Flood early warning method based on health state and resilience evaluation of drainage pipe network under heavy rain weather

Abstract

The application discloses a heavy rain weather-based flood warning method based on drainage pipe network health state and resilience evaluation, relates to the technical field of urban flood prevention, and is constructed through a pressure-state-response three-dimensional index system, combined with an entropy weight method and a comprehensive evaluation method, which avoids single-dimension evaluation limitations, and makes flood resilience evaluation more objective and accurate. Relying on drainage pipe network health state investigation and water power model dynamic correction, the flood accumulation prediction accuracy is greatly improved, and the problem that a traditional model is disconnected with actual working conditions is solved. The method can not only accurately locate high-risk fragile areas, provide data support for flood control resource optimal allocation and targeted reconstruction, promote the change of flood control work from experience driving to data driving, but also can reduce resource mismatch and redundancy waste through high-precision early warning and post-disaster recovery quantitative analysis, improve flood control efficiency and post-disaster recovery ability, provide strong support for resilience city planning and construction, and effectively reduce life and property losses caused by floods and waterlogging.

Description

Technical Field

[0001] This application relates to the field of urban flood prevention technology, specifically to a flood early warning method based on the health status and resilience assessment of drainage pipe networks during rainstorms. Background Technology

[0002] To fundamentally reduce loss of life and property caused by natural disasters such as floods, and to ensure the stable operation and sustainable development of urban systems, it has become an urgent need to break through the limitations of traditional management models and build resilient cities with the ability to predict disaster risks, withstand impacts, and recover rapidly after disasters. Against this backdrop, how to establish a scientific and reasonable evaluation index system to accurately measure and quantify urban flood disaster recovery capabilities, and to provide data support and decision-making basis for the planning, construction, and management of resilient cities, has become a core scientific problem that urgently needs to be solved in the field of urban safety and disaster risk management.

[0003] The concept of resilience originated in the field of ecology and has gradually extended to multiple fields such as sociology, management, and urban planning. Its connotation has evolved from "restoring the original state" to "adaptive evolution," emphasizing the dynamic process by which a system adjusts its structure, optimizes its functions, and achieves a better state in the face of disturbances. As a complex system with highly coupled multiple elements, cities face multiple disturbances, giving rise to the concept of urban resilience. Its core is the comprehensive characteristic of an urban system in maintaining uninterrupted core services, rapid recovery, and enhanced long-term resilience in the face of internal and external disturbances. Flooding, as one of the most frequent natural disasters in cities, is increasingly affected by climate change and urbanization. Traditional flood control and drainage models have become limited, highlighting the growing necessity of conducting urban flood resilience assessments. These assessments not only quantify a city's "resistance-adaptation-recovery" capacity in response to floods but also provide targeted optimization solutions for flood control and drainage.

[0004] In the framework of flood resilience assessment, the construction of the evaluation index system is crucial. The representativeness, completeness, and scientific validity of the indicators directly affect the evaluation results. The construction of an effective index system requires both solid theoretical support and clearly defined indicators that characterize the mechanism and development trend of flood disaster resilience. While current research has expanded from a single perspective to a city ecosystem framework perspective, covering multi-dimensional analysis, shortcomings remain: existing measurement methods often treat resilience as a result or process, and traditional indicator selection suffers from problems such as "more theoretical derivation than measured data" and "more static description than dynamic characterization." Therefore, it is necessary to integrate hydrodynamic model simulation and on-site monitoring methods. The former can quantify inundation characteristics under different flood scenarios, providing data support for selecting risk response indicators, while the latter can capture dynamic information in real time and correct model parameters. The synergy of both can optimize and form a more realistic evaluation index system. Among these, urban waterlogging hydrodynamic models that support joint one-dimensional and two-dimensional simulations can achieve high-precision and high-reliability simulation of water accumulation results by reasonably generalizing the pipe network, refining the catchment area division, and introducing high-precision topographic data, providing accurate quantitative results for understanding the distribution patterns of waterlogging.

[0005] In summary, this embodiment proposes a flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain conditions. Combined with the refined simulation results of the hydrodynamic model, it provides a realistic basis for decision-making under extreme weather conditions in the study area. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems, this application proposes the following technical solution:

[0007] In a first aspect, embodiments of this application provide a flood early warning method based on the health status and resilience assessment of drainage pipe networks during heavy rain, including:

[0008] A city flood resilience assessment index system is constructed by identifying multiple key indicator elements from three dimensions: stress, state, and response, to evaluate the city's flood resilience.

[0009] A basic hydrodynamic numerical simulation model was established for the study area, and the key indicator elements collected in the study area were normalized. The entropy weight method was used to calculate the weight of each indicator element. The flood disaster resilience index of the study area was calculated according to the comprehensive evaluation method, and the regional resilience level was divided.

[0010] The establishment of a basic hydrodynamic numerical simulation model for the study area includes:

[0011] Statistical processing was performed on the stormwater pipe network data map of the study area. Stormwater wells and stormwater branch pipes with diameters lower than the preset standard in the road green isolation belt were removed, and the main stormwater pipes were retained. The pipe network topology was checked according to the drawings, and errors such as pipe reversal, missing pipes, reverse slope, and mixed stormwater and sewage connections were corrected to ensure the rationality of the pipe network model.

[0012] The original elevation data points are densified by linear interpolation to generate an irregular TIN model. The ground dem model reflects the regional topographic relief and confluence path.

[0013] The land use types were analyzed and surface runoff coefficients were assigned to them to reflect the actual surface runoff situation.

[0014] The simulation area is divided into 2D grids, and the grid resolution is adjusted according to the simulation accuracy requirements to accurately represent surface runoff;

[0015] Statistical calculations of rainfall intensity change time series were performed on measured rainfall data, and the output of flow and water level results for key pipelines were compared with actual observation data to analyze the causes of flooding in flood-prone areas.

[0016] Monitoring equipment was installed in key flood-prone areas of the study area to enable the model to perform detailed simulations of these areas.

[0017] The health status of the drainage network in the study area is investigated, and the basic hydrodynamic numerical simulation model is revised in real time to establish a refined prediction model for urban flooding and waterlogging, thereby enabling flood early warning for cities under extreme rainstorm weather. In one possible implementation, the urban flood resilience assessment index system is constructed by determining multiple key indicator elements from three dimensions: pressure, state, and response, to evaluate urban flood resilience, including:

[0018] Pipeline siltation, pipeline blockage, pipeline rupture, geographical elevation, population density, built-up area, per capita road area in built-up areas, green coverage rate in built-up areas, and flood control information engineering were selected as the key indicator elements.

[0019] An urban flood resilience evaluation model is established based on PSR theory and combined with the aforementioned key indicator elements.

[0020] In one possible implementation, the installation of monitoring equipment in key flood-prone areas of the study area to enable the model to perform a refined simulation of these areas includes:

[0021] By comparing the peak flow rate and its changing trend with those monitored by flow meters on important pipelines upstream and downstream of flood-prone areas, the error between the simulated peak value and the measured peak value is made smaller than the preset difference, so as to achieve a more refined simulation of flood-prone areas by the model.

[0022] In one possible implementation, the key indicator elements collected within the study area are subjected to data normalization processing, the weight of each indicator element is calculated using the entropy weight method, the flood resilience index of the study area is calculated according to the comprehensive evaluation method, and the regional resilience level is classified, including:

[0023] The study area is divided into m partitions, and an original matrix of m study partitions and n evaluation indicators is established:

[0024] ;

[0025] in: These are the original indicator values ​​for key indicator elements in each sub-region of the study area;

[0026] The original index values ​​of the key index elements in the original matrix are standardized and normalized to obtain the proportion of the key index elements.

[0027] The information entropy of the proportion of the key indicator elements after normalization is calculated, the uncertainty is transformed into a calculable value, and the indicator weights of different key indicator elements are determined.

[0028] The flood resilience index of the study area is calculated by combining the indicator weights of the key indicator elements.

[0029] In one possible implementation, the step of standardizing and normalizing the original index values ​​of the key index elements in the original matrix to obtain the weights of the key index elements includes:

[0030] The original index values ​​of the key indicator elements are standardized to obtain standardized evaluation index values:

[0031] Positive evaluation indicators: ;

[0032] Negative evaluation indicators: ;

[0033] in, X ij The evaluation index values ​​are the standardized data of key indicator elements for each research region. , Let j be the maximum and minimum index values ​​of the j-th index in the total study area;

[0034] The evaluation index values ​​are normalized to determine the weight of the j-th index in the t-th research partition. :

[0035] ;

[0036] in: Let be the standardized evaluation index value of the j-th indicator in the t-th research partition.

[0037] In one possible implementation, the calculation of the information entropy of the proportion of the key indicator elements after normalization transforms uncertainty into a calculable value and determines the indicator weights of different key indicator elements, including:

[0038] Calculate the information entropy of the key indicator elements to transform uncertainty into a calculable value; information entropy. The formula is:

[0039] ;

[0040] The information redundancy of the key indicator elements is determined based on the information entropy. : ;

[0041] Utilizing the aforementioned information redundancy Determine the indicator weights of the key indicator elements. : .

[0042] In one possible implementation, calculating the flood resilience index of the study area by combining the indicator weights of the key indicator elements includes: determining the flood resilience index of the study area by combining the standardized evaluation indicator values ​​of the key indicator elements with the indicator weights. :

[0043] ;

[0044] Wherein: FRI is the resilience index for flood disasters in the study area. The larger the FRI, the greater the resilience, and vice versa.

[0045] In one possible implementation, the investigation of the health status of the drainage network in the study area and the real-time correction of the hydrodynamic numerical simulation model are used to establish a refined prediction model for urban flooding and waterlogging, thereby enabling flood early warning for cities under extreme rainstorm weather, including:

[0046] The basic model of hydrodynamic numerical simulation is modified in real time based on the health status of the drainage pipe network in the study area. The sensitivity parameters of the basic model are adjusted and optimized in real time, and the optimized basic model is used as the basis.

[0047] By integrating real-time information on the health status of the pipeline network, a targeted and refined prediction model for urban flooding is established, ultimately enabling refined simulation and quantitative prediction of urban flooding processes under extreme rainstorms.

[0048] In this embodiment, a three-dimensional index system of pressure-state-response is constructed, combined with the entropy weight method and comprehensive evaluation method, avoiding the limitations of single-dimensional evaluation and making the assessment of flood resilience more objective and accurate. Relying on the health status investigation of drainage pipe networks and dynamic correction of hydrodynamic models, the accuracy of urban flooding prediction is significantly improved, solving the problem of the disconnect between traditional models and actual working conditions. It can not only accurately locate high-risk and vulnerable areas, providing data support for the optimized allocation and targeted transformation of flood control resources, and promoting the transformation of flood control work from experience-driven to data-driven, but also reduce resource misallocation and redundant waste through high-precision early warning and post-disaster recovery quantitative analysis, improving flood control efficiency and post-disaster recovery capabilities, providing strong support for resilient urban planning and construction, and effectively reducing the loss of life and property caused by floods. Attached Figure Description

[0049] Figure 1 A flowchart illustrating a flood early warning method based on the health status and resilience assessment of drainage pipe networks during heavy rain, provided as an embodiment of this application;

[0050] Figure 2 A schematic diagram illustrating the results of calculating the weights of key indicators in the study area using the entropy weight method, provided for an embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the accuracy of the pipeline flow rate before calibration, provided in an embodiment of this application.

[0052] Figure 4 This is a schematic diagram illustrating the accuracy of pipeline flow rate calibration provided in an embodiment of this application. Detailed Implementation

[0053] The present solution will now be described in conjunction with the accompanying drawings and specific embodiments.

[0054] See Figure 1 The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather provided in this embodiment includes:

[0055] S101 uses a multi-key indicator system to assess urban flood resilience by identifying multiple key indicators from three dimensions: pressure, state, and response.

[0056] This embodiment uses the PSR theory, or "Stress-State-Response" model, which originated in the environmental field and has since expanded to resource management, disaster assessment, and other fields. Its core is a framework built upon the logical chain of "Stress-State-Response": the "Stress" dimension focuses on the disturbances caused by human activities or natural factors to the research object (such as the environment or urban systems) (e.g., industrial pollution, flood threats); the "State" dimension reflects the current attributes and functional status of the research object after being affected by stress (e.g., water quality indicators, urban flood resilience); and the "Response" dimension covers intervention measures taken in response to changes in stress and state (e.g., environmental policy formulation, flood control engineering construction). These three dimensions form a dynamic feedback loop, enabling a systematic analysis of the research object's "problem causes - current status - response direction," and providing clear logical support for scientific decision-making. It is widely applied in practical scenarios such as environmental quality assessment, urban disaster resilience analysis, and sustainable resource utilization planning.

[0057] Pipeline siltation, pipeline blockage, pipeline rupture, geographical elevation, population density, built-up area, per capita road area in built-up areas, green coverage rate in built-up areas, and flood control information engineering are selected as key indicator elements. Based on PSR theory and combined with the key indicator elements, an urban flood resilience evaluation model is established.

[0058] In this embodiment, the urban flood resilience evaluation model includes a target layer, multiple criterion layers, and multiple index layers contained in each criterion layer; the target layer is urban flood resilience, and the criterion layers include a pressure layer, a state layer, and a response layer.

[0059] The pressure layer includes the following indicators: geographical elevation, reflecting the degree of flood risk in each zone; pipe siltation, reflecting the reduced water flow capacity of the drainage network; pipe blockage, reflecting the complete blockage of water flow in the drainage network due to siltation, resulting in paralysis of drainage function; pipe rupture, reflecting damage to the network structure leading to backflow of external rainwater, weakening the drainage efficiency of the network; and population density, reflecting the exposure risk to the affected population and the difficulty of emergency response to flooding.

[0060] The state layer includes the following indicators: built-up area, reflecting the spatial scope and complexity of flood response; number of overflow points, reflecting the drainage pipeline capacity affected by floods; water depth, reflecting the degree of impact of floods on human activities; per capita road area, reflecting the capacity for emergency access and post-disaster traffic recovery; green coverage rate of built-up area, reflecting the natural rainwater storage capacity; and per capita fixed asset investment, reflecting the infrastructure for flood control and post-disaster recovery capabilities.

[0061] The response layer includes the following indicators: the number of internet users per capita, reflecting the efficiency of information dissemination and public responsiveness; and the flood control information engineering, reflecting the 'intelligent' and 'precise' capabilities in flood response.

[0062] S102. Establish a basic hydrodynamic numerical simulation model for the study area, normalize the key indicator elements collected in the study area, calculate the weight of each indicator element using the entropy weight method, calculate the flood disaster resilience index of the study area according to the comprehensive evaluation method, and classify the regional resilience level.

[0063] In this embodiment, Infoworks ICM was used to perform hydrodynamic numerical simulation of the study area to complete the construction of the basic model for the hydrodynamic numerical simulation. The simulation adopted unsteady flow control equations, and the calculation step was set to 60 seconds. Doppler flow meters, water level gauges, and rain gauges were installed at key upstream and downstream pipeline nodes in the key flood-prone areas of the study area, with a monitoring frequency of 1 minute / time, to achieve a refined simulation of the flood-prone areas by the model.

[0064] The measured maps of the stormwater pipe network in the study area were statistically processed using CAD spatial analysis. The statistics included pipe diameter, length, burial depth, location, depth, and number of stormwater wells. Stormwater wells within road greenbelts and stormwater branch pipes with diameters lower than DN300 were removed, retaining the main stormwater pipes. A GIS topology inspection tool was used to check the pipe network topology based on the drawings. Errors were corrected by redefining pipe direction to correct reverse pipe errors, supplementing missing pipes and assigning attributes to correct missing pipe errors, recalibrating pipe elevation to correct reverse slope errors, separating stormwater and sewage pipe layers, and sealing joints to correct mixed stormwater and sewage connections, ensuring the rationality of the pipe network model.

[0065] The original elevation data points were densified using linear interpolation to generate an irregular TIN model. The ground DEM model reflects the regional topographic relief and runoff paths. GIS was used to define parameters for the land use type vector data of the study area, classifying it into six categories: buildings, roads, vegetation cover, and rivers, and assigning surface runoff coefficients to each. The simulation area was divided into 2D grids, and the grid resolution was adjusted according to the simulation accuracy requirements to accurately represent surface runoff.

[0066] The study calculated the time series of rainfall intensity changes based on the measured rainfall data from rain gauges in the study area. The simulation output of key pipeline flow and water level results were quantitatively compared with the actual observation data. The causes of flooding in flood-prone areas were analyzed from four dimensions: insufficient drainage capacity of the pipe network, poor topographical confluence, combined sewer overflows encroaching on drainage space, and pipe blockage leading to reduced flow capacity.

[0067] Error analysis was conducted by comparing four indicators: peak flow rate, peak occurrence time, and peak water level, as monitored by flow meters and water level gauges, across important upstream and downstream pipelines in flood-prone areas. Adjustments were made to the surface runoff coefficient and pipeline blockage status based on the actual health condition of the drainage pipelines, ensuring that the relative error between the simulated peak value and the measured peak value was less than a preset difference of 10%, thus achieving a more refined simulation of flood-prone areas.

[0068] In this embodiment, the key indicator elements collected within the study area are normalized, the entropy weight method is used to calculate the weight of each indicator element, the flood resilience index of the study area is calculated according to the comprehensive evaluation method, and the regional resilience level is divided, including:

[0069] The study area is divided into m partitions, and an original matrix of m study partitions and n evaluation indicators is established:

[0070] ;

[0071] in: These are the original indicator values ​​for key indicator elements in each sub-region of the study area.

[0072] The original index values ​​of the key index elements in the original matrix are standardized and normalized to obtain the weights of the key index elements, including:

[0073] The original index values ​​of the key indicator elements are standardized to obtain standardized evaluation index values:

[0074] Positive evaluation indicators: ;

[0075] Negative evaluation indicators: ;

[0076] in, X ij The evaluation index values ​​are the standardized data of key indicator elements for each research region. , Let j be the maximum and minimum index values ​​of the j-th index in the total study area;

[0077] The evaluation index values ​​are normalized to determine the weight of the j-th index in the t-th research partition. :

[0078] ;

[0079] in: Let be the standardized evaluation index value of the j-th indicator in the t-th research partition.

[0080] The information entropy of the proportions of the key indicator elements after normalization is calculated, transforming uncertainty into a calculable value, and the indicator weights of different key indicator elements are determined. Specifically, the information entropy of the key indicator elements is calculated, transforming uncertainty into a calculable value; information entropy... The formula is:

[0081] ;

[0082] The information redundancy of the key indicator elements is determined based on the information entropy. : ;

[0083] Utilizing the aforementioned information redundancy Determine the indicator weights of the key indicator elements. : .

[0084] Finally, the flood resilience index of the study area is calculated by combining the indicator weights of the key indicator elements. Specifically, the flood resilience index of the study area is determined by combining the standardized evaluation indicator values ​​of the key indicator elements with the indicator weights. :

[0085] ;

[0086] Wherein: FRI is the resilience index for flood disasters in the study area. The larger the FRI, the greater the resilience, and vice versa.

[0087] S103, Investigate the health status of the drainage network in the study area and correct the basic hydrodynamic numerical simulation model in real time to establish a refined prediction model for urban flooding and waterlogging, so as to realize the early warning of urban flooding under extreme rainstorm weather.

[0088] In this embodiment, the basic hydrodynamic numerical simulation model is modified in real time according to the health status of the drainage pipe network in the study area. The sensitivity parameters of the basic model are adjusted and optimized in real time. Based on the optimized basic model, the real information of the pipe network health status is integrated to establish a targeted refined prediction model for urban waterlogging. Finally, the refined simulation and quantitative prediction of urban waterlogging process under extreme rainstorm weather are realized.

[0089] Taking the Dajiangou area of ​​Jinan City as the study area, a flood early warning simulation was conducted using the flood early warning method provided in the above embodiments.

[0090] The statistical data in this embodiment covers four areas within the core area of ​​Dajiangou in Jinan City: Wenzhuangou Sub-district 1, Wenzhuangou Sub-district 2, Qixian Community 3, and Jiuquzhuang Sub-district 4. The data sources mainly include: (1) land use data: mainly including pipeline health status, green space, construction land, road network, water system, etc.; (2) population and economic data of the study area, which are from the Jinan Statistical Yearbook 2024.

[0091] The data processing steps in this embodiment include: processing regional geographic elevation and population density information through GIS; and using Microsoft EXCEL software, which has excellent calculation functions and charting tools, to process data including data standardization, entropy, and weight calculations.

[0092] Combining PSR theory with consideration of data availability, see [link to relevant documentation]. Figure 2 In this embodiment, 12 representative indicators will be selected from three dimensions—pressure layer, state layer, and response layer—to measure flood resilience. (1) Urban flood resilience is manifested in the ability of cities to withstand flood disasters and their recovery and adaptation capabilities after the disaster. Among them, the recovery and adaptation capabilities of cities are closely related to the response capabilities of cities. Therefore, in this embodiment, three indicators will be selected from the aspects of property density and early warning capability: per capita fixed asset investment, per capita number of Internet users, and flood control information engineering. (2) Flood resilience of mountainous cities is also reflected in the impact of drainage pressure, geographical pressure, and population pressure caused by pipe network defects in the context of rainstorms on the city's adaptability and resilience. Therefore, in this embodiment, geographical elevation, population density, and drainage pipe network health status are selected as indicators for pressure layer assessment. (3) The urban resilience state layer plays an important role in the city's resistance to flood disasters and recovery from trauma and adaptation to new environments. It is mainly manifested in the defects of basic building facilities, such as the sharp reduction of public green space area in urban building land. Therefore, in this embodiment, three indicators will be selected as state layer indicators: built-up area, per capita road area, and green coverage rate of built-up area.

[0093] A basic hydrodynamic numerical simulation model was built, incorporating topographic data, stormwater drainage network data, land use type maps, and other fundamental information about the study area. This resulted in the construction of the network topology, the division of sub-catchments, and the initial assignment of hydraulic parameters, thus establishing a hydrodynamic simulation framework covering the entire study area. To further enhance the model's relevance to the actual scenario, real-time adjustments were made based on the current status of stormwater drainage network modifications in the study area (e.g., completed expansion sections, pipeline repair areas, etc.) to ensure the model accurately reflects the current operational status of the network system.

[0094] Rainfall collection devices were scientifically deployed within the study area, and rain gauges were installed at representative locations (such as low-lying areas and around key nodes of the pipeline network) to achieve real-time monitoring of rainfall intensity, duration, and spatial distribution. Combined with real-time pipeline health status monitoring simulation, this provided the model with high-precision measured rainfall input data. Considering that in flood resilience assessment, the three parameters of per capita road area (affecting surface runoff confluence velocity and emergency accessibility), green coverage (affecting rainwater infiltration and retention capacity), and population density (affecting the degree of exposure to urban flooding risk and emergency response needs) have significant weights and are directly related to the formation and impact range of urban flooding, these three parameters need to be used as core variables to correct the sensitivity parameters of the basic model: for example, adjusting the surface Manning coefficient and optimizing the soil permeability coefficient based on the per capita road area of ​​different areas.

[0095] The weights of the basic working condition flood resilience index and the weights of the pipeline network defect-weighted flood resilience index, calculated using the entropy weight method, are shown in Tables 1 and 2 below:

[0096] The basic working condition is a flood resilience index system consisting of 10 indicators, excluding the number of drainage pipe network blockages and pipe network ruptures;

[0097] The weighted working condition of drainage pipe network defects is a flood resilience index system consisting of 12 indicators, including the number of drainage pipe network blockages and the number of pipe network ruptures;

[0098] Table 1 Evaluation Index System for Flood Resilience of Basic Engineering Conditions in Dajiangou Area, Jinan City

[0099]

[0100] Table 2. Evaluation Index System for Weighted Flood Resilience of Pipeline Defects in Dajiangou Area, Jinan City

[0101]

[0102] Referring to the calculation results in Tables 1 and 2, and based on the comparative analysis of the weighted graphs of each indicator, it is found that, without considering the basic working conditions of drainage network defects, the indicators with the greatest impact on urban flood resilience are water depth and the number of overflow points. Considering network defects, the weighted results show that network blockage has the greatest impact on urban flood resilience, followed by green coverage and the number of overflow points. Other indicators have relatively small weight differences and therefore have a relatively small impact on resilience assessment. Therefore, the health status of the urban drainage system has a significant impact on urban flood resilience assessment, while green coverage and pressure factors such as road surface water accumulation and storm drain overflows caused by rainfall also play important roles.

[0103] The flood resilience index of each sub-district in the study area was calculated using the comprehensive evaluation method, and the results are shown in Table 3. Sub-district 1 of Wenzhuangou has the highest comprehensive resilience value; the higher the index value, the stronger the disaster resistance, and vice versa. The area with the lowest resilience is Qixian Community, due to its location affected by the flood discharge from Shifangyu Mountain and the incomplete drainage network caused by on-site stormwater and sewage renovations, resulting in poor drainage capacity and significant losses from flooding.

[0104] Table 3. Calculation results of flood resilience index in four study areas of Dajiangou District, Jinan City

[0105]

[0106] In flood resilience assessment, the flood resilience resilience index can be divided into four levels. Based on the actual application scenarios and index distribution characteristics, the classification results are shown in Table 4:

[0107] Table 4 Classification of Flood Resilience and Recovery Index

[0108]

[0109] Through the above multi-dimensional optimization, a model optimization system was ultimately formed, which "determines the direction through resilience assessment, defines parameters through pipeline health, and specifies inputs through measured data." This effectively improves the model's fitting accuracy to urban flooding scenarios and pushes the error of urban flooding simulation models to less than 15%. (See also...) Figure 3 and Figure 4 It can be seen that the calibrated pipeline flow rate is closer to the measured flow rate. This not only clarifies the key value of resilience assessment and pipeline health status for model optimization, but also constructs a feasible model iteration path, providing standardized technical support for accurate prediction of urban flood risks and scientific planning of flood control and drainage projects. It can effectively improve the comprehensive resilience of cities in responding to flood disasters and has significant practical application value and promotion potential.

[0110] This embodiment takes the Dajiangou area of ​​Jinan City as an example, using the PSR theory combined with the health status of the drainage network to assess the urban flood resilience of the study area. It calculates the impact of various influencing factors on the city's resistance, recovery, and adaptation capabilities in the face of flood disasters, thereby refining the numerical simulation model and achieving a more detailed simulation. From the perspective of "coexisting with floods," and combining pressure, state, and response studies to examine the actual disaster recovery capabilities under urban flood scenarios, it can identify potential problems in areas with weak flood resilience, such as pipeline infrastructure and geographical environment, thus providing new ideas and methods for flood prevention and control in the study area. This research can provide a decision-making basis for subsequent urban planning and construction, and can therefore be promoted and applied in other rapidly urbanizing cities.

[0111] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0112] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A flood early warning method based on the health status and resilience assessment of drainage pipe networks during heavy rain, characterized in that, include: A city flood resilience assessment index system is constructed by identifying multiple key indicator elements from three dimensions: stress, state, and response, to evaluate the city's flood resilience. A basic hydrodynamic numerical simulation model was established for the study area, and the key indicator elements collected in the study area were normalized. The entropy weight method was used to calculate the weight of each indicator element. The flood disaster resilience index of the study area was calculated according to the comprehensive evaluation method, and the regional resilience level was divided. The establishment of a basic hydrodynamic numerical simulation model for the study area includes: Statistical processing was performed on the stormwater pipe network data map of the study area. Stormwater wells and stormwater branch pipes with diameters lower than the preset standard in the road green isolation belt were removed, and the main stormwater pipes were retained. The pipe network topology was checked according to the drawings, and errors such as pipe reversal, missing pipes, reverse slope, and mixed stormwater and sewage connections were corrected to ensure the rationality of the pipe network model. The original elevation data points are densified by linear interpolation to generate an irregular TIN model. The ground dem model reflects the regional topographic relief and confluence path. The land use types were analyzed and surface runoff coefficients were assigned to them to reflect the actual surface runoff situation. The simulation area is divided into 2D grids, and the grid resolution is adjusted according to the simulation accuracy requirements to accurately represent surface runoff; Statistical calculations of rainfall intensity change time series were performed on measured rainfall data, and the output of flow and water level results for key pipelines were compared with actual observation data to analyze the causes of flooding in flood-prone areas. Monitoring equipment was installed in key flood-prone areas of the study area to enable the model to perform detailed simulations of these areas. The health status of the drainage network in the study area was investigated, and the basic hydrodynamic numerical simulation model was corrected in real time to establish a refined prediction model for urban waterlogging and flooding, so as to realize the early warning of urban flooding under extreme rainstorm weather. The aforementioned urban flood resilience assessment index system, which identifies multiple key indicator elements from three dimensions—pressure, state, and response—is used to evaluate urban flood resilience. This includes: Pipeline siltation, pipeline blockage, pipeline rupture, geographical elevation, population density, built-up area, per capita road area in built-up areas, green coverage rate in built-up areas, and flood control information engineering were selected as the key indicator elements. An urban flood resilience evaluation model was established based on PSR theory and combined with the aforementioned key indicator elements. Error analysis was conducted by comparing four indicators of changes: peak flow rate, peak occurrence time, peak water level, and peak time, as monitored by flow meters of important pipelines upstream and downstream of flood-prone areas. Combined with the actual health status of drainage pipelines, adjustments were made to the surface runoff coefficient and pipeline blockage status to ensure that the relative error between the model-simulated peak value and the measured peak value is less than 10% of the preset difference, thereby achieving a refined simulation of flood-prone areas by the model.

2. The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather as described in claim 1, characterized in that, The key indicator elements collected within the study area are subjected to data normalization processing, and the weight of each indicator element is calculated using the entropy weight method. The flood resilience index of the study area is calculated based on the comprehensive evaluation method, and the regional resilience levels are classified, including: The study area is divided into m partitions, and an original matrix of m study partitions and n evaluation indicators is established: ； in: These are the original indicator values ​​for key indicator elements in each sub-region of the study area; The original index values ​​of the key index elements in the original matrix are standardized and normalized to obtain the proportion of the key index elements. The information entropy of the proportion of the key indicator elements after normalization is calculated, the uncertainty is transformed into a calculable value, and the indicator weights of different key indicator elements are determined. The flood resilience index of the study area is calculated by combining the indicator weights of the key indicator elements.

3. The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather as described in claim 2, characterized in that, The step of standardizing and normalizing the original index values ​​of the key index elements in the original matrix to obtain the weight of the key index elements includes: The original index values ​​of the key indicator elements are standardized to obtain standardized evaluation index values: Positive evaluation indicators: ; Negative evaluation indicators: ; in, X ij The evaluation index values ​​are the standardized data of key indicator elements for each research region. , Let j be the maximum and minimum index values ​​of the j-th index in the total study area; The evaluation index values ​​are normalized to determine the weight of the j-th index in the t-th research partition. : ； in: Let be the standardized evaluation index value of the j-th indicator in the t-th research partition.

4. The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather as described in claim 3, characterized in that, The calculation of the information entropy of the proportion of the key indicator elements after normalization transforms uncertainty into a calculable value and determines the indicator weights of different key indicator elements, including: Calculate the information entropy of the key indicator elements to transform uncertainty into a calculable value; information entropy. The formula is: ； The information redundancy of the key indicator elements is determined based on the information entropy. : ; Utilizing the aforementioned information redundancy Determine the indicator weights of the key indicator elements. : .

5. The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather as described in claim 4, characterized in that, The calculation of the flood resilience index of the study area by combining the indicator weights of the key indicator elements includes: determining the flood resilience index of the study area by combining the standardized evaluation indicator values ​​of the key indicator elements with the indicator weights. : ； Wherein: FRI is the resilience index for flood disasters in the study area. The larger the FRI, the greater the resilience, and vice versa.

6. The flood early warning method based on the health status and resilience assessment of drainage pipe networks under heavy rain weather as described in claim 1, characterized in that, The study investigates the health status of the drainage network in the research area and corrects the basic hydrodynamic numerical simulation model in real time to establish a refined prediction model for urban flooding and waterlogging, thereby enabling flood early warning for cities under extreme rainstorm weather, including: The basic model of hydrodynamic numerical simulation is modified in real time based on the health status of the drainage pipe network in the study area. The sensitivity parameters of the basic model are adjusted and optimized in real time, and the optimized basic model is used as the basis. By integrating real-time information on the health status of the pipeline network, a targeted and refined prediction model for urban flooding is established, ultimately enabling refined simulation and quantitative prediction of urban flooding processes under extreme rainstorms.

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