Multi-scale urban rainstorm flood risk assessment method

Abstract

The invention relates to the technical field of risk management, in particular to a multi-scale urban rainstorm flood risk assessment method. The method comprises the following steps: acquiring a regional basic data set; performing data preprocessing on the regional basic data set to generate a regional basic preprocessed data set; constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set to obtain an urban scale flood assessment index, and performing urban scale rainstorm flood risk assessment to obtain an urban scale rainstorm flood risk assessment result; according to the method, the index information is obtained from the open source data, and the defects that in previous researches, part of index data is poor in spatial precision and long in time are overcome; the multi-scale urban rainstorm flood risk assessment method comprehensively considering the urban scale and the block scale is provided, and the accuracy, scientificity and comprehensiveness of urban rainstorm flood risk assessment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk management, and in particular, to a multi-scale urban rainstorm and flood risk assessment method. Background Art

[0002] Urban extreme rainstorm and flood disasters occur frequently in China. Non-engineering measures represented by risk management have gradually become an important technical means for urban rainstorm and flood prevention and mitigation, and play a positive role in formulating effective prevention and mitigation strategies. However, most of the current urban rainstorm and flood risk assessments focus on the entire urban scale alone and lack consideration of the block scale. The present invention combines the urban scale and the block scale, and proposes a multi-scale urban rainstorm and flood risk assessment method based on an index evaluation and a hydrological-hydrodynamic coupling model. The technical results provide strong technical support for flood emergency management and improve the accuracy, scientificity and comprehensiveness of urban rainstorm and flood risk assessment. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-scale urban rainstorm and flood risk assessment method to solve at least one of the above technical problems.

[0004] To achieve the above object, a multi-scale urban rainstorm and flood risk assessment method includes the following steps:

[0005] Step S1: Obtain a regional basic data set; perform data preprocessing on the regional basic data set to generate a regional basic preprocessed data set;

[0006] Step S2: Construct a rainstorm and flood risk assessment index system based on the regional basic preprocessed data set to obtain urban-scale flood assessment indexes, and perform urban-scale rainstorm and flood risk assessment to obtain an urban-scale rainstorm and flood risk assessment result;

[0007] Step S3: Construct design rainstorm scenarios with different return periods, construct a hydrological-hydrodynamic coupling model, carry out simulations of design rainstorm scenarios with different return periods, and perform block-scale rainstorm and flood risk assessment to obtain a block-scale rainstorm and flood risk assessment result.

[0008] The beneficial effects of the present invention are as follows. By obtaining the regional basic data set and performing preprocessing, the standardization and consistency of the input data are ensured, providing a reliable data basis for subsequent analysis. Based on the regional basic data set, a rainstorm and flood risk assessment index system is constructed and risk assessment at the urban scale is carried out. In this process, a data-driven assessment model is introduced, making the rainstorm and flood risk assessment results not only have high spatial accuracy but also be able to reflect the risk differences in different regions. Different design rainstorm scenarios with different return periods are adopted, and combined with the hydrological and hydrodynamic coupling model, the rainstorm and flood situations under different scenarios are simulated. This step combines dynamic simulation with risk assessment, accurately captures the flood risk at the block scale, and reveals the risk status of each block in the face of rainstorm scenarios through refined analysis, further improving the refinement degree of flood warning. In addition, the construction of multiple design rainstorm scenarios makes the assessment results more flexible and adaptable, capable of coping with different climate conditions and return period requirements. Generally speaking, the step-by-step assessment process of this method from the region to the block not only improves the hierarchy and systematicness of the assessment but also significantly enhances the prediction ability of flood risk through refined data models and scenario simulations, and can provide more scientific data support and decision-making basis for urban management and disaster prevention and control. Therefore, the present invention solves the problems of lack of refinement and adaptability in traditional rainstorm and flood risk assessment through a phased and multi-scale risk assessment method, and improves the accuracy and practicability of the assessment results.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Obtain the regional basic data set, where the regional basic data set includes basic geographic information data, hydrological data, and social development data;

[0011] Step S12: Obtain the regional population heat map data; classify the regional population heat map data and social development data, and assign values according to different color blocks to obtain the population density distribution data;

[0012] Step S13: Clean the basic geographic information data and hydrological data, and perform data standardization processing to obtain the regional basic preprocessing data set, where the regional basic preprocessing data set includes basic geographic information data and hydrological data, the regional basic preprocessing data set includes river system data, surface terrain data, land use data, urban rainwater pipe network data, and the hydrological data includes historical measured precipitation data, historical waterlogging inundation depth range data, and design rainstorm manuals.

[0013] Through the obtained regional basic dataset, including basic geographical information data, hydrological data, and social development data, multi-dimensional and multi-source information support is provided for rainstorm and flood risk assessment. This dataset not only covers environmental factors such as geography, meteorology, and hydrology, but also involves the social and economic development situation, enabling the risk assessment to more comprehensively consider the interaction between the natural environment and human activities in different regions. Secondly, by obtaining regional population heat map data and classifying and assigning color blocks to it and the social development data, population density distribution data is generated. This process effectively quantifies social factors into spatial data, ensuring that the impact of population distribution and social activities on flood risk assessment is accurately reflected, thus taking into account the vulnerability of different regions in the assessment process and avoiding the deviation caused by the traditional method's neglect of social factors. Next, data cleaning and standardization processing are carried out on the basic geographical information data and hydrological data, ensuring the consistency and accuracy of the data and providing high-quality input data for subsequent analysis. By standardizing multi-dimensional geographical data such as river system data, surface terrain data, land use data, and urban rainwater pipe network data, errors caused by inconsistent data sources or scale differences can be avoided, ensuring the comparability and consistency of the analysis results. In terms of hydrological data, by standardizing historical measured precipitation data, historical waterlogging inundation depth range data, and design rainstorm manuals, the spatio-temporal consistency of the data is further improved, enabling hydrological data in different historical stages and climate scenarios to be compared and analyzed within a unified framework. This high-quality data integration and processing process not only provides more comprehensive and accurate data support for rainstorm and flood risk assessment, but also enhances the scientificity and operability of the assessment results and improves the decision-making ability to respond to rainstorm and flood disasters.

[0014] Preferably, step S2 includes the following steps:

[0015] Step S21: Based on the regional basic preprocessing dataset, construct an index system for rainstorm and flood risk assessment to obtain urban-scale flood assessment indicators, where the urban-scale flood assessment indicators include an objective layer, a criterion layer, and an index layer;

[0016] Step S22: Based on the urban-scale flood assessment indicators, calculate the urban-scale rainstorm and flood risk assessment for the basic geographical information data and hydrological data to obtain the values of each index for the urban-scale rainstorm and flood risk assessment;

[0017] Step S23: Use the analytic hierarchy process and the entropy weight method to calculate the weights of the criterion layer and the index layer respectively to obtain the comprehensive criterion-index weight value;

[0018] Step S24: Based on the single-index quantification - multi-index synthesis - multi-criterion integration evaluation method and the criterion-index comprehensive weight value, perform urban-scale rainstorm and flood risk calculation processing on each index value of the urban-scale rainstorm and flood risk assessment to generate the urban-scale rainstorm and flood risk assessment result.

[0019] The present invention constructs an index system for rainstorm and flood risk assessment based on a regional basic preprocessing data set. By dividing the urban-scale rainstorm and flood risk assessment into an objective layer, a criterion layer, and an index layer, each risk factor can be decomposed layer by layer in the hierarchical structure. This structured design not only improves the readability of the data but also provides a clear framework for assessments at different levels, helping to clarify the roles and relationships of indicators at each level in complex flood risk analysis. In the urban-scale flood assessment calculation, basic geographic information data and hydrological data are combined to ensure that, based on multi-source data, the risk assessment can take into account the interactions of multiple factors such as space, time, and society. This comprehensive analysis can reflect a more realistic urban flood risk situation. Next, the analytic hierarchy process and the entropy weight method are used to calculate the weights of the criterion layer and the index layer to obtain the criterion-index comprehensive weight value, further improving the weight allocation of different factors in the assessment process. This weight calculation method fully considers the relative importance of each criterion and index in the entire assessment, thus effectively avoiding the interference of subjective factors in traditional methods and ensuring the objectivity and scientific nature of the assessment process. Finally, the single-index quantification - multi-index synthesis - multi-criterion integration evaluation method is used to process each index value and combine it with the criterion-index comprehensive weight value for risk calculation, enabling effective comprehensive assessment among different indicators. By integrating the influences of multiple levels and multiple factors, this method obtains a more comprehensive and accurate urban-scale rainstorm and flood risk assessment result, not only enhancing the multi-dimensionality of the assessment but also strengthening the reliability and practicality of the result.

[0020] The objective layer is determined to be the urban-scale rainstorm and flood risk assessment;

[0021] The criterion layer is defined as hazard and vulnerability;

[0022] The design rainfall amount, ground elevation, and terrain standard deviation are used as hazard indicators;

[0023] The population density, distance from hospitals, distance from emergency shelters, etc. are used as vulnerability indicators;

[0024] By limiting the evaluation scope to the urban-scale rainstorm and flood risk assessment, the results of the risk assessment can reflect the overall flood risk level of the city, rather than being limited to a specific area or scale, thus ensuring the wide applicability of the assessment results at the spatial level. In the setting of the criterion layer, by selecting "hazard" and "vulnerability" as the core assessment criteria, the assessment of flood risk is systematically divided into two key dimensions, considering both the potential risk factors in the natural environment and the vulnerability factors of society and infrastructure. Hazard indicators include design rainfall, ground elevation, and terrain standard deviation, etc. These indicators directly reflect the natural disaster risks faced by a certain area during rainstorms. As an important meteorological factor, the design rainfall determines the precipitation during rainstorms in the region, while the ground elevation and terrain standard deviation reflect the influence of geographical terrain on water accumulation and drainage capacity, further enriching the dimensions of natural disaster assessment. These hazard indicators ensure the full consideration of geographical and meteorological conditions at the data level, enabling the assessment results to accurately reflect the natural risk differences of each region in the face of rainstorm floods. The vulnerability indicators are evaluated through social factors such as population density, distance to hospitals, and distance to emergency shelters, significantly enhancing the attention to the vulnerability of the population and infrastructure. The level of population density directly affects the number of people affected during disasters, while the distances to hospitals and emergency shelters reflect the ease of post-disaster rescue and evacuation. On the one hand, by incorporating socio-economic data, the assessment can consider the impact of population distribution and social infrastructure on disaster response capabilities. On the other hand, it optimizes the operability and pertinence of the risk assessment, ensuring that social and natural factors can be scientifically weighed during the rainstorm and flood risk assessment process.

[0025] Preferably, step S23 includes the following steps:

[0026] Step S231: Obtain the subjective weights of each criterion and each indicator using the analytic hierarchy process;

[0027] Step S232: Obtain the objective weights of each criterion and each indicator using the entropy weight method;

[0028] Step S233: Obtain the comprehensive weights of each criterion and each indicator by integrating the subjective weights and the objective weights.

[0029] In the present invention, the subjective weights of each criterion and each index are obtained by using the Analytic Hierarchy Process (AHP). This method can convert subjective opinions into quantitative weight data through expert judgment and empirical knowledge, thereby providing professional domain knowledge support for the evaluation model. The advantage of the Analytic Hierarchy Process lies in its ability to hierarchize complex problems and assign corresponding importance weights to each level, which enables the relative importance of each criterion and index to be clearly reflected in the weight calculation process, thus ensuring the theoretical rigor of the evaluation system. The objective weights of each criterion and each index are obtained by the entropy weight method. The entropy weight method objectively reflects the information contribution degree of each index in the dataset by analyzing the discreteness of the data, and thus calculates the objective weight of each index. Different from the subjective judgment of the Analytic Hierarchy Process, the entropy weight method provides a data-driven weight calculation method, which avoids the interference of human factors, makes the weight assignment more dependent on the variability and internal laws of the actual data, and enhances the objectivity of the evaluation. By combining the subjective weight and the objective weight, the comprehensive weight of each criterion and each index is obtained synthetically, which reflects a decision-making method that balances subjective experience and objective data. The introduction of the comprehensive weight makes the evaluation process consider both the expert's empirical knowledge and fully utilize the potential information in the data, thus avoiding the biases or limitations brought by a single weight calculation method. Through this method, the weight assignment is more scientific and reasonable, which not only ensures the professionalism of the evaluation system, but also enhances the objectivity of the results and the reliability of practical applications.

[0030] Preferably, the comprehensive weight calculation formula is:

[0031] w i = aw i '+ (1 - a)w i ”

[0033] In the formula, w i is the comprehensive weight, w i ' is the weight obtained by the Analytic Hierarchy Process, w i ” is the weight obtained by the entropy weight method, and a is an empirical value.

[0034] Through the comprehensive weight w i of the present invention, it is possible to better balance the expert's subjective experience and the objective performance of the data, eliminate the biases that occur when using the two alone, and make the entire evaluation process both professional and maximally dependent on the performance of the actual data. Finally, the weight system obtained through this comprehensive method not only improves the accuracy of the rainstorm and flood risk assessment results, but also enhances its adaptability and operability, ensuring the comprehensiveness, objectivity and scientificity of the evaluation results.

[0035] Preferably, step S24 includes the following steps:

[0036] Step S241: Quantify single indicators using the fuzzy membership method, map all indicators to the range of [0, 1] uniformly, and calculate positive and negative indicators respectively. The specific calculation formulas are as follows:

[0037]

[0038] In the formula, and are the quantified values of single positive and negative indicators respectively, x i is the value of the i th indicator, a i , b i , c i , d i , e i , f i are the worst value, passing value, relatively good value, optimal value, larger value, and maximum value respectively;

[0039] Step S242: For multiple indicators in the criterion layer that can reflect the urban-scale rainstorm and flood risk, use the multi-index weighted calculation method to obtain the membership degree of each criterion layer. The specific calculation formula is as follows:

[0040]

[0041] In the formula, RI t represents the score level of each criterion layer, w i is the comprehensive weight, which is obtained by comprehensively calculating the analytic hierarchy process and the entropy weight method.

[0042] Step S243: Based on the calculation results of different criterion layers, weight the membership degrees of each criterion layer to obtain the urban-scale rainstorm and flood risk degree:

[0043]

[0044] In the formula, R is the urban-scale rainstorm and flood risk degree, w t represents the weight of each criterion, which is obtained by comprehensively calculating the analytic hierarchy process and the entropy weight method;

[0045] Step S244: According to the differences in the urban-scale rainstorm and flood risk degree (R), the urban-scale rainstorm and flood risk degree levels are divided into four levels: low risk, [0, 0.25]; medium risk, (0.25, 0.5]; medium-high risk, (0.5, 0.75]; and high risk (0.75, 1);

[0046] Step S245: Based on the R value and the urban-scale rainstorm and flood risk degree classification standard, obtain the urban-scale rainstorm and flood risk level.

[0047] The present invention adopts the maximum - minimum normalization method to uniformly map all indicators to the range of [0, 1]. This process ensures the dimensional consistency and numerical consistency of each indicator through the normalization of the minimum and maximum values of each indicator, so that the subsequent analysis is not affected by the differences in data dimensions. This method enables different types of data to be compared under the same standard, thereby improving the consistency and reliability of data processing. By calculating the weights of each indicator, this step weights the relative importance of each indicator through a subjective assignment method that introduces professional knowledge or experience, and then forms a comprehensive weight for risk assessment. In this process, the role of each indicator in the rainstorm and flood risk assessment is considered, reasonably reflecting the relative contributions of each indicator in decision - making. This weight calculation method based on expert assignment enables the evaluation model to better fit the actual risk management requirements and provides a weight setting that is more in line with actual needs. Further, in combination with the weighted calculation results, the weighted summation method is used to obtain the final risk assessment result, further quantifying the relative vulnerability of different regions when facing flood risks, and reflecting the different risk levels of each region in the rainstorm scenario through layer - by - layer analysis. Finally, the subdivision based on different rainstorm risk levels in step S244 enables the final assessment result to accurately classify different risk levels, providing a detailed basis for subsequent decision - making and emergency management. By comprehensively considering different indicators and weights, the finally obtained risk assessment result not only has strong scientificity and accuracy, but also can provide reliable decision - making support for actual disaster warning and prevention and control.

[0048] Preferably, step S3 includes the following steps:

[0049] Step S31: Re - create the rainstorm scenario according to the design rainstorm manual to generate different - period design rainstorm scenarios;

[0050] Step S32: Based on different - period design rainstorm scenarios, construct a surface runoff generation and concentration model, a pipe network runoff concentration model, a one - dimensional river model, and a two - dimensional surface model, and couple and integrate the surface runoff generation and concentration model, the pipe network runoff concentration model, the one - dimensional river model, and the two - dimensional surface model to obtain a hydro - dynamic coupling model;

[0051] Step S33: Calibrate the parameters of the hydro - dynamic coupling model according to historical measured precipitation data and historical waterlogging inundation depth range data to generate the parameter calibration of the hydro - dynamic coupling model;

[0052] Step S34: Input the parameter calibration of the hydro - dynamic coupling model into the hydro - dynamic coupling model to calculate the inundation calculation result;

[0053] Step S35: Obtain the water accumulation depth data; divide the block-scale rainstorm flood risk levels based on the water accumulation depth data, and evaluate the block-scale rainstorm flood risks under different return periods. The division of the block-scale rainstorm flood risk levels by the water accumulation depth data is divided into four levels, namely: low risk, 0 < h < 0.15; medium risk, 0.15 ≤ h < 0.30; medium-high risk, 0.30 ≤ h < 0.50; and high risk, 0.50 ≤ h, where h is the water accumulation depth data.

[0054] The present invention reproduces different-period design rainstorm scenarios according to the design rainstorm manual, providing diverse meteorological inputs for subsequent hydrological models. The generation of these design rainstorm scenarios combines precipitation data of different return periods, which can provide basic data for evaluating the intensity and occurrence frequency of rainstorms, and further provide different simulation scenarios for flood risks under different rainstorm conditions, thereby increasing the adaptability and representativeness of the evaluation results. Based on these design rainstorm scenarios, multiple models are constructed, including surface runoff and confluence models, pipe network confluence models, one-dimensional river models, and two-dimensional surface models. These models simulate the changes in water flow after rainstorms from different hydrological process perspectives, covering various hydrological dynamic processes such as surface runoff, pipe network flow, river flow, and surface water flow. Coupling and integrating these models form a hydrological-hydrodynamic coupling model, which can more comprehensively and accurately reflect the dynamic changes in the hydrological environment after rainstorms, improving the reliability and accuracy of flood risk prediction. Step S33 further calibrates the model through historical measured precipitation data and historical waterlogging inundation depth range data, thereby ensuring the consistency between the model output results and the actual situation. The introduction of historical data enables the model to be calibrated based on real precipitation and waterlogging depth conditions, enhancing the authenticity and applicability of the simulation results. By inputting and calculating the calibrated hydrological-hydrodynamic coupling model, the inundation calculation results are obtained. These calculation results provide detailed data support for evaluating the waterlogging inundation situation under different rainstorm scenarios, providing a quantitative basis for subsequent risk assessment. Finally, by obtaining the water accumulation depth data and using it to divide the block-scale rainstorm flood risk levels, the flood risk is effectively refined to the block level, enabling the evaluation results to accurately reflect the risk status of different regions under different rainstorm scenarios. This refined risk division provides more targeted flood control and disaster reduction strategies for urban managers, enhancing the risk response ability. Generally speaking, this method provides a high-precision, multi-dimensional rainstorm flood risk assessment framework through systematic data integration, historical data calibration, and model coupling, ensuring the scientificity, accuracy, and practicality of the evaluation results.

[0055] Preferably, step S32 includes the following steps:

[0056] Step S321: Divide the surface sub - catchment areas for the land use data to generate surface sub - catchment area division data; Select the time - varying gain model as the surface runoff generation model and the non - linear reservoir method as the surface runoff concentration model, couple the time - varying gain model and the non - linear reservoir method with different - period design storm scenarios to construct a surface runoff generation and concentration model, and generate a surface runoff generation and concentration model;

[0057] Step S322: Analyze the pipe network topological relationship for the urban rainwater pipe network data to generate pipe network topological relationship data; Construct a pipe network runoff concentration model based on the pipe network topological relationship data and the dynamic wave method, and generate a pipe network runoff concentration model;

[0058] Step S323: Analyze the river network topological relationship for the river system data to generate river network topological relationship data; Construct a one - dimensional river channel model based on the river network topological relationship data and the one - dimensional Saint - Venant equation, and generate a one - dimensional river channel model;

[0059] Step S324: Conduct surface grid meshing on the surface terrain data to generate surface grid meshing data; Construct a two - dimensional surface model based on the two - dimensional shallow water equation and the surface grid meshing data, and generate a two - dimensional surface model;

[0060] Step S325: Couple and integrate the surface runoff generation and concentration model, the pipe network runoff concentration model, the one - dimensional river channel model, and the two - dimensional surface model to obtain a hydrological - hydrodynamic coupling model.

[0061] The present invention divides the surface sub - catchments of land use data, and constructs a surface runoff generation and concentration model by combining the time - varying gain model and the non - linear reservoir method, which can accurately simulate the precipitation transformation and surface runoff processes under different land use types. This process couples land use information, rainfall scenarios, and the dynamic changes of water flow, effectively improving the accuracy and adaptability of the model. Secondly, through the analysis of the data topological relationship of urban rainwater pipe networks and in combination with the dynamic wave method, a pipe network runoff concentration model is constructed. This method can comprehensively consider the complexity and spatio - temporal dynamics of urban rainwater pipe networks, providing a more accurate prediction for the urban discharge of rainwater runoff. In addition, the river channel model constructed by combining the river network topological relationship data with the one - dimensional Saint - Venant equation effectively simulates the water flow process in the river system, thereby improving the prediction ability of parameters such as water level and flow rate in the river basin. The two - dimensional surface model constructed by the two - dimensional shallow water equation based on the surface grid division data not only improves the detailed simulation of surface water flow but also enhances the analysis ability of the interaction between topographic features and hydrological characteristics. Finally, by coupling and integrating these independent hydrological and hydrodynamic models, the simulation of surface runoff generation, pipe network runoff concentration, river channel flow, and surface water flow can be linked, thus forming a more complete, comprehensive, and efficient hydrological - hydrodynamic coupling model. This coupling model has strong practical application value in dealing with urban flood, rainwater discharge, river channel water flow changes, etc., can provide accurate predictions and decision - making support for fields such as water resource management, urban flood control, and environmental protection, and can show strong adaptability and fineness at different time and space scales.

[0062] Preferably, step S33 includes the following steps:

[0063] Step S331: Input the historical measured precipitation data into the hydrological - hydrodynamic coupling model for urban rainstorm flood simulation;

[0064] Step S332: Compare the simulated inundation results with the actual inundation results. When the differences in inundation scope and inundation depth between the two are within an acceptable range, the model parameters are obtained.

[0065] The present invention uses historical measured precipitation data as input, which has real spatio-temporal distribution characteristics and can fully reflect the precipitation intensity, duration, and spatial distribution of rainstorm events at different time and spatial scales. These data not only provide a scientific input basis for the model but also ensure the practical relevance of the simulation results. By simulating rainstorm flood scenarios, the inundation area and water depth data of flood events can be obtained, providing crucial reference bases for urban flood control, drainage system design, and flood disaster warning. Next, through comparison with actual inundation results, the effectiveness of the model is further verified. The gap between the inundation area and water depth is controlled within an acceptable range, and this verification process demonstrates the high precision and credibility of the model in reflecting actual inundation phenomena. This not only improves the rationality of the model parameters but also ensures that the hydrological-hydrodynamic coupling model can reflect the actual dynamics and distribution of water flow in urban areas when simulating rainstorm flood events. In addition, by continuously comparing and adjusting the model parameters, the errors of the model can be effectively corrected, thereby improving its prediction accuracy under different regions and conditions. Finally, this optimization process based on the comparison between measured data and simulation results not only enhances the adaptability and reliability of the model in practical applications but also provides strong scientific support for the formulation of subsequent urban flood control and water resource management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic flow chart of the steps of a multi-scale urban rainstorm flood risk assessment method;

[0067] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0068] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in

[0069] The realization, functional features, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0072] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve the above object, please refer to Figures 1 to 3 , a multi-scale urban rainstorm flood risk assessment method, the method comprising the following steps:

[0074] Step S1: Obtain a regional basic data set; perform data preprocessing on the regional basic data set to generate a regional basic preprocessed data set;

[0075] Step S2: Based on the regional basic preprocessed data set, construct a rainstorm flood risk assessment index system to obtain urban-scale flood assessment indexes, and perform urban-scale rainstorm flood risk assessment to obtain urban-scale rainstorm flood risk assessment results;

[0076] Step S3: Construct design rainstorm scenarios with different return periods, construct a hydrological-hydrodynamic coupling model, conduct simulations of design rainstorm scenarios with different return periods, and perform block-scale rainstorm flood risk assessment to obtain block-scale rainstorm flood risk assessment results.

[0077] The beneficial effects of the present invention are as follows. By obtaining the regional basic data set and performing preprocessing, the standardization and consistency of the input data are ensured, providing a reliable data basis for subsequent analysis. Based on the regional basic data set, a rainstorm and flood risk assessment index system is constructed, and risk assessment at the urban scale is carried out. In this process, a data-driven assessment model is introduced, making the rainstorm and flood risk assessment results not only have high spatial accuracy but also be able to reflect the risk differences in different regions. Different design rainstorm scenarios with different return periods are adopted, and combined with the hydrological and hydrodynamic coupling model, the rainstorm and flood conditions under different scenarios are simulated. This step combines dynamic simulation with risk assessment to accurately capture the flood risk at the block scale, and through refined analysis, reveals the risk status of each block in the face of rainstorm scenarios, further improving the refinement degree of flood warning. In addition, the construction of multiple design rainstorm scenarios makes the assessment results more flexible and adaptable, and can cope with different climate conditions and return period requirements. Generally speaking, the step-by-step assessment process of this method from the region to the block not only improves the hierarchy and systematicness of the assessment but also significantly enhances the prediction ability of flood risk through refined data models and scenario simulations, and can provide more scientific data support and decision-making basis for urban management and disaster prevention and control. Therefore, the present invention solves the problems of lack of refinement and adaptability in traditional rainstorm and flood risk assessment through a phased and multi-scale risk assessment method, and improves the accuracy and practicality of the assessment results.

[0078] In the embodiment of the present invention, with reference to Figure 1 As shown, it is a schematic flow chart of the steps of a multi-scale urban rainstorm and flood risk assessment method of the present invention. In this example, the multi-scale urban rainstorm and flood risk assessment method includes the following steps:

[0079] Step S1: Obtain the regional basic data set; perform data preprocessing on the regional basic data set to generate a regional basic preprocessed data set;

[0080] In the embodiments of the present invention, obtaining the regional basic data set means collecting multi-dimensional data related to the region, including but not limited to geographic information data, climate data, land use data, transportation network data, etc. The sources of these data are remote sensing images, meteorological stations, ground monitoring devices or public databases, etc., and cover various information such as spatial distribution, time series and geographic coordinates. In order to ensure that these data can be effectively applied in subsequent analyses, data preprocessing must be carried out. The purpose of data preprocessing is to make the original data available and consistent by performing operations such as cleaning, denoising, format conversion and standardization on the original data. Specific technical means include steps such as missing value processing, outlier detection and correction, data format unification, coordinate system conversion, etc. For example, for the processing of missing values, the missing part can be estimated by interpolation or nearest neighbor filling; while the processing of outliers depends on statistical methods such as Z-value detection or outlier removal based on the distribution model. At the same time, the standardization of data formats, such as unifying data units and converting different data formats (such as CSV, Shapefile, GeoTIFF, etc.), can enable seamless docking of multi-source data in subsequent steps. In addition, the preprocessing of spatial data also includes projection conversion and coordinate accuracy adjustment to meet the requirements of different spatial analyses. All these preprocessing operations help to transform complex and heterogeneous data sets into standardized data sets that meet the analysis requirements, and thus provide high-quality inputs for subsequent model construction, analysis and calculation, and decision support.

[0081] Step S2: Construct a rainstorm and flood risk assessment index system based on the regional basic preprocessing data set to obtain urban-scale flood assessment indicators, and conduct urban-scale rainstorm and flood risk assessment to obtain urban-scale rainstorm and flood risk assessment results;

[0082] In the embodiments of the present invention, constructing an index system for rainstorm and flood risk assessment is a comprehensive analysis process involving multiple dimensions and factors. This process relies on key data in the regional basic preprocessing dataset, including precipitation, surface slope, soil type, land use pattern, terrain features, distribution of drainage facilities, historical flood data, etc. These data reflect the potential impacts of rainstorm events on floods in different geographical units and different time periods. Through data-level analysis and modeling, a series of indicators related to rainstorm and flood risk can be determined, such as waterlogging depth, flood duration, drainage capacity, soil permeability, etc. The construction of the index system depends on expert experience, statistical analysis, or machine learning methods to determine the weights and relative importance of each indicator for flood risk, and to adapt it in combination with regional characteristics. In addition, in order to ensure the scientificity and applicability of the evaluation indicators, methods such as multi-criteria decision analysis (MCDA) and analytic hierarchy process (AHP) are also used to normalize and weight the indicators, so as to obtain a comprehensive risk score. Based on the constructed evaluation index system, the next step is to conduct rainstorm and flood risk assessment at the urban scale. In this process, according to the rainstorm intensity, duration, and frequency under different design scenarios, by inputting these evaluation indicators into a hydrological model or a flood simulation system, dynamic calculations and risk predictions are carried out. These models combine spatio-temporal data, precipitation patterns, and ground features to simulate the urban flood situation after a rainstorm, and obtain the flood risk levels of each region. The results of the risk assessment include the division of regions with different risk levels, the probability of flood occurrence, the distribution of waterlogging depth, etc., and can provide a strong decision-making basis for urban flood emergency management, infrastructure planning, and disaster prevention and mitigation measures.

[0083] Step S3: Construct design rainstorm scenarios with different return periods, construct a coupled hydro-hydraulic model, conduct simulations of design rainstorm scenarios with different return periods, and conduct rainstorm and flood risk assessment at the block scale to obtain the rainstorm and flood risk assessment results at the block scale.

[0084] In the embodiments of the present invention, constructing design rainstorm scenarios with different return periods is based on historical precipitation data, meteorological data, and climate change prediction results. Multiple design scenarios of rainstorm events are generated through statistical analysis or probability models. These scenarios include rainstorm situations with different intensities, durations, and frequencies to simulate the flood impacts of different rainstorm events on urban areas. When inputting data, the setting of the return period needs to be considered. Common return periods such as 10 years, 50 years, 100 years, etc. represent the probability and intensity of rainstorm events occurring. In this process, the quality and coverage of historical precipitation data are crucial to ensure the representativeness and reliability of the design scenarios. Next, the construction of the hydrological-hydrodynamic coupling model combines a hydrological model and a hydrodynamic model. By considering factors such as precipitation, surface runoff, water flow conveyance in drainage pipe networks and river channels, etc., it simulates the hydrological response and water flow evolution process under rainstorm scenarios. The hydrological model generally simulates the process of precipitation being converted into runoff based on factors such as precipitation-runoff relationship, soil moisture dynamics, evapotranspiration, etc.; the hydrodynamic model is used to simulate the propagation and accumulation behavior of water flow in pipe networks, river channels, and on the surface. In addition to precipitation data, the model inputs also include basic data such as topography, land use, soil type, drainage capacity, river channel geometry, etc. These data need to be preprocessed, including spatial interpolation, coordinate transformation, format unification, etc., to ensure the compatibility and accuracy of different data sources. Through this coupled modeling, the hydrological and hydrodynamic responses of rainstorm events at different spatial and temporal scales can be accurately captured. Based on the constructed hydrological-hydrodynamic coupling model, a rainstorm flood risk assessment at the block scale is further carried out. By inputting the design rainstorm scenarios into this model, indicators such as flood inundation range, water depth, and flow velocity within the block are simulated to obtain the rainstorm flood risk assessment results at the block scale. This assessment not only involves the physical process of flood but also combines social and economic factors such as the carrying capacity of the urban drainage system and land use types, further improving the comprehensiveness and accuracy of the risk assessment. Finally, the assessment results provide a detailed flood risk map for urban blocks, which can identify high-risk areas and provide decision-making support for relevant departments to optimize the design of urban disaster prevention, drainage systems, and emergency response plans.

[0085] Preferably, step S1 includes the following steps:

[0086] Step S11: Obtain a regional basic data set, where the regional basic data set includes basic geographic information data, hydrological data, and social development data;

[0087] Step S12: Obtain regional population heat map data; classify the regional population heat map data and social development data, and assign values according to different color blocks to obtain population density distribution data;

[0088] Step S13: Clean the basic geographic information data and hydrological data, and perform data standardization processing to obtain a regional basic preprocessing data set. The regional basic preprocessing data set includes basic geographic information data and hydrological data. The regional basic preprocessing data set includes river system data, surface terrain data, land use data, and urban rainwater pipe network data. The hydrological data includes historical measured precipitation data, historical waterlogging inundation depth range data, and design storm manuals.

[0089] In the embodiments of the present invention, obtaining the regional basic data set includes the collection of multi-source data. Among them, the basic geographic information data, hydrological data, and social development data respectively reflect the geographical, climatic, and social and economic conditions. These data come from different monitoring platforms, satellite remote sensing, meteorological stations, public databases and other channels, covering multiple aspects such as the basic terrain, meteorological information, land use, and population distribution of the region, and need to be uniformly processed and integrated to ensure their data quality and consistency. After the data is obtained, data cleaning becomes a key step. The basic geographic information data and hydrological data often contain noise, missing values, or outliers, so data cleaning must be carried out. This process includes operations such as identifying and correcting missing values, outlier detection and processing, and removing duplicate data. Common data cleaning methods such as interpolation methods (such as Kriging interpolation, spline interpolation, etc.) are used to complement missing data; outliers can be identified through statistical methods such as box plot method, Z-value detection, etc., and then removed or corrected accordingly. After data cleaning, data standardization processing is to eliminate the influence brought by differences in scale and unit between different data sources, so that different types of data are comparable on the same platform. Standardization methods include Z-score standardization, maximum-minimum standardization, etc., aiming to make the mean of the data 0 and the standard deviation 1, or compress the data into a unified numerical range to ensure unity when input into the model. For the processing of regional population heat map data and social development data, through classification processing and assignment, population density distribution data can be obtained. In this process, the population heat map data shows the spatial distribution of the population within the region, and the social development data includes social factors such as economy, education, and transportation in each region. By combining these data and performing classification processing, different regions or regional units are assigned different risk levels, population density values, or social development indices, etc., and population density distribution data with geographical attributes is generated. This data can divide different densities into different categories through color block assignment, providing a basis for subsequent regional analysis. Finally, the generated regional basic preprocessing data set contains key data such as basic geographic information data, hydrological data, and population density distribution data, which provide high-quality data support for subsequent hydrological analysis, urban flood risk assessment, and emergency response planning, etc.

[0090] As an example of the present invention, refer to Figure 2As shown, in this example, step S2 includes:

[0091] Step S21: Construct a rainstorm and flood risk assessment index system based on the regional basic preprocessing data set to obtain urban-scale flood assessment indicators, where the urban-scale flood assessment indicators include an objective layer, a criterion layer, and an index layer;

[0092] Step S22: Perform urban-scale rainstorm and flood risk assessment calculations on the basic geographic information data and hydrological data based on the urban-scale flood assessment indicators to obtain the values of each index for the urban-scale rainstorm and flood risk assessment;

[0093] Step S23: Use the analytic hierarchy process and the entropy weight method to calculate the weights of the criterion layer and the index layer respectively to obtain the comprehensive criterion-index weight value;

[0094] Step S24: Perform urban-scale rainstorm and flood risk calculation processing on the values of each index for the urban-scale rainstorm and flood risk assessment based on the single-index quantification - multi-index synthesis - multi-criterion integration evaluation method and the comprehensive criterion-index weight value to generate the urban-scale rainstorm and flood risk assessment result.

[0095] In the embodiments of the present invention, a rainstorm and flood risk assessment index system is constructed based on a regional basic preprocessing data set, which is a process of quantifying and hierarchizing multiple factors. The regional basic preprocessing data set provides data support in multiple dimensions, including basic geographic information data, hydrological data, land use data, etc. Through comprehensive analysis of these data, a multi-level index system is formed. The system includes an objective layer, a criterion layer, and an index layer. The objective layer reflects the overall objective of the assessment, that is, the rainstorm and flood risk assessment at the urban scale. The criterion layer and the index layer represent different factors and specific indicators affecting the rainstorm and flood risk, such as precipitation intensity, basin area, land use type, etc. On this basis, the index values of the criterion layer and the index layer are obtained through data calculation, providing a clear calculation basis for subsequent assessments. After the index system is constructed, the rainstorm and flood risk assessment calculation at the urban scale is carried out on the basic geographic information data and hydrological data. This process is based on the aforementioned assessment indicators. By using relevant hydrological models and statistical analysis methods, the data is calculated and analyzed to obtain the index values of the rainstorm and flood risk at the urban scale. Through spatial analysis, overlay calculation, and normalization processing of each data set, the flood risk assessment of different urban areas under different rainstorm scenarios can be realized. The analytic hierarchy process (AHP) and the entropy weight method are used to calculate the weights of the criterion layer and the index layer. The analytic hierarchy process determines the weight values of the criterion layer and the index layer by constructing a hierarchical structure model and relying on expert experience and relative judgment matrices. The entropy weight method automatically assigns different weights to each indicator based on the calculation of information entropy and the diversity and uncertainty of the data, ensuring that the assessment results are highly objective and scientific. The combination of the two methods provides quantitative and systematic support for the comprehensive weight value of the criterion-index in the rainstorm and flood risk assessment. Finally, based on the single-index quantification, multi-index synthesis, and multi-criterion integration evaluation methods, combined with the comprehensive weight value of the criterion-index, the calculation process of the rainstorm and flood risk assessment at the urban scale is carried out. At this stage, the weights and calculation results of each indicator are synthesized through technologies such as weighted average, fuzzy mathematics, and comprehensive evaluation methods to finally obtain the rainstorm and flood risk assessment results at the urban scale.

[0096] Preferably, the construction of the rainstorm and flood risk assessment index system includes the following steps:

[0097] Determine the objective layer as the rainstorm and flood risk assessment at the urban scale;

[0098] Define the criterion layer as hazard and vulnerability;

[0099] Use the design rainfall amount, ground elevation, and terrain standard deviation indicators as hazard indicators;

[0100] Use indicators such as population density, distance from hospitals, and distance from emergency shelters as vulnerability indicators;

[0101] In the embodiments of the present invention, according to the determination of the target layer, that is, the urban-scale rainstorm and flood risk assessment, by defining the assessment target layer as the urban scale, it means that the assessment object is the overall rainstorm and flood risk of the city, rather than just a local area or a single rainstorm event. This provides a global framework for the subsequent risk assessment. The establishment of the target layer makes all the data and models involved in the assessment process point to a unified goal, avoiding the fragmentation problem of local analysis and ensuring the coordination and comprehensiveness of the entire assessment system. Secondly, based on the determination that the criterion layer is hazard and vulnerability, technically, it can be understood as a hierarchical analysis method based on multi-criteria decision-making. Hazard and vulnerability respectively represent the possibility of the occurrence of rainstorm and flood events and their impact on the urban system. Hazard mainly refers to the occurrence probability and intensity of rainstorms or floods, while vulnerability focuses on the vulnerability of urban elements to rainstorms and floods. The determination of these two criterion layers unfolds the rainstorm and flood risk assessment from two dimensions of the probability of risk occurrence and the degree of impact, ensuring that the assessment can comprehensively reflect risk factors. Then, by designing the rainfall amount, ground elevation, and terrain standard deviation as hazard indicators, these indicators provide the potentiality of the occurrence of rainstorm and flood events at the data level. The designed rainfall amount is obtained through meteorological data and represents the intensity of rainstorms occurring in a specific area within a certain period; the ground elevation data is obtained through remote sensing technology and geographic information system (GIS) and is used to determine the water flow direction and water accumulation situation in different areas during rainstorms; the terrain standard deviation evaluates the ease of flood diffusion by analyzing the undulation degree of the terrain. Areas with larger terrain undulations will result in more complex water flow distributions. These data can be used for spatial analysis and modeling, combined with different rainstorm scenarios, to obtain corresponding hazard assessments. Finally, the vulnerability indicators are defined by population density, distance from hospitals, distance from emergency shelters, etc. The acquisition of these data depends on the integration of socio-economic data and spatial data. Population density data can be obtained through remote sensing data or mobile communication data, etc., and can reflect the potential exposure risk in rainstorm and flood events; the distances from hospitals and emergency shelters are based on the analysis of urban public facility data and can evaluate whether the population in vulnerable areas can be quickly rescued or transferred during rainstorms and floods. These vulnerability indicators further optimize the assessment of vulnerable areas through means such as spatial analysis technology and network analysis method.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Use the analytic hierarchy process to obtain the subjective weights of each criterion and each indicator;

[0104] Step S232: Use the entropy weight method to obtain the objective weights of each criterion and each indicator;

[0105] Step S233: Obtain the comprehensive weights of each criterion and each indicator by integrating the subjective weight and the objective weight.

[0106] In the embodiment of the present invention, the analytic hierarchy process (AHP) is used to determine the subjective weights of each criterion and indicator. The AHP method constructs a hierarchical structure model, enabling the criteria and indicators at different levels to be assigned corresponding subjective weights during the process of mutual comparison. At the data level, AHP needs to construct a judgment matrix, and experts score the importance of the criteria and indicators based on experience and knowledge. The scoring is reflected by a ratio scale from 1 to 9. Then, through the consistency test, the consistency of the scoring is ensured, and finally, the subjective weights of each criterion and indicator are obtained. These weights reflect the degree of emphasis of experts or decision-makers on each criterion and indicator. Secondly, the entropy weight method is used to calculate the objective weights of each criterion and indicator. This method mainly relies on the data distribution information of each indicator, specifically by calculating the information entropy of each indicator to reflect its variation degree in the data. An indicator with a larger information entropy contains more information in the data, so it will be assigned a larger objective weight, while an indicator with a smaller information entropy will be assigned a smaller weight. Through the calculation of the entropy value, the importance of each indicator can be reflected from the objective data level without relying on expert judgment, thus providing a quantitative and data-driven weight allocation method. Finally, in step S233, the subjective weight and the objective weight are weighted and integrated, which is achieved by the weighted average method. The subjective weight and the objective weight are synthesized to obtain the final comprehensive weight of each criterion and indicator. This process combines the knowledge of experts and the objective information of the data, making the weight calculation result reflect both the actual data characteristics and the professional judgment of the decision-maker.

[0107] Preferably, the comprehensive weight calculation formula is:

[0108] w i =aw i '+(1 - a)w i ”

[0110] In the formula, w i is the comprehensive weight, w i ' is the weight obtained by the analytic hierarchy process, w i ” is the weight obtained by the entropy weight method, and a is an empirical value.

[0111] In the embodiment of the present invention, the comprehensive weight w i can better balance the expert's subjective experience and the objective performance of the data, eliminate the biases that occur when using them separately, and make the entire evaluation process both professional and rely on the performance of the actual data to the greatest extent.

[0112] Preferably, step S24 includes the following steps:

[0113] Step S241: Quantify single indicators using the fuzzy membership method, map all indicators to the range of [0, 1] uniformly, and calculate positive and negative indicators respectively. The specific calculation formulas are as follows:

[0114]

[0115] In the formula, and are the quantified values of positive and reverse single indicators respectively. x i is the value of the i th indicator. a i , b i , c i , d i , e i , f i are the worst value, passing value, relatively good value, optimal value, larger value, and maximum value respectively;

[0116] Step S242: For multiple indicators in the criterion layer that can reflect the urban-scale storm flood risk, use the multi-index weighted calculation method to obtain the membership degree of each criterion layer. The specific calculation formula is as follows:

[0117]

[0118] In the formula, RI t represents the score level of each criterion layer. w i is the comprehensive weight, which is obtained by comprehensively calculating the analytic hierarchy process and the entropy weight method. V i is the and arithmetic conditional sum;

[0119] Step S243: Based on the calculation results of different criterion layers, weight the membership degrees of each criterion layer to obtain the urban-scale storm flood risk degree:

[0120]

[0121] In the formula, R is the urban-scale storm flood risk degree. w t represents the weight of each criterion, which is obtained by comprehensively calculating the analytic hierarchy process and the entropy weight method.

[0122] Step S244: According to the differences in the urban-scale storm flood risk degree (R), the urban-scale storm flood risk degree is divided into four levels: low risk, [0, 0.25]; medium risk, (0.25, 0.5]; medium-high risk, (0.5, 0.75]; and high risk (0.75, 1);

[0123] Step S245: Based on the R value and the urban-scale storm flood risk degree grading standard, obtain the urban-scale storm flood risk level.

[0124] In the embodiments of the present invention, the quantification of positive indicators and negative indicators is calculated through formulas, where x i represents the value of the i-th indicator, and a i and b i represent the worst value and the best value of the indicator respectively. This step ensures the comparability between different indicators, enabling each indicator to be evaluated on the same scale. Next, a multi-index weighted calculation method is used to comprehensively evaluate multiple indicators in the criterion layer, and the membership degrees of each criterion layer are calculated, which are obtained by comprehensively calculating the analytic hierarchy process and the entropy method. This step incorporates the comprehensive influence of multiple indicators into the evaluation system through weighted summation, enhancing the comprehensiveness and accuracy of the evaluation results. On this basis, the membership degrees of each criterion layer are further weighted and summed to calculate the urban-scale rainstorm and flood risk degree R, where represents the weights of each criterion. Finally, according to the differences in the R values, the urban-scale rainstorm and flood risk degrees are divided into four levels.

[0125] As an example of the present invention, as shown in Figure 3 In this example, step S3 includes:

[0126] Step S31: Replicate the rainstorm scenario according to the design rainstorm manual to generate different-period design rainstorm scenarios;

[0127] Step S32: Based on different-period design rainstorm scenarios, construct surface runoff and confluence models, pipe network confluence models, one-dimensional river models, and two-dimensional surface models, and couple and integrate the surface runoff and confluence models, pipe network confluence models, one-dimensional river models, and two-dimensional surface models to obtain a hydrological and hydrodynamic coupling model;

[0128] Step S33: Calibrate the parameters of the hydrological and hydrodynamic coupling model according to historical measured precipitation data and historical waterlogging inundation depth range data to generate the parameter calibration of the hydrological and hydrodynamic coupling model;

[0129] Step S34: Input the parameter calibration of the hydrological and hydrodynamic coupling model into the hydrological and hydrodynamic coupling model to calculate the inundation calculation results;

[0130] Step S35: Obtain the water depth data; divide the block-scale rainstorm and flood risk levels based on the water depth data, and evaluate the block-scale rainstorm and flood risks under different return periods. The division of the block-scale rainstorm and flood risk levels based on the water depth data is divided into four levels, namely: low risk, 0 < h < 0.15; medium risk, 0.15 ≤ h < 0.30; medium-high risk, 0.30 ≤ h < 0.50; and high risk, 0.50 ≤ h, where h is the water depth data.

[0131] In the embodiments of the present invention, by reproducing different design storm scenarios according to the design storm manual, from the data level, this process needs to rely on historical meteorological data and factors such as design storm intensity and frequency. By analyzing historical precipitation data, the probability and intensity of rainstorms, design storm scenarios with different return periods can be generated. This process involves the construction and parameter setting of the rainstorm model to ensure that the model can reflect the characteristics of the actual precipitation scenario. Secondly, in step S32, based on different design storm scenarios, a surface runoff generation and concentration model, a pipe network runoff concentration model, a one-dimensional river model, and a two-dimensional surface model are constructed and coupled and integrated. Specifically, the surface runoff generation and concentration model reflects processes such as precipitation infiltration and surface runoff. The pipe network runoff concentration model considers the drainage capacity of the urban rainwater pipe network for precipitation. The one-dimensional river model simulates the flow of water along the river, and the two-dimensional surface model considers the flow characteristics of water on the surface and its interaction with the terrain. These models are coupled and integrated to form a hydrological and hydrodynamic coupling model, so as to comprehensively simulate the water flow movement and inundation process caused by rainstorms. The core technology of model coupling is multi-model integration and collaborative calculation, which requires reasonable setting of the parameters of each model and determination of the interaction relationship from the data level. Step S33 involves the parameter calibration of the hydrological and hydrodynamic coupling model. This process is based on historical measured precipitation data and the data range of the waterlogging inundation depth, and uses optimization algorithms (such as the minimum error method or genetic algorithm) to adjust the model parameters to ensure that the difference between the model simulation results and the actual observation data is minimized. The key to this process lies in how to select the appropriate parameter range and the design of the error function. Step S34 then inputs the calibrated model parameters into the hydrological and hydrodynamic coupling model for actual inundation calculation to obtain the simulation results of the inundation range and water depth distribution. This calculation process relies on numerical solution methods, such as the finite element method or the finite difference method, to transform the physical process into a discretized numerical model, so as to obtain the spatial distribution of the inundated area. Finally, step S35 obtains the water depth data and divides the rainstorm flood risk level at the block scale through these data. This division process relies on the preset risk level standard, and different regions are divided into different risk levels according to the water depth, so as to provide a basis for subsequent flood risk assessment. Through the water depth data, the rainstorm flood risk with different return periods can be quantified, and the risk levels in different regions and different scenarios can be evaluated.

[0132] Preferably, step S32 includes the following steps:

[0133] Step S321: Divide the surface sub-catchment area for the land use data to generate surface sub-catchment area division data; select the time-varying gain model as the surface runoff generation model and the non-linear reservoir method as the surface runoff concentration model, couple the time-varying gain model and the non-linear reservoir method with different design storm scenarios to construct the surface runoff generation and concentration model, and generate the surface runoff generation and concentration model;

[0134] Step S322: Analyze the pipeline network topological relationship of urban rainwater pipeline network data to generate pipeline network topological relationship data; construct a pipeline network confluence model based on the pipeline network topological relationship data and the dynamic wave method to generate a pipeline network confluence model;

[0135] Step S323: Analyze the river network topological relationship of river water system data to generate river network topological relationship data; construct a one-dimensional river channel model based on the river network topological relationship data and the one-dimensional Saint-Venant equation to generate a one-dimensional river channel model;

[0136] Step S324: Conduct surface grid meshing on surface terrain data to generate surface grid meshing data; construct a two-dimensional surface model based on the two-dimensional shallow water equation and the surface grid meshing data to generate a two-dimensional surface model;

[0137] Step S325: Coupling and integrating the surface runoff and confluence model, the pipeline network confluence model, the one-dimensional river channel model, and the two-dimensional surface model to obtain a hydrological and hydrodynamic coupling model.

[0138] In the embodiments of the present invention, the surface sub-catchments of land use data are divided. This process involves identifying the land use types and analyzing their spatial distribution, and using GIS technology to delimit the catchments. According to the division results, the spatial distribution data of different surface sub-catchments can be obtained. Next, the time-varying gain model is selected as the surface runoff generation model. The time-varying gain model can dynamically reflect the non-linear relationship between precipitation and surface runoff, and is particularly suitable for flow prediction under complex terrain and variable climate conditions. When combined with the non-linear reservoir method, it can better simulate the infiltration, ponding and flow processes of precipitation, and then generate a surface runoff and confluence model. The introduction of the non-linear reservoir method can simulate the accumulation and infiltration processes of water flow in the groundwater layer. First, the topological relationship analysis of urban rainwater pipe network data is carried out. Using the spatial data of the pipe network, the pipe network structure is analyzed by graph theory methods to generate the topological relationship data of the pipe network, which provides a basis for the subsequent pipe network confluence model. On this basis, combined with the dynamic wave method, the dynamic wave equation is used to describe the propagation process of water flow in the pipe network. The dynamic wave method can accurately simulate the non-linear fluctuation and reverse flow of water flow, and is suitable for complex flow conditions in urban pipe networks. The topological relationship analysis of river system data is carried out. Using the river channel spatial data and the basin structure, the topological relationship data of the river network is generated. Based on the one-dimensional Saint-Venant equation, a river channel flow model is constructed. The one-dimensional Saint-Venant equation is used to simulate the water flow in a one-dimensional river channel, and can calculate parameters such as flow velocity and water level change, and then generate a one-dimensional river channel model. For the surface terrain data, the surface is meshed by terrain dissection technology to obtain the surface mesh dissection data. Based on the two-dimensional shallow water equation, combined with the surface mesh data, a two-dimensional surface model is constructed. The two-dimensional shallow water equation can accurately describe the flow law of water flow on complex surfaces, especially when considering factors such as terrain undulation and surface reflection, and calculate the flow velocity and water level change of water flow through numerical solution methods, so as to generate a two-dimensional surface model. Finally, the above four models are coupled and integrated to obtain a hydrological and hydrodynamic coupling model. The core of the coupling and integration technology is to comprehensively simulate the complex hydrological and hydrodynamic processes caused by rainstorms through the collaborative calculation of multiple disciplines and multiple physical fields, and to make the results of different models influence and restrict each other.

[0139] Preferably, step S33 includes the following steps:

[0140] Step S331: Input the historical measured precipitation data into the hydrological and hydrodynamic coupling model for urban rainstorm and flood simulation;

[0141] Step S332: Compare the simulated inundation results with the actual inundation results. When the differences in inundation range and inundation depth between the two are within an acceptable range, the model parameters are obtained.

[0142] In the embodiments of the present invention, historical measured precipitation data is input into a hydro - hydrological dynamic coupling model for simulation. This step uses the measured precipitation data as input and conducts urban rainstorm flood simulation through the hydro - hydrological dynamic coupling model. The hydro - hydrological dynamic coupling model combines hydrological processes (such as precipitation, evaporation, basin runoff, etc.) and hydrodynamic processes (such as water flow, sediment transport, river flow regime, etc.), and can accurately simulate the whole process of urban rainstorm flood. The historical precipitation data input into the model includes parameters such as precipitation intensity and duration, and these data are input into the model in a time - series manner to restore the precipitation process in a specific time period. Secondly, by comparing the simulated inundation results with the actual inundation results, the simulation effect is evaluated and the model parameters are optimized. The actual inundation results are derived from historical measured waterlogging data or urban water accumulation data obtained through remote sensing technology, and these data provide information on the actual inundation range and water depth during rainstorms. By comparing the simulated results with the actual results, the differences in the inundation range and inundation water depth between the two are mainly analyzed, and the error range of these differences is calculated to ensure that it is within an acceptable range. If there is a significant deviation between the simulated results and the actual data, it is necessary to correct by adjusting the model parameters (such as precipitation intensity, soil permeability, river channel friction coefficient, etc.). The key technical means of this process is error analysis and parameter optimization. Optimization algorithms (such as the least - squares method, genetic algorithm, etc.) are used to repeatedly adjust the model parameters to minimize the gap between the model output results and the actual data.

[0143] The beneficial effects of the present invention are as follows. By obtaining the regional basic data set and performing preprocessing, the standardization and consistency of the input data are ensured, providing a reliable data basis for subsequent analysis. Based on the regional basic data set, a rainstorm and flood risk assessment index system is constructed and a risk assessment at the urban scale is carried out. In this process, a data-driven assessment model is introduced, making the rainstorm and flood risk assessment results not only have high spatial accuracy but also be able to reflect the risk differences in different regions. Different design rainstorm scenarios with different return periods are adopted, and combined with the hydrological and hydrodynamic coupling model, the rainstorm and flood situations under different scenarios are simulated. This step combines dynamic simulation with risk assessment, accurately captures the flood risk at the block scale, and reveals the risk status of each block in the face of rainstorm scenarios through refined analysis, further improving the refinement degree of flood warning. In addition, the construction of multiple design rainstorm scenarios makes the assessment results more flexible and adaptable, capable of coping with different climate conditions and return period requirements. Generally speaking, the step-by-step assessment process of this method from the region to the block not only improves the hierarchy and systematicness of the assessment but also significantly enhances the flood risk prediction ability through refined data models and scenario simulations, and can provide more scientific data support and decision-making basis for urban management and disaster prevention and control. Therefore, the present invention solves the problems of lack of refinement and adaptability in traditional rainstorm and flood risk assessment through a phased and multi-scale risk assessment method, and improves the accuracy and practicality of the assessment results.

[0144] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application document within the present invention.

[0145] The above are only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A multi-scale urban rainstorm flood risk assessment method, characterized in that: The following steps are involved: Step S1: Obtain regional basic data set; Perform data preprocessing on the regional basic data set to generate a regional basic preprocessed data set; Step S2: constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set, obtaining a city-scale flood assessment index, and conducting a city-scale rainstorm flood risk assessment to obtain a city-scale rainstorm flood risk assessment result; Step S3: constructing design rainstorm scenarios with different return periods, constructing a hydrological and hydrodynamic coupling model, carrying out simulations of design rainstorm scenarios with different return periods, and conducting a block-scale rainstorm and flood risk assessment to obtain a block-scale rainstorm and flood risk assessment result; Step S4: Perform flood risk assessment based on the block-scale rainstorm flood risk assessment results to generate flood risk assessment data; Conduct neural differential comparative analysis on flood risk assessment data and generate reports to generate multi-scale urban rainstorm flood risk assessment reports.

2. The multi-scale urban rainstorm flood risk assessment method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire a regional basic data set, wherein the regional basic data set includes basic geographic information data, hydrological data, and social development data; Step S12: Acquire regional population heat map data; classify and process the regional population heat map data and social development data, and assign values ​​to different color blocks to obtain population density distribution data; Step S13: Clean the basic geographic information data and hydrological data, and perform data standardization to obtain a regional basic preprocessing data set, wherein the regional basic preprocessing data set includes basic geographic information data and hydrological data, the regional basic preprocessing data set includes river system data, surface topography data, land use data, urban rainwater pipe network data, and the hydrological data includes historical measured precipitation data, historical waterlogging inundation depth range data, and a design rainstorm manual.

3. The multi-scale urban rainstorm flood risk assessment method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set to obtain a city-scale flood assessment index, wherein the city-scale flood assessment index includes a target layer, a criterion layer, and an index layer; Step S22: performing city-scale rainstorm flood risk assessment calculation on basic geographic information data and hydrological data based on city-scale flood assessment indicators to obtain the values ​​of various indicators for city-scale rainstorm flood risk assessment; Step S23: Use the analytic hierarchy process and the entropy weight method to calculate the weights of the criterion layer and the indicator layer respectively to obtain the criterion-indicator comprehensive weight value; Step S24: Based on the single-index quantification-multi-index synthesis-multi-criteria integrated evaluation method and the criterion-indicator comprehensive weight value, the urban-scale rainstorm and flood risk assessment index values ​​are calculated and processed to generate the urban-scale rainstorm and flood risk assessment results.

4. The multi-scale urban rainstorm flood risk assessment method according to claim 3 is characterized in that: The construction of the rainstorm flood risk assessment indicator system includes the following steps: According to the target layer, it is determined as a city-scale rainstorm flood risk assessment; Determined as dangerous and vulnerable according to the criteria level; The design rainstorm volume, ground elevation, and terrain standard deviation indicators are used as hazard indicators; Population density, distance to hospitals, distance to emergency shelters and other indicators are used as vulnerability indicators.

5. The multi-scale urban rainstorm flood risk assessment method according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: Obtain the subjective weights of each criterion and each indicator using the analytic hierarchy process; Step S232: Obtain the objective weights of each criterion and each indicator using the entropy weight method; Step S233: Combine the subjective weights and objective weights to obtain the comprehensive weights of each criterion and each indicator.

6. The multi-scale urban rainstorm flood risk assessment method according to claim 3 is characterized in that: The formula for calculating the comprehensive weight is: In i =aw i '+(1-a)in i ” In the formula, w i is the comprehensive weight, w i ' is the weight obtained by the hierarchical analysis method, w i ” is the weight obtained by entropy weight method, and a is the empirical value.

7. The urban-scale rainstorm flood risk calculation method according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Quantify single indicators using the fuzzy membership method, map all indicators to the range of [0, 1] uniformly, and calculate positive and negative indicators respectively. The specific calculation formulas are: In the formula, and They are the single indicator quantitative values ​​for the positive and negative directions, x i For the i The value of an indicator, a i 、b i 、c i ,d i 、e i 、f i They are the worst value, passing value, better value, optimal value, larger value, and maximum value respectively; Step S242: For multiple indicators in the criterion layer that can reflect the urban-scale storm flood risk, use the multi-index weighted calculation method to obtain the membership degrees of each criterion layer. The specific calculation formulas are as follows: In the formula, RI t Indicates the score level of each criterion layer, w i is the comprehensive weight, which is calculated by the analytic hierarchy process and the entropy weight method. i for and The arithmetic conditions and; Step S243: Based on the calculation results of different criterion layers, weight the membership degrees of each criterion layer to obtain the urban-scale storm flood risk degree: Where R is the city-scale rainstorm flood risk, w t It represents the weight of each criterion, which is calculated by the analytic hierarchy process and entropy weight method; Step S244: According to the differences in the urban-scale storm flood risk degree (R), the urban-scale storm flood risk degree levels are divided into four levels: low risk, [0, 0.25]; medium risk, (0.25, 0.5]; medium-high risk, (0.5, 0.75]; and high risk (0.75, 1]; Step S245: Based on the R value and the urban-scale storm flood risk degree grading standard, obtain the urban-scale storm flood risk level.

8. The multi-scale urban rainstorm flood risk assessment method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Recreate the storm scenarios according to the design storm manual to generate different-period design storm scenarios; Step S32: Based on different-period design storm scenarios, construct a surface runoff and confluence model, a pipe network confluence model, a one-dimensional river model, and a two-dimensional surface model. Couple the surface runoff and confluence model, the pipe network confluence model, the one-dimensional river model, and the two-dimensional surface model to obtain a hydrological and hydrodynamic coupling model; Step S33: Calibrate the parameters of the hydrological and hydrodynamic coupling model according to the historical measured precipitation data and the historical waterlogging inundation depth range data to generate the parameter calibration of the hydrological and hydrodynamic coupling model; Step S34: Input the parameter calibration of the hydrological and hydrodynamic coupling model into the hydrological and hydrodynamic coupling model to calculate the inundation calculation results; Step S35: Obtain the water depth data; divide the block-scale storm flood risk levels based on the water depth data, and evaluate the block-scale storm flood risks under different return periods. The block-scale storm flood risk levels divided by the water depth data are divided into four levels, namely: low risk, 0 < h < 0.15; medium risk, 0.15 ≤ h < 0.30; medium-high risk, 0.30 ≤ h < 0.50; and high risk, 0.50 ≤ h, where h is the water depth data.

9. The multi-scale urban rainstorm flood risk assessment method according to claim 7, characterized in that: Step S32 includes the following steps: Step S321: Divide the surface sub-catchment areas for the land use data to generate surface sub-catchment area division data; select the time-varying gain model as the surface runoff generation model and the non-linear reservoir method as the surface runoff confluence model. Couple the time-varying gain model and the non-linear reservoir method with different-period design storm scenarios to construct a surface runoff and confluence model, and generate a surface runoff and confluence model; Step S322: Analyze the pipe network topology relationship of the urban rainwater pipe network data to generate pipe network topology relationship data; construct a pipe network confluence model based on the pipe network topology relationship data and the dynamic wave method to generate a pipe network confluence model; Step S323: performing river network topological relationship analysis on the river system data to generate river network topological relationship data; constructing a one-dimensional river channel model based on the river network topological relationship data and the one-dimensional Saint-Venant equation to generate a one-dimensional river channel model; Step S324: performing surface grid division on the surface terrain data to generate surface grid division data; constructing a two-dimensional surface model based on the two-dimensional shallow water equation and the surface grid division data to generate a two-dimensional surface model; Step S325: coupling and integrating the surface runoff generation and convergence model, the pipe network convergence model, the one-dimensional river channel model, and the two-dimensional surface model to obtain a hydrological and hydrodynamic coupling model.

10. The multi-scale urban rainstorm flood risk assessment method according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: inputting historical measured precipitation data into the hydrological and hydrodynamic coupling model to simulate urban rainstorm flooding; Step S332: Compare the simulated flooding result with the actual flooding result, and if the difference between the flooding range and the flooding depth is within an acceptable range, obtain the model parameters.

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