Urban inland inundation toughness evaluation method based on multi-dimensional indexes and coupling model

Abstract

The invention discloses an urban inland inundation toughness evaluation method based on a multi-dimensional index and a coupling model, and belongs to the technical field of urban inland inundation prevention and toughness evaluation. The method comprises the following steps: constructing a toughness evaluation index system covering three dimensions of urban space, lifeline engineering and rapid recovery; establishing a two-dimensional coupling hydraulic model, simulating different rainfall scenes, and outputting dynamic waterlogging parameters; quantifying the toughness score of each space unit by adopting a comprehensive weighting method and a TOPSIS model; identifying a low-toughness aggregation area and positioning key influence indexes; and formulating targeted improvement measures according to an identification result, and verifying the effect of the measures in a closed-loop manner by means of a coupling model. According to the method, the problems of one-sided index system, lack of dynamic simulation and lack of treatment closed loop in the existing evaluation method are solved, and high-precision and dynamic evaluation and scientific optimization of urban waterlogging toughness are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban waterlogging prevention and resilience assessment, and particularly relates to a method for evaluating urban waterlogging resilience based on multi-dimensional indexes and coupling models. BACKGROUND

[0002] Global climate change and accelerated urbanization have led to frequent extreme rainfall events, and urban waterlogging disasters have become a key problem restricting the sustainable development of cities. Since the concept of "resilient city" was proposed, the evaluation of urban waterlogging resilience has become a research hotspot in the field of disaster prevention and mitigation. However, the existing technology has obvious limitations: first, the index system is one-sided, focusing on physical facilities (such as pipe networks and pump stations) and ignoring social and economic dimensions and emergency support, making it difficult to cover the entire process before, during and after the disaster; second, dynamic simulation is missing, traditional methods rely on static statistical data and do not combine hydrological and hydrodynamic models to obtain dynamic parameters of waterlogging, resulting in a gap between the evaluation results and the actual waterlogging process; third, the weight calculation is single, only using the analytic hierarchy process (subjective) or entropy weight method (objective), leading to significant deviations in the results; fourth, there is a lack of closed-loop improvement, and the evaluation process is mostly a one-way model of "data input - result output", which is difficult to guide actual governance work. In view of the above problems, the present application proposes an evaluation method that integrates multi-dimensional indexes, coupling models and combined weights, filling the gap in existing technology. SUMMARY

[0003] Therefore, the present application aims to provide a method for evaluating urban waterlogging resilience based on multi-dimensional indexes and coupling models, which realizes high-precision evaluation and optimization of urban waterlogging resilience by constructing a full-dimensional index system, precise dynamic simulation, scientific quantification and closed-loop improvement.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A method for evaluating urban waterlogging resilience based on multi-dimensional indexes and coupling models, comprising the following five core steps: Step 1: Based on the resilience theory and disaster chain theory, identify the influencing factors of urban waterlogging resilience, select 20 indexes from three dimensions of urban space, lifeline engineering and rapid recovery, and construct a three-level urban waterlogging resilience evaluation index system; Step 2, build IFMS / Urban two-dimensional coupled urban flood simulation model. First, collect hydro-meteorological, topography, river network, land use data in the study area, and perform the following operations through IFMS / Urban software: import DEM data to generate two-dimensional surface grid, and raise the grid elevation of building area; digitize river network and drainage pipe network, and generalize river section, pipe parameters and inspection well attributes to build one-dimensional model; adopt orifice connection method to establish one-two dimensional coupling relationship, and set parameters such as infiltration area storage capacity, Manning coefficient and soil infiltration rate; input measured rainfall data to verify the model; simulate different return period rainfall scenarios, and output ground water depth and vulnerable point distribution data; Step 3, quantitative evaluation of urban waterlogging resilience level, adopt comprehensive weighting method combining analytic hierarchy process (subjective weighting) and entropy weight method (objective weighting) to determine the weight of each index, calculate the resilience score of each evaluation unit through TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), divide the resilience level into five levels: high resilience, higher resilience, medium resilience, lower resilience and low resilience, and identify resilience aggregation area and weak area through spatial autocorrelation analysis.

[0005] Step 4, identify low resilience aggregation area through Moran's I index and LISA cluster analysis, and screen key influence indexes such as drainage pipe network density and distance to vulnerable point by combining index weight and average value of different resilience level indexes. Step 5, according to the demand of different target level years and different catchment areas in the study area, develop measures such as adding sponge facilities, transforming pipe network and raising river bank, and cyclically execute steps 2-4 to verify the effect of measures, so as to ensure that the resilience of more than 80% area is improved, and form a closed-loop resilience improvement process.

[0006] Further, the obtaining of the urban waterlogging resilience evaluation index system in step 1 comprises the following steps: Step 11, select 7 indexes in urban space dimension: green coverage rate (positive index), water body coverage rate (positive index), terrain slope (positive index), ground elevation (positive index), distance to river network (positive index), distance to vulnerable point (positive index), and ground water depth (negative index); Step 12, select 8 indexes in lifeline engineering dimension: distance to medical institution (negative index), road network density (positive index), distance to traffic station (negative index), drainage pipe network density (positive index), distance to communication maintenance station (negative index), water supply pipe network density (positive index), distance to power maintenance station (negative index), and population density (negative index); Step 13: Select 5 indicators for rapid recovery: nighttime lighting (positive indicator), distance to drainage pumping station (positive indicator), distance to sluice gate (positive indicator), distance to emergency rescue station (negative indicator), and distance to disaster shelter (negative indicator). Step 14: Form a three-level indicator system that includes a target layer (urban flood resilience), a criterion layer (3 dimensions), and an indicator layer (20 indicators).

[0007] Furthermore, the construction of the IFMS / Urban one-to-two-dimensional coupled urban flood simulation model described in step 2 includes the following steps: Step 21, collect basic data for the study area: use a combination of field surveys and data collection to obtain basic geographic and infrastructure data required for model construction, boundary condition data required for model operation, and measured data required for model accuracy verification.

[0008] Step 22: Basic data preprocessing and one-dimensional model construction. Geographic Information System (GIS) software is used to standardize and preprocess the collected basic data, including digital elevation model smoothing and slope analysis, land use classification system regularization, and verification of river and pipe network topology. A one-dimensional river network model is constructed using a river generalization method, with a focus on densifying key sections. Through the structured generalization of the pipe network system, a one-dimensional drainage pipe network model system of "node-pipe segment-outlet" is formed, clarifying the geometric attributes and hydraulic characteristic parameters of the pipe segments.

[0009] Step 23, Sub-catchment Division and 2D Model Construction: A hierarchical division method of "coarse division followed by fine division" is adopted. First, the primary drainage zones are delineated according to the distribution of the river network and pipe network. Then, the secondary sub-catchments are subdivided based on the Thiessen polygon method (areas without pipe network coverage are not divided separately). The core topography and underlying surface parameters of the sub-catchments are extracted. The surface 2D model is constructed using unstructured quadrilateral meshing technology. The mesh scale is determined according to the principle of denser meshing around the river channel and conventional meshing in other areas. The main roads are used as meshing constraints. The mesh elevation is assigned in combination with topographic data. The effect of buildings on water flow obstruction is simulated by adjusting the elevation.

[0010] Step 24: Coupling of one-dimensional and two-dimensional models. In the coupling module of IFMS / Urban software, inspection wells are used as key connection nodes of the one-dimensional and two-dimensional models. Through orifice connection technology, a two-way hydraulic coupling relationship between the one-dimensional river network-pipeline network model and the two-dimensional surface model is established to realize the dynamic water volume interaction simulation among the river, pipeline network and surface.

[0011] Step 25, Model Validation: Select actual rainfall events as input conditions, and use a combination of matching degree verification of flood-prone area inundation range and quantitative error analysis of inundation depth. By comparing simulation results with actual data, verify the model's ability to replicate flood processes and its accuracy and reliability.

[0012] Step 26: Model parameter setting and calculation scheme determination. Based on the characteristics of the underlying surface and the hydraulic motion law of the study area, core hydraulic parameters such as permeable zone, impermeable zone, pipe network and river channel are set. The design rainfall intensity of different return periods is calculated using the official rainfall intensity formula published by the study area. Referring to the spatiotemporal distribution characteristics of regional rainfall, the Chicago rainfall pattern is used to allocate the rainfall time history and determine the model simulation calculation scheme.

[0013] Step 27: Extract key parameters of urban flooding. Extract key parameters of urban flooding such as surface water depth and distribution of flood-prone points in the current year from the coupled simulation results for subsequent index quantification.

[0014] Furthermore, in step 3, the weight calculation of indicators and the quantification of resilience levels are achieved by using a comprehensive weighting method combining the analytic hierarchy process (AHP) (subjective weighting) and the entropy weighting method (objective weighting) to determine the weights of each indicator. The resilience level is then quantified by calculating the resilience score of each evaluation unit using the best-in-best solution distance method (TOPSIS). The specific steps are as follows: Step 31: Standardize the indicator data. Organize the original data of the 20 indicators (including the waterlogging parameters extracted in Step 2 and the distance and density parameters obtained from GIS spatial analysis) into an n×m evaluation matrix (n is the number of evaluation units, m=20) in 100m×100m evaluation units. Use the extreme value method to eliminate the influence of dimensions. Calculate the standardized values ​​of both positive and negative indicators according to the formula.

[0015] Furthermore, the evaluation matrix is ​​as follows: For n evaluation objects and m evaluation indicators, establish an evaluation matrix. In the formula, i = 1, 2, ..., n; j = 1, 2, ..., m.

[0016] Furthermore, the formula for calculating the positive indicator is as follows: In the formula, This is the standardized value (range 0~1) of the j-th indicator for the i-th evaluation unit. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; if all evaluation units of an indicator have the same value, its standardized value is set to 0.5. Furthermore, the formula for calculating the negative index is as follows: In the formula, This is the standardized value (range 0~1) of the j-th indicator for the i-th evaluation unit. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; if all evaluation units of an indicator have the same value, its standardized value is set to 0.5. Step 32: Calculate the comprehensive weight using the comprehensive weighting method. Calculate the objective weight using the entropy weight method. First, calculate the proportion of the i-th evaluation unit under the j-th indicator using the formula. Then, the information entropy is calculated using the formula. Finally, the objective weights are calculated using a formula. Calculating subjective weights using the analytic hierarchy process (AHP). First, invite 5-10 experts in water conservancy, planning, and emergency response to construct a judgment matrix for indicators within the same criterion layer using a 1-9 scaling method; then calculate the maximum eigenvalue of the judgment matrix. The corresponding feature vectors are then normalized through a consistency test. The global subjective weights are calculated by combining the criterion layer weights (0.35 for urban space, 0.4 for lifeline engineering, and 0.25 for rapid recovery). The overall weight is calculated by combining subjective and objective factors according to the formula. This reflects the overall impact of the indicators on resilience; Furthermore, the formula for calculating the proportion of the i-th evaluation unit under the j-th indicator is as follows: If in the formula ,set up Avoid logarithms being meaningless.

[0017] Furthermore, the formula for calculating the information entropy is as follows: In the formula .

[0018] Furthermore, the objective weight calculation formula is as follows: In the formula Must meet .

[0019] Furthermore, the consistency test calculation formula is as follows: In the formula , RI represents the number of criteria-level indicators, and RI is the average random consistency index.

[0020] Furthermore, the formula for calculating the comprehensive weight is as follows: Step 33: Quantify resilience level using the TOPSIS method. First, evaluate the parameter matrix from step S31. Forward processing yields For the normalized matrix Standardization is performed to obtain the standardized decision matrix. Then, the standardized decision matrix is... After weighting, a weighted standardized decision matrix is ​​obtained. Determine the ideal solution. With negative ideal solution ; Through formula , Calculate the geometric distance between each evaluation unit and the positive and negative ideal solutions; use the formula... Calculate the similarity in toughness. The range is 0~1, with values ​​closer to 1 indicating stronger toughness; the natural discontinuity grading method is used to classify... Divided into high, that is Higher, that is Medium, that is Lower, i.e. Low, that is Five resilience levels; Furthermore, the positiveization process is applied to positive indicators. = Regarding negative indicators = ; Furthermore, the construction of the standardized decision matrix involves standardizing the forward-oriented parameter matrix using a normalization formula, as shown below: Furthermore, the weighted standardized decision matrix is ​​shown below: = In the formula, The comprehensive weights calculated in step 32 are... For standardized decision matrices; Furthermore, step 4, which involves identifying low-toughness regions and locating key indicators, includes the following steps: Step 41, Identification of low-resilience areas. Load resilience proximity data in ArcGIS software. For raster data, calculate Moran's I index (if Moran's I index is lower than Moran's I index). and (This indicates a significant spatial clustering of resilience levels); cluster maps are generated using LISA cluster analysis to identify "low-low clustering" areas (areas with concentrated distribution of low-resilience units, which are key targets for remediation). Step 42, Key Indicator Identification. Calculate the weighted standard deviation of each indicator. Compared to high toughness ( ) and low toughness ( ) regional indicator mean, filter Large, significant difference in mean ( Key indicators.

[0021] Furthermore, the formula for calculating the weighted standard deviation is as follows: In the formula The standard deviation of the standardized values ​​of the indicator. This is the mean.

[0022] Furthermore, the resilience enhancement measures and closed-loop verification described in step 5 include the following steps: Step 51: Develop regional measures. For older urban areas with low-lying terrain and challenging pipe network upgrades, construct water storage tanks to collect surface water. Additionally, for areas with high upstream flow and significant pipe network drainage pressure, consider adding drainage pumping stations to improve regional pumping capacity. In areas with insufficient pipe network drainage capacity and low pipe network density, increase pipe size and slope to enhance drainage capacity.

[0023] Step 52: Verify the effectiveness of the measures. Adjust the corresponding parameters of the measures in the IFMS / Urban model, re-simulate 10-50 year return period rainfall, and calculate the resilience approximation. The requirement is that the proportion of resilience enhancement evaluation units is ≥80%.

[0024] Step 53: Closed-loop optimization. Combined with dynamic adjustment measures of urban development planning, a closed-loop resilience enhancement process of "assessment-identification-improvement-verification" is formed to ensure continuous optimization of urban flood resilience.

[0025] The beneficial effects of this invention are as follows: This invention presents a method for assessing urban flood resilience and optimizing the configuration of flood control facilities. It integrates resilience theory, disaster chain theory, and hydrological and hydrodynamic mechanisms, overcoming the limitations of traditional assessments. It constructs an indicator system covering multiple dimensions—natural, social, economic, and emergency—and the entire process from pre-disaster to post-disaster, comprehensively reflecting the core connotations of urban flood resilience. Through a one- or two-dimensional coupled IFMS / Urban model, it completes design storm calculations, data processing, modeling simulations, and coupled verification according to specifications, providing high-precision flood parameters with a ≥85% hit rate for flood-prone areas, thus solving the problem of disconnect between static assessments and actual processes. It employs a comprehensive weighting method to balance subjective experience and objective data patterns, combined with the TOPSIS method to intuitively quantify resilience levels, and uses spatial analysis to accurately locate weak areas, avoiding biases from single methods. Targeted improvement measures are formulated for different regions, and the optimization effect is verified through model iterations, forming a dynamic closed-loop improvement mechanism. This method is applicable to long-term flood resilience management in cities of different sizes, playing a core role in scientifically and quantitatively guiding urban flood resilience assessment, optimizing facility layout, and improving control effectiveness.

[0026] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0027] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 is a flowchart of the urban flooding resilience evaluation method of the present invention; Figure 2 is Figure 1 System structure diagram of urban waterlogging resilience evaluation index system in medium-sized cities; Figure 3 yes Figure 1 A schematic diagram showing the distribution of urban spatial resilience levels in the study area. Figure 4 yes Figure 1 A schematic diagram showing the distribution of resilience levels across different urban spatial dimensions in the study area. Figure 5 yes Figure 1 A schematic diagram showing the distribution of lifeline engineering resilience levels in the central study area; Figure 6 yes Figure 1 A schematic diagram showing the distribution of resilience levels across different zones within the lifeline engineering dimension of the central study area; Figure 7 yes Figure 1 A schematic diagram illustrating the distribution of rapid recovery dimension resilience levels in the study area. Figure 8 yes Figure 1A schematic diagram showing the distribution of resilience levels across different regions of the rapid recovery dimension in the study area. Figure 9 yes Figure 1 A schematic diagram showing the distribution of the overall resilience level in the study area. Figure 10 yes Figure 1 A schematic diagram showing the overall distribution of resilience levels across different zones in the central study area; Figure 11 is Figure 1 Average values ​​of various indicators for different resilience levels in the study area; Figure 12 is Figure 1 A schematic diagram of LISA clustering in the low-toughness region of the study area; Detailed Implementation like Figures 1-12 As shown, this invention discloses a method for evaluating urban flood resilience based on multi-dimensional indicators and a coupled model.

[0028] The study area is the southwestern part of Suqian City, covering an area of ​​164.28 km², with a terrain that slopes from north to south (ground elevation 15-58 m). The average annual precipitation is 915 mm, with the flood season (June to September) accounting for 70% of the annual rainfall. The area includes 14 rivers such as the ancient Yellow River and the Ximinbian River. The drainage network in the old city is designed for a 1-2 year return period, which aligns with the application scenario of this invention.

[0029] like Figure 1 As shown in the figure, a method for evaluating urban flood resilience based on multi-dimensional indicators and a coupled model in this specific embodiment includes the following steps: Step 1, obtaining the urban flood resilience evaluation indicator system, includes the following steps: Step 1: Based on resilience theory and disaster chain theory, identify the influencing factors of urban flooding resilience, select 20 indicators from three dimensions of urban space, lifeline engineering, and rapid recovery, and construct a three-level urban flooding resilience evaluation index system.

[0030] Step 11, Implementation of Urban Spatial Dimension Indicators. ArcGIS 10.8 software was used to process the basic data, obtaining data for seven indicators: green coverage rate, water coverage rate, topographic slope, ground elevation, distance to river network, distance to flood-prone areas, and surface water depth. Of these indicators, the first six are positive, while surface water depth is a negative indicator. The data was processed using 3... The principle test showed no abnormalities.

[0031] Step 12, Implementation of Lifeline Engineering Dimension Indicators. Eight indicators are obtained through POI data and infrastructure data: distance to medical institutions, road network density, distance to transportation hubs, drainage network density, distance to telecommunications maintenance stations, distance to power maintenance stations, water supply network density, and population density. Among these, road network density, drainage network density, and water supply network density are positive indicators, while the rest are negative indicators.

[0032] Step 13, implement rapid recovery of dimensional indicators. Integrate multi-source data to obtain 5 indicators: nighttime light, distance to drainage pumping station, distance to drainage sluice gate, distance to emergency rescue station, and distance to disaster relief shelter. The first 3 are positive indicators, and the last 2 are negative indicators. Combined with the data from steps 11 and 12, a total of 20 comprehensive indicators (x1~x) are finally formed. 20 ).

[0033] Step 14: A three-level indicator system is formed, including the target layer (urban flood resilience), the criterion layer (3 dimensions), and the indicator layer (20 indicators), as shown in Table 1.

[0034] Table 1. Evaluation Index System for Urban Flood Resilience Step 2: Construct a coupled one-dimensional and two-dimensional urban flood simulation model using IFMS / Urban. First, collect hydrological, meteorological, topographic, river network, and land use data for the study area. Then, use IFMS / Urban software to perform the following operations: import DEM data to generate a two-dimensional surface grid, and perform elevation uplift processing on the grid in the building area; digitize the river network and drainage network, generalize river cross-sections, pipe parameters, and manhole attributes to construct a one-dimensional model; establish a one-dimensional and two-dimensional coupling relationship using orifice connections, and set parameters such as permeable depression storage capacity, Manning coefficient, and soil infiltration rate; input measured rainfall data to validate the model, requiring a simulation hit rate of ≥85% for flood-prone points and an average relative error of ≤20% for the maximum inundation depth; simulate rainfall scenarios with different return periods and output data on surface water depth and distribution of flood-prone points. Step 21: Collect basic data for the study area, including model construction data such as 5m resolution DEM, 5m resolution land use, measured river cross sections, pipeline network data of manholes and pipes, and building distribution; boundary condition data such as hourly rainfall at rain gauge stations and river water level-flow relationship; and model validation data such as measured rainfall inundation depth and distribution of flood-prone areas.

[0035] Step 22: Basic data preprocessing and one-dimensional model construction. Data preprocessing is completed using ArcGIS software, including DEM depression smoothing and slope analysis, land use reclassification, and river network topology inspection; rivers are generalized and key cross-sections are densified to construct a one-dimensional river network model; the drainage system is generalized into a "manhole-pipe-outlet" system, and parameters such as pipe cross-sectional shape and pipe diameter are defined to construct a one-dimensional drainage network model.

[0036] Step 23: Sub-catchment division and 2D model construction. First, the area is divided into 21 drainage zones according to the distribution of rivers and pipe networks. Then, 2244 sub-catchments are divided using the Thiessen polygon method (farmland / village areas without pipe networks are not divided). Parameters such as sub-catchment area, slope, and proportion of impermeable areas are extracted. 395469 unstructured quadrilateral grids are divided into the modeling area (excluding river channels). The main roads are used as grid control lines. The grid elevation is assigned based on the 2.5m precision DEM. The grid elevation of the building is raised to construct the 2D surface model.

[0037] Step 24: Coupling of one-dimensional and two-dimensional models. In the "Coupled Modeling Module" of IFMS / Urban software, the hydraulic coupling relationship between the one-dimensional river network-pipeline model and the two-dimensional surface model is established using the manhole as the connection node and the orifice connection method, so as to realize the dynamic interaction between the river, pipeline and surface water volume.

[0038] Step 25, Model Validation: Using two measured rainfall events as input, the flooding situation of 21 flood-prone points was simulated. The flooding rates of the flood-prone points in the two rainfall events were 90.5% and 85.7%, respectively. The average relative errors of the measured and simulated flooding depths were 18.1% and 19.3%, respectively (absolute error < 0.1m), thus validating the model accuracy.

[0039] Step 26: Model parameter setting and calculation scheme determination. Set core modeling parameters, including Manning coefficient for permeable areas, Manning coefficient for impermeable areas, Manning coefficient for pipes, and maximum soil infiltration rate. Use the local official rainstorm intensity formula to calculate the design rainstorm intensity for 10-year, 20-year, 30-year, and 50-year return periods. Use the Chicago rain pattern to distribute the rainfall time history from 120 to 180 minutes and determine the simulation calculation scheme.

[0040] Furthermore, this specific example uses Suqian City as an example, so the intensity of the aforementioned rainstorm is as follows: In the formula, i represents the intensity of the rainstorm (mm / min); T represents the return period (years); and t represents the duration of rainfall (min).

[0041] Calculate the 3-hour design rainfall for 10a to 50a return periods: 105.1 mm cumulative rainfall for 10a return period (maximum rainfall intensity 3.1 mm / min), 121.3 mm for 20a return period (maximum rainfall intensity 3.6 mm / min), 130.8 mm for 30a return period (maximum rainfall intensity 3.9 mm / min), and 142.8 mm for 50a return period (maximum rainfall intensity 4.2 mm / min); allocate the 180-minute rainfall duration according to the Chicago rainfall pattern (peak rainfall coefficient 0.4), with the peak rainfall occurring at the 72nd minute.

[0042] Step 27: Extraction of key flooding parameters. Key flooding parameters are extracted from the coupled simulation results: Surface water depth: Under a 50-year return period rainfall, the water depth in the eastern old urban area is 0.8~2.24m, while in the western rural area it is <0.3m; Distribution of flood-prone points: Of the 21 measured flood-prone points, 19 were simulated to have water accumulation; Pipeline overflow nodes: Of the 3079 inspection wells, 58 experienced overflow. These parameters are used for subsequent quantification of the "surface water depth" and "distance from flood-prone points" indicators.

[0043] Step 3, Quantitative assessment of urban flood resilience: The weight of each indicator is determined by a comprehensive weighting method combining the analytic hierarchy process (subjective weighting) and the entropy weighting method (objective weighting). The resilience score of each assessment unit is calculated by the top-inferior solution distance method (TOPSIS). The resilience level is divided into five levels: high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience. Spatial autocorrelation analysis is used to identify resilience clusters and weak areas.

[0044] Step 31, Standardization of Indicator Data: The raw data of the 20 indicators (including the waterlogging parameters extracted in Step 2 and the distance and density parameters obtained from GIS spatial analysis) are divided into evaluation units of 100m×100m, totaling 17317 units. The raw data of the 20 indicators are then organized into a 17317×20 matrix. The extreme value method is used to eliminate the influence of dimensions. Positive indicators (such as green coverage rate) are standardized using the formula, and negative indicators (such as ground water depth) are also standardized using the formula. The water supply network density has a consistent value in some areas, so its standardized value is set to 0.5. After standardization, the data range is 0~1, with no outliers.

[0045] Furthermore, the evaluation matrix is ​​as follows: For n evaluation objects and m evaluation indicators, establish an evaluation matrix. In the formula, i = 1, 2, ..., n; j = 1, 2, ..., m.

[0046] Furthermore, the formula for calculating the positive indicator is as follows: In the formula, This is the standardized value (range 0~1) of the j-th indicator for the i-th evaluation unit. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; if all evaluation units of an indicator have the same value, its standardized value is set to 0.5. Furthermore, the formula for calculating the negative index is as follows: In the formula, This is the standardized value (range 0~1) of the j-th indicator for the i-th evaluation unit. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; if all evaluation units of an indicator have the same value, its standardized value is set to 0.5. Step 32: Calculate the comprehensive weight using the comprehensive weighting method. Calculate the objective weight using the entropy weight method. First, calculate the proportion of the i-th evaluation unit under the j-th indicator using the formula. Then, the information entropy is calculated using the formula. Finally, the objective weights are calculated using a formula. Calculating subjective weights using the analytic hierarchy process (AHP). First, invite 5-10 experts in water conservancy, planning, and emergency response to construct a judgment matrix for indicators within the same criterion layer using a 1-9 scaling method; then calculate the maximum eigenvalue of the judgment matrix. The corresponding feature vectors are then normalized through a consistency test. The global subjective weights are calculated by combining the criterion layer weights (0.35 for urban space, 0.4 for lifeline engineering, and 0.25 for rapid recovery). The overall weight is calculated by combining subjective and objective factors according to the formula. The results show: ground elevation =0.081, distance from medical institution =0.074, Distance from disaster shelter =0.072 and terrain slope Dj=0.071 are the top four weighted indicators.

[0047] Furthermore, the formula for calculating the proportion of the i-th evaluation unit under the j-th indicator is as follows: If in the formula ,set up Avoid logarithms being meaningless.

[0048] Furthermore, the formula for calculating the information entropy is as follows: In the formula .

[0049] Furthermore, the objective weight calculation formula is as follows: In the formula Must meet .

[0050] Furthermore, the consistency test calculation formula is as follows: In the formula , RI represents the number of criteria-level indicators, and RI is the average random consistency index.

[0051] Furthermore, the formula for calculating the comprehensive weight is as follows: Step 33: Quantify resilience level using the TOPSIS method. First, evaluate the parameter matrix from step S31. Forward processing yields For the normalized matrix Standardization is performed to obtain the standardized decision matrix. Then, the standardized decision matrix is... After weighting, a weighted standardized decision matrix is ​​obtained. Determine the ideal solution. With negative ideal solution ; Through formula , Calculate the geometric distance between each evaluation unit and the positive and negative ideal solutions; use the formula... Calculate the similarity in toughness. The range is 0~1, with values ​​closer to 1 indicating stronger toughness; the natural discontinuity grading method is used to classify... Divided into high, that is Higher, that is Medium, that is Lower, i.e. Low, that is Five resilience levels, spatial distribution of each resilience level as follows: Figures 3-10 As shown.

[0052] Furthermore, the positiveization process is applied to positive indicators. = Regarding negative indicators = ; Furthermore, the construction of the standardized decision matrix involves standardizing the forward-oriented parameter matrix using a normalization formula, as shown below: Furthermore, the weighted standardized decision matrix is ​​shown below: = In the formula, The comprehensive weights calculated in step 32 are... For standardized decision matrices; Step 4: Identify low-resilience clustering areas through Moran's I index and LISA cluster analysis, and screen key influencing indicators such as drainage network density and distance from flood-prone areas by combining indicator weights and the mean values ​​of indicators for different resilience levels. Step 41, Identification of low-resilience areas. Load resilience proximity data in ArcGIS software. The raster data was used to calculate Moran's I index = 0.708 (P<0.05), indicating significant spatial clustering of resilience levels. LISA cluster analysis generated a cluster map, identifying "low-low clustering" areas accounting for 18.29%, which are key areas for remediation. Overlaying land use data revealed that low-resilience areas are mostly located in old urban areas and low-lying areas, such as... Figure 12 As shown.

[0053] Step 42, Key Indicator Identification. Calculate the weighted standard deviation of each indicator. and screening Key indicators; comprehensive and The study identified several key indicators that significantly impact resilience assessment results, including green coverage rate, distance from river network, distance from flood-prone areas, sluice gate scheduling capacity, and drainage network density. These indicators are considered the core entry points for resilience enhancement.

[0054] Furthermore, the formula for calculating the weighted standard deviation is as follows: In the formula The standard deviation of the standardized values ​​of the indicator. This is the mean.

[0055] Step 5: Based on the different target level years and different catchment area needs of the study area, formulate measures such as adding sponge city facilities, upgrading pipe networks, and raising riverbanks. Execute steps 2-4 in a cycle to verify the effectiveness of the measures, ensuring that more than 80% of the area has improved resilience, thus forming a closed-loop resilience improvement process.

[0056] Step 51: Develop regional measures. For older urban areas with low-lying terrain and challenging pipe network upgrades, construct water storage tanks to collect surface water. Additionally, for areas with high upstream flow and significant pipe network drainage pressure, consider adding drainage pumping stations to improve regional pumping capacity. In areas with insufficient pipe network drainage capacity and low pipe network density, increase pipe size and slope to enhance drainage capacity.

[0057] Step 52: Verify the effectiveness of the measures. Adjust the corresponding parameters of the measures in the IFMS / Urban model. Resimulate using a 50-year return period for rainfall, and repeat steps 3-4 to calculate the resilience approximation. The results showed that the resilience level of 85.8% of the regions improved after the measures were implemented, and the measures achieved their intended effect.

[0058] Step 53: Closed-loop optimization. Combined with dynamic adjustment measures of urban development planning, a closed-loop resilience enhancement process of "assessment-identification-improvement-verification" is formed to ensure continuous optimization of urban flood resilience.

[0059] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for evaluating urban flood resilience based on multi-dimensional indicators and a coupled model, characterized in that, Includes the following steps: S1. Select indicators from three dimensions—urban space, lifeline engineering, and rapid recovery—to construct a three-level urban flood resilience evaluation index system; S2. Construct a one-dimensional coupled urban flood simulation model using IFMS / Urban. Collect basic data of the study area and preprocess it. Perform the following operations using IFMS / Urban software: import DEM data to generate a two-dimensional surface grid, and perform elevation uplift processing on the grid in the building area; digitize the river network and drainage network, generalize river cross-sections, pipeline parameters, and manhole attributes, and construct a one-dimensional model; establish a one-dimensional coupling relationship using orifice connection method and set core modeling parameters; input measured rainfall data to simulate rainfall scenarios with different return periods, and extract key flooding parameters from the coupled simulation results for quantification of evaluation indicators. S3. Quantitative assessment of urban waterlogging resilience level: Based on key waterlogging parameters and a three-level urban waterlogging resilience evaluation index system, the weight of each index is determined by a comprehensive weighting method combining the analytic hierarchy process and the entropy weight method. The resilience score of each assessment unit is calculated by the superior-inferior solution distance method, and the resilience level is divided into five levels: high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience.

2. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 1, characterized in that: It also includes S4 and S5; S4. Identify low-resilience clustering areas through Moran's I index and LISA cluster analysis, and screen key influencing indicators by combining indicator weights and the mean values ​​of indicators at different resilience levels. S5. Based on the identified low-resilience clusters and key influencing indicators, formulate resilience enhancement measures and iteratively execute S2~S4 to verify the effectiveness of the measures, forming a closed-loop resilience enhancement process.

3. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 2, characterized in that: The three-level urban flood resilience evaluation index system described in S1 includes a target layer, a criterion layer, and an indicator layer. The target layer is urban flood resilience, the criterion layer includes three dimensions: urban space, lifeline engineering, and rapid recovery, and the indicator layer includes 20 indicators. The urban spatial dimension includes seven indicators: green coverage rate, water coverage rate, topographic slope, ground elevation, distance from river network, distance from flood-prone areas, and ground water depth. Among them, green coverage rate, water coverage rate, topographic slope, ground elevation, distance from river network, and distance from flood-prone areas are positive indicators, while ground water depth is a negative indicator. The lifeline project dimension includes eight indicators: distance to medical institutions, road network density, distance to transportation hubs, drainage network density, distance to telecommunications maintenance stations, water supply network density, distance to power maintenance stations, and population density. Among these, road network density, drainage network density, and water supply network density are positive indicators, while distance to medical institutions, distance to transportation hubs, telecommunications maintenance stations, power maintenance stations, and population density are negative indicators. The rapid recovery dimension includes five indicators: nighttime lighting, distance to drainage pumping stations, distance to sluice gates, distance to emergency rescue stations, and distance to disaster shelters. Among them, nighttime lighting, distance to drainage pumping stations, and distance to sluice gates are positive indicators, while distance to emergency rescue stations and distance to disaster shelters are negative indicators.

4. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 3, characterized in that: S2 includes the following steps: S21. Collect basic data for the study area: Use a combination of field surveys and data collection to obtain basic data, including basic geographic and infrastructure data required for model building, boundary condition data required for model operation, and measured data required for model accuracy verification. S22. Basic data preprocessing and one-dimensional model construction: The collected basic data is standardized and preprocessed using geographic information system software, including digital elevation model filling and smoothing, slope analysis, land use classification system regularization, and verification of river network and pipeline network topology. A one-dimensional river network model is constructed using the river channel generalization method in IFMS / Urban software. A one-dimensional drainage pipeline network model system is formed by structural generalization of the pipeline system, consisting of nodes, pipe segments, and discharge outlets. S23. Sub-catchment delineation and 2D model construction: Primary drainage zones are delineated according to the distribution of river network and pipe network, and then secondary sub-catchments are subdivided based on the Thiessen polygon method to extract the core topography and underlying surface parameters of the sub-catchments; a 2D surface model is constructed using unstructured quadrilateral meshing technology, and the mesh scale is determined according to the principle of denser meshing around the river channel and conventional meshing in other areas. Main roads are used as meshing constraints, and the mesh elevation is assigned in combination with topographic data. The effect of buildings on water flow obstruction is simulated by adjusting the elevation. S24. Coupling of one-dimensional and two-dimensional models: In the coupling module of IFMS / Urban software, inspection wells are used as key connection nodes of one-dimensional and two-dimensional models. Through orifice connection technology, a two-way hydraulic coupling relationship between the one-dimensional river network-pipeline network model and the two-dimensional surface model is established to realize the dynamic water volume interaction simulation among the river, pipeline and surface. S25. Model Validation: Selecting actual rainfall events as input conditions, a combination of matching degree verification of flood-prone areas and quantitative error analysis of flood depth is adopted. By comparing simulation results with actual data, the model's ability to replicate flood processes and its accuracy and reliability are verified. S26. Model parameter settings and calculation scheme determination: Based on the characteristics of the underlying surface and the hydraulic motion law of the study area, the core hydraulic parameters of the permeable zone, impermeable zone, pipe network and river channel are set; the design rainfall intensity of different return periods is calculated using the rainfall intensity formula published for the study area; the rainfall time history is allocated by adopting the Chicago rainfall pattern through the spatiotemporal distribution characteristics of regional rainfall; and the model simulation calculation scheme is determined. S27. Extraction of key parameters for urban flooding: Extract key parameters for urban flooding in the study area at the current level of the year from the coupled simulation results, including the surface water depth and the distribution of flood-prone points, for the quantification of evaluation indicators.

5. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 4, characterized in that: S3 includes the following steps: S31. Standardization of indicator data: The original data of 20 indicators are organized into an n×m evaluation matrix in 100m×100m evaluation units, where n is the number of evaluation units and m=20. The original data includes the key waterlogging parameters extracted in S2 and the distance and density parameters obtained from GIS spatial analysis. The extreme value method is used to eliminate the influence of dimensions. The standardized values ​​of both positive and negative indicators are calculated according to the formula. For n evaluation objects and m evaluation indicators, establish an evaluation matrix. The evaluation matrix is ​​shown below: In the formula, i = 1, 2, ..., n; j = 1, 2, ..., m; The formula for calculating the positive indicator is as follows: In the formula, Let j be the standardized value of the j-th indicator in the i-th evaluation unit, ranging from 0 to 1. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; if all evaluation units of the indicator have the same value, its standardized value is set to 0.

5. The formula for calculating the negative indicator is as follows: In the formula, Let j be the standardized value of the j-th indicator in the i-th evaluation unit, ranging from 0 to 1. The original value of the indicator. , These are the maximum and minimum values ​​of the i-th indicator, respectively; If all evaluation units of a certain indicator have the same value, its standardized value is set to 0.5; S32. Calculate the comprehensive weight using the comprehensive weighting method: calculate the objective weight using the entropy weighting method. First, calculate the proportion of the i-th evaluation unit under the j-th indicator using the formula. ; Then calculate the information entropy using the formula. Finally, the objective weights are calculated using a formula. ; Analytic Hierarchy Process (AHP) for calculating subjective weights First, invite 5-10 experts in water conservancy, planning, and emergency response to construct a judgment matrix for indicators within the same criterion layer using a 1-9 scaling method; then calculate the maximum eigenvalue of the judgment matrix. The corresponding feature vectors are then normalized through a consistency test, and the global subjective weights are calculated by combining the criterion layer weights. The weights for the criteria layer include urban space, lifeline engineering, and rapid recovery, with weights of 0.35, 0.4, and 0.25 respectively; the comprehensive weight is calculated by integrating subjective and objective weights. This reflects the overall impact of the indicators on resilience; The formula for calculating the proportion of the i-th evaluation unit under the j-th indicator is as follows: The formula for calculating information entropy is as follows: The formula for calculating objective weights is as follows: The formula for calculating the consistency test is as follows: In the formula, , RI represents the number of criteria-level indicators, and RI is the average random consistency index. The formula for calculating the overall weight is as follows: S33, the TOPSIS method quantifies resilience level, first evaluating the parameter matrix in S31. Forward processing yields For the already normalized matrix Standardization is performed to obtain the standardized decision matrix. Then, the standardized decision matrix is ​​used. After weighting, a weighted standardized decision matrix is ​​obtained. Determine the ideal solution With negative ideal solution ; Through formula , Calculate the geometric distance between each evaluation unit and the positive and negative ideal solutions; use the formula... Calculate the similarity in toughness. The range is 0~1, with values ​​closer to 1 indicating stronger toughness; the natural discontinuity grading method is used to classify... Divided into high, that is Higher, that is Medium, that is Lower, i.e. Low, that is Five resilience levels; Positive processing is for positive indicators = Regarding negative indicators = ; A standardized decision matrix is ​​constructed, and the normalized parameter matrix is ​​standardized using a normalization formula, as shown below: The weighted standardized decision matrix is ​​shown below: = In the formula, The overall weights calculated for S32 are... This is a standardized decision matrix.

6. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 5, characterized in that: S4 includes the following steps: S41. Identification of low-resilience areas: Loading resilience proximity in ArcGIS software. For raster data, calculate Moran's I index, if and This indicates a significant spatial clustering of toughness levels; cluster maps are generated using LISA cluster analysis to identify concentrated distribution areas of low-toughness units; S42. Key indicator identification and calculation of weighted standard deviation for each indicator. In contrast to high toughness With low toughness Regional indicator mean, filtering Key influencing indicators with large and significant differences in means; the criteria for determining significant differences in means are as follows: ; The formula for calculating the weighted standard deviation is as follows: In the formula The standard deviation of the standardized values ​​of the indicator. This is the mean.

7. The urban flood resilience evaluation method based on multi-dimensional indicators and a coupled model according to claim 6, characterized in that: S5 includes the following steps: S51. Develop regional measures. For old urban areas with low terrain and difficult pipeline renovation, construct water storage tanks to collect surface water in the area. For areas with large upstream flow and high pipeline drainage pressure, add drainage pumping stations to improve the area's pumping capacity. In areas with insufficient pipeline drainage capacity and low pipeline density, increase pipeline size and slope to improve drainage capacity. S52. Verification of the effectiveness of the measures: Adjust the corresponding parameters of the measures in the IFMS / Urban one-dimensional coupled urban flood simulation model, re-simulate 10-50 year return period rainfall and calculate the resilience approximation. The proportion of resilience enhancement evaluation units should be ≥80%. S53. Closed-loop optimization: Combined with dynamic adjustment measures of urban development planning, a closed-loop resilience enhancement process of assessment, identification, improvement and verification is formed to continuously optimize urban flood resilience.

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