calculated, and the network state is not restored, and the next node is removed; this strategy can comprehensively evaluate the robustness of the network as a whole and identify the weak links under multiple risk superposition.

[0085] The maximum connected subgraph proportion is σ, and the specific expression is as follows:

[0086]

[0087] In the formula, S is the number of nodes contained in the maximum connected subgraph, S = max(Nj'), Nj' is the set of node numbers in the connected subgraph after the network node j' is destroyed.

[0088] The expression of network efficiency is as follows:

[0089]

[0090] In the formula: Ej'j = 1 / dj'j, dj'j is the shortest path length between node j' and node j, and N is the total number of network nodes. Node j' and node j represent any two different nodes in the network, and need to satisfy j' ≠ j. They can be river section nodes, engineering nodes or sources and outlets of rivers. The summation symbol in the formula traverses all possible node pairs in the network, ensuring that the connection efficiency between all nodes is considered. They can be directly connected, indirectly connected, or in some cases may have no connection relationship.

[0091] In static attack analysis, by removing different nodes one by one and observing the changes of network indicators, key nodes are identified, that is, those nodes that will cause significant decline in the maximum connected subgraph proportion or global efficiency after removal. In dynamic attack analysis, based on different centrality indexes, nodes are sorted and removed step by step, and the trend of the overall robustness level of the network is observed, so as to determine the evaluation index that has the most significant impact on the global robustness of the network;

[0092] Specifically, based on the edge betweenness centrality and the three ability indexes (flood control ability, water supply ability, ecological ability) calculated in step 2, the river sections are sorted, and the sorted river network is subjected to edge removal attack simulation, and the maximum connected subgraph proportion and global efficiency of the network after removing the river sections are calculated. The attack strategy is the same as the attack strategy based on node removal in step 5, including static attack and dynamic attack. In static attack analysis, by removing different river sections one by one and observing the changes of network indicators, key river sections are identified, that is, those river sections that will cause significant decline in the maximum connected subgraph proportion or global efficiency after removal. In dynamic attack analysis, based on different neutral indexes and function weighting, the river sections are sorted and removed step by step.

[0093] The above merely illustrates the specific embodiments of the present application, and the above implementation steps are only used to help understand the specific method and core idea of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of several changes or equivalent replacements, combinations and refinements within the technical range disclosed by the present application and according to the idea of the present application without departing from the principle of the present application. These changes or equivalent replacements, combinations and refinements should be considered to fall within the protection scope of the present application, and should be covered within the protection scope of the present application.

Claims

1. A river network robustness evaluation method based on complex network theory, characterized by: include: S1, collect and preprocess terrain characteristics, river network function index and river network vector data; S2, based on the terrain characteristics, river network function index and river network vector data, performs river network function analysis and calculates all function indices of each river section in the river network, including flood control capacity index, water supply capacity index and ecological capacity index; further comprising: calculating the flood control capacity index of each river section, the expression is as follows: ; ; Where, is the flood control capacity improvement index of the i-th river section, is the flood control capacity index of the i-th river section, is the path length from the river section to the upstream reservoir in the graph model, is the storage capacity of the reservoir upstream of the river section, 、 are the weights of the flood discharge capacity and flood control improvement index of the river section respectively; among them, the average flood discharge capacity index of each river section is calculated, and the expression is as follows: ; Where A is the cross-sectional water area, C is the Xie Cai coefficient, R is the hydraulic radius, and J is the hydraulic slope. is the average flood discharge capacity index of the i-th river section, i is the river section number, 、 、 are the flow rates at the upper, middle and lower reaches of the river respectively; The water supply capacity index of each river section is evaluated using the following expression: ; ; in, is the network efficiency index, is the efficiency index of node s and node t in the network, is the path distance between node s and node t in the network, N is the number of nodes in the graph model, is the water supply improvement index of the i-th river section, is the cultivated land area in the watershed of the i-th river section, is the water supply capacity index of the i-th river section, that is, the local water supply capacity index, a3 is the influence of the river section network efficiency index on the water supply capacity index, a4 represents the influence of cultivated land distribution on the water supply capacity index, and a5 is the influence of the water supply improvement index on the water supply capacity index; Functional connectivity is used to characterize the ecological capacity index of each river section, and the expression is as follows: ; In the formula, BC is the network betweenness centrality index, TWI is the terrain wetness index, and TCI is the terrain connectivity index. is the water network connectivity correction coefficient, is the ecological capacity index of the i-th river section; S3, based on the preprocessed river network vector data, construct a river network complex network model G = (V, E) for the basin to be evaluated. The node set V represents the source of the river, the intersection, and the key locations of the control engineering within the river section; the edge set E represents the river sections that serve as nodes and edges in the network. The input of this model is the river network vector data, the attribute data of the nodes, and the flood control capacity index, water supply capacity index, and ecological capacity index of each river section. The output of this model is the abstract network topology of the river network represented by graph theory, the network calculation index, and the weight attributes of the edge connection. S4, based on the river network complex network model, describes and quantifies the topological structural characteristics of the river network, specifically including: calculating the node and edge centrality indicators such as degree centrality, betweenness centrality, and closeness centrality of the river network; further including: The expression of the degree centrality of each node in the river network complex network model is as follows: ; Where: is the degree of node j, N is the total number of nodes; node s, node t, and node j are any three nodes in the network; The expression of the betweenness centrality of each node in the river network complex network model is as follows: ; Where, is the number of shortest paths from node s to node t, is the number of shortest paths from node s to node t through node j; The expression of the closeness centrality of each node in the river network complex network model is as follows: ; Where N is the total number of nodes, d sj is the shortest path length between node s and node j; S5. Based on the three indicators of degree centrality, betweenness centrality and closeness centrality, the river network complex network model is simulated to undergo node removal attack. The maximum connected subgraph ratio and network efficiency of the network after the river section is removed are analyzed. The changing trend of the overall robustness level of the river network complex network model subjected to the node removal attack is observed to determine the evaluation indicator with the most significant impact on the global robustness of the network.

2. A river network robustness evaluation method based on complex network theory according to claim 1, characterized in that: The removal attack in S5 further includes static attacks and dynamic attacks, and further includes static attack analysis, by removing different nodes one by one and observing the changes in network indicators, identifying nodes that will cause a significant decrease in the maximum connected subgraph ratio or global efficiency after removal; dynamic attack analysis, sorting nodes based on different centrality indicators and gradually removing them, observing the changing trend of the overall robustness level of the network, so as to determine the evaluation indicators that have the most significant impact on the global robustness of the network.

3. The river network robustness evaluation method based on complex network theory according to claim 1 is characterized in that: The S5 further includes: assigning weights based on the edge betweenness centrality and the flood control capacity index, water supply capacity index and ecological capacity index calculated in S2, sorting the river sections according to the node centrality index, performing edge removal attack simulation on the sorted river networks, and calculating the maximum connected subgraph ratio and global efficiency of the network after removing the river sections.

4. A river network robustness evaluation method based on complex network theory according to claim 1, characterized in that: in, The expression of the maximum connected subgraph ratio σ is as follows: ; In the formula, S is the number of nodes contained in the maximum connected subgraph, , Nj' is the set of the number of nodes in the connected subgraph after the network node j' is destroyed.

5. The river network robustness evaluation method based on complex network theory according to claim 1 is characterized in that: in, The expression of network efficiency is as follows: ; Where: , is the shortest path length between node j' and node j, and N is the total number of nodes in the network.

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