Urban waterlogging model-based evaluation method for storage and drainage facilities
By constructing an SWMM model and combining it with FMEA and Bayesian networks, the problem of insufficient assessment of single facilities in existing technologies is solved, enabling quantitative assessment of complex environmental changes and improving the scheduling capability of flood control and drainage systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YANGTZE ECOLOGY & ENVIRONMENT CO LTD
- Filing Date
- 2025-07-10
- Publication Date
- 2026-07-24
AI Technical Summary
Existing methods for assessing urban flood control and drainage effectiveness often focus on individual facilities, lack systematic evaluation, and fail to adequately consider the impact of complex external conditions and environmental changes.
Based on the urban flooding model, a SWMM model was constructed for parameter calibration and verification. FMEA analysis and Bayesian networks were used, combined with normalized indices of multiple failure modes, to calculate RPN values, screen key failure modes, and conduct scenario simulation and failure probability calculation.
It enables the quantification of the impact of extreme rainfall on water storage and drainage facilities from a system perspective, identifies the shortcomings of flood control and drainage systems, improves the scheduling capacity of watershed-city flood control and drainage systems, and provides a system-level performance assessment of water storage and drainage facilities.
Smart Images
Figure CN120952597B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban flooding simulation and control, and in particular to a method for evaluating the effectiveness of water storage and drainage facilities based on urban flooding models. Background Technology
[0002] The combined effects of climate change and human activities have exacerbated the complexity of urban flooding. Although urban drainage and flood control systems have been further improved and some positive progress has been made in urban flooding management, urban flooding remains a significant constraint on high-quality urban development.
[0003] In response to urban flooding problems, recent research progress and technological achievements both domestically and internationally have mainly focused on the following five aspects: (1) The mechanism of urban flooding, including the basic characteristics and evolution of urban rainfall, with extreme rainstorms as the typical example, the evolution of urban surface runoff generation and quantitative attribution; (2) The simulation methods of urban flooding, including two main methods: mechanism-driven models represented by hydrological models, hydrodynamic models and hydro-hydrodynamic coupling models, and data-driven models trained using machine learning and deep learning technologies. The simulation methods also consider factors such as water accumulation time and inundation range as important indicators for urban flooding simulation. Furthermore, the simulation methods also focus on improving accuracy and efficiency, deep integration of urban-scale and watershed-scale models, and the application of mechanism models and... Significant progress has been made in the dynamic integration of numerical weather prediction, the real-time coupling of mechanism-driven simulation and data-driven simulation; (3) risk assessment of urban waterlogging disasters, including the assessment index system of urbanization process, flood disaster assessment index and method, etc.; (4) strategies for dealing with urban waterlogging, including urban waterlogging monitoring and early warning, joint scheduling of multi-level facilities of urban drainage system, planning and design of urban flood control and drainage system based on source control and emission reduction, etc.; (5) design of urban waterlogging drainage facilities and devices, including the structural design and simulation of storage and drainage facilities such as dredging devices, road surface structure, diversion well, rainwater grate and interception well.
[0004] Current research on the mechanisms and simulations of urban flooding risk has some limitations, mainly in the following aspects: (1) As an important component of the urban flood control and drainage system, urban flood control and drainage facilities suffer from problems such as unsystematic design and unscientific operation, which seriously restricts the effectiveness of the facilities. At present, quantitative assessment of the effectiveness of flood control and drainage systems is mainly based on single facilities, while theoretical research on the effectiveness of flood control and drainage systems from the perspective of watershed flood control-urban drainage systems is insufficient.
[0005] (2) The effectiveness of urban flood control, drainage and storage facilities is constrained by a variety of external conditions. At present, the simulation and regulation of storage and drainage facilities do not take into account external conditions sufficiently, especially the extreme events that are becoming increasingly prominent in changing environments.
[0006] (3) Domestic and foreign scholars have conducted research on the effectiveness of runoff control of sponge city infrastructure, analysis of pipeline carrying capacity and influencing factors, and analysis of runoff reduction rate of storage tanks and pumping stations using urban flood models. However, most studies mainly focus on the effectiveness of single-type facilities, and the evaluation methods for the effectiveness of storage and drainage facilities at the system level still need to be improved. Summary of the Invention
[0007] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method for evaluating the effectiveness of urban flood control and drainage facilities based on an urban flood model. This method addresses the technical problems that existing urban flood control and drainage effectiveness evaluation methods often focus on single facilities, lack systematic evaluation, and do not adequately consider the impact of complex external conditions and environmental changes.
[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model, which includes the following steps: S1. Obtain basic data on elevation, drainage network, soil and land use, rainfall, and historical waterlogging in the study area. The data formats include raster, vector, DWG, and tables. The reliability, consistency, and representativeness of the basic data are reviewed. S2. Construct the SWMM urban flooding model and perform model parameter calibration and verification; S3. Perform parameter sensitivity analysis: Select one parameter from the model parameters, randomly change the parameter value within the range of possible values, run the model to obtain a series of different output values, and then use the ratio of the change in output value to the change in parameter value as the evaluation criterion for the influence of the parameter on the model output value. S4. Use measured flow rate or water level data to calibrate and verify the parameters; S5. The urban stormwater storage and drainage system is divided into three parts: source control, process control, and end control. These parts include LID facilities, stormwater inlets, drainage pipe networks, canals, storage tanks, pumping stations, and sluice gates. Various failure modes are summarized. Then, FMEA analysis is conducted on the failure modes of the storage and drainage facilities using five normalized indicators: Severity (S), Occurrence (O), Detectivity (D), Propagation (P), and Recoverability (R). S6. Calculate the risk priority number (RPN) for each failure mode. S7. Based on the RPN value, screen the key failure modes of the storage and discharge facilities, generalize the key failure modes into several nodes, and construct a Bayesian network. S8. Based on the key failure modes screened by FMEA analysis of the storage and drainage system, scenario combination settings are made for the model, and the total overflow, overflow nodes and node failure probability are selected as evaluation indicators of the storage and drainage facility effectiveness; the PYSWMM platform is used to automatically call up the SWMM model and perform combined scenario simulation. S9. Based on the simulation results of the SWMM model of the critical failure modes, calculate the failure probability of each failure mode according to the Bayesian network.
[0009] Preferably, step S2 includes the following steps: S2.1 Divide the study area into sub-catchments and calculate empirical values of runoff generation and runoff parameters for each sub-catchment based on elevation, soil and land use data. S2.2. The flow parameters are calculated by solving the continuity equation and Manning's equation simultaneously to calculate the surface flow process; the hydrodynamic process in the pipeline network is calculated by the steady flow method, the kinematic wave method and the dynamic wave method; the Manning formula is used to link the flow velocity, water depth and pipeline friction, and the principles of mass conservation and momentum conservation are used to calculate the water flow in the pipeline. More preferably, in step S2.1, the empirical values for the runoff generation and collection parameters of each sub-catchment area are selected using the Horton equation method to simulate the infiltration process. This model describes a long-duration precipitation event, where the infiltration decay index decreases exponentially from the maximum infiltration rate to a certain minimum over time, as shown in the following formula:
[0010] In the formula, f is the infiltration rate (mm / h); f t The stable infiltration rate is (mm / h); f0 is the initial infiltration rate (mm / h); t is the precipitation duration (h); and k is the infiltration attenuation coefficient (1 / h).
[0011] More preferably, in step S2.2, the basic equation for calculating the water flow in the pipe is as follows, based on the principles of conservation of mass and momentum:
[0012]
[0013] In the formula, Q is the flow rate; A is the cross-sectional area of the water passage; q L t is the inflow rate per unit length; v is the flow velocity; h is the hydrostatic head; t is the time; x is the distance; S0 is the pipe bottom slope; S f Frictional gradient.
[0014] Preferably, in step S3, the Morris screening method is used for parameter sensitivity analysis.
[0015] Preferably, in step S3, the ratio of the output value change amplitude to the parameter change amplitude is the parameter sensitivity coefficient, which is calculated using the following formula:
[0016] In the formula: Y i+1 and Y i These are the output results obtained from the i-th and i+1-th runs of the model, respectively; Y 0 represents the model's output without adjusting the parameters; P i+1 and P i These represent the changes in parameter values used in the i-th and i+1-th runs of the model compared to the initial parameter values; n This represents the number of simulations performed by the model.
[0017] Preferably, step S4 specifically includes the following process: first, determining the objective function for model parameter calibration; then, simulating the rainfall-runoff process in the study area by changing the values of the sensitivity parameters; iterating the model parameters step by step based on the model simulation values and the objective function values; and finally obtaining the optimal parameter calibration result.
[0018] Preferably, in step S6, the formula for calculating the RPN value is as follows:
[0019] Among them, the five normalized indicators were assigned values using a combination of objective data from the historical operation of facilities in the study area and subjective experience from experts.
[0020] Preferably, in step S7, the Bayesian network construction method uses prior probability to infer the probability that the failure of the storage and drainage facility system is caused by failure mode i.
[0021] More preferably, the specific formula for inferring the probability that a failure of the storage and drainage facility system is caused by failure mode i using prior probability is as follows:
[0022] Among them, P(M) i |R) represents the failure of the storage and pumping system attributing the failure to the failure node M. i The probability of failure, P(R), is given by the law of total probability.
[0023] In the formula, Indicates the failed node M i The probability of causing the storage and discharge system to fail; Indicates the failed node Mi The probability of failure.
[0024] Beneficial effects of this invention: 1. The method described in this invention primarily evaluates the effectiveness of flood control and drainage systems based on urban flooding models. Compared to existing flood control and drainage effectiveness evaluation methods, this invention's method, based on the simulation of urban flood control and drainage physical processes, couples storage and drainage facility modules to further consider the impact of complex environmental changes, conducting evaluations of the flood control and drainage system and identifying the shortcomings of its storage and drainage facilities. Test results show that the flood control and drainage system effectiveness evaluation method based on urban flooding models proposed in this invention can quantify the impact of changes in extreme rainfall frequency and intensity, multiple disaster-causing factors, and other factors on the flood control and drainage effectiveness of storage and drainage facilities from a system perspective of watershed flood control and urban drainage. Furthermore, it can quantify the failure probability of each storage and drainage facility under various scenarios from a system perspective. This solves the technical problems of existing urban flood control and drainage effectiveness evaluation methods, which often focus on single facilities, lack system evaluation, and fail to adequately consider the impact of complex external conditions and environmental changes.
[0025] 2. This invention solves the problem of improving the efficiency of old flood control and drainage facilities in watersheds and cities. It implements the scheduling of watershed-city flood control and drainage systems and carries out supporting construction of related urban infrastructure.
[0026] 3. This invention solves the technical problem that existing urban flood control and drainage efficiency assessment methods mostly focus on single facilities, lack systematic assessment, and do not adequately consider the impact of complex external conditions and environmental changes. Attached Figure Description
[0027] Figure 1 This is a flowchart illustrating a method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model. Figure 2 This is a schematic diagram of the Morris screening method; Figure 3 FMEA analysis framework diagram; Figure 4 A graph showing the RPN value calculation results; Figure 5 Construct the resulting graph for the Bayesian network; Figure 6 The graph shows the results of the failure probability calculation. Detailed Implementation
[0028] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0029] Example 1: As Figure 1 As shown, a method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model includes the following steps: S1. Obtain basic data on elevation, drainage network, soil and land use, rainfall, and historical waterlogging in the study area. The data formats include raster, vector, DWG, and tables. The reliability, consistency, and representativeness of the basic data are reviewed. S2. Construct the SWMM urban flooding model and perform model parameter calibration and verification; S2.1. Divide the study area into sub-catchments and calculate empirical values of runoff generation and runoff parameters for each sub-catchment based on elevation, soil, and land use data. The Horton equation method is selected to simulate the infiltration process. This model describes a long-duration precipitation event where the infiltration decay index decreases exponentially from its maximum value to a minimum value over time. The calculation is shown in the following formula:
[0030] In the formula, f is the infiltration rate (mm / h); f t The stable infiltration rate is (mm / h); f0 is the initial infiltration rate (mm / h); t is the precipitation duration (h); and k is the infiltration attenuation coefficient (1 / h).
[0031] S2.2. The runoff parameters are calculated by simultaneously solving the continuity equation and Manning's equations to determine the surface runoff process; the hydrodynamic process within the pipe network is calculated using the steady flow method, the kinematic wave method, and the dynamic wave method; the Manning formula is used to link flow velocity, water depth, and pipe friction, and the principles of mass and momentum conservation are applied to calculate the water flow within the pipe. The basic equations are as follows:
[0032]
[0033] In the formula, Q is the flow rate; A is the cross-sectional area of the water passage; q L t is the inflow rate per unit length; v is the flow velocity; h is the hydrostatic head; t is the time; x is the distance; S0 is the pipe bottom slope; S f Frictional gradient; S3. Parameter sensitivity analysis using the Morris screening method: Select one parameter from the model parameters, randomly change the parameter value within its possible range, run the model to obtain a series of different output values, and then use the ratio of the output value change to the parameter change as the evaluation criterion for the parameter's influence on the model output value; the ratio of the output value change to the parameter change is the parameter's sensitivity coefficient, which is calculated using the following formula:
[0034] In the formula: Y i+1 and Y iThese are the output results obtained from the i-th and i+1-th runs of the model, respectively; Y 0 represents the model's output without adjusting the parameters; P i+1 and P i These represent the changes in parameter values used in the i-th and i+1-th runs of the model compared to the initial parameter values; n This represents the number of simulations performed by the model.
[0035] S4. Calibration and verification of parameters using measured flow or water level data: First, determine the objective function for model parameter calibration. Then, by changing the values of sensitive parameters, simulate the rainfall-runoff process in the study area. Iterate the model parameters step by step based on the model simulation values and the objective function values, and finally obtain the optimal parameter calibration results.
[0036] S5. The urban stormwater storage and drainage system is divided into three parts: source control, process control, and end control. These parts include LID facilities, stormwater inlets, drainage pipe networks, canals, storage tanks, pumping stations, and sluice gates. Various failure modes are summarized. Then, FMEA analysis is conducted on the failure modes of the storage and drainage facilities using five normalized indicators: Severity (S), Occurrence (O), Detectivity (D), Propagation (P), and Recoverability (R). S6. Calculate the Risk Priority Number (RPN) for each failure mode; the formula for calculating the RPN value is as follows:
[0037] Among them, the five normalized indicators were assigned values using a combination of objective data from the historical operation of facilities in the study area and subjective experience from experts.
[0038] S7. A Bayesian network is a directed acyclic graph model, consisting of nodes representing variables and connecting arcs representing the relationships between nodes. Based on probabilistic inference, and combining the conditional independence of the node variables, Bayesian networks use probabilistic inference algorithms to calculate the posterior probability of events. Since we cannot exhaustively enumerate all failure modes and calculate their classical probabilities, we can screen the key failure modes of storage and drainage facilities based on the RPN value, generalize these key failure modes into several nodes, and construct a Bayesian network. The Bayesian network construction method uses prior probability to infer the probability that the failure of the storage and drainage facility system is caused by failure mode i. The specific formula is as follows:
[0039] Among them, P(M) i |R) represents the failure of the storage and pumping system attributing the failure to the failure node M. i The probability of failure, P(R), is given by the law of total probability.
[0040] In the formula, Indicates the failed node M i The probability of causing the storage and discharge system to fail; Indicates the failed node M i The probability of failure.
[0041] S8. Based on the key failure modes screened by FMEA analysis of the storage and drainage system, scenario combination settings are made for the model, and the total overflow, overflow node and node failure probability are selected as evaluation indicators of the storage and drainage facility effectiveness; the PYSWMM platform is used to automatically call up the SWMM model and perform scenario combination simulation.
[0042] S9. Based on the simulation results of the SWMM model of the critical failure modes, calculate the failure probability of each failure mode according to the Bayesian network.
[0043] Example 2: Taking the failure probability assessment of urban flood storage and drainage facilities in Yuanjiang City, Hunan Province as an example, a method for evaluating the effectiveness of flood storage and drainage facilities based on an urban flooding model includes the following steps: Step A. Acquire basic data on elevation, drainage network, rainfall, and historical urban flooding in the study area, and download Jilin-1 satellite imagery with a resolution of 0.5m and ASTGTM2 DEM data with a resolution of 30m. Data formats include raster, vector, DWG, and tables. Data preprocessing includes: reviewing the reliability, consistency, and representativeness of the basic data; Step B. Constructing the SWMM urban flooding model: Based on the basic geographic information data of the study area and the processed pipe network data, sub-catchments and drainage pipe networks were delineated, resulting in 145 sub-catchments, 160 pipe network nodes, and 161 pipe segments. Parameters such as pipe diameter, pipe roughness, manhole bottom elevation, and maximum depth at nodes and drainage outlets were set using measured pipe network data. Furthermore, parameters such as drainage pipe length, slope, average slope of sub-catchments, and impermeability were calculated based on DEM data and GIS spatial analysis methods.
[0044] Step C. Based on the Morris screening method, the design rainfall is input, and the model parameters are set to vary within six ranges of -30% to 30%. The simulated total runoff of the study area is used as the objective function. The resulting sensitivity parameters include N-Imperv, Roughness, Destore-Imperv, Max Rate, and Decay constant. A schematic diagram of the Morris screening method is shown below. Figure 2 As shown.
[0045] Step D. The comprehensive runoff coefficient method is used to validate the model parameters. Based on the planning report of the study area, the surface runoff coefficient for densely built-up areas is determined to be 0.7, and the surface runoff coefficient for other areas is 0.65, which are used as the target runoff coefficients. The simulated runoff coefficients are calculated based on rainfall data and runoff simulation results. By changing the values of the sensitivity parameters, the rainfall-runoff process of the study area is simulated. The model parameters are iteratively adjusted based on the simulated values and the objective function values to finally obtain the optimized parameter calibration results.
[0046] Step E. Based on the current status of stormwater storage and drainage facilities in the central urban area of Yuanjiang City, the urban stormwater storage and drainage system is divided into seven parts according to three stages: source control, process control, and end-of-pipe control: LID facilities, stormwater inlets, drainage pipe networks, canals, storage tanks, pumping stations, and sluice gates. n common failure modes are summarized. Then, FMEA analysis is conducted on the failure modes of the stormwater storage and drainage facilities using five normalized indicators: Severity (S), Occurrence (O), Detectivity (D), Propagation (P), and Recoverability (R). The FMEA analysis framework is as follows: Figure 3 As shown.
[0047] Step F. Calculate the Risk Priority Number (RPN) for each failure mode. The RPN calculation results are as follows: Figure 4 As shown.
[0048] Step G. Based on the RPN values, select the critical failure modes of the storage and drainage facilities, generalize the critical failure modes into several nodes, and construct a Bayesian network. The Bayesian network method can use prior probabilities to infer the probability that a failure of the storage and drainage facility system is caused by failure mode i. The Bayesian network construction result is as follows: Figure 5 As shown.
[0049] Step H. Based on the key failure modes identified through FMEA analysis of the storage and drainage system, scenario combinations are set up for the model, and total overflow, overflow nodes, and node failure probabilities are selected as evaluation indicators for the effectiveness of the storage and drainage facilities. The PYSWMM platform is used to automatically call up and combine scenario simulations of the SWMM model.
[0050] Step I. Based on the SWMM model simulation results of the critical failure modes, calculate the failure probability of each failure mode using a Bayesian network. The failure probability calculation results are as follows: Figure 6 As shown.
[0051] Test results show that the failure probability assessment technology for storage and drainage facilities based on SWMM model and Bayesian network proposed in this invention can carry out process simulation and reliability assessment of storage and drainage systems from a system perspective, and accurately calculate the failure probability of various complex failure modes of storage and drainage facilities.
[0052] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.
Claims
1. A method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model, characterized in that: It includes the following steps: S1. Obtain basic data on elevation, drainage network, soil and land use, rainfall, and historical waterlogging in the study area. The data formats include raster, vector, DWG, and tables. The reliability, consistency, and representativeness of the basic data are reviewed. S2. Construct the SWMM urban flooding model and perform model parameter calibration and verification; S3. Perform parameter sensitivity analysis: Select one parameter from the model parameters, randomly change the parameter value within the range of possible values, run the model to obtain a series of different output values, and then use the ratio of the change in output value to the change in parameter value as the evaluation criterion for the influence of the parameter on the model output value. S4. Use measured flow rate or water level data to calibrate and verify the parameters; S5. The urban stormwater storage and drainage system is divided into three parts: source control, process control, and end control. These parts include LID facilities, stormwater inlets, drainage pipe networks, canals, storage tanks, pumping stations, and sluice gates. Various failure modes are summarized. Then, FMEA analysis is conducted on the failure modes of the storage and drainage facilities using five normalized indicators: Severity (S), Occurrence (O), Detectivity (D), Propagation (P), and Recoverability (R). S6. Calculate the risk priority number (RPN) for each failure mode. S7. Based on the RPN value, screen the key failure modes of the storage and discharge facilities, generalize the key failure modes into several nodes, and construct a Bayesian network. S8. Based on the key failure modes screened by FMEA analysis of the storage and drainage system, scenario combination settings are made for the model, and the total overflow, overflow nodes and node failure probability are selected as evaluation indicators of the storage and drainage facility effectiveness; the PYSWMM platform is used to automatically call up the SWMM model and perform combined scenario simulation. S9. Based on the simulation results of the SWMM model of the critical failure modes, calculate the failure probability of each failure mode according to the Bayesian network.
2. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 1, characterized in that: Step S2 includes the following steps: S2.1 Divide the study area into sub-catchments and calculate empirical values of runoff generation and runoff parameters for each sub-catchment based on elevation, soil and land use data. S2.
2. The flow parameters are calculated by solving the continuity equation and Manning's equation simultaneously to calculate the surface flow process; the hydrodynamic process in the pipeline network is calculated by the steady flow method, the kinematic wave method and the dynamic wave method; the Manning formula is used to link the flow velocity, water depth and pipeline friction, and the principles of mass conservation and momentum conservation are used to calculate the water flow in the pipeline.
3. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 2, characterized in that: In step S2.1, the empirical values of the runoff generation and collection parameters for each sub-catchment area are selected using the Horton equation method to simulate the infiltration process, describing a long-duration precipitation event where the infiltration decay index decreases exponentially from the maximum infiltration rate to a certain minimum over time. The calculation is shown in the following formula: In the formula, f is the infiltration rate, mm / h; f t The infiltration rate is constant, in mm / h; f0 is the initial infiltration rate, in mm / h; t is the precipitation duration, in h; and k is the infiltration attenuation coefficient, 1 / h.
4. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 3, characterized in that: In step S2.2, the basic equation for calculating the water flow in the pipe is based on the principles of conservation of mass and momentum, as follows: In the formula, Q is the flow rate; A is the cross-sectional area of the water passage; q L t is the inflow rate per unit length; v is the flow velocity; h is the hydrostatic head; t is the time; x is the distance; S0 is the pipe bottom slope; S f Frictional gradient.
5. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 1, characterized in that: In step S3, the Morris screening method is used to perform parameter sensitivity analysis.
6. A method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model, as described in claim 1 or 5, characterized in that: In step S3, the ratio of the output value change amplitude to the parameter change amplitude is the parameter sensitivity coefficient, which is calculated using the following formula: ； In the formula: Y i+1 and Y i These are the output results obtained from the i-th and (i+1)-th runs of the model, respectively; Y 0 represents the model's output without adjusting the parameters; P i+1 and P i These represent the changes in parameter values used in the i-th and (i+1)-th runs of the model compared to the initial parameter values; n This represents the number of simulations performed by the model.
7. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 1, characterized in that: Step S4 specifically includes the following process: First, determine the objective function for model parameter calibration, then simulate the rainfall-runoff process in the study area by changing the values of the sensitivity parameters, iterate the model parameters step by step based on the model simulation values and the objective function values, and finally obtain the optimal parameter calibration results.
8. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 1, characterized in that: In step S6, the formula for calculating the RPN value is as follows: ； Among them, the five normalized indicators were assigned values using a combination of objective data from the historical operation of facilities in the study area and subjective experience from experts.
9. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 1, characterized in that: In step S7, the Bayesian network construction method uses prior probability to infer the probability that the failure of the storage and drainage facility system is caused by failure mode i.
10. The method for evaluating the effectiveness of water storage and drainage facilities based on an urban flooding model according to claim 9, characterized in that: The specific formula for inferring the probability that a failure of a storage and drainage facility system is caused by failure mode i using prior probability is as follows: Among them, P(M) i |R) represents the failure of the storage and pumping system attributing the failure to the failure node M. i The probability of failure, P(R), is given by the law of total probability. In the formula, Indicates the failed node M i The probability of causing the storage and discharge system to fail; Indicates the failed node M i The probability of failure.