A multi-objective decision-making method, program product, equipment, and medium for flood control measures.

Abstract

This invention discloses a multi-objective decision-making method, program product, equipment, and medium for flood control measures, comprising: constructing an uncertain scenario set based on pre-determined uncertainty parameters and flood control data; determining the inundation results of each uncertain scenario according to a pre-determined hydrodynamic model, and selecting representative scenarios from the uncertain scenario set based on the inundation results; for each representative scenario, calculating the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and selecting candidate flood control measures based on the risk reduction rate; determining the multi-objective evaluation results of the candidate flood control measures under all uncertain scenarios according to pre-determined evaluation indicators; and generating an optimized selection strategy for flood control measures based on the multi-objective evaluation results to achieve robust flood control decision-making. This invention improves the accuracy and precision of flood control measure evaluation, and further enhances the scientific rigor and reliability of flood control decision-making.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of water conservancy engineering technology, and in particular to a multi-objective decision-making method, program product, equipment and medium for flood control measures. Background Technology

[0002] With the acceleration of global climate change and urbanization, urban rainstorms and floods are becoming increasingly frequent. The scientific assessment and optimized allocation of flood prevention measures have become a core issue in urban safety governance. Existing assessment methods for various flood prevention measures mainly include evaluation methods based on multidimensional indicators.

[0003] Evaluation methods based on multidimensional indicators mainly rely on indicator calculation and weight aggregation to comprehensively assess urban flooding risk. Although their indicator system can achieve multi-factor analysis, their evaluation process depends on statistical or empirical models, making it difficult to accurately reflect the dynamic evolution of urban flooding from occurrence to disaster, resulting in insufficient precision and reliability of the evaluation results. Furthermore, these methods are usually based on deterministic or limited scenarios, making it difficult to comprehensively evaluate the adaptability and stability of different measures under various complex conditions, resulting in incomplete evaluation results and limiting the scientific nature and reliability of flood control decisions. Summary of the Invention

[0004] This invention provides a multi-objective decision-making method, program product, equipment, and medium for flood control measures. It can combine the dynamic evolution of urban flooding from occurrence to disaster, accurately and comprehensively assess the flood control performance of different flood control measures under various complex conditions, improve the accuracy and precision of flood control measure evaluation, and further enhance the scientificity and reliability of flood control decision-making.

[0005] In a first aspect, embodiments of the present invention provide a multi-objective decision-making method for flood control measures, including: Acquire flood control data for the area to be analyzed, and construct a set of uncertainty scenarios based on pre-determined uncertainty parameters and the flood control data; the uncertainty parameters include rainfall variation and drainage capacity; The inundation results for each uncertainty scenario are determined based on a predetermined hydrodynamic model, and representative scenarios are selected from the set of uncertainty scenarios based on the inundation results. For each representative scenario, the risk reduction rate of each pre-constructed flood control measure under the current representative scenario is calculated based on the hydrodynamic model, and each candidate flood control measure is determined based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measure; the flood control measure can be a single measure or a combination of measures; Based on the hydrodynamic model, the flood control assessment results of each candidate flood control measure under all uncertainty scenarios are determined, and a flood control measure selection strategy is generated based on the flood control assessment results under all uncertainty scenarios.

[0006] Secondly, embodiments of the present invention provide a multi-objective decision-making device for flood control measures, the device comprising: The scenario construction module is used to acquire flood control data of the area to be analyzed, and construct a set of uncertainty scenarios based on pre-determined uncertainty parameters and the flood control data; The scenario screening module is used to determine the inundation results of each uncertainty scenario based on a predetermined hydrodynamic model, and to screen out representative scenarios from the uncertainty scenario set based on the inundation results. The measure evaluation module is used to calculate the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and to screen out each candidate flood control measure based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measure; the flood control measure can be a single measure or a combination of measures; The multi-objective decision-making module is used to determine the multi-objective evaluation results of the candidate flood control measures under all uncertain situations based on the pre-determined evaluation indicators; and to generate an optimized selection strategy for flood control measures based on the multi-objective evaluation results.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the multi-objective decision-making method for flood control measures as described in any of the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective decision-making method for flood control measures as described in any of the embodiments of the present invention.

[0009] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the multi-objective decision-making method for flood control measures as described in any of the embodiments of the present invention.

[0010] In this embodiment of the invention, a scenario set is constructed based on uncertainty parameters. The uncertain scenarios are simulated using a hydrodynamic model. Representative scenarios are first selected based on the simulation results, laying a data foundation for subsequent two-layer analysis of various flood control measures using the hydrodynamic model. A preliminary analysis of the representative scenarios is conducted using the hydrodynamic model, followed by precise calculation and verification of candidate measures for all scenarios. This ensures that the evaluation results not only reflect performance under typical conditions but also reflect the characteristics of all complex scenarios, including extreme and boundary conditions, improving the comprehensiveness of the performance evaluation of flood control measures under various complex situations. By calculating the risk reduction rate of each flood control measure under different scenarios, the flood control effect of each measure can be quantified. Simultaneously, the simulation calculation of flood control measures using the hydrodynamic model achieves deep coupling between the measure's action process and the physical hydrodynamic process, improving the accuracy and precision of flood control measure evaluation and further enhancing the scientific rigor and reliability of flood control decision-making. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart of a multi-objective decision-making method for flood control measures provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the multi-objective decision-making device for flood control measures provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all structures. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant national laws and regulations. It should be noted that, in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used the relevant content of such solutions.

[0014] Figure 1This is a flowchart illustrating a multi-objective decision-making method for flood control measures provided in an embodiment of the present invention. The method of this embodiment can combine the dynamic evolution of urban flooding from occurrence to disaster, accurately and comprehensively assess the flood control performance of different flood control measures under various complex conditions, improve the accuracy and precision of flood control measure evaluation, and further enhance the scientific rigor and reliability of flood control decision-making. This method can be executed by a multi-objective decision-making device for flood control measures provided in this embodiment of the present invention, which can be implemented using software and / or hardware. The following embodiments will illustrate this using the integration of this device into an electronic device as an example. The electronic device can be a server or computer device, etc., used to implement a multi-objective decision-making method for flood control measures. (Refer to...) Figure 1 The method may specifically include the following steps:

[0015] Step 101: Obtain flood control data for the area to be analyzed, and construct a set of uncertainty scenarios based on predetermined uncertainty parameters and flood control data.

[0016] The area to be analyzed is the geographical region where flood control measures need to be analyzed, such as a city. Uncertain parameters include rainfall variation and drainage capacity. Of course, specific uncertain parameters can be expanded to include other uncertain factors affecting urban flood control effectiveness based on the actual application scenario. Flood control data includes meteorological and hydrological data, topographic data, drainage network data, land use data, and asset values ​​of the area to be analyzed. Uncertain parameters are pre-determined based on domain big data and historical meteorological and urban basic data of the area to be analyzed. The uncertainty scenario set consists of multiple uncertainty scenarios. Uncertain scenarios include precipitation sequences and drainage system parameters. In this scheme, the uncertainty scenarios are composed of calculable numerical values. Each uncertainty scenario corresponds to a set of defined rainfall variation rates and drainage capacity variation rates. This parameterization of uncertainty scenarios facilitates subsequent calculations by the hydrodynamic model.

[0017] In one optional implementation, an uncertainty scenario set is constructed based on predetermined uncertainty parameters and flood control data, including: determining the value range of the uncertainty parameters; generating combinations of rainfall changes and drainage capacity according to predetermined stratified sampling methods and value ranges to obtain each uncertainty scenario.

[0018] The core idea of ​​stratified sampling is to divide the range of each parameter into several equally probable intervals, then randomly select a sample from each interval, and finally combine these samples to obtain multiple combinations (uncertainty scenarios). In this scheme, the stratified sampling method can be Latin hypercube sampling. Specifically, the uncertainty parameters include rainfall variation and drainage capacity. Rainfall variation represents the rate of change in future extreme precipitation, that is, the degree of change in the intensity of extreme rainstorm events encountered in the analyzed area relative to historical baselines under future climate change conditions. To indicate rainfall changes, based on historical data of the area to be analyzed and meteorological data from flood control data, settings can be configured. The value ranges from 5% to 15%, meaning that future extreme rainfall may increase by 5% to 15% from current levels. This range reflects the uncertainty range of current climate models' predictions of regional rainfall changes. Drainage capacity represents the degree of decline in the drainage system's capacity in the analyzed area due to factors such as pipe network aging, siltation, and pump station degradation. To indicate rainfall changes, based on historical data and meteorological data from flood control data of the area to be analyzed, a set... The value ranges from 0% to 50%. 0% indicates that the drainage system's ability to maintain its current state remains unchanged, while 50% indicates that the drainage capacity has been severely reduced to half of its current state.

[0019] Furthermore, a Latin hypercube sampling method was used to generate a combination of rainfall variations and drainage capacity. and A predetermined number (e.g., 100) of representative sample points are generated within the constructed two-dimensional parameter space. For The parameters divide the range of 5% to 15% into 100 smaller intervals, each with a width of 0.1%. A single sample is randomly selected from each smaller interval. Value, ensure The values ​​are evenly distributed across the entire range, avoiding clustering or blank areas that may occur with simple random sampling. For The parameters also divide the range of 0% to 50% into a preset number (e.g., 100) small intervals, with each interval having a width of 0.5%. A value is randomly selected from each small interval. Value. Take 100 Value and 100 The values ​​are randomly paired to form 100 two-dimensional parameter combinations. Each combination represents a unique uncertainty scenario; for example, a scenario might be... equals 8.3%. Equals 23.6%, another scenario might be equals 12.7%. It equals 5.4%.

[0020] Specifically, each uncertainty scenario corresponds to a defined set of rainfall variation rates and drainage capacity variation rates; that is, the uncertainty scenario includes precipitation sequences and drainage system parameters. Based on the historical rainfall characteristics and flood control design standards of the area to be analyzed, a 50-year return period design storm is used as the baseline rainfall process. The baseline rainfall process defines the temporal distribution of rainfall, including the start and end times of rainfall, the peak occurrence time, and the total rainfall. A corresponding precipitation sequence is generated for each scenario: for each of the 100 scenarios, based on its... The values ​​are corrected for the baseline rainfall event. For example, the rainfall amount at each moment of the baseline rainfall is multiplied by a preset amplification factor to obtain the final precipitation sequence. Simultaneously, the drainage system parameters for each scenario are adjusted. For example, based on... The value is proportionally reduced for parameters such as the flow capacity of the drainage network and the pumping capacity of the pumping station. When γ is 0%, the current parameters remain unchanged; when γ is 50%, the relevant drainage capacity parameters are multiplied by 0.5. Furthermore, the generated precipitation sequences and drainage parameters are numbered to obtain a set of uncertainty scenarios. Each scenario in the set has a unique number, corresponding to a set of defined precipitation sequences and drainage parameters.

[0021] The Latin hypercube sampling method is used to generate uncertainty scenarios covering two-dimensional parameter spaces of rainfall variation and drainage capacity decay, which facilitates comprehensive testing of the performance of various flood control measures under different future conditions.

[0022] Step 102: Determine the inundation results for each uncertainty scenario based on the predetermined hydrodynamic model, and select representative scenarios from the uncertainty scenario set based on the inundation results.

[0023] The hydrodynamic model used in this scheme is the High-performance Integrated Hydrodynamic Modelling System (HiPIMS). HiPIMS is a numerical simulation model based on two-dimensional shallow water equations, used to simulate complex hydrodynamic processes such as urban surface floods and river floods. The model employs the finite volume method or finite difference method for discretization, dividing the study area into regular computational grids and tracking the evolution of the two core state variables, water level and flow velocity, over time in each grid cell. The inundation results are the regional inundation characteristics calculated by the hydrodynamic model under various uncertainty scenarios through numerical simulation of the physical processes. Representative scenarios include low-risk, medium-risk, and high-risk scenarios. The high-risk scenario represents the most unfavorable combination of conditions for the analyzed area in the future, namely, significantly increased rainfall and severely degraded drainage capacity. The medium-risk scenario represents the average combination of conditions with a relatively high probability of future risk for the analyzed area, used to assess the routine effectiveness of flood control measures. The low-risk scenario represents the relatively favorable combination of conditions for the analyzed area in the future, used to identify whether flood control measures are being overused.

[0024] In one optional implementation, the inundation results for each uncertainty scenario are determined according to a predetermined hydrodynamic model, including: constructing input data for the hydrodynamic model based on elevation data, land use data, precipitation sequence, drainage system parameters, and a predetermined initial water depth; inputting the input data into the hydrodynamic model; and performing numerical simulations of each uncertainty scenario using a two-dimensional shallow water model of the hydrodynamic model to obtain the inundation results for each uncertainty scenario.

[0025] Elevation data is typically used to accurately characterize the topographic relief features of the area under analysis, including elevation differences between different geomorphic units such as roads, buildings, and green spaces. Elevation data is fundamental for determining water flow direction and calculating water depth. Land use data processing is used to determine roughness parameters for different surface types. Roughness is a key parameter reflecting the degree to which the surface impedes water flow; for example, asphalt pavements have low roughness, resulting in fast water flow; grasslands and green spaces have high roughness, resulting in slow water flow and strong infiltration. To reflect the spatial unevenness of rainfall distribution, precipitation masking data is generated using the Thiessen polygon method based on the distribution of meteorological observation stations within the area under analysis. Specifically, this involves expanding outwards from each observation station to form a polygonal region, with rainfall data from that station used as input at any location within the polygon. The initial water depth can be zero or a very small value, indicating that there is no water accumulation on the surface of the area under analysis at the start of the simulation. Furthermore, the input data for the hydrodynamic model is constructed based on elevation data, land use data, precipitation sequences, drainage system parameters, and the pre-determined initial water depth.

[0026] The inundation results include water depth changes, maximum inundation depth, and spatial distribution at different time steps. Input data is fed into the HiPIMS model, which performs numerical simulations for each uncertainty scenario using either the finite volume method or the finite difference method. The simulation process involves: for each uncertainty scenario, the model reads the corresponding input data and progresses step-by-step according to a preset time step, starting from the initial moment. At each time step, the model updates the water level and flow velocity of each grid cell based on the rainfall input and boundary conditions using the two-dimensional shallow water equation. The simulation continues throughout the entire rainfall process, during which the model automatically records the water depth values ​​for each grid cell at each time step. After the simulation, based on the raster map of the spatial distribution of the maximum inundation depth for each scenario and the time series data of the inundation depth, the water depth changes at different time steps, the corresponding maximum inundation depth, and the spatial distribution results for each scenario are determined.

[0027] Furthermore, index analysis is performed based on the inundation results of each uncertain scenario, and low-risk, medium-risk, and high-risk scenarios are selected based on the analysis results. The 90th percentile inundation depth, first-level inundation area, and average inundation depth are extracted from the raster data of each uncertain scenario. Only one of the first-level inundation area or average inundation depth can be selected. The 90th percentile inundation depth is calculated as follows: identify all inundated grid cells in the scenario, extract the maximum inundation depth value of each grid cell; sort the maximum inundation depth values ​​in ascending order, and take the depth value corresponding to the 90th percentile as the 90th percentile inundation depth. The 90th percentile water depth reflects the degree of water accumulation in the most severe area of ​​the region under analysis in this scenario. The proportion of first-level inundation area can also be expressed as the Class I inundation area, which is calculated according to a preset inundation level standard. For example, less than 0.1 meters is considered slight water accumulation, 0.1 to 0.25 meters is Class IV inundation, 0.25 to 0.5 meters is Class III inundation, more than 0.5 meters is Class II inundation, and more than 0.8 meters is Class I inundation. The total area of ​​grids reaching Level 1 or higher inundation standards in each scenario is calculated to obtain the Level 1 inundation area. The average inundation depth is the arithmetic mean of the maximum water depth of all inundated grids in that scenario, reflecting the severity of water accumulation in the area under analysis in that scenario.

[0028] After determining the corresponding index values ​​for the inundation outcomes of each uncertainty scenario, a small number of representative scenarios are selected based on these index values ​​for subsequent multi-objective decision-making regarding flood control measures. For example, using the 90th percentile inundation depth as the primary ranking index, 100 scenarios are arranged from largest to smallest according to this index. Then, combined with the primary inundation area as an auxiliary verification index, three representative scenarios are selected from the ranked sequence: the scenario ranked high with both a large 90th percentile water depth and a large primary inundation area is selected as a high-risk representative scenario; the scenario in the middle of the ranking with both indices at a moderate level is selected as a medium-risk representative scenario; and the scenario ranked low with both a small 90th percentile water depth and a small primary inundation area is selected as a low-risk representative scenario. The number of representative scenarios for each risk level can be 3-5, and can be adjusted according to the actual situation.

[0029] Step 103: For each representative scenario, calculate the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and select each candidate flood control measure based on the risk reduction rate.

[0030] The risk reduction rate is used to quantify the flood control effectiveness of flood control measures; flood control measures can be single measures or combined measures. Single measures include measures to improve drainage capacity; combined measures include measures to improve drainage capacity and increase public green space, measures to improve drainage capacity and permeable paving, measures to increase public green space and permeable paving, and measures to improve drainage capacity, increase public green space and permeable paving.

[0031] Specifically, after obtaining the representative scenarios, the waterlogging risk corresponding to each uncertainty scenario can be calculated, that is, the waterlogging risk of the area to be analyzed without waterlogging prevention measures under each uncertainty scenario, providing a baseline reference for the subsequent comparison of the effectiveness of waterlogging prevention measures. Further, the risk reduction rate of each waterlogging prevention measure is determined based on the waterlogging risk without measures under the representative scenarios. Optionally, in this scheme, the risk reduction rate of each pre-constructed waterlogging prevention measure under the current representative scenario is calculated based on the hydrodynamic model, and each candidate waterlogging prevention measure is determined based on the risk reduction rate, including the following steps A1-A3:

[0032] Step A1: Construct spatial distribution data of disaster-bearing bodies based on flood control data; use a hydrodynamic model to overlay and analyze the inundation results and spatial distribution data of disaster-bearing bodies under the current representative scenario to obtain the degree of disaster under the current scenario.

[0033] Specifically, based on land use data, disaster-bearing bodies within the analysis area are classified and identified, including residential buildings, commercial buildings, public buildings, and industrial buildings. The spatial distribution range and area information of each type of disaster-bearing body are extracted to construct spatial distribution data. The disaster-bearing body vector layer is overlaid with an inundation depth raster layer. For each disaster-bearing body patch, the maximum inundation depth value of all raster cells within its coverage area is extracted. For partially inundated disaster-bearing bodies, only the area and corresponding water depth of the actually inundated portion are calculated. For completely inundated disaster-bearing bodies, their total area and internal water depth distribution are recorded. The result of the overlay analysis can be a correlation table containing multiple records. Each record may include: disaster-bearing body number, type, total area, inundated area, average inundation depth, maximum inundation depth, and the proportion of inundated area to total area. This correlation table can represent the degree of disaster in the analysis area under a corresponding representative scenario.

[0034] Step A2: Determine the risk of unmanaged flooding in the area to be analyzed under the current representative scenario based on the preset water depth loss function and the degree of disaster.

[0035] The water depth loss function describes the loss rate of a certain type of disaster-bearing body at different inundation depths. The water depth loss function is predetermined based on domain-specific big data and other factors. The loss rate is the proportion of value loss due to flooding to the total value. In this scheme, the representative scenario is... i The formula for calculating the risk of urban flooding without measures is as follows: ;in, Describing a scenario i The area to be analyzed below has no measures to prevent waterlogging risk, which can also be understood as the comprehensive loss caused by waterlogging; For the context i Underlying disaster body j The flooded area; Disaster-bearing body j The value per unit area; For the context i The inundation depth of the underlying disaster-bearing area; This is the water depth loss function.

[0036] Step A3: Determine the risk reduction rate of each flood prevention measure under the current representative scenario based on the risk of flooding without measures.

[0037] Specifically, flood control measures can be individual or combined. Individual measures include measures to improve drainage capacity; combined measures include measures to improve drainage capacity and increase public green space, measures to improve drainage capacity and permeable paving, measures to increase public green space and permeable paving, and measures to improve drainage capacity and increase public green space and permeable paving. Measures to improve drainage capacity enhance the city's drainage system's transport and discharge capacity through methods such as expanding the pipeline network, upgrading pumping stations, and adding new drainage channels. For example, the design standard of the current drainage system can be upgraded from a two-year return period to a five-year return period. Specifically, this is achieved by proportionally increasing the drainage capacity parameters of the HiPIMS model according to the ratio of the rainfall intensity of a five-year return period to a two-year return period. Measures to increase public green space improve the infiltration and retention capacity of the urban underlying surface by constructing green infrastructure such as rain gardens, sunken green spaces, and vegetated swales, thereby reducing surface runoff and peak runoff. For example, green facilities can be configured in the built-up area of ​​the area to be analyzed according to a preset ratio. Specifically, this is achieved by adjusting the infiltration parameters of the corresponding area in the model; an increased infiltration parameter reflects the soil's absorption capacity for rainwater. Meanwhile, water storage capacity parameters can be set for some sunken green areas to simulate their function of retaining rainwater. Permeable paving measures enhance the surface's rainwater infiltration capacity by transforming traditional hardened paving such as sidewalks, parking lots, and plazas into permeable materials such as permeable concrete and permeable bricks. For example, permeable paving can be configured in a preset proportion within the built-up area of ​​the region to be analyzed. Specifically, the infiltration parameters of the paved area are adjusted to approximate the infiltration capacity of natural soil, while the Manning coefficient is appropriately adjusted to reflect the characteristics of permeable paving. Through the above parameterization, each flood control measure is transformed into a key parameter adjustment scheme that can be directly input into the HiPIMS model, realizing the conversion from engineering language to model language. This allows the disaster reduction effects of different measures to be quantitatively simulated and objectively compared through a unified physical model.

[0038] After determining the risk of flooding without measures under each representative scenario, the same method as in step A2 is used to determine the flooding risk of each flood control measure under each representative scenario. Then, the risk reduction rate is determined based on the flooding risk without measures and the flooding risk with measures. Optionally, in this scheme, the risk reduction rate of each flood control measure under the current representative scenario is determined based on the flooding risk without measures under the current representative scenario. This includes: calculating the flooding risk of each flood control measure under the current representative scenario based on the water depth loss function; and determining the risk reduction rate under the current representative scenario based on the hydrodynamic model, the flooding risk without measures under the current representative scenario, and the flooding risk with measures under the current representative scenario.

[0039] Specifically, under the current representative scenario, various flood control measures are applied, and the risk of flooding within the affected area is calculated based on the water depth loss function. The formula for calculating the risk reduction rate is: ; Measures to mitigate the risk of flooding should be implemented. As a representative scenarioi The risk reduction rate.

[0040] Furthermore, based on representative scenarios, the risk reduction rate of each flood control measure is calculated, and flood control measures with weak disaster reduction effects or unstable performance are screened out. For example, only flood control measures with risk reduction rates greater than the preset risk rate under all representative scenarios are retained as candidate flood control measures.

[0041] Step 104: Determine the multi-objective evaluation results of candidate flood control measures under all uncertainty scenarios based on the predetermined evaluation indicators; and generate an optimization selection strategy for flood control measures based on the multi-objective evaluation results.

[0042] The multi-objective assessment results include disaster reduction assessment results and cost assessment results. Evaluation indicators include the effective inundation area percentage, the ratio of the 90th percentile inundation depth to the reference water depth, the percentage of first-order inundation area, and the cost-benefit ratio. Of course, the evaluation indicators can be defined and expanded according to specific application scenarios (e.g., cities with different weather conditions or geographical locations) to achieve robust flood control decisions. After determining each candidate flood control measure, each candidate measure is precisely verified, i.e., simulated under all uncertainty scenarios using a hydrodynamic model. In one optional implementation, the flood control assessment results of each candidate flood control measure under all uncertainty scenarios are determined based on the hydrodynamic model, including: for each uncertainty scenario, flood control simulation of each candidate flood control measure under the current uncertainty scenario is performed using a hydrodynamic model to obtain the indicator values ​​of each evaluation indicator; the disaster reduction assessment results are determined based on the effective inundation area percentage, the ratio of the 90th percentile inundation depth to the reference water depth, and the percentage of first-order inundation area; and the cost assessment results are determined based on the cost-benefit ratio.

[0043] Each candidate flood control strategy is input into the hydrodynamic model as part of the input data. The precipitation sequence and drainage parameters generated in the previous steps are then used to perform hydrodynamic simulations on each candidate flood control strategy. The simulation results of each candidate flood control measure under each uncertainty scenario are obtained, including the maximum inundation depth, spatial distribution, and risk reduction rate of each candidate flood control measure under each scenario.

[0044] The final flood control assessment results include disaster reduction assessment results and cost assessment results. After simulating various candidate flood control measures, a multi-objective evaluation system is constructed to achieve a comprehensive comparison between the disaster reduction effect and economic efficiency of different flood control measures. The evaluation indicators include two optimization objectives: comprehensive inundation index (corresponding to the disaster reduction assessment result) and cost-benefit ratio (corresponding to the cost assessment result). The comprehensive inundation index includes the effective inundation area percentage, the ratio of the 90th percentile inundation depth to the reference water depth, and the first-order inundation area percentage.

[0045] The first objective is to minimize the comprehensive inundation index, which is calculated as follows: ;in To effectively flood the area, The area to be analyzed is . To effectively determine the proportion of the submerged area, The 90th percentile flood depth For reference water depth, This refers to the first-level inundation area. , and These are pre-set weighting coefficients. This is a comprehensive inundation index.

[0046] The second objective is to maximize the cost-benefit ratio, which is calculated as follows: Wherein, PVB represents the net present value of income, which can be determined based on the average risk reduction rate: ;in, n This refers to the number of scenarios. PVC is calculated as follows: ;in, ICW For initial construction costs, For the first Annual maintenance and operating costs, where r is the discount rate and T is the lifespan in years. The lifespan and cost parameters of each flood control measure can be set according to the project type and the actual conditions of the area to be analyzed. For example, the lifespan of drainage capacity improvement measures is 50 years, the lifespan of measures to increase public green space is 70 years, and the lifespan of permeable pavement measures is 50 years.

[0047] The flood control measure selection strategy guides subsequent engineering projects, determining which flood control measure should be chosen under specific scenarios. Furthermore, based on the flood control assessment results of each candidate flood control strategy under all uncertainty scenarios, a scheme selection logic matching future scenario conditions is established. This enables decision-makers to dynamically select the most suitable combination of flood control measures based on the detection and prediction of future rainfall and drainage capacity changes, achieving flexibility and foresight in decision-making. Specifically, statistical analysis is conducted on the risk reduction rate, comprehensive inundation index, and cost-effectiveness ratio of each flood control measure under different uncertainty scenarios to obtain the overall performance level and fluctuation characteristics of each flood control measure under different uncertainty scenarios, thereby characterizing its stability and adaptability. A comparative analysis of the performance of flood control measures under different uncertainty scenarios is performed to identify the differences in response of different flood control measures under different uncertainty scenarios. This includes analyzing the changing trends of the risk reduction rate, the changing characteristics of the comprehensive inundation index, and the differences in cost-effectiveness ratio of each flood control measure under different uncertainty scenarios. This study comprehensively evaluates various flood control measures from two dimensions: disaster reduction effectiveness and economic efficiency. It focuses on identifying measures that demonstrate high risk reduction rates and reasonable cost-effectiveness in most scenarios, while also considering the degree of fluctuation in their performance under different uncertainty scenarios to assess their stability. Based on this, combinations of measures that demonstrate stability and superior overall performance under multiple uncertainty scenarios are selected as the preferred flood control solutions. Furthermore, a selection logic for flood control measures under different uncertainty scenarios is constructed. When the scenario involves low rainfall enhancement and minimal drainage capacity decline, priority is given to flood control measures with lower costs and stable disaster reduction effects. When the scenario involves significant rainfall enhancement or severe drainage capacity decline, priority is given to flood control measures with stronger disaster reduction effects and stable performance under high-risk scenarios.

[0048] The technical solution of this embodiment acquires flood control data of the area to be analyzed and constructs a set of uncertain scenarios based on pre-determined uncertainty parameters and flood control data. Uncertainty parameters include rainfall variation and drainage capacity. The inundation results of each uncertain scenario are determined according to a pre-determined hydrodynamic model, and representative scenarios are selected from the set of uncertain scenarios based on the inundation results. For each representative scenario, the risk reduction rate of each pre-constructed flood control measure under the current representative scenario is calculated based on the hydrodynamic model, and candidate flood control measures are selected based on the risk reduction rate. The risk reduction rate is used to quantify the flood control effect of the flood control measures. Flood control measures can be single measures or combined measures. The multi-objective evaluation results of the candidate flood control measures under all uncertain scenarios are determined according to pre-determined evaluation indicators. An optimized selection strategy for flood control measures is generated based on the multi-objective evaluation results. The technical solution of this embodiment, by parameterizing flood control measures, directly transforms engineering measures such as drainage capacity improvement, increasing public green space, and permeable paving into key parameters identifiable by the hydrodynamic model, achieving deep coupling between the action process of the measures and the physical hydrodynamic process. Compared to existing evaluation methods that rely on statistical indicators or empirical models, this approach quantifies the actual regulatory effects of different flood control measures on inundation depth and spatial distribution of water accumulation. This ensures that the evaluation results are based on rigorous physical mechanisms, improving the accuracy and reliability of measure effectiveness assessments. By introducing quantitative indicators such as risk reduction rate and cost-benefit ratio, the physical simulation results are transformed into economic loss assessments. Combined with life-cycle cost analysis, a multi-objective evaluation system encompassing disaster reduction effectiveness, economic rationality, and risk stability is constructed. This avoids situations where only disaster reduction effects are considered while ignoring cost constraints, or where excessive cost reduction sacrifices safety levels, thereby improving the scientific rigor of flood control measure selection. Robustness analysis identifies candidate solutions that perform well and have controllable fluctuations in most scenarios. Adaptive selection strategies for different uncertainty scenarios are constructed, ensuring that decision-making results do not become invalid due to biases in single-scenario assumptions, guaranteeing the reliability and sustainability of flood control measures under future complex environmental changes.

[0049] Figure 2 This is a schematic diagram of the structure of a multi-objective decision-making device for flood control measures provided in an embodiment of the present invention. This device is suitable for executing the multi-objective decision-making method for flood control measures provided in an embodiment of the present invention. Figure 2 As shown, the device may specifically include:

[0050] The scenario construction module 201 is used to acquire flood control data of the area to be analyzed, and construct an uncertain scenario set based on predetermined uncertainty parameters and the flood control data; the uncertainty parameters include rainfall changes and drainage capacity. The scenario screening module 202 is used to determine the inundation results of each uncertainty scenario according to a predetermined hydrodynamic model, and to screen out representative scenarios from the uncertainty scenario set based on the inundation results. The measure evaluation module 203 is used to calculate the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and to screen candidate flood control measures based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measures; the flood control measures are single measures or combined measures; The multi-objective decision module 204 is used to determine the multi-objective evaluation results of the candidate flood control measures under all uncertainty scenarios based on the pre-determined evaluation indicators; and to generate an optimized selection strategy for flood control measures based on the multi-objective evaluation results.

[0051] Optionally, the set of uncertainty scenarios includes various uncertainty scenarios; the scenario construction module 201 is specifically used to: determine the value range of the uncertainty parameter; Based on a predetermined stratified sampling method and the range of values, combinations of rainfall variations and drainage capacity are generated to obtain the uncertain scenarios; the uncertain scenarios include precipitation sequences and drainage system parameters.

[0052] Optionally, the flood control data includes elevation data and land use data; the representative scenarios include low-risk scenarios, medium-risk scenarios, and high-risk scenarios; the scenario screening module 202 is specifically used to: construct input data for the hydrodynamic model based on the elevation data, the land use data, the precipitation sequence, the drainage system parameters, and the predetermined initial water depth; The input data is input into the hydrodynamic model, and the two-dimensional shallow water model of the hydrodynamic model is used to perform numerical simulations of the uncertain scenarios to obtain the inundation results of the uncertain scenarios.

[0053] Optionally, the first measure analysis module 203 is specifically used to: construct spatial distribution data of disaster-bearing bodies based on the flood control data; By superimposing the inundation results under the current representative scenario and the spatial distribution number of disaster-bearing bodies using the hydrodynamic model, the degree of disaster under the current representative scenario can be obtained. The risk of unmanaged flooding in the area to be analyzed under the current scenario is determined based on a preset water depth loss function and the degree of disaster. Based on the risk of flooding without measures under the current representative scenario, determine the risk reduction rate of each flood prevention measure under the current representative scenario.

[0054] Optionally, the measure evaluation module 203 is further configured to: calculate the flood risk of each flood control measure under the current representative scenario based on the water depth loss function; The risk reduction rate under the current representative scenario is determined based on the hydrodynamic model, the risk of waterlogging without measures under the current representative scenario, and the risk of waterlogging with measures.

[0055] Optionally, the multi-objective evaluation results include disaster reduction evaluation results and cost evaluation results; the multi-objective decision module 204 is specifically used to: for each uncertainty scenario, perform flood control simulation on each candidate flood control measure under the current uncertainty scenario through the hydrodynamic model, and obtain the index values ​​of each evaluation index; the evaluation index includes the effective inundation area ratio, the ratio of the 90th percentile inundation depth to the reference water depth, the first-level inundation area ratio, and the cost-benefit ratio; The disaster reduction assessment result is determined based on the effective inundation area ratio, the ratio of the 90th percentile inundation depth to the reference water depth, and the first-level inundation area ratio. The cost assessment result is determined based on the cost-benefit ratio.

[0056] The individual measures include measures to improve drainage capacity; the combined measures include measures to improve drainage capacity and increase public green space, measures to improve drainage capacity and permeable paving, measures to increase public green space and permeable paving, and measures to improve drainage capacity, increase public green space and permeable paving.

[0057] The multi-objective decision-making device for flood control measures provided in this embodiment of the invention can execute the multi-objective decision-making method for flood control measures provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Content not described in detail in this embodiment can be referred to the description in any method embodiment of the invention.

[0058] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, with reference to... Figure 3 , Figure 3 The electronic device 12 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 3 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0059] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0060] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0061] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0062] A program / utility 40 having a set (at least one) of program modules 46 may be stored, for example, in system memory 28. Such program modules 46 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 46 typically perform the functions and / or methods described in the embodiments of this application.

[0063] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0064] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing a multi-objective decision-making method for flood control measures provided in this embodiment of the invention: acquiring flood control data of the area to be analyzed, and constructing a set of uncertain scenarios based on predetermined uncertainty parameters and the flood control data; the uncertainty parameters include rainfall changes and drainage capacity; determining the inundation results of each uncertain scenario according to a predetermined hydrodynamic model, and selecting representative scenarios from the set of uncertain scenarios based on the inundation results; for each representative scenario, calculating the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and selecting candidate flood control measures based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measures; the flood control measures are single measures or combined measures; determining the multi-objective evaluation results of the candidate flood control measures under all uncertain scenarios according to predetermined evaluation indicators; and generating an optimized selection strategy for flood control measures based on the multi-objective evaluation results.

[0065] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-objective decision-making method for flood control measures as provided in all embodiments of this invention: acquiring flood control data for the area to be analyzed, and constructing a set of uncertain scenarios based on pre-determined uncertainty parameters and the flood control data; the uncertainty parameters include rainfall variation and drainage capacity; determining the inundation result of each uncertain scenario according to a pre-determined hydrodynamic model, and selecting representative scenarios from the set of uncertain scenarios based on the inundation result; for each representative scenario, calculating the risk reduction rate of each pre-constructed flood control measure under the current representative scenario based on the hydrodynamic model, and selecting candidate flood control measures based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measures; the flood control measures are single measures or combined measures; determining the multi-objective evaluation results of the candidate flood control measures under all uncertain scenarios according to pre-determined evaluation indicators; and generating an optimized selection strategy for flood control measures based on the multi-objective evaluation results. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor electronic devices, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM or flash memory); optical fiber; portable compact disk read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an electronic device, apparatus, or apparatus that can be executed.

[0066] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in conjunction with an electronic device, apparatus, or device that executes instructions.

[0067] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0068] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0069] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A multi-objective decision-making method for flood control measures, characterized in that, The method includes: Acquire flood control data for the area to be analyzed, and construct a set of uncertainty scenarios based on predetermined uncertainty parameters and the flood control data; The inundation results for each uncertainty scenario are determined based on a predetermined hydrodynamic model, and representative scenarios are selected from the set of uncertainty scenarios based on the inundation results. For each representative scenario, the risk reduction rate of each pre-constructed flood control measure under the current representative scenario is calculated based on the hydrodynamic model, and candidate flood control measures are selected based on the risk reduction rate; the risk reduction rate is used to quantify the flood control effect of the flood control measures; the flood control measures are single measures or combined measures; The multi-objective evaluation results of the candidate flood control measures under all uncertain scenarios are determined based on the pre-determined evaluation indicators; and an optimization selection strategy for flood control measures is generated based on the multi-objective evaluation results.

2. The method according to claim 1, characterized in that, The set of uncertainty scenarios includes various uncertainty scenarios; the set of uncertainty scenarios is constructed based on predetermined uncertainty parameters and the flood control data, including: Determine the range of values ​​for the uncertainty parameter; Based on a predetermined stratified sampling method and the range of values, combinations of rainfall variations and drainage capacity are generated to obtain the uncertain scenarios; the uncertain scenarios include precipitation sequences and drainage system parameters.

3. The method according to claim 2, characterized in that, The flood control data includes elevation data and land use data; the representative scenarios include low-risk, medium-risk, and high-risk scenarios. The inundation results for each uncertainty scenario are determined based on a pre-defined hydrodynamic model, and representative scenarios are selected from the set of uncertainty scenarios based on these inundation results, including: The input data for constructing the hydrodynamic model is based on the elevation data, the land use data, the precipitation sequence, the drainage system parameters, and the predetermined initial water depth. The input data is input into the hydrodynamic model, and the two-dimensional shallow water model of the hydrodynamic model is used to perform numerical simulations of the uncertain scenarios to obtain the inundation results of the uncertain scenarios.

4. The method according to claim 1, characterized in that, Based on the hydrodynamic model, the risk reduction rate of each pre-constructed flood control measure under the current representative scenario is calculated, including: Based on the flood control data, spatial distribution data of disaster-bearing bodies are constructed; By superimposing the inundation results under the current representative scenario and the spatial distribution number of disaster-bearing bodies using the hydrodynamic model, the degree of disaster under the current representative scenario can be obtained. The risk of unmanaged flooding in the area to be analyzed under the current scenario is determined based on a preset water depth loss function and the degree of disaster. Based on the risk of flooding without measures under the current representative scenario, determine the risk reduction rate of each flood prevention measure under the current representative scenario.

5. The method according to claim 4, characterized in that... Based on the risk of flooding without measures under the current representative scenario, determine the risk reduction rate of each flood prevention measure under the current representative scenario, including: Based on the water depth loss function, the risk of waterlogging for each flood control measure under the current representative scenario is calculated. The risk reduction rate under the current representative scenario is determined based on the hydrodynamic model, the risk of waterlogging without measures under the current representative scenario, and the risk of waterlogging with measures.

6. The method according to claim 1, characterized in that, The multi-objective evaluation results include disaster reduction evaluation results and cost evaluation results; based on pre-determined evaluation indicators, the multi-objective evaluation results of the candidate flood control measures under all uncertainty scenarios are determined, including: For each uncertainty scenario, the hydrodynamic model is used to simulate the flood control measures under the current uncertainty scenario, and the index values ​​of each evaluation index are obtained. The evaluation index includes the effective inundation area ratio, the ratio of the 90th percentile inundation depth to the reference water depth, the first-level inundation area ratio, and the cost-benefit ratio. The disaster reduction assessment result is determined based on the effective inundation area ratio, the ratio of the 90th percentile inundation depth to the reference water depth, and the first-level inundation area ratio. The cost assessment result is determined based on the cost-benefit ratio.

7. The method according to claim 1, characterized in that, The individual measures include measures to improve drainage capacity; the combined measures include measures to improve drainage capacity and increase public green space, measures to improve drainage capacity and permeable paving, measures to increase public green space and permeable paving, and measures to improve drainage capacity, increase public green space and permeable paving.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-objective decision-making method for flood control measures as described in any one of claims 1-7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-objective decision-making method for flood control measures as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multi-objective decision-making method for flood control measures as described in any one of claims 1-7.

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