A city flood risk assessment method and device based on open source data

Abstract

The application discloses a kind of urban flood risk assessment method and device based on open source data, belong to urban flood technical field, the method is by obtaining open source road data and simplifying it to drainage pipe network initial topological graph, determine outlet and further iteration optimization obtain drainage pipe network model, according to the drainage pipe network model, the drainage effect of urban actual drainage system is predicted, and the urban flood risk is evaluated.The method completely relies on open source data, completely gets rid of the dependence on special drainage pipe network data difficult to obtain;Modeling efficiency is improved, and through reasonable physical rules and optimization algorithm, the model generated is consistent with the real model on the system level (such as total overflow flow, outlet flow) and flooded range, which can support the planning level risk assessment requirements.

Description

Technical Field

[0001] This invention belongs to the field of urban flood control technology, and in particular relates to a method and apparatus for urban flood risk assessment based on open source data. Background Technology

[0002] Urban flooding has become a major challenge for many cities worldwide. Climate change is leading to more frequent extreme rainfall events, and urbanization is exacerbating surface hardening, putting increasing pressure on urban drainage systems and significantly raising the risk of flooding. Accurate and efficient flood risk assessment is crucial for urban planning, emergency management, and disaster early warning.

[0003] Currently, mainstream assessment methods rely on physical mechanism models. However, the core challenge in building such models lies in obtaining complete and accurate data on underground drainage networks (such as pipe diameter, slope, and connection relationships). This data is typically managed by municipal departments, and suffers from difficulties in acquisition, slow updates, inconsistent formats, and varying accuracy. Traditional model building methods require significant investment of manpower and resources for data collection, organization, verification, and digitization, a process that often takes months, severely limiting the application of these models in data-scarce areas and rapid assessment scenarios.

[0004] To address these issues, existing technologies have proposed several solutions: First, simplified models are employed, such as implicitly representing drainage capacity in surface models by adding infiltration or setting up catchment channels. However, the physical mechanisms of this method are unclear, resulting in limited accuracy. Second, data-driven artificial intelligence methods are utilized, but these require a large amount of historical inundation data for training and exhibit poor generalization ability. Third, attempts are made to use the spatial correlation between road networks and drainage pipe networks to infer the drainage system structure. However, existing research mainly focuses on generating pipe network layouts and has failed to establish a complete and automated workflow that includes hydrological parameterization, hydraulic design, outlet identification, and dynamic simulation.

[0005] Therefore, there is an urgent need for an open-source solution that does not rely on dedicated drainage data, is highly automated, computationally efficient, and can provide reliable flood risk assessment. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies in urban flooding model construction, which heavily rely on difficult-to-obtain underground pipe network data, resulting in long modeling cycles, high costs, and inability to be applied quickly. This invention provides an urban flooding risk assessment method and device based on open-source data.

[0007] According to a first aspect of the embodiments of this application, a method for urban flood risk assessment based on open-source data is provided, comprising: S1: Obtain road network data, satellite remote sensing imagery, and digital elevation model data for the target area from an open-source geographic information platform, and perform preprocessing. S2: Convert the preprocessed road network data into a graph structure and perform skeletonization processing to generate an initial topology graph of the drainage pipe network consisting of nodes and edges, and generate a sub-catchment area corresponding to each node. In the initial topology graph of the drainage pipe network, the edges represent candidate drainage pipes and the nodes represent candidate manholes or catchment points. S3: Based on the preprocessed satellite remote sensing imagery, land use classification is performed, hydrological parameters are assigned to the sub-catchment areas represented by each node in the initial topology map, the distance between the node and the surface water body is calculated, and the elevation of the node is calculated based on the preprocessed digital elevation model data. S4: Based on the elevation of the nodes and their distance from the surface water body, an iterative sorting algorithm is used to determine the outlet of the drainage system from all nodes; S5: Using the outlet as the root node, generate a random drainage network layout and assign a pipe diameter to each edge; using the minimization of total excavation cost as the objective function, use an optimization algorithm to iteratively optimize the network layout to obtain the optimal drainage network model; S6: Based on the optimal drainage network model, coupled with the surface runoff model, simulate the urban flooding process under the design rainfall scenario to obtain the inundation range and water depth, thereby completing the urban flood risk assessment.

[0008] Furthermore, the skeletonization process described in step S2 is the cleaning up of some nodes and edges in the graph structure, including: (1) Merge small road segments: Traverse all nodes in the graph. For a node that connects two edges, if the angle between its two incident edges is greater than a first predetermined angle... ; or the included angle is greater than the second predetermined angle. And the shorter of the two incident sides is less than the first predetermined length. Meters; or the sum of the lengths of both sides is less than the second predetermined length. If the value is less than 1 meter, then delete the node and merge the two sides. (2) Remove the turning lane: Traverse all edges in the graph. For edge AB, if there is a common neighbor node C and ∠ACB is within the third predetermined angle. To the fourth predetermined angle Between, the length ratio of edge CA to CB is within a first predetermined ratio. to Between the second predetermined ratio, remove edge AB and merge the small road segments again; (3) Remove parallel connections: Traverse all edges in the graph. For edge AB, if its length is less than the third predetermined length... Meters, and nodes A and B are each connected to three edges, and the included angles of the two edges other than AB at the two nodes are both greater than the fifth predetermined angle. If so, remove edge AB and merge the smaller road segments again; (4) Node clustering and merging: Calculate the straight-line distance between nodes in the graph, and merge nodes whose straight-line distance is less than a fourth predetermined length. Nodes in the same cluster are marked as belonging to the same cluster; the center position of each node in each cluster is calculated, and the single center node located at the center position is used to replace other nodes in the cluster, and all edges are deleted; if there is a connection between the original clusters corresponding to the center node, a new edge is added between the corresponding center nodes; (5) Remove triangles: Traverse all nodes in the graph and check whether they form a triangle with their adjacent nodes. If such a triangle exists, remove the longest side of the triangle. (6) Eliminate overlapping edges: Traverse all edges in the graph. If an edge is found to overlap with another edge, remove the longer edge. (7) Remove sharp polygons: Traverse the polygons formed by the edges in the graph, calculate the ratio of the area of ​​each polygon to the length of its longest side, and remove any sharp polygons if the ratio exceeds a third predetermined ratio. If the polygon is sharp, then mark it as a sharp polygon and remove the longest side that makes up each sharp polygon from the graph; (8) Remove the end: Traverse all nodes. If there is a node connected to only one edge, remove the node and the edge it is connected to. Repeat this process until there are no nodes in the graph that meet the removal conditions, thus obtaining the initial topology of the drainage network.

[0009] Further, in step S2, the sub-catchment area corresponding to each node is generated, specifically as follows: In the initial topology diagram of the drainage network, nodes are inserted at fixed intervals on the edges, and Thiessen polygons are generated based on all nodes as sub-catchment areas.

[0010] Further, in step S4, the iterative sorting algorithm includes: a) Sort all nodes according to their distance from the nearest surface water body, and select the fourth predetermined proportion that is closest to the water body. The nodes are used as the candidate set; b) Select the node with the lowest elevation from the candidate set as the first outlet; c) For the k-th outlet, calculate the shortest path distance from each node in the candidate set to the (k-1)-th outlet on the graph topology, where k=2,3,…,N, and N is the preset total number of outlets; d) Arrange the candidate nodes in descending order according to the shortest path distance, and select the node with the lowest elevation from the first 1 / (k-1) of the nodes as the kth outlet; e) Repeat steps c and d until N outlets are selected.

[0011] Furthermore, in step S5, if there is more than one outlet determined in S4, the graph structure is pre-divided into N parts according to the number of outlets N, and a tree structure is generated for each part. The division method is as follows: calculate the distance of each edge to all outlets, and each edge and its corresponding node are assigned to the nearest outlet. If there is an edge that is closest to all outlets, then the edge is deleted.

[0012] According to a second aspect of the embodiments of this application, an urban flood risk assessment device based on open-source data is provided, comprising: The data acquisition and preprocessing module is used to acquire road network data, satellite remote sensing imagery, and digital elevation model data of the target area from an open-source geographic information platform, and to perform preprocessing. The drainage network topology reconstruction module is used to convert the preprocessed road network data into a graph structure and perform skeletonization processing to generate an initial drainage network topology graph consisting of nodes and edges, and generate a sub-catchment area corresponding to each node. In the initial drainage network topology graph, the edges represent candidate drainage pipes, and the nodes represent candidate manholes or catchment points. The hydrological parameterization module is used to classify land use based on preprocessed satellite remote sensing images, assign hydrological parameters to the sub-catchment area represented by each node in the initial topology map, calculate the distance between the node and the surface water body, and calculate the elevation of the node based on the preprocessed digital elevation model data. The outlet identification module is used to determine the outlet of the drainage system from all nodes based on the elevation of the nodes and their distance from the surface water body, using an iterative sorting algorithm. The hydraulic design and optimization module is used to generate a random drainage network layout with the outlet as the root node and assign pipe diameter to each edge; with minimizing the total excavation cost as the objective function, the optimization algorithm is used to iteratively optimize the network layout to obtain the optimal drainage network model. The flood simulation and assessment module is used to simulate urban flooding processes under design rainfall scenarios based on the optimal drainage network model and coupled with the surface runoff model, to obtain the inundation range and water depth, thereby completing the urban flood risk assessment.

[0013] According to a third aspect of the embodiments of this application, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method described in the first aspect.

[0014] According to a fourth aspect of the embodiments of this application, an electronic device is provided, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0015] According to a fifth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0016] The technical solutions provided by the embodiments of this application may include the following beneficial effects: (1) Extremely low data acquisition cost: It relies entirely on open source data, completely eliminating the dependence on hard-to-obtain dedicated drainage network data.

[0017] (2) Revolutionary improvement in modeling efficiency: The traditional model development process, which takes months, is shortened to a few hours, achieving a truly “fast” evaluation.

[0018] (3) High degree of automation and intelligence: From data processing to pipeline design, the entire process is driven by preset rules and algorithms, which greatly reduces human intervention.

[0019] (4) The method is highly versatile and reproducible: Based on open source data and public algorithms, the method process is standardized and transparent, and can be reproduced and compared in different cities.

[0020] (5) Accuracy and reliability: Although based on speculation, the generated model shows good consistency with the real model at the system level (such as total overflow and outlet flow) and inundation range through reasonable physical rules and optimization algorithms, which is sufficient to support the planning-level risk assessment needs.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] Figure 1 This is a flowchart illustrating a rapid urban flood assessment method based on open-source data, according to an exemplary embodiment.

[0024] Figure 2 This is a schematic diagram illustrating the process of data skeletonization of road network in town S, a case study area, according to an exemplary embodiment.

[0025] Figure 3 This is a comparison diagram of the automatically reconstructed pipeline network and the baseline pipeline network in the case area S town, according to an exemplary embodiment.

[0026] Figure 4 This is a specific rainstorm curve used in the case area S town as illustrated in an exemplary embodiment.

[0027] Figure 5 This is a comparison diagram of the flooding extent in case area S town under a specific rainstorm, according to an exemplary embodiment.

[0028] Figure 6 This is a block diagram illustrating a rapid urban flood assessment device based on open-source data, according to an exemplary embodiment.

[0029] Figure 7 This is a schematic diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0031] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0032] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0033] Figure 1 This is a flowchart illustrating an urban flood risk assessment method based on open-source data, according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps: S1: Obtain road network data, satellite remote sensing imagery, and digital elevation model data for the target area from an open-source geographic information platform, and perform preprocessing. Specifically, road network data, satellite remote sensing imagery, and digital elevation model data for the target area are acquired from open platforms (such as OpenStreetMap, Sentinel-2, and ALOS). GIS software (such as QGIS) is then used to perform preprocessing operations on the data, including cropping, coordinate system unification, and filling.

[0034] In one embodiment, taking Town S (approximately 3 square kilometers) in a city in China as an example, the QuickOSM plugin of QGIS was used to download all highway features within the boundary of Town S to obtain the road network; recent Sentinel-2L2A remote sensing imagery was downloaded from the USGS website; and ALOS AW3D30 30-meter resolution elevation data was used. Then, in QGIS, all data were unified to the WGS84 UTM projection coordinate system, and the "Fill Sinks" tool was used to fill depressions in the elevation data.

[0035] S2: Convert the preprocessed road network data into a graph structure and perform skeletonization to generate an initial topology graph of the drainage network consisting of nodes and edges, and generate the sub-catchment area corresponding to each node; Specifically, the skeletonization process involves cleaning up some nodes and edges in the graph structure. In one embodiment, for Figure 2 The skeletonization process, which includes the original road shown in (a) in the figure, may include: (1) Merging small road segments: Traverse all nodes in the graph. For a node connecting two edges, if the angle between its two incident edges is greater than 1, then 1 / 2 merging the small road segments. or the included angle is greater than And the shorter of the two incident sides is less than meters; or the sum of the lengths of the two sides is less than 1 meter. If the distance is less than 180°, then delete the node and merge the two sides. This step aims to merge road segments with large angles (close to 180°) and shorter road segments. , , , In this embodiment, the settings can be adjusted according to the actual situation. , , , The structure after merging the smaller road segments is as follows: Figure 2 As shown in (b) of the diagram.

[0036] (2) Remove the turning lane: Traverse all edges in the graph. For edge AB, if there is a common neighbor node C, and ∠ACB is between to Between, the length ratio of edge CA to CB is between to Between them, edge AB is removed. Then, the merging of small road segments is performed again. The purpose of this step is to remove the lane used for turning / turning, which typically does not require drainage pipes under it. In this embodiment, , , , The structure after removing the turning lane is as follows: Figure 2 As shown in (c) in the figure.

[0037] (3) Remove parallel connections: Traverse all edges in the graph. For edge AB, if its length is less than 1, remove parallel connections. Meters, and nodes A and B are each connected to three edges, and the included angles of the two edges other than AB at the two nodes are both greater than 1. If so, then remove edge AB. Then, perform the merge small road segment operation again. In this embodiment, , The diagram structure after removing parallel connections is as follows: Figure 2 As shown in (d) in the figure.

[0038] (4) Node clustering and merging: Calculate the pairwise linear distance between nodes in the graph, and merge nodes with a distance less than 1. Nodes with a distance of 1 meter are marked as belonging to the same cluster; then, the center position (average coordinates) of the nodes in each cluster is calculated, and the single node located at the center position is used to replace the other nodes in that cluster, and all edges are deleted; finally, if there is a connection between the original clusters corresponding to the center node (then there is at least one edge between the two clusters), a new edge is added between the corresponding center nodes. Cluster merging can remove some closely spaced nodes, avoiding the issues present in this embodiment. The graph structure after node clustering and merging is as follows: Figure 2 As shown in (e) in the diagram.

[0039] (5) Remove triangles: Traverse all nodes in the graph and check if they form a triangle with their adjacent nodes. If such a triangle exists, remove the longest side of the triangle. The graph structure after removing the triangle is as follows: Figure 2 As shown in (f) in the figure.

[0040] (6) Eliminate overlapping edges: Traverse all edges. If an edge is found to overlap with another edge, remove the longer of the two edges. The graph structure after eliminating overlapping edges is as follows: Figure 2 As shown in (g) in the diagram.

[0041] (7) Remove sharp polygons: Traverse the polygons formed by the edges in the graph. Calculate the ratio of the area of ​​each polygon to the length of its longest side. If the ratio exceeds... If the polygon is sharp, then it is marked as a sharp polygon. Finally, the longest side that makes up each sharp polygon is removed from the graph. In this embodiment, The structure of the image after removing sharp polygons is as follows: Figure 2 As shown in (h) in the diagram.

[0042] (8) Remove the end node: Traverse all nodes. If a node is connected to only one edge, remove the node and its connected edge. Repeat this process until there are no nodes in the graph that meet the removal conditions. The graph structure after removing the end node is as follows: Figure 2 As shown in (i), this is the initial topology diagram of the final drainage network.

[0043] The above process removes redundant nodes and edges from the road graph structure, resulting in an initial topology graph of the drainage network. In this graph, edges represent candidate drainage pipes, and nodes represent candidate manholes or catchment points. Subsequently, nodes are inserted at fixed intervals along the edges, and Thiessen polygons are generated based on all nodes to serve as sub-catchment areas.

[0044] In this embodiment, the NetworkX library of the Python programming language is used to convert the downloaded road network data into a graph structure. The graph is cleaned by executing sub-steps in the order of the skeletonization process described above, and the graph topology is recalculated after each round of rule execution. After skeletonization, nodes are inserted at 30-meter intervals along the edges of the graph structure, and sub-catchment areas are generated using QGIS's "Voronoi Polygons" tool.

[0045] S3: Based on the preprocessed satellite remote sensing imagery, land use classification is performed, hydrological parameters are assigned to the sub-catchment areas represented by each node in the initial topology map, the distance between the node and the surface water body is calculated, and the elevation of the node is calculated based on the preprocessed digital elevation model data. In practical implementation, tools such as QGIS can be used to perform supervised classification of satellite imagery to obtain land use classifications (e.g., buildings, vegetation, water bodies). Topographic and hydrological parameters such as area, average slope, and proportion of impervious area are calculated for each sub-catchment. Based on land type, Manning roughness, infiltration parameters (e.g., Horton formula parameters), and depression depth are assigned to each sub-catchment, and the Euclidean distance from each node to the nearest surface water body is calculated. Simultaneously, the elevation of each node is extracted from the elevation data.

[0046] In this embodiment, the QGIS SCP plugin is used to perform supervised classification on Sentinel-2 imagery, outputting land type data. The Zonal Statistics tool is used to calculate the impervious area ratio and average slope for each sub-catchment. Based on the land type lookup table, SWMM hydrological parameters (such as Manning roughness, depression depth, and infiltration parameters) are assigned to each sub-catchment and written to the attribute table. The elevation of each node is extracted, and the straight-line distance from each node to the nearest water body (extracted from the classification results) is calculated.

[0047] S4: Based on the elevation of the nodes and their distance from the surface water body, an iterative sorting algorithm is used to determine the outlet of the drainage system from all nodes; Specifically, considering that the actual outlet cannot be too far from the water body, nor will it be higher than the non-outlet elevation, since the drainage network is driven by gravity flow and discharges mainly from high to low, the iterative sorting algorithm is as follows: a) Sort all nodes according to their distance from the nearest surface water body, and then select the nodes closest to the water body. Nodes as candidate set, ; b) Select the node with the lowest elevation from the candidate set as the first outlet; c) For the k-th outlet, calculate the shortest path distance from each node in the candidate set to the (k-1)-th outlet on the graph topology, where k=2,3,…,N, and N is the preset total number of outlets; d) Arrange the candidate nodes in descending order according to the shortest path distance, and select the node with the lowest elevation from the first 1 / (k-1) of the nodes as the kth outlet; e) Repeat steps c and d until N outlets are selected.

[0048] The outlet of the drainage system is the connection point between the pipe network and the receiving water body, and its location is crucial. The iterative sorting algorithm cleverly combines geographical proximity (close to the water body), topographic conditions (low elevation), and the uniformity of the pipe network topology distribution (long distance in graph theory).

[0049] In this embodiment, the number of water outlets is preset to N=1 (it is known that there is only one main water outlet in this area), and the top 10% of candidate nodes that are closest to the river are found. Among these candidate nodes, the node with the lowest elevation is selected and marked as the water outlet.

[0050] S5: Using the outlet as the root node, generate a random drainage network layout and assign a pipe diameter to each edge; using the minimization of total excavation cost as the objective function, use an optimization algorithm to iteratively optimize the network layout to obtain the optimal drainage network model; Specifically, using the outlet determined in S4 as the root node, each edge of the skeleton graph is assigned a random weight, and the minimum spanning tree algorithm is applied to generate a tree-like candidate layout of the drainage network flowing towards the outlet. If there is more than one outlet determined in S4, the graph structure is pre-divided into N parts based on the number of outlets N. The division method can be as follows: calculate the distance from each edge to all outlets (the number of interval edges), and assign each edge and its corresponding node to the nearest outlet. If an edge has the shortest distance to all outlets, it is deleted.

[0051] For candidate layouts, the pipe burial depth is calculated backward from the outlet, following the minimum slope constraint (e.g., 0.003). Based on local engineering conditions, the upstream total catchment area and design rainfall intensity (e.g., 30-minute rainfall with a 3-year return period) of each node are determined. The required pipe diameter is calculated using the Manning formula, and a pipe diameter that meets the requirements is selected from the standard pipe diameter series.

[0052] With the goal of minimizing the total excavation volume (cost function) of the entire pipeline network, a genetic algorithm (such as NSGA-II) is used to iteratively optimize the random weights, evolve hundreds of candidate layouts and evaluate their costs, and finally output the drainage pipeline network design scheme with the lowest cost (i.e. optimal).

[0053] In this embodiment, the NSGA-II algorithm is implemented using the Platypus library (Python). First, the algorithm generates 20 sets of random weights and assigns them to the edges of the skeleton graph, generating the corresponding minimum spanning tree layout as the initial population. Hydraulic calculations are performed on each layout: a minimum slope of 0.003 and a minimum cover thickness of 2 meters are set, and the pipe diameter is calculated based on the upstream catchment area and the design rainfall intensity (in this example, a 3-year return period 30-minute rainfall intensity is used). The total excavation volume (cost function) for each layout is calculated. Based on the weights and corresponding costs of the initial population, the NSGA-II algorithm is used to iteratively optimize the population's weight parameters. After 100 generations of optimization, the solution with the lowest cost is selected as the final drainage network design and exported as the file S_town_swmm.inp.

[0054] S6: Based on the optimal drainage network model, coupled with the surface runoff model, simulate the urban flooding process under the design rainfall scenario to obtain the inundation range and water depth, thereby completing the urban flood risk assessment; The optimal drainage network model obtained from S5 is imported into hydrological and hydraulic simulation software (such as SWMM). The overflow process lines of each node calculated are then input as point sources into the corresponding grid cells of a two-dimensional hydrodynamic model (such as LISFLOOD-FP) for unidirectional coupled simulation to obtain the inundation range and water depth of the surface. By analyzing the inundation map and water depth, the flood risk of the study area can be assessed.

[0055] In summary, this method addresses situations where the actual urban drainage network is already constructed but its data is difficult to obtain (due to underground locations and confidentiality requirements). It acquires open-source road data, simplifies it into an initial topology map of the drainage network, identifies outlets, and further iteratively optimizes to obtain a drainage network model. Based on this model, it predicts the drainage effect of the actual urban drainage system and assesses urban flood risk. The model generated in this embodiment is called the automatic model, and a corresponding model of the local actual drainage network is selected for comparison, called the baseline model. Figure 2As shown, the automatic model and the baseline model have good consistency in the spatial distribution of the pipeline network.

[0056] In this embodiment, S_town_swmm.inp is opened in EPA SWMM 5.2, and a simulation of a real rainstorm scenario is run. The rainfall curve is as follows. Figure 4 As shown. Export the overflow time series of all nodes in the SWMM simulation results as a CSV file. In LISFLOOD-FP, construct a 5-meter resolution 2D grid terrain (based on elevation data). Process the overflow CSV file into the LISFLOOD-FP point source input format. Run the LISFLOOD-FP simulation to obtain the maximum inundation depth raster and inundation time series.

[0057] The flooding results simulated by this invention are compared with those of a baseline model constructed using conventional methods. The maximum water depth and flooding range of the baseline model and the automated model are as follows: Figure 5 As shown in the figure. The results show that the method of the present invention is in high agreement with the benchmark model in terms of main inundation areas and peak flow (NSE > 0.8), and successfully achieves rapid and accurate assessment of flood risk in S town.

[0058] Corresponding to the aforementioned embodiments of the urban flood risk assessment method based on open-source data, this application also provides embodiments of an urban flood risk assessment device based on open-source data.

[0059] Figure 6 This is a block diagram illustrating an urban flood risk assessment device based on open-source data, according to an exemplary embodiment. (Refer to...) Figure 6 The device may include: The data acquisition and preprocessing module 21 is used to acquire road network data, satellite remote sensing images and digital elevation model data of the target area from the open source geographic information platform, and to perform preprocessing. The drainage network topology reconstruction module 22 is used to convert the preprocessed road network data into a graph structure and perform skeletonization processing to generate an initial drainage network topology graph composed of nodes and edges, and generate a sub-catchment area corresponding to each node. In the initial drainage network topology graph, the edges represent candidate drainage pipes, and the nodes represent candidate manholes or catchment points. The hydrological parameterization module 23 is used to classify land use based on preprocessed satellite remote sensing images, assign hydrological parameters to the sub-catchment area represented by each node in the initial topology map, calculate the distance between the node and the surface water body, and calculate the elevation of the node based on the preprocessed digital elevation model data. The outlet identification module 24 is used to determine the outlet of the drainage system from all nodes based on the elevation of the nodes and their distance from the surface water body using an iterative sorting algorithm. The hydraulic design and optimization module 25 is used to generate a random drainage network layout with the outlet as the root node and assign pipe diameter to each edge; with minimizing the total excavation cost as the objective function, the optimization algorithm is used to iteratively optimize the network layout to obtain the optimal drainage network model. The flood simulation and assessment module 26 is used to simulate the urban flooding process under the design rainfall scenario based on the optimal drainage network model and coupled with the surface runoff model, to obtain the inundation range and water depth, thereby completing the urban flood risk assessment.

[0060] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0061] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0062] Accordingly, this application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the urban flood risk assessment method based on open-source data as described above.

[0063] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the urban flood risk assessment method based on open-source data as described above. Figure 7 The diagram shown is a hardware structure diagram of any data processing-capable device, including an urban flood risk assessment device based on open-source data provided in an embodiment of the present invention. (Except for...) Figure 7 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0064] Accordingly, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the urban flood risk assessment method based on open-source data as described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0065] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A method for urban flood risk assessment based on open-source data, characterized in that, include: S1: Obtain road network data, satellite remote sensing imagery, and digital elevation model data for the target area from an open-source geographic information platform, and perform preprocessing. S2: Convert the preprocessed road network data into a graph structure and perform skeletonization processing to generate an initial topology graph of the drainage pipe network consisting of nodes and edges, and generate a sub-catchment area corresponding to each node. In the initial topology graph of the drainage pipe network, the edges represent candidate drainage pipes and the nodes represent candidate manholes or catchment points. S3: Based on the preprocessed satellite remote sensing imagery, land use classification is performed, hydrological parameters are assigned to the sub-catchment areas represented by each node in the initial topology map, the distance between the node and the surface water body is calculated, and the elevation of the node is calculated based on the preprocessed digital elevation model data. S4: Based on the elevation of the nodes and their distance from the surface water body, an iterative sorting algorithm is used to determine the outlet of the drainage system from all nodes; S5: Using the outlet as the root node, generate a random drainage network layout and assign a pipe diameter to each edge; using the minimization of total excavation cost as the objective function, use an optimization algorithm to iteratively optimize the network layout to obtain the optimal drainage network model; S6: Based on the optimal drainage network model, coupled with the surface runoff model, simulate the urban flooding process under the design rainfall scenario to obtain the inundation range and water depth, thereby completing the urban flood risk assessment; The skeletonization process described in step S2 involves cleaning up some nodes and edges in the graph structure, including: (1) Merge small road segments: Traverse all nodes in the graph. For a node that connects two sides, if the angle between its two incident sides is greater than the first predetermined angle α1; or the angle is greater than the second predetermined angle α2 and the shorter side of the two incident sides is less than the first predetermined length l1 meters; or the sum of the lengths of the two sides is less than the second predetermined length l2 meters, then delete the node and merge the two sides. (2) Remove turning lanes: Traverse all edges in the graph. For edge AB, if there is a common neighbor node C, and ∠ACB is between the third predetermined angle β1 and the fourth predetermined angle β2, and the length ratio of edge CA to CB is between the first predetermined ratio r1 and the second predetermined ratio r2, then remove edge AB and merge the small road segments again. (3) Remove parallel connections: Traverse all edges in the graph. For edge AB, if its length is less than the third predetermined length l3 meters, and nodes A and B are each connected to three edges, and the included angle between the two edges other than AB at the two nodes is greater than the fifth predetermined angle γ, then remove edge AB and merge the small road segments again. (4) Node cluster merging: Calculate the straight-line distance between nodes in the graph, and mark nodes whose straight-line distance is less than the fourth predetermined length l4 meters as belonging to the same cluster; calculate the center position of the nodes in each cluster, use the single center node located at the center position to replace other nodes in the cluster, and delete all edges; if there is a connection relationship between the original clusters corresponding to the center node, add new edges between the corresponding center nodes; (5) Remove triangles: Traverse all nodes in the graph and check whether they form a triangle with their adjacent nodes. If such a triangle exists, remove the longest side of the triangle. (6) Eliminate overlapping edges: Traverse all edges in the graph. If an edge is found to overlap with another edge, remove the longer edge. (7) Remove sharp polygons: Traverse the polygons formed by the edges in the graph, calculate the ratio of the area of ​​each polygon to the length of its longest side. If the ratio exceeds the third predetermined ratio r3, mark the polygon as a sharp polygon and remove the longest side that makes up each sharp polygon from the graph. (8) Remove the end: Traverse all nodes. If there is a node connected to only one edge, remove the node and the edge it is connected to. Repeat this process until there are no nodes in the graph that meet the removal conditions, thus obtaining the initial topology of the drainage network.

2. The method according to claim 1, characterized in that, In step S2, the sub-catchment area corresponding to each node is generated, specifically as follows: In the initial topology diagram of the drainage network, nodes are inserted at fixed intervals on the edges, and Thiessen polygons are generated based on all nodes as sub-catchment areas.

3. The method according to claim 1, characterized in that, In step S4, the iterative sorting algorithm includes: a) Sort all nodes according to their distance from the nearest surface water body, and select the nodes that are closest to the water body at the fourth predetermined ratio r4 as the candidate set; b) Select the node with the lowest elevation from the candidate set as the first outlet; c) For the k-th outlet, calculate the shortest path distance from each node in the candidate set to the (k-1)-th outlet on the graph topology, where k=2,3,…,N, and N is the preset total number of outlets; d) Arrange the candidate nodes in descending order according to the shortest path distance, and select the node with the lowest elevation from the first 1 / (k-1) of the nodes as the kth outlet; e) Repeat steps c and d until N outlets are selected.

4. The method according to claim 1, characterized in that, In step S5, if there is more than one outlet determined in S4, the graph structure is pre-divided into N parts according to the number of outlets N. A tree structure is generated for each part. The division method is as follows: calculate the distance of each edge to all outlets, and each edge and its corresponding node are assigned to the nearest outlet. If there is an edge that is closest to all outlets, then the edge is deleted.

5. An apparatus for an urban flood risk assessment method based on open-source data as described in claim 1, characterized in that, include: The data acquisition and preprocessing module is used to acquire road network data, satellite remote sensing imagery, and digital elevation model data of the target area from an open-source geographic information platform, and to perform preprocessing. The drainage network topology reconstruction module is used to convert the preprocessed road network data into a graph structure and perform skeletonization processing to generate an initial drainage network topology graph consisting of nodes and edges, and generate a sub-catchment area corresponding to each node. In the initial drainage network topology graph, the edges represent candidate drainage pipes, and the nodes represent candidate manholes or catchment points. The hydrological parameterization module is used to classify land use based on preprocessed satellite remote sensing images, assign hydrological parameters to the sub-catchment area represented by each node in the initial topology map, calculate the distance between the node and the surface water body, and calculate the elevation of the node based on the preprocessed digital elevation model data. The outlet identification module is used to determine the outlet of the drainage system from all nodes based on the elevation of the nodes and their distance from the surface water body, using an iterative sorting algorithm. The hydraulic design and optimization module is used to generate a random drainage network layout with the outlet as the root node and assign pipe diameter to each edge; with minimizing the total excavation cost as the objective function, the optimization algorithm is used to iteratively optimize the network layout to obtain the optimal drainage network model. The flood simulation and assessment module is used to simulate urban flooding processes under design rainfall scenarios based on the optimal drainage network model and coupled with the surface runoff model, to obtain the inundation range and water depth, thereby completing the urban flood risk assessment.

6. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method as described in any one of claims 1-4.

7. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-4.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-4.

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