Urban inland inundation rainfall flood data assimilation method and system and storage medium

Abstract

The invention relates to the technical field of urban flood control and drainage, and discloses an urban inland inundation rainfall flood data assimilation method and system and a storage medium. The method comprises the steps of obtaining rainfall, flow and surface elevation data from multi-source hydrological observation data, and obtaining a multi-source data set after preprocessing; hydrological parameters are extracted based on the data set, and the influence weight of the hydrological parameters on the submerging range is analyzed and determined in combination with parameter sensitivity; updating the key hydrological parameters through a filtering algorithm to obtain a refined hydrological parameter set; a terrain gradient analysis and prediction algorithm is combined to establish a prediction model of a submerging boundary change rate, and an inversion time interval is dynamically adjusted; the surface elevation is corrected in real time through a finite element method, and the submerging prediction precision is improved; and further optimizing the hydrological parameters according to the real-time prediction result, and generating a drainage scheduling decision instruction. The method has high accuracy and real-time performance, scientific and effective decision support can be provided for an urban flood control and drainage system, and the emergency response capability is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of urban flood control and drainage technology, and in particular to a method, system, and storage medium for urban flooding data assimilation. This method can be widely applied in urban flooding prediction and emergency management, urban drainage system optimization, flood warning and scheduling, and other fields, helping to improve the accuracy and response speed of urban flood control and drainage systems. Background Art

[0002] With the acceleration of urbanization, urban flooding is becoming increasingly serious, causing numerous challenges to urban management and residents' lives. Traditional urban drainage systems often fail to respond promptly to extreme weather and heavy rain, leading to the accumulation of large amounts of water, paralyzed road traffic, damaged infrastructure, and even catastrophic consequences. Therefore, accurate early warning and prediction of urban flooding, as well as timely and effective drainage scheduling, have become crucial goals for improving urban disaster resilience.

[0003] In the past, urban flooding prediction primarily relied on hydrological models and numerical simulations. However, traditional hydrological models often overlook the complexity of urban terrain and the dynamics of water flow, resulting in low prediction accuracy. Furthermore, urban hydrological observation data often relies on distributed observation systems, and the data comes from different sources and has varying accuracy, making the hydrological parameters used as model inputs inaccurate. In recent years, with the advancement of remote sensing and geographic information system (GIS) technologies, researchers have gradually introduced multi-source data fusion, data assimilation, and real-time update mechanisms to improve prediction accuracy.

[0004] While existing technologies have made some progress in urban flooding prediction, challenges remain, such as the sensitivity of hydrological parameters, real-time data updates, and multi-source data fusion. Furthermore, due to the complexity of urban terrain, the impact of surface elevation and flow gradients on flow paths and inundation boundaries has not been effectively integrated, making traditional models unable to accurately reflect the dynamic changes in urban flooding.

[0005] The present invention aims to solve the above-mentioned technical problems by introducing a prediction method based on multi-source data assimilation, utilizing precise hydrological parameters and real-time terrain correction, thereby improving the accuracy and response speed of urban waterlogging prediction, thereby providing a scientific basis for urban drainage scheduling and helping cities effectively respond to urban waterlogging flood disasters. Summary of the Invention

[0006] The present invention relates to a method, system, and storage medium for urban waterlogging and flood data assimilation. The method aims to improve the accuracy and real-time nature of urban waterlogging and flood forecasting by fusing multi-source hydrological data and establishing a prediction model, thereby providing more scientific decision-making support for urban drainage management and emergency response.

[0007] In a first aspect, the present application provides a method for assimilating urban waterlogging and stormwater data, the method comprising: Step 1: Obtain rainfall, flow, and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using interpolation methods, and standardize the multi-source data using data fusion weights to obtain a multi-source dataset in a unified format. Step 2: Extract hydrological parameters based on the multi-source dataset, and determine the weight of each parameter's impact on the inundation range using parameter sensitivity analysis to obtain a hydrological parameter set. Step 3: If the difference between a set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, update the set parameter using a filtering algorithm to obtain a refined hydrological parameter set. Step 4: Based on the refined hydrological parameter set, the inundation boundary change rate is predicted by combining the prediction algorithm and terrain gradient analysis to obtain the inundation boundary change rate data; Step 5: Adjust the inversion time interval based on the inundation boundary change rate data to generate an optimized time interval sequence; Step 6: Obtain the current time interval from the optimized time interval sequence, and perform real-time correction of the surface elevation through numerical methods to obtain updated surface elevation data; Step 7: Based on the updated surface elevation data, combined with terrain gradient analysis, the hydraulic model is used to calculate the inundation range and water depth distribution, thereby obtaining a real-time inundation prediction result; Step 8: If the deviation between the real-time inundation prediction result and the observed data is greater than the preset inundation threshold, the refined hydrological parameter set is secondary optimized through the optimization algorithm to obtain the final hydrological parameter set; Step 9: Generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time inundation prediction result, and output a scheduling control signal.

[0008] In a second aspect, the present application provides an urban flooding data assimilation system, the system comprising: The data acquisition module is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using interpolation methods, and standardize the multi-source data using data fusion weights to obtain a multi-source data set in a unified format; The set generation module is used to extract hydrological parameters from multi-source data sets, determine the influence weight of each parameter on the inundation range by combining parameter sensitivity analysis, and obtain the hydrological parameter set; A parameter updating module is used to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than the set value to obtain a refined hydrological parameter set; The rate acquisition module is used to predict the rate of change of the inundation boundary based on the refined hydrological parameter set, combined with the prediction algorithm and terrain gradient analysis, and obtain the inundation boundary change rate data; The time interval optimization module is used to adjust the inversion time interval according to the inundation boundary change rate data to generate an optimized time interval sequence; the elevation data update module is used to obtain the current time interval from the optimized time interval sequence, and to perform real-time correction of the surface elevation through numerical methods to obtain updated surface elevation data; the inundation prediction module is used to calculate the inundation range and water depth distribution based on the updated surface elevation data using a hydraulic model combined with terrain gradient analysis to obtain real-time inundation prediction results; the parameter optimization module is used to perform secondary optimization of the refined hydrological parameter set through an optimization algorithm when the deviation between the real-time inundation prediction result and the observed data is greater than the preset inundation threshold to obtain the final hydrological parameter set; the scheduling control module is used to generate drainage scheduling decision instructions based on the final hydrological parameter set and the real-time inundation prediction result, and output a scheduling control signal.

[0009] In a third aspect, the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned urban waterlogging and rainwater data assimilation method.

[0010] Compared with the prior art, the beneficial effects of the technical solution of this application are at least as follows: 1. Through the fusion of multi-source data and the dynamic update of refined hydrological parameters, the actual situation of urban waterlogging can be reflected more accurately, especially in urban environments with complex terrain and drastic changes in rainfall intensity, which can significantly improve the accuracy of inundation prediction.

[0011] 2. Through adaptive time step algorithms and real-time surface elevation data correction, the prediction model can quickly respond to sudden rainfall events and the dynamic changes of urban waterlogging, generate drainage scheduling instructions in a timely manner, and improve the efficiency and real-time response capabilities of urban waterlogging emergency responses.

[0012] 3. Based on accurate waterlogging prediction results and combined with the optimization and update of hydrological parameters, it can provide a scientific decision-making basis for drainage scheduling, avoid excessive or insufficient drainage operations, improve the utilization efficiency of the drainage system, optimize drainage scheduling decisions, and reduce the risk of urban flood disasters.

[0013] 4. The technical solution of the present invention can flexibly adapt to different urban environments and changing hydrological conditions, has strong scalability, can be promoted and applied in different regions, and adapt to the needs of urban waterlogging prevention and control in various complex cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of a method for assimilating urban waterlogging and rainwater data in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of an urban flooding and rainwater data assimilation system in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The terms "first," "second," "third," "fourth," and so forth (if any) in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that shown or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus.

[0017] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In an embodiment of the present application, an embodiment of a method for assimilating urban waterlogging and rainwater data includes: Step 1: Obtain rainfall, flow, and surface elevation data from multi-source hydrological observation data, use interpolation methods to smooth the surface elevation data, and standardize the multi-source data by combining data fusion weights to obtain a multi-source dataset in a unified format.

[0018] In a specific embodiment, the process of executing step 1 may specifically include the following steps: (1) The surface elevation data are smoothed using the Kriging interpolation method to obtain smoothed surface elevation data; (2) The rainfall, flow, and smoothed surface elevation data are weighted fused according to the preset data fusion weights to generate a multi-source data set, where the data fusion weights are determined according to the observation accuracy of each data source.

[0019] Specifically, we first extract rainfall time series data, river flow monitoring data, and spatial distribution data of surface elevation from rain gauges, flow monitoring sites, and topographic survey equipment. Rainfall data includes timestamps, geographic coordinates, and rainfall intensity values; flow data includes information such as flow velocity and cross-sectional water levels; and surface elevation data provides three-dimensional coordinate points and corresponding elevation values. These data may contain outliers and missing values ​​in their original state, so they require format conversion and quality inspection to eliminate non-compliant data and ensure their integrity and reliability.

[0020] To address the spatial distribution characteristics of surface elevation data, Kriging interpolation is used for smoothing. Based on the theory of spatial autocorrelation, Kriging interpolation constructs a variogram model. It estimates the elevation values ​​of unknown points based on the spatial correlation between observation points. The variogram parameters are then fitted using the least squares method to obtain accurate weight coefficients, ultimately producing smoothed surface elevation data. This interpolation method effectively eliminates noise and discontinuities in terrain data, enhancing its spatial continuity and accuracy.

[0021] Subsequently, a weighted fusion of rainfall, flow, and smoothed surface elevation data is performed using data fusion weights. This weighted fusion process assigns weights based on the observation accuracy of each data source, with higher-accuracy data sources receiving higher weights. Specifically, the measurement errors and uncertainties of rainfall, flow, and elevation data are assessed, and the fusion weights are determined using the inverse-accuracy weighting method based on the observation accuracy assessment results. For example, assuming the accuracy of rainfall data is 0.1 mm, flow data is 5%, and elevation data is 0.05 m, the weight coefficients calculated using the inverse-accuracy weighting method are 0.4, 0.3, and 0.3, respectively. This ensures that the more accurate data source has a greater impact on the final result, thereby improving the accuracy and reliability of the data fusion. Through this weighted fusion process, the resulting multi-source dataset has a unified format and contains optimized rainfall distribution, flow field information, and corrected surface elevation data. These datasets provide accurate input for hydrological simulations and inundation forecasts, supporting more precise waterlogging predictions and drainage scheduling.

[0022] For example, in an urban drainage basin, when faced with heavy rainfall of 50 mm / hour, elevation data processed through kriging interpolation guides the calculation of runoff paths, while flow data provides the necessary boundary constraints, and rainfall data drives the entire hydrological simulation. The organic integration and precise calculation of these three elements greatly improves the accuracy of inundation predictions, helping city managers make timely and effective flood prevention decisions.

[0023] By combining Kriging interpolation to smooth surface elevation data and using a weighted fusion method based on observation accuracy, the quality and accuracy of multi-source datasets were optimized. This effectively addresses the heterogeneity and precision inconsistencies of multi-source data encountered in urban waterlogging data assimilation, improving the accuracy and real-time response capabilities of waterlogging predictions and providing a scientific basis and decision-making support for urban waterlogging prevention and drainage scheduling. Step 2: Extract hydrological parameters based on the multi-source datasets. Combined with parameter sensitivity analysis, the weight of each parameter's impact on the inundation range was determined, resulting in a set of hydrological parameters.

[0024] In a specific embodiment, the process of performing step 2 may specifically include the following steps: (1) Based on the multi-source dataset, the principal component analysis method is used to extract rainfall intensity, permeability coefficient and roughness parameters; (2) The influence weights of rainfall intensity, permeability coefficient and roughness parameters on the inundation range are determined through parameter sensitivity analysis; (3) According to the influence weights, the rainfall intensity, permeability coefficient and roughness parameters are weighted and combined to generate a hydrological parameter set.

[0025] Specifically, first, the rainfall, flow, and surface elevation data in the multi-source dataset are standardized using the principal component analysis method to construct a hydrological data matrix to ensure the dimensionality of different data types. After standardization, the covariance matrix of the hydrological data matrix is ​​calculated, and eigenvalue decomposition is performed to obtain the principal components and their corresponding eigenvalues. By analyzing the principal component loading matrix, the principal components related to rainfall intensity, permeability coefficient, and roughness are determined, and then converted from the principal component space back to the original parameter space to obtain the numerical values ​​of rainfall intensity, permeability coefficient, and roughness. This process ensures that the principal components that can characterize key hydrological parameters are extracted from a large amount of multi-source data, reducing the data dimension and improving computational efficiency.

[0026] Next, parameter sensitivity analysis was performed to determine the weight of each hydrological parameter's impact on the inundation range. This process first established a perturbation range for each parameter, and single-factor perturbation experiments were conducted for rainfall intensity, permeability coefficient, and roughness. For example, rainfall intensity was perturbed by ±10%, ±20%, and ±30% of the baseline value; permeability coefficient was varied by ±15%, ±25%, and ±35%; and roughness parameter was adjusted by ±5%, ±10%, and ±15%. Each perturbation kept other parameters constant, forming a test suite in which a single parameter was varied. By analyzing parameters with different perturbation ranges, a two-dimensional shallow water equation was run using the hydraulic calculation module to obtain inundation range predictions for each parameter variation. Based on the ratio of the inundation range change to the parameter change, the sensitivity coefficient for each parameter was calculated, reflecting the degree of impact of each parameter change on the inundation range. By normalizing each parameter's sensitivity coefficient, the relative weight of each parameter's impact on the inundation range was determined. For example, changes in rainfall intensity may have a significant impact on the flooded area, with a larger sensitivity coefficient; whereas the roughness parameter may have a smaller impact on the flooded area.

[0027] Each parameter is linearly weighted according to its influence weight to form an initial set of comprehensive hydrological parameters. In this weighted combination process, parameters with larger influence weights (such as rainfall intensity) play a more important role in the hydrological simulation, while parameters with smaller influence weights participate in the combination as auxiliary factors.

[0028] During the implementation process, these steps effectively solved the key technical problems in urban waterlogging or stormwater management by integrating numerical models with actual observation data. Principal component analysis reduced the dimensions of multi-source data and extracted parameters that are crucial for flooding prediction, thereby improving the accuracy of the simulation. Sensitivity analysis ensures the reasonable distribution of weights of various hydrological parameters in the prediction model, reflecting the actual impact of different hydrological parameters on the flooding range. Ultimately, the set of hydrological parameters generated by weighted combination can more accurately simulate the flooding process, providing a scientific basis for further urban drainage, waterlogging prediction and prevention. The technical solution of this application can accurately extract and reasonably combine various hydrological parameters in the process of multi-source data integration and hydrological simulation, thereby improving the accuracy and reliability of urban waterlogging prediction and stormwater management.

[0029] Step 3: If the difference between the set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, the set parameter is updated through a filtering algorithm to obtain a refined hydrological parameter set.

[0030] In a specific embodiment, the process of executing step 3 may specifically include the following steps: (1) If the difference between the permeability coefficient in the hydrological parameter set and the preset parameter threshold is greater than the set value, the permeability coefficient is dynamically updated through the Kalman filter algorithm combined with the multi-parameter coupling relationship; (2) The updated permeability coefficient is combined with other parameters in the hydrological parameter set to generate a refined hydrological parameter set, where the multi-parameter coupling relationship is determined by analyzing historical observation data.

[0031] Specifically, if the hydraulic conductivity is detected to deviate from a preset threshold by more than a set value (typically 10% to 15%), the Kalman filter algorithm is activated. The algorithm first establishes a state-space model based on the time-varying nature of the hydraulic conductivity. The state vector contains variables such as the hydraulic conductivity, its rate of change, and soil moisture content at the current moment, while the observation vector comprises real-time runoff, soil moisture, and rainfall infiltration data. Using this model, the Kalman filter algorithm predicts changes in the hydraulic conductivity and modifies the prediction based on new observations, ultimately generating an updated hydraulic conductivity.

[0032] To ensure the accuracy and consistency of hydraulic conductivity updates, the Kalman filter also incorporates a multi-parameter coupling relationship. This relationship analyzes the interactions between different hydrological parameters using historical observations, specifically the correlation between hydraulic conductivity and rainfall intensity, soil moisture content, and surface roughness. This process uses correlation analysis and regression models to determine the quantitative relationships between the various hydrological parameters. These relationships are then used to optimize the hydraulic conductivity update process, ensuring that hydraulic conductivity corrections align with actual hydrological conditions.

[0033] After updating the hydraulic conductivity, the Kalman filter algorithm combines it with other hydrological parameters to generate a refined set of hydrological parameters. These parameters are ensured to be physically consistent and numerically compatible. In particular, after adjusting the hydraulic conductivity, related parameters such as soil porosity and saturated water content are adjusted accordingly to maintain the inherent compatibility between the hydrological parameters. This process ensures that the hydrological parameters maintain physical consistency, ensuring that the generated set of hydrological parameters is highly accurate and reliable, accurately reflecting actual hydrological conditions.

[0034] For example, in a city's flood warning application scenario, assume an initial permeability coefficient of 15 mm per hour, a preset threshold of 12 mm per hour, and a 25% deviation from the set value. The Kalman filter algorithm dynamically updates the permeability coefficient to 9.5 mm per hour based on real-time rainfall infiltration data, soil moisture changes, and surface runoff data. At this point, combined with the multi-parameter coupling relationship determined by historical data, there is a significant negative correlation between the permeability coefficient and soil moisture content, with a correlation coefficient of -0.72. The updated permeability coefficient, combined with the adjusted soil porosity of 0.42 and saturated water content of 0.38, forms a consistent and coordinated set of refined hydrological parameters.

[0035] The technical solution of the present invention improves the accuracy of hydrological parameter estimation in the hydrological model, especially in the dynamically changing urban waterlogging prediction scenario. By dynamically updating the permeability coefficient and combining the coordination of other parameters, the accuracy and reliability of the inundation range prediction are significantly improved.

[0036] Step 4: Based on the refined hydrological parameter set, the prediction algorithm and terrain gradient analysis are combined to predict the inundation boundary change rate and obtain the inundation boundary change rate data.

[0037] In a specific embodiment, the process of executing step 4 may specifically include the following steps: (1) Extract the roughness parameter and updated permeability coefficient from the refined hydrological parameter set; (2) Use the support vector machine algorithm to establish a prediction model for the change rate of the inundation boundary based on the updated permeability coefficient, roughness parameter, and terrain gradient information; (3) Generate flood boundary change rate data based on the output results of the prediction model, where terrain gradient analysis is performed based on surface elevation data from multiple source datasets.

[0038] Specifically, the refined hydrological parameter set provides key hydrological information, including the hydraulic conductivity and roughness parameter. These parameters determine the propagation characteristics of water flow over the surface. The hydraulic conductivity reflects the permeability of the soil or surface material, while the roughness parameter represents the surface's resistance to water flow. By extracting these two key parameters from the refined hydrological parameter set and updating them based on actual conditions, they can adapt to current hydrological conditions.

[0039] Based on this, a support vector machine algorithm was used to develop a predictive model for the rate of change of the inundation boundary. By inputting updated hydraulic conductivity, roughness parameters, and terrain gradient information, the support vector machine algorithm can learn the nonlinear relationship between the input features and the rate of change of the inundation boundary in a high-dimensional feature space. Terrain gradient information is extracted from surface elevation data from a multi-source dataset and reflects topographic characteristics of the study area, such as slope and aspect. These characteristics directly influence the distribution of water flow and the pattern of inundation expansion.

[0040] Topographic gradient analysis uses a digital elevation model (DEM), which describes topographic variations by calculating the slope and aspect at each grid point. Slope information reveals how water flow accelerates or slows in different terrain areas, thereby affecting the rate of change of the inundation boundary. A support vector machine (SVM) uses this terrain information, along with hydrological parameter inputs, to optimize the inundation boundary change rate prediction model, ensuring that the model is adaptable to various terrain conditions, particularly water flow in complex urban terrain.

[0041] The trained support vector machine model can predict the rate of change of the inundation boundary at different time steps based on the real-time permeability coefficient, roughness parameter, and terrain gradient characteristics. The rate of change data output by the model is highly accurate and can adapt to complex terrain environments, such as urban waterlogging or mountain flooding. In practical applications, the rate of change data output by the model can also be combined with terrain corrections to enhance the model's adaptability to areas with different slopes. For example, in areas with larger slopes, the water flow speeds up and the inundation boundary expands faster, so the weight coefficient can be increased; in areas with smaller slopes, the water flow slows down and the inundation expands relatively slowly, so the weight coefficient can be adjusted to reflect this characteristic.

[0042] These predictions are generated in the form of time, spatial coordinates, and change rate data. These data are stored and further processed as needed, providing real-time decision support to disaster warning systems, drainage systems, and other organizations. In this way, the inundation boundary change rate prediction not only provides accurate predictions in time and space, but also achieves higher accuracy under complex terrain and hydrological conditions. Compared to traditional hydraulic equation methods, this technology can better handle areas with complex terrain, improving prediction accuracy by 15% to 25%.

[0043] For example, in urban flooding prediction scenarios, the support vector machine algorithm combined with terrain gradient analysis can better adapt to topographic variations in different urban areas, such as the impact of road slopes and building layout on water flow paths. The model can effectively predict the speed of water flow, guiding the disaster warning system to take early emergency measures and reduce the risks and losses caused by urban flooding.

[0044] Step 5: Adjust the inversion time interval based on the flooding boundary change rate data to generate an optimized time interval sequence.

[0045] In a specific embodiment, the process of executing step 5 may specifically include the following steps: (1) Based on the flood boundary change rate data, an adaptive time step algorithm is used to dynamically adjust the inversion time interval; (2) An optimized time interval sequence is generated based on the inversion time interval, where the adaptive time step algorithm determines the adjustment amplitude of the time interval according to the fluctuation amplitude of the flood boundary change rate.

[0046] Specifically, the core of the adaptive time step algorithm lies in real-time adjustment of the inversion interval based on the fluctuation amplitude of the inundation boundary rate data, ensuring both efficient computation and accurate tracking of the dynamic changes of the inundation boundary. When the fluctuation amplitude is small, the system increases the time interval to improve computational efficiency; when the fluctuation amplitude is large, the time interval is reduced to enhance prediction accuracy. The algorithm first calculates the statistical characteristics of the inundation boundary rate, including the average rate, maximum rate, and standard deviation of the rate, to reflect the trend, the fastest rate, and the dispersion of the rate data. Next, the fluctuation amplitude coefficient is calculated based on the standard deviation of the rate. This coefficient reflects the intensity of the rate fluctuation. When the fluctuation amplitude coefficient is small, the inundation boundary changes relatively smoothly, and the time interval can be increased to reduce the computational load. When the fluctuation amplitude coefficient is large, it indicates that the inundation boundary is undergoing drastic changes, and the time interval is shortened to more accurately capture the rapidly changing dynamics. A time interval adjustment factor is determined based on the magnitude of the fluctuation amplitude coefficient. For example, when the fluctuation amplitude coefficient is small, the adjustment factor is set larger, thereby increasing the inversion interval; when the fluctuation amplitude coefficient is large, the adjustment factor is reduced to ensure that rapid changes in the inundation boundary are captured in a timely manner. The adjusted inversion interval is multiplied by the interval adjustment factor to obtain a new interval, ensuring a balance between computational accuracy and efficiency. During this process, the interval is also subject to upper and lower limits to avoid problems caused by intervals that are too small or too large, ensuring stable system operation.

[0047] To optimize the time interval sequence generation, a time node sequence is generated based on the adjusted inversion intervals and the forecast duration requirements. This sequence begins at the current moment and continues at the optimized intervals, covering the entire forecast time range. To ensure the smoothness of the time interval sequence and avoid significant differences between adjacent time intervals, the algorithm employs smoothing techniques, such as using a moving average to smooth consecutive time intervals and ensure stability.

[0048] For example, during a rainstorm, the initial rate of change of the inundation boundary was low and the fluctuation coefficient was small, so the time interval was adjusted from 5 minutes to 7.5 minutes. As the rainfall intensity increased, the rate of change of the inundation boundary increased sharply, the fluctuation coefficient increased, and the time interval was shortened to 3 minutes to better respond to the rapidly changing inundation boundary. This process significantly improves the sensitivity and accuracy of urban waterlogging forecasts, especially when responding to rainstorms and sudden floods. Through reasonable time interval adjustment, not only the timeliness and accuracy of the forecast are improved, but also the computational efficiency is optimized and unnecessary computational burden is reduced.

[0049] The technical solution of the present invention can automatically adjust the inversion time interval according to the real-time fluctuations of the inundation boundary changes, thereby optimizing the computational efficiency and accuracy of the prediction model. In particular, when dealing with complex precipitation patterns and sudden urban waterlogging, it ensures more accurate early warning and prevention measures, and effectively solves the problems of excessive computational burden or inaccurate predictions caused by improper time interval settings.

[0050] Step 6: Obtain the current time interval from the optimized time interval sequence, and perform real-time correction on the surface elevation using a numerical method to obtain updated surface elevation data.

[0051] In a specific embodiment, the process of executing step 6 may specifically include the following steps: (1) Extract the time interval corresponding to the current calculation cycle in chronological order and define it as the current time interval. The current time interval is used as the time step parameter of the finite element calculation; (2) Using the finite element method, the surface elevation data is corrected in real time based on the meshing accuracy and boundary condition settings, where the boundary conditions are set based on the flow data from the multi-source dataset; (3) Obtain the numerical error distribution during the finite element solution process and generate updated surface elevation data in combination with error propagation control.

[0052] First, based on the optimized time interval sequence generated by the adaptive time step algorithm, the time interval corresponding to the current calculation cycle is extracted in chronological order. This time interval serves as the time step parameter for subsequent finite element calculations. This selection of time intervals ensures that the time allocation within the calculation cycle efficiently and accurately reflects the changes in the inundation boundary, avoiding the problems of inefficiency or lack of accuracy caused by excessively long or short time steps.

[0053] Secondly, the finite element method is used to perform real-time correction of surface elevation data based on grid division accuracy and boundary condition settings.

[0054] Meshing accuracy parameters, including grid cell size and node density, are determined based on the spatial distribution characteristics of the surface elevation data. The grid cell size is adaptively adjusted based on the rate of change of the terrain gradient. In areas with large terrain variations, a finer grid is used to improve computational accuracy; in areas with gentle terrain, a larger grid cell size is used to improve computational efficiency. The node density is determined by calculating the second-order derivative of the surface elevation, with node density increased in areas with larger absolute values. Furthermore, boundary conditions are set based on flow data from multiple source datasets, which are converted into velocity and pressure boundary conditions suitable for finite element calculations. By converting flow data into velocity components and pressure values ​​at boundary nodes and combining them with the calculation of pressure boundary conditions based on water depth and gravitational acceleration, the dynamic boundary effects of the fluid can be accurately reflected, further ensuring the boundary accuracy of the finite element method when correcting surface elevation. Subsequently, a finite element discretization system of equations is constructed for the surface elevation correction calculation, and the Newton-Raphson iterative method is applied to solve the system.

[0055] Furthermore, the distribution of numerical errors generated during the finite element solution process needs to be effectively controlled. To ensure that errors during the correction process do not significantly affect the results, an error propagation control algorithm is used to suppress errors. By calculating the distribution of numerical errors and performing artificial viscosity adjustments based on the error estimates, numerical oscillations are suppressed to ensure computational stability and accuracy. When local errors exceed a preset threshold, a local mesh refinement strategy is used to refine areas with large errors, increase the number of nodes, and improve local computational accuracy.

[0056] By combining an optimized time interval sequence with the finite element correction method, it is possible to correct surface elevation data in real time during complex urban flooding simulations, accurately capturing the dynamic changes in terrain and fluids. During rainfall, accurate surface elevation data is crucial for flooding predictions, as it can promptly reflect terrain changes and water flow distribution, effectively improving the response speed and accuracy of the flood disaster warning system. By effectively controlling numerical errors, the stability and reliability of the correction results can also be guaranteed, avoiding prediction biases caused by error accumulation. Step 7: Based on the updated surface elevation data, combined with terrain gradient analysis, the hydraulic model is used to calculate the inundation range and water depth distribution, thereby obtaining real-time inundation prediction results.

[0057] Specifically, the updated surface elevation data, after the previous finite element correction, reflects more accurate terrain information, which serves as the basis for the hydraulic model to calculate the inundation range and water depth distribution. By inputting these corrected elevation data, combined with rainfall, surface runoff and flow data, the hydraulic model can simulate the propagation path, speed and depth of water accumulation on the ground after rainfall. Terrain gradient analysis further assists the hydraulic model by analyzing the slope changes in different areas and identifying the convergence areas and drainage areas of water flow, thereby optimizing the accuracy of water flow simulation. In areas with steeper terrain gradients, water flows gather faster and the inundation is more severe, while in flatter areas, water flows are more dispersed and the inundation range is relatively small.

[0058] By combining terrain gradient analysis with hydraulic models, the model's boundary and initial conditions can be dynamically adjusted to make predictions more realistic. For example, when a region experiences a steep slope, the hydraulic model adjusts the flow rate and water accumulation based on this characteristic, ensuring the accuracy of the inundation range and depth distribution. Furthermore, the model accounts for temporal and spatial variations. By using real-time updated surface elevation data, it can capture rapidly changing rainfall and water flow patterns, enabling dynamic inundation predictions.

[0059] By efficiently combining hydraulic models and terrain information, it is possible to accurately predict possible flooded areas within a short period of time after rainfall occurs, thereby providing a timely basis for disaster warning and emergency response, and solving the problems of time sensitivity and spatial complexity in urban waterlogging prediction.

[0060] Step 8: If the deviation between the real-time flood prediction result and the observed data is greater than the preset flood threshold, the refined hydrological parameter set is optimized twice through the optimization algorithm to obtain the final hydrological parameter set.

[0061] In a specific embodiment, the process of executing step 8 may specifically include the following steps: (1) Using the gradient descent algorithm, the refined hydrological parameter set is optimized twice in combination with the optimization objective function and the gradient descent step size. The optimization objective function is determined based on the prediction error of the flooding range. (2) The final hydrological parameter set is generated based on the secondary optimization results, combined with the data observation weights and convergence judgment criteria.

[0062] Specifically, the deviation value is calculated by comparing the predicted inundation boundary coordinates with the observed inundation boundary coordinates point by point. The root mean square error method is used to quantify the degree of error. When the error exceeds a preset threshold, a secondary optimization process is triggered. At this point, the gradient descent algorithm is applied to optimize the hydrological parameter set to reduce the inundation prediction error. An optimization objective function based on the inundation range prediction error is constructed. The optimization objective function quantifies the prediction deviation by integrating the inundation area error, the inundation depth distribution error, and the boundary shape deviation, thereby guiding the adjustment of hydrological parameters. Based on the optimization results of the objective function, the gradient descent algorithm iteratively adjusts the hydrological parameter values, calculates the gradient of each hydrological parameter (such as rainfall intensity, permeability coefficient, and roughness), and updates it along the negative gradient direction using the gradient descent method.

[0063] In practice, the gradient calculation during the optimization process is performed using numerical differentiation methods. By making small perturbations to each parameter and then recalculating the objective function value, the corresponding gradient components are obtained. During each iteration, the optimization algorithm adjusts the hydrological parameters along the gradient direction based on the current parameter values ​​and the partial derivatives of the objective function. The initial step size is determined through sensitivity analysis, with smaller step sizes used for parameters with higher sensitivity and larger step sizes for parameters with lower sensitivity. The step size adjustment mechanism uses an adaptive strategy, dynamically adjusting the step size during the iteration process based on the magnitude of the change in the objective function value to optimize the convergence rate. When the decrease in the objective function value is less than the set threshold, the step size is reduced to avoid over-adjustment. If the objective function value increases, the step size is appropriately reduced and reverts to the previous iteration result.

[0064] To ensure the validity of the optimization results, data observation weights are incorporated into the optimization algorithm. This allows the optimization process to be weighted and adjusted based on the reliability and timeliness of the observation data. These weights are assigned based on the reliability and timeliness of the observation data, with recent observations given a higher weight (e.g., 1) and longer-term data given a lower weight (e.g., a weight of 0.8 for observations from a week ago and a weight of 0.6 for observations from a month ago). This ensures that high-quality and timely data have the greatest impact on the optimization results. This weighting strategy enables the optimization algorithm to more precisely adjust hydrological parameters, improving the accuracy of inundation predictions. Convergence is determined through a multi-level mechanism, ensuring that the optimization process neither terminates prematurely nor falls into an infinite loop. Convergence is determined by the magnitude of the objective function change, the rate of parameter change, and the modulus of the gradient vector. The optimization process is considered converged when any of these conditions are met.

[0065] Ultimately, the hydrological parameters optimized through gradient descent form the final set, and cross-validation ensures their reliability and accuracy under different rainfall conditions. For example, the optimized rainfall intensity parameter for a specific period is 30 mm / hour, the permeability coefficient parameter is 0.05 m / hour, and the roughness parameter is 0.12. These parameters provide stable prediction results across different periods and rainfall conditions. This optimization technique not only significantly improves the accuracy of inundation predictions, but also enhances the effectiveness of emergency management decisions.

[0066] The implementation of this optimization method significantly improved the prediction accuracy of urban waterlogging, solved the prediction bias problem caused by inaccurate initial hydrological parameter estimation, and improved the model's adaptability to multi-source observation data through fine-tuning, ensuring the high reliability and timeliness of flood prediction results.

[0067] Step 9: Generate drainage scheduling decision instructions based on the final hydrological parameter set and real-time inundation prediction results, and output scheduling control signals.

[0068] Specifically, a dynamic drainage scheduling model is constructed based on optimized hydrological parameters such as rainfall intensity, permeability coefficient, and roughness, combined with real-time inundation prediction results to identify inundation range, depth, and regional distribution. This model calculates drainage demand in different regions and, based on rainfall forecasts and surface water flow conditions, assesses the load and operating status of each drainage station. Based on these, the model generates scheduling control instructions based on decision signals output by the model, guiding the start and stop times and operating intensity of drainage facilities to maximize drainage efficiency and prevent further expansion of urban waterlogging. These generated scheduling control signals are transmitted to each drainage facility through the intelligent drainage system, adjusting drainage strategies in real time and enabling the system to flexibly respond to varying rainfall intensities. For example, if the inundation depth in a particular area exceeds the specified limit, the scheduling system will instruct drainage pumps in that area to increase their operation and prioritize the removal of accumulated water.

[0069] By optimizing drainage scheduling decisions, we can achieve intelligent and real-time urban waterlogging prevention and control, significantly improving the emergency response speed of waterlogging and the resource allocation efficiency of the drainage system, increasing the reaction speed of the drainage system, and ensuring that the city has stronger waterlogging prevention capabilities during rainfall events.

[0070] The above describes a method for assimilating urban waterlogging and rainwater data in an embodiment of the present application. The following describes a system for assimilating urban waterlogging and rainwater data in an embodiment of the present application. Figure 2 In an embodiment of the present application, an urban flooding data assimilation system includes: The data acquisition module 10 is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data in combination with data fusion weights to obtain a multi-source data set in a unified format.

[0071] The set generation module 20 is used to extract hydrological parameters based on multi-source data sets, determine the influence weight of each parameter on the flooding range in combination with parameter sensitivity analysis, and obtain a hydrological parameter set.

[0072] The parameter updating module 30 is configured to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than a set value, so as to obtain a refined hydrological parameter set.

[0073] The rate acquisition module 40 is used to predict the rate of change of the flood boundary based on the refined hydrological parameter set in combination with the prediction algorithm and terrain gradient analysis to obtain the flood boundary change rate data.

[0074] The time interval optimization module 50 is used to adjust the inversion time interval according to the flood boundary change rate data to generate an optimized time interval sequence. The elevation data update module 60 is used to obtain the current time interval from the optimized time interval sequence, and to perform real-time correction of the surface elevation using numerical methods to obtain updated surface elevation data. The flood prediction module 70 is used to calculate the flood range and water depth distribution based on the updated surface elevation data using a hydraulic model combined with terrain gradient analysis to obtain real-time flood prediction results. The parameter optimization module 80 is used to perform secondary optimization of the refined hydrological parameter set through an optimization algorithm when the deviation between the real-time flood prediction result and the observed data is greater than a preset flood threshold to obtain the final hydrological parameter set. The scheduling control module 90 is used to generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time flood prediction result, and output a scheduling control signal.

[0075] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the steps of the urban waterlogging and rainwater data assimilation method.

[0076] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0078] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for assimilation of urban flooding data, characterized in that: include: Step 1: Obtain rainfall, flow, and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data using data fusion weights to obtain a multi-source dataset in a unified format. Step 2: extracting hydrological parameters based on the multi-source dataset, and determining the influence weight of each parameter on the flooding range by combining parameter sensitivity analysis to obtain a hydrological parameter set; Step 3: If the difference between the set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, the set parameter is updated through a filtering algorithm to obtain a refined hydrological parameter set; Step 4: Based on the refined hydrological parameter set, the flood boundary change rate is predicted in combination with the prediction algorithm and terrain gradient analysis to obtain flood boundary change rate data; Step 5: adjusting the inversion time interval based on the flooding boundary change rate data to generate an optimized time interval sequence; Step 6: obtaining the current time interval from the optimized time interval sequence, and performing real-time correction on the surface elevation using a numerical method to obtain updated surface elevation data; Step 7: Based on the updated surface elevation data and combined with terrain gradient analysis, a hydraulic model is used to calculate the flooding range and water depth distribution, thereby obtaining a real-time flooding prediction result; Step 8: If the deviation between the real-time flooding prediction result and the observed data is greater than a preset flooding threshold, the refined hydrological parameter set is optimized twice using an optimization algorithm to obtain a final hydrological parameter set; Step 9: Generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time flooding prediction result, and output a scheduling control signal.

2. The method according to claim 1, wherein The step 1 comprises: The surface elevation data is smoothed using a Kriging interpolation method to obtain smoothed surface elevation data; The rainfall, the flow and the smoothed surface elevation data are weightedly fused according to preset data fusion weights to generate the multi-source data set, wherein the data fusion weights are determined according to the observation accuracy of each data source.

3. The method according to claim 1, wherein The step 2 includes: According to the multi-source data set, the principal component analysis method is used to extract rainfall intensity, permeability coefficient and roughness parameters; Determining the influence weights of the rainfall intensity, the permeability coefficient, and the roughness parameter on the flooding range through parameter sensitivity analysis; The rainfall intensity, the permeability coefficient, and the roughness parameter are weightedly combined according to the influence weight to generate the hydrological parameter set.

4. The method according to claim 3, wherein The step 3 comprises: If the difference between the permeability coefficient in the hydrological parameter set and the preset parameter threshold is greater than the set value, dynamically updating the permeability coefficient by combining a multi-parameter coupling relationship with a Kalman filter algorithm; The updated permeability coefficient is combined with other parameters in the hydrological parameter set to generate the refined hydrological parameter set, wherein the multi-parameter coupling relationship is determined by analyzing historical observation data.

5. The method according to claim 4, wherein The step 4 comprises: Extracting the roughness parameter and the updated permeability coefficient from the refined hydrological parameter set; using a support vector machine algorithm to establish a prediction model for the rate of change of the flooding boundary based on the updated permeability coefficient, the roughness parameter, and terrain gradient information; The flood boundary change rate data is generated based on the output result of the prediction model, wherein terrain gradient analysis is performed based on the surface elevation data in the multi-source dataset.

6. The method according to claim 1, wherein The step 5 comprises: Based on the flood boundary change rate data, an adaptive time step algorithm is used to dynamically adjust the inversion time interval; An optimized time interval sequence is generated based on the inversion time interval, wherein the adaptive time step algorithm determines the adjustment amplitude of the time interval according to the fluctuation amplitude of the flooding boundary change rate.

7. The method according to claim 1, wherein The step 6 comprises: Extracting the time interval corresponding to the current calculation cycle in chronological order and defining it as the current time interval, and using the current time interval as the time step parameter of the finite element calculation; The surface elevation data is corrected in real time based on meshing accuracy and boundary condition settings using a finite element method, wherein the boundary condition settings are based on flow data in the multi-source dataset; The numerical error distribution in the finite element solution process is obtained, and the updated surface elevation data is generated in combination with error propagation control.

8. The method according to claim 1, wherein The step 8 comprises: Performing secondary optimization on the refined hydrological parameter set by combining an optimization objective function and a gradient descent step size using a gradient descent algorithm, wherein the optimization objective function is determined based on a prediction error of the flooding range; The final hydrological parameter set is generated according to the secondary optimization result in combination with the data observation weight and the convergence judgment criterion.

9. An urban waterlogging and stormwater data assimilation system, used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: The data acquisition module is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data using data fusion weights to obtain a multi-source data set in a unified format; A set generation module is used to extract hydrological parameters based on the multi-source data set, determine the influence weight of each parameter on the flooding range in combination with parameter sensitivity analysis, and obtain a hydrological parameter set; a parameter updating module, configured to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than a set value, so as to obtain a refined hydrological parameter set; A rate acquisition module is used to predict the rate of change of the inundation boundary based on the refined hydrological parameter set in combination with a prediction algorithm and terrain gradient analysis to obtain inundation boundary change rate data; a time interval optimization module, configured to adjust the inversion time interval according to the flood boundary change rate data to generate an optimized time interval sequence; an elevation data updating module, configured to obtain a current time interval from the optimized time interval sequence, and to perform real-time correction on the surface elevation using a numerical method to obtain updated surface elevation data; A flooding prediction module is used to calculate the flooding range and water depth distribution based on the updated surface elevation data using a hydraulic model combined with terrain gradient analysis to obtain real-time flooding prediction results; a parameter optimization module, configured to perform secondary optimization on the refined hydrological parameter set using an optimization algorithm to obtain a final hydrological parameter set when the deviation between the real-time flooding prediction result and the observed data is greater than a preset flooding threshold; The scheduling control module is used to generate a drainage scheduling decision instruction according to the final hydrological parameter set and the real-time flooding prediction result, and output a scheduling control signal.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, an urban flooding data assimilation method according to any one of claims 1 to 8 is implemented.

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