High-dimensional statistics and hydrodynamic force coupled flood risk assessment method and system

Abstract

The invention relates to a high-dimensional statistics and hydrodynamic force coupled flood risk assessment method and system. The method comprises the following steps: acquiring historical observation data covering river flow, tide level and rainfall events; constructing a univariate marginal distribution function; connecting the univariate marginal distribution functions of the river flow, the tide level and the rainfall event, and simulating a joint probability distribution function; coupling the joint probability distribution function with the mechanism model to obtain a probability-mechanism coupling model; inputting the determined joint probability boundary condition into a probability-mechanism coupling model, simulating to obtain composite flood scenes in different return periods, and comparing the composite flood scenes with single variable flood scenes in different return periods; and quantizing the contribution of each variable to the change of the submerging area by using the obtained submerging area and the corresponding rainfall event, river flow and coastal tide level data. According to the method, the defect that traditional single-factor evaluation cannot capture multi-factor interaction is overcome, and the accuracy of flood disaster evaluation is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster prediction and prevention, and in particular to a flood risk assessment method and system coupling high-dimensional statistics with hydrodynamics. Background Art

[0002] Under the combined effects of global climate change and intensified urbanization, coastal cities are facing an increasingly serious risk of compound flood disasters. These disasters are caused by the coupling of factors such as heavy rainfall, high tide, river floods and rapid urbanization, which have a significant impact on society and the economy. In the field of multi-hazard integrated risk assessment research, the interaction between different types of disasters and the compound disaster process have become one of the key topics in geography and disaster research. Especially in coastal urban areas, where the population is highly dense and economic activities are extremely prosperous, various exposed elements are more sensitive and fragile, which undoubtedly further increases the risk of urban flood disasters.

[0003] Traditional urban flood risk research mainly focuses on a single factor, such as only considering the impact of storm surges, rainfall or river floods, which makes it difficult to comprehensively and accurately capture the overall evolution process and statistical characteristics of disaster risks. At present, most of the research on compound floods is based on the superposition of two disaster factors, such as the superposition of storm surges and surface runoff, and the superposition of storm surges and river floods. Although such two-factor superposition research has enhanced the understanding of some characteristics of compound floods to a certain extent, the compound flood disasters actually faced by coastal cities are often the result of the interweaving of multiple complex factors. Its complexity is far beyond the coverage of the superposition of two factors. It is impossible to determine the dependency between the various disaster factors and it is difficult to effectively assess the risk of compound flood disasters.

[0004] In the traditional numerical research field, few studies have been able to comprehensively consider the combined effects of upstream high flow, coastal high tide and local extreme rainfall. Under the combined action of these multiple factors, the factors are not simply superimposed, and their interaction mechanism is extremely complex. Therefore, overall, the current analysis of the coupled effects of composite floods is still insufficient. There are still significant scientific gaps in the understanding of the disaster formation mechanism and the interaction between multiple factors that need to be filled. It is urgent to propose an innovative flood risk assessment method that couples high-dimensional statistics and hydrodynamics to overcome the above technical defects. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the technical defects existing in the prior art, and propose a flood risk assessment method and system that couples high-dimensional statistics with hydrodynamics. It innovatively constructs a high-dimensional statistical model, uses the Copula function to connect the marginal distribution of single variables, and realizes the accurate simulation of the joint probability distribution, which changes the disadvantage that the traditional single factor evaluation cannot capture the interaction of multiple factors, and greatly improves the accuracy of flood disaster assessment.

[0006] In order to solve the above technical problems, the present invention provides a flood risk assessment method coupled with high-dimensional statistics and hydrodynamics, comprising the following steps: S1. Obtain historical observation data covering three variables: river flow, tidal level, and precipitation events; S2. Based on historical observation data, univariate marginal distribution functions are constructed for river flow, tidal level and precipitation events; S3, using the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; S4, constructing a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; S5. Determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate and obtain the composite flood scenarios under different return periods, compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; S6. Using the inundated areas obtained under different flood scenarios and the corresponding precipitation events, river flow and tidal level data, a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels is established to quantify the contribution of each variable to the change in the inundated area.

[0007] In one embodiment of the present invention, in S3, the method of connecting the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events using a Copula function to simulate the joint probability distribution function of at least two variables includes: Use a two-dimensional copula function to connect the univariate marginal distribution functions of the two variables in river discharge, tidal level and precipitation events to simulate the bivariate joint probability distribution function of the two variables; and / or The univariate marginal distribution functions of the three variables of river discharge, tidal level and precipitation events are connected using a three-dimensional Copula function to simulate the multivariate joint probability distribution function of the three variables.

[0008] In one embodiment of the present invention, after obtaining the univariate marginal distribution function, the bivariate joint probability distribution function and the multivariate joint probability distribution function, the univariate recurrence period is calculated based on the univariate marginal distribution function, and the bivariate recurrence period and the multivariate recurrence period are calculated based on the bivariate joint probability distribution function and the multivariate joint probability distribution function, respectively.

[0009] In one embodiment of the present invention, the calculation formulas for the univariate return period, the bivariate return period and the multivariate return period are: ; ; ; ; ; In the formula, T X is the univariate return period, is a variable, For variables The threshold value, is a random variable X Greater than or equal to threshold x The probability of For variables X The cumulative distribution function of F X ( x ) is a variable X The cumulative distribution function is obtained by fitting historical observation data and is used to describe the probability distribution characteristics of a single variable; is the bivariate return period (OR), is the bivariate return period (AND), , For two variables, and Variables and The threshold value, and Variables X and Y The cumulative distribution function of is the Copula function, To connect via Copula function X and Y The marginal distribution and joint distribution of H(x,y) are constructed. H(x,y) is the joint cumulative distribution function of two variables, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of two variables. is the multivariate return period (OR), is the multivariate return period (AND), , , For three variables, , , Variables , , The threshold value, To connect via Copula function , , The marginal distribution of , , Construct the joint distribution, H ( x , y , z ) is the multivariate joint cumulative distribution function, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of the three variables.

[0010] In one embodiment of the present invention, in S4, the method for constructing a mechanism model includes: The calculation formula for constructing a one-dimensional river network hydrodynamic model is: ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, is the water level value obtained by fitting the joint probability distribution function, is the cross-sectional area of ​​water flow, For water depth, is the river bottom gradient, is the friction ratio, is the average flow velocity in the cross section, is the width of the water-passing section; and The calculation formula for constructing a one-dimensional pipe network model is: ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, C is the cross-sectional area of ​​the pipe, is the acceleration due to gravity, is the angle between the bottom of the pipe and the horizontal plane, For water depth, i is the pipeline slope, is the coefficient.

[0011] In one embodiment of the present invention, in S4, the calculation formula of the probability-mechanism coupling model obtained by coupling the joint probability distribution function of at least two variables with the mechanism model is: ; ; ; In the formula, For water depth, and are the velocity components in the x and y directions respectively, is the source flow generated by rainfall on the grid cell, and its value is obtained by fitting the joint probability distribution function. and are the flow velocities caused by rainfall on the grid cells, is the acceleration due to gravity, is the eddy viscosity coefficient, and are the bed slopes in the x and y directions, and are the bed friction in the x and y directions, is the number of source items.

[0012] In one embodiment of the present invention, in S6, a relationship model between the ratio of flooded area and the ratio of precipitation events, river flow and coastal tide level under different risk levels is established, and a method for quantifying the contribution of each variable to the change of flooded area includes: Obtain inundated area data under composite flood scenarios and single variable flood scenarios with different return periods; The inundated area ratio Ar is defined as the ratio of the inundated area under the composite flood scenario to the inundated area under the single variable flood scenario, and the precipitation ratio Pr, flow ratio Qr and tide level ratio Zr are defined as the ratios of precipitation, flow and water level under the composite flood scenario to precipitation, flow and water level under the single variable flood scenario, respectively; A linear regression model was established to represent the relationship between Ar and Pr, Qr and Zr. ; In the formula, is a constant term, , and are the regression coefficients of precipitation ratio, discharge ratio and tide level ratio, respectively; The linear regression model was fitted using the nonlinear least squares method, and we obtained , , and The estimated value of .

[0013] In addition, the present invention also provides a flood risk assessment method system coupling high-dimensional statistics and hydrodynamics, comprising: A data acquisition module, which is used to obtain historical observation data covering three variables: river flow, tidal level and precipitation events; Univariate marginal distribution construction module, which is used to construct univariate marginal distribution functions for river flow, tidal level and precipitation events based on historical observation data; The joint probability distribution construction module uses the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; A coupling module is used to construct a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; Composite flood scenario design and analysis module, which is used to determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate the composite flood scenarios under different return periods, and compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; The flood disaster assessment module is used to use the inundated areas under different flood scenarios and the corresponding precipitation events, river flow and tidal level data to establish a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels, and quantify the contribution of each variable to the change in the inundated area.

[0014] Furthermore, the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method when executing the program.

[0015] Furthermore, the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned method are implemented.

[0016] The above technical solution of the present invention has the following advantages compared with the prior art: The present invention provides a flood risk assessment method and system that couples high-dimensional statistics with hydrodynamics. In terms of assessment accuracy, it innovatively constructs a high-dimensional statistical model, uses the Copula function to connect the marginal distribution of single variables, and achieves accurate simulation of the joint probability distribution, which changes the disadvantage that the traditional single-factor assessment cannot capture the interaction of multiple factors, greatly improves the accuracy of flood disaster assessment, and lays a solid foundation for scientific decision-making. The flood risk assessment method and system coupled with high-dimensional statistics and hydrodynamics provided by the present invention has excellent generalization ability in terms of scope of application. It not only accurately considers the unique geographical and climatic factors of the coastal areas to assess flood risks, but also can seamlessly adapt to flood risk prediction scenarios of various cities such as inland and mountainous areas after reasonable variable selection and model adaptability adjustment. In addition, this technical concept can be successfully transferred to complex systems such as urban drainage system optimization and efficient water resources management, helping multiple fields to efficiently deal with related problems, fully demonstrating its wide application value and innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0018] Figure 1 It is a flow chart of a flood risk assessment method combining high-dimensional statistics and hydrodynamic coupling proposed in the present invention.

[0019] Figure 2 It is the cumulative distribution (CDF) diagram of rainfall in the case of the present invention.

[0020] Figure 3 It is the cumulative distribution (CDF) diagram of tide level in the case of the present invention.

[0021] Figure 4 It is the cumulative distribution (CDF) diagram of river flow in the case of the present invention.

[0022] Figure 5 It is the design value of the annual maximum daily rainfall under different return periods in the case of the present invention.

[0023] Figure 6 It is the design value of the annual highest tidal level under different return periods in the case of the present invention.

[0024] Figure 7 It is the design value of annual upstream flow under different return periods in the case of the present invention.

[0025] Figure 8 It is the waterlogging map with a return period of 5 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under the composite flood scenario.

[0026] Fig. 9 It is the waterlogging map with a return period of 5 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under a single variable flood scenario.

[0027] Fig.10 It is the waterlogging map with a return period of 10 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under the composite flood scenario.

[0028] Fig.11 It is the waterlogging map with a return period of 10 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under a single variable flood scenario.

[0029] Fig.12 It is the waterlogging map with a return period of 25 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under the composite flood scenario.

[0030] Fig.13 It is the waterlogging map with a return period of 25 years in the case of the present invention, which is the inundated area of ​​the study area under different return periods under a single variable flood scenario. DETAILED DESCRIPTION

[0031] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0032] Reference Figure 1 As shown, an embodiment of the present invention provides a flood risk assessment method coupling high-dimensional statistics and hydrodynamics, comprising the following steps: S1. Obtain historical observation data covering three variables: river flow, tidal level, and precipitation events; S2. Based on historical observation data, univariate marginal distribution functions are constructed for river flow, tidal level and precipitation events; S3, using the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; S4, constructing a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; S5. Determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate and obtain the composite flood scenarios under different return periods, compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; S6. Using the inundated areas obtained under different flood scenarios and the corresponding precipitation events, river flow and tidal level data, a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels is established to quantify the contribution of each variable to the change in the inundated area.

[0033] The present invention provides a flood risk assessment method that couples high-dimensional statistics with hydrodynamics. In terms of assessment accuracy, it innovatively constructs a high-dimensional statistical model, uses the Copula function to connect the marginal distribution of single variables, and realizes the precise simulation of the joint probability distribution. It changes the disadvantage that the traditional single-factor assessment cannot capture the interaction of multiple factors, greatly improves the accuracy of flood disaster assessment, and lays a solid foundation for scientific decision-making.

[0034] Among them, in step S1, a data sharing cooperation relationship can be established with the meteorological monitoring stations of the meteorological department. These stations are widely distributed and can continuously and accurately record precipitation event data. For example, hourly or daily precipitation data for the past n years can be regularly downloaded through the professional data interface of the meteorological department, covering the occurrence time, duration and precipitation amount of precipitation of different intensities. For river flow data, it is possible to collaborate with the hydrological observation stations under the water conservancy department. The hydrological observation stations monitor the flow and water level changes of the river in real time through water level meters, flow meters and other equipment installed at key locations of the river. You can apply to obtain river flow data for many years in its database, including flow peaks, means and change trends in different seasons and different hydrological conditions. Tide data is mainly obtained from marine monitoring agencies. These agencies use tide monitoring stations to conduct long-term observations of tide levels in coastal areas. Through official data platforms or cooperation agreements, you can obtain observation records of tide levels over the years, including information such as tide height, occurrence time and tidal cycle.

[0035] After obtaining the data, the statistical analysis method is used to detect outliers in the data of each variable, and then the data of each variable is checked for missing values ​​in the time series. For data with missing values, interpolation methods (such as linear interpolation, spline interpolation, etc.) are used to fill in the missing values ​​according to the change trend of the previous and subsequent data. Finally, the data of the three variables are cross-validated to check the logical consistency between the data. For example, during the time period of the precipitation event, whether the corresponding river flow and tide level data show a reasonable change trend. If contradictions or unreasonable situations are found between the data, the data source is further traced, the accuracy of the data is verified, and the relevant data is re-acquired when necessary to ensure that the quality of the entire data set meets the needs of subsequent research.

[0036] Wherein, in step S2, the distribution functions include Lognorm distribution, Gamma distribution, Weibull distribution, GEV distribution and Normal distribution, and the parameters of the above distribution functions are estimated by using maximum likelihood estimation (MLE) and moment estimation method to ensure the accuracy and reliability of parameter estimation.

[0037] The Lognorm distribution function is: ; In the formula, is a positional parameter, are shape parameters, and both parameters are estimated using historical observational data (e.g., river discharge, tidal level, and precipitation events); is the error function, which can be expressed as .

[0038] The Gamma distribution function is: ; In the formula, is the scale parameter, are shape parameters, and the other two parameters are estimated through historical observation data to describe the probability distribution characteristics of historical observation data (such as river flow, tide level and precipitation events).

[0039] The Weibull distribution function is: ; In the formula, is a positional parameter, is the scale parameter, are shape parameters, and the other three parameters are estimated through historical observation data to describe the probability distribution characteristics of historical observation data (such as river flow, tide level and precipitation events).

[0040] The GEV distribution function is: ; In the formula, is a positional parameter, is the scale parameter, are shape parameters, and the other three parameters are estimated through historical observation data to describe the probability distribution characteristics of historical observation data (such as river flow, tide level and precipitation events).

[0041] The Normal distribution function is: ; In the formula, is a positional parameter, are scale parameters, and the other two parameters are estimated through historical observation data and are used to describe the probability distribution characteristics of historical observation data (such as river flow, tide level and precipitation events).

[0042] As an example, taking the Lognorm distribution function as an example, assuming that the historical observation data collected is river flow, the specific data is {x1, x2, x3, …, xn}, and by using the maximum likelihood estimation method to estimate and , the formula is as follows: ; ; In the formula, It represents the i-th sample value in the historical observation data, n is the total number of samples, and the historical observation data is directly used for estimating the distribution function parameters through calculation.

[0043] Among them, in step S3, the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events are connected using Copula functions to simulate the joint probability distribution function of at least two variables, including using a two-dimensional Copula function to connect the univariate marginal distribution functions of two variables in river flow, tidal level and precipitation events to simulate the bivariate joint probability distribution function of the two variables; and / or using a three-dimensional Copula function to connect the univariate marginal distribution functions of three variables in river flow, tidal level and precipitation events to simulate the multivariate joint probability distribution function of the three variables.

[0044] The above two-dimensional Copula function may include Gaussian Copula function, Clayton Copula function, Frank Copula function and Gumbel Copula function. Specifically, the Gaussian Copula function is: ; In the formula, Φ −1 (u) and Φ −1 (v) is the inverse cumulative distribution function of the standard normal distribution, r and s are random variables under the standard normal distribution, C(u,v) is the function value of the two-dimensional Gaussian copula, which represents the joint distribution function of the two variables U and V. u and v are the cumulative distribution function values ​​of the univariate marginal distribution functions of the two variables (such as river flow and tide level), respectively. The two are connected by the Gaussian Copula function to simulate the joint probability distribution of the two variables. ρ is the correlation coefficient, which represents the correlation between the two variables.

[0045] The Clayton Copula function is: ; ; In the formula, Generator Parameters, ,parameter Kendall rank correlation coefficient The relationship is , u and v are the cumulative distribution function values ​​of the univariate marginal distribution functions of two variables (e.g., river discharge and tidal level), respectively.

[0046] The Frank Copula function is: ; ; In the formula, Generator Parameters, ,parameter Kendall correlation coefficient The relationship is , , u and v are the cumulative distribution function values ​​of the univariate marginal distribution functions of two variables (e.g., river discharge and tidal level), respectively.

[0047] The Gumbel Copula function is: ; ; In the formula, Generator Parameters, , parameter and Kendall rank correlation coefficient The relationship is , , u and v are the cumulative distribution function values ​​of the univariate marginal distribution functions of two variables (e.g., river discharge and tidal level), respectively.

[0048] The above three-dimensional Copula functions include Clayton Copula function, Gumbel-Hougaard Copula function, Joe Copula function and Gaussian Copula function. Specifically, the expression of Clayton Copula function is: ; Where u, v and w are the univariate marginal distribution function values ​​of river flow, tidal level and precipitation events, respectively. The three are connected by the Clayton Copula function to simulate the joint probability distribution of the three variables. θ is the parameter of Clayton Copula. θ>0 indicates positive tail dependence, that is, when the value of one variable is larger, the probability that the value of the other variable is also larger is higher; on the contrary, when θ<0, it indicates negative tail dependence, that is, when the value of one variable is larger, the probability that the value of the other variable is smaller is higher.

[0049] The expression of the Gumbel-Hougaard Copula function is: ; In the formula, Generator , u, v and w are the univariate marginal distribution function values ​​of river flow, tidal level and precipitation events, respectively.

[0050] The expression of Joe Copula function is: ; In the formula, Generator , u, v and w are the univariate marginal distribution function values ​​of river flow, tidal level and precipitation events, respectively.

[0051] The expression of Gaussian Copula function is: ; In the formula, Generator Parameters, is the joint cumulative distribution function (or CDF) of the standard three-variable normal distribution with correlation matrix R, is the inverse cumulative distribution function of the standard univariate normal distribution, and u, v, and w are the univariate marginal distribution function values ​​of river discharge, tidal level, and precipitation events, respectively.

[0052] After obtaining the univariate marginal distribution function, the bivariate joint probability distribution function and the multivariate joint probability distribution function, the univariate return period is calculated based on the univariate marginal distribution function, and the bivariate return period and the multivariate return period are calculated based on the bivariate joint probability distribution function and the multivariate joint probability distribution function, respectively.

[0053] Specifically, the calculation formulas for the univariate return period, bivariate return period, and multivariate return period are: ; ; ; ; ; In the formula, T X is the univariate return period, is a variable, For variables The threshold value, is a random variable X Greater than or equal to threshold x The probability of For variables X The cumulative distribution function of F X ( x ) is a variable XThe cumulative distribution function is obtained by fitting historical observation data and is used to describe the probability distribution characteristics of a single variable; is the bivariate return period (OR), is the bivariate return period (AND), , For two variables, and Variables and The threshold value, and Variables X and Y The cumulative distribution function of is the Copula function, To connect via Copula function X and Y The marginal distribution and joint distribution of H(x,y) are constructed. H(x,y) is the joint cumulative distribution function of two variables, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of two variables. is the multivariate return period (OR), is the multivariate return period (AND), , , For three variables, , , Variables , , The threshold value, To connect via Copula function , , The marginal distribution of , , Construct the joint distribution, H ( x , y , z ) is the multivariate joint cumulative distribution function, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of the three variables.

[0054] The return period refers to the average number of years that a particular flood event occurs in a statistical sense. In this embodiment, the purpose of calculating the univariate return period, the bivariate return period, and the multivariate return period is to evaluate the extreme nature of flood events under different combinations and provide a basis for subsequent flood risk assessment. Through these return periods, the probability of different flood factors (such as river flow, tidal level, and precipitation events) occurring alone or in combination can be better understood, thereby providing a scientific basis for the comprehensive assessment of flood disasters in coastal cities.

[0055] Among them, in step S4, the mechanism model includes a one-dimensional river network hydrodynamic model and a one-dimensional pipe network model. The calculation formula of the one-dimensional river network hydrodynamic model is: ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, is the water level value obtained by fitting the joint probability distribution function, is the cross-sectional area of ​​water flow, For water depth, is the river bottom gradient, is the friction ratio, is the average flow velocity in the cross section, is the width of the water-passing section; and the calculation formula of the one-dimensional pipe network model is ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, C is the cross-sectional area of ​​the pipe, is the acceleration due to gravity, is the angle between the bottom of the pipe and the horizontal plane, For water depth, i is the pipeline slope, is the coefficient.

[0056] Then, the joint probability distribution function of at least two variables is coupled with the mechanism model to obtain the calculation formula of the probability-mechanism coupling model: ; ; ; In the formula, For water depth, and are the velocity components in the x and y directions respectively, is the source flow generated by rainfall on the grid cell, and its value is obtained by fitting the joint probability distribution function. and are the flow velocities caused by rainfall on the grid cells, is the acceleration due to gravity, is the eddy viscosity coefficient, and are the bed slopes in the x and y directions, and are the bed friction in the x and y directions, is the number of source items.

[0057] Then, the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R) were used to evaluate the model performance and calibrate the model parameters: ; ; In the formula, is the i-th observation data sequence, is the ith simulation data, is the average value of the observed data sequence, n is the total number of observed data, is the observed data sequence, is the mean value of the observed data series, To simulate the data series, is the mean value of the observed data series.

[0058] In S6, the relationship model between the ratio of inundated area and the ratio of precipitation events, river discharge and coastal tide level under different risk levels is established, and the method of quantifying the contribution of each variable to the change of inundated area includes the following steps: S61. Obtain inundated area data under composite flood scenarios and single variable flood scenarios with different return periods; S62. Define the inundation area ratio Ar as the ratio of the inundation area under the composite flood scenario to the inundation area under the single variable flood scenario, and define the precipitation ratio Pr, the flow ratio Qr and the tide level ratio Zr as the ratios of the precipitation, flow and water level under the composite flood scenario to the precipitation, flow and water level under the single variable flood scenario, respectively; S63. Establish a linear regression model to represent the relationship between Ar and Pr, Qr and Zr ; In the formula, is a constant term, , and are the regression coefficients of precipitation ratio, discharge ratio and tide level ratio, respectively; S64, using nonlinear least squares method to fit the linear regression model, we get , , and The estimated value of .

[0059] The flood risk assessment method provided by the present invention, which is coupled with high-dimensional statistics and hydrodynamics, has excellent generalization ability in terms of its scope of application. It not only accurately considers the unique geographical and climatic factors of the coastal areas to assess flood risks, but also can seamlessly adapt to flood risk prediction scenarios of various cities such as inland and mountainous areas after reasonable variable selection and model adaptability adjustment. In addition, this technical concept can be successfully transferred to complex systems such as urban drainage system optimization and efficient water resources management, helping multiple fields to efficiently deal with related problems, fully demonstrating its wide application value and innovation.

[0060] The flood risk assessment method of coupling high-dimensional statistics with hydrodynamics proposed in the present invention is described in detail below through specific implementation methods.

[0061] This example selects the urban area of ​​City B in Province A for testing. The test results are as follows: Figure 2-4 shown. Figure 2-4 They are the cumulative distribution diagrams of different marginal distributions of rainfall, tide level and river flow. All analysis and graphical test measures point to the good fitting effect of Gamma distribution in rainfall series, Weibull distribution in tide level series and GEV distribution in river flow. Figure 5-7 The joint probability boundary conditions (rainfall, tide level, flow process) under different return periods (5 years-200 years) are determined based on the joint probability distribution function of the three variables. The joint probability boundary condition data will then be input into the probability-mechanism coupling model. Table 1 below shows the main joint return periods of the three variables for different flood characteristic combinations.

[0062] Table 1

[0063] It can be seen from Table 1 that when considering the three-variable flood characteristics, the co-occurrence return period is greater than the joint return period, that is. This shows that the simultaneous occurrence of the three-variable flood characteristics is more frequent in the "OR" joint case and less common in the "AND" joint case. For example, when calculated according to the single frequency method, the maximum daily rainfall of 158.32 in the return period corresponds to a frequency of once in 5 years, while if the three-variable joint function is used for calculation, it will be found that the corresponding return periods are 25.54 years and 22.64 years, and the return periods are 2.30 years and 2.30 years, that is, the simultaneous occurrence of the three-variable flood characteristics is more frequent in the "OR" joint case and less common in the "AND" joint case. T OR This method better reflects the correlation and joint effect among the three-variable flood characteristics and is more specific and advanced.

[0064] Figure 8-13 The figures are the waterlogging conditions in the central urban area of ​​City B under different recurrence periods. From the comparison, it can be seen that the actual flooded area calculated using the composite flood method is larger and more suitable for risk deduction and simulation under extreme rainstorm scenarios under future climate change. It can be seen that the method of the present invention performs well in the test of flood disaster assessment in City B. Table 2 is a comparison of simulated flooding data under the two scenarios.

[0065] Table 2

[0066] Corresponding to the above method embodiment, the present invention also provides a flood risk assessment system coupled with high-dimensional statistics and hydrodynamics, comprising: A data acquisition module, which is used to obtain historical observation data covering three variables: river flow, tidal level and precipitation events; Univariate marginal distribution construction module, which is used to construct univariate marginal distribution functions for river flow, tidal level and precipitation events based on historical observation data; The joint probability distribution construction module uses the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; A coupling module is used to construct a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; Composite flood scenario design and analysis module, which is used to determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate the composite flood scenarios under different return periods, and compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; The flood disaster assessment module is used to use the inundated areas under different flood scenarios and the corresponding precipitation events, river flow and tidal level data to establish a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels, and quantify the contribution of each variable to the change in the inundated area.

[0067] The flood risk assessment system of high-dimensional statistics and hydrodynamic coupling of this embodiment is used to implement the aforementioned flood risk assessment method of high-dimensional statistics and hydrodynamic coupling. Therefore, its specific implementation method can refer to the description of the corresponding various partial embodiments and will not be introduced in detail here.

[0068] In addition, since the high-dimensional statistics and hydrodynamics coupled flood risk assessment system of this embodiment is used to implement the aforementioned high-dimensional statistics and hydrodynamics coupled flood risk assessment method, its function corresponds to that of the aforementioned method and will not be repeated here.

[0069] Corresponding to the above method embodiment, an embodiment of the present invention further provides a computer device, including: A memory for storing computer programs; A processor is used to implement the steps of the flood risk assessment method of coupling high-dimensional statistics with hydrodynamics when executing a computer program.

[0070] In the embodiment of the present invention, the processor may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0071] The processor may call a program stored in the memory. Specifically, the processor may execute operations in the above-mentioned embodiment of the flood risk assessment method coupling high-dimensional statistics and hydrodynamics.

[0072] The memory is used to store one or more programs, which may include program codes, and the program codes include computer operating instructions.

[0073] In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0074] Corresponding to the above method embodiment, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the flood risk assessment method coupling high-dimensional statistics and hydrodynamics are implemented.

[0075] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0076] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A flood risk assessment method based on high-dimensional statistics and hydrodynamic coupling, characterized by: The following steps are involved: S1. Obtain historical observation data covering three variables: river flow, tidal level, and precipitation events; S2. Based on historical observation data, univariate marginal distribution functions are constructed for river flow, tidal level and precipitation events; S3, using the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; S4, constructing a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; S5. Determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate and obtain the composite flood scenarios under different return periods, compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; S6. Using the inundated areas obtained under different flood scenarios and the corresponding precipitation events, river flow and tidal level data, a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels is established to quantify the contribution of each variable to the change in the inundated area.

2. The flood risk assessment method of high-dimensional statistics coupled with hydrodynamics according to claim 1 is characterized by: In S3, a method for connecting the constructed univariate marginal distribution functions of river flow, tidal level, and precipitation events using a Copula function to simulate a joint probability distribution function of at least two variables includes: Use a two-dimensional copula function to connect the univariate marginal distribution functions of the two variables in river discharge, tidal level and precipitation events to simulate the bivariate joint probability distribution function of the two variables; and / or The univariate marginal distribution functions of the three variables of river discharge, tidal level and precipitation events are connected using a three-dimensional Copula function to simulate the multivariate joint probability distribution function of the three variables.

3. A flood risk assessment method based on high-dimensional statistics and hydrodynamic coupling according to claim 1 or 2, characterized in that: After obtaining the univariate marginal distribution function, the bivariate joint probability distribution function and the multivariate joint probability distribution function, the univariate return period is calculated based on the univariate marginal distribution function, and the bivariate return period and the multivariate return period are calculated based on the bivariate joint probability distribution function and the multivariate joint probability distribution function, respectively.

4. The flood risk assessment method of high-dimensional statistics coupled with hydrodynamics according to claim 3 is characterized by: The calculation formulas for univariate return period, bivariate return period and multivariate return period are: ； ； ； ； ； In the formula, T X is the univariate return period, is a variable, For variables The threshold value, is a random variable X Greater than or equal to threshold x The probability of For variables X The cumulative distribution function of F X ( x ) is a variable X The cumulative distribution function of is obtained by fitting historical observation data and is used to describe the probability distribution characteristics of a single variable; is the bivariate return period (OR), is the bivariate return period (AND), , For two variables, and Variables and The threshold value, and Variables X and Y The cumulative distribution function of is the Copula function, To connect via Copula function X and Y The marginal distribution and joint distribution of H(x,y) are constructed. H(x,y) is the joint cumulative distribution function of two variables, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of two variables. is the multivariate return period (OR), is the multivariate return period (AND), , , For three variables, , , Variables , , The threshold value, To connect via Copula function , , The marginal distribution of , , Construct the joint distribution, H ( x , y , z ) is the multivariate joint cumulative distribution function, which is obtained by fitting historical observation data and is used to describe the joint probability distribution characteristics of the three variables.

5. A flood risk assessment method based on high-dimensional statistics and hydrodynamic coupling according to claim 1 or 2, characterized in that: In S4, the method for constructing a mechanism model includes: The calculation formula for constructing a one-dimensional river network hydrodynamic model is: ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, is the water level value obtained by fitting the joint probability distribution function, is the cross-sectional area of ​​water flow, For water depth, is the river bottom gradient, is the friction ratio, is the average flow velocity in the cross section, is the width of the water-passing section; and The calculation formula for constructing a one-dimensional pipe network model is: ; ; In the formula, is the flow value obtained by fitting the joint probability distribution function, C is the cross-sectional area of ​​the pipe, is the acceleration due to gravity, is the angle between the bottom of the pipe and the horizontal plane, For water depth, i is the pipeline slope, is the coefficient.

6. The flood risk assessment method of high-dimensional statistics coupled with hydrodynamics according to claim 5 is characterized by: In S4, the calculation formula of the probability-mechanism coupling model obtained by coupling the joint probability distribution function of at least two variables with the mechanism model is: ； ； ； In the formula, For water depth, and are the velocity components in the x and y directions respectively, is the source flow generated by rainfall on the grid cell, and its value is obtained by fitting the joint probability distribution function. and are the flow velocities caused by rainfall on the grid cells, is the acceleration due to gravity, is the eddy viscosity coefficient, and are the bed slopes in the x and y directions, and are the bed friction in the x and y directions, is the number of source items.

7. A flood risk assessment method based on high-dimensional statistics and hydrodynamic coupling according to claim 1 or 2, characterized in that: In S6, the relationship model between the ratio of inundated area and the ratio of precipitation events, river discharge and coastal tidal level under different risk levels is established, and the method of quantifying the contribution of each variable to the change of inundated area includes: Obtain inundated area data under composite flood scenarios and single variable flood scenarios with different return periods; The inundated area ratio Ar is defined as the ratio of the inundated area under the composite flood scenario to the inundated area under the single variable flood scenario, and the precipitation ratio Pr, flow ratio Qr and tide level ratio Zr are defined as the ratios of precipitation, flow and water level under the composite flood scenario to precipitation, flow and water level under the single variable flood scenario, respectively; A linear regression model was established to represent the relationship between Ar and Pr, Qr and Zr. ; In the formula, is a constant term, , and are the regression coefficients of precipitation ratio, discharge ratio and tide level ratio, respectively; The linear regression model was fitted using the nonlinear least squares method, and we obtained , , and The estimated value of .

8. A flood risk assessment method system coupling high-dimensional statistics and hydrodynamics, characterized by: include: A data acquisition module, which is used to obtain historical observation data covering three variables: river flow, tidal level and precipitation events; Univariate marginal distribution construction module, which is used to construct univariate marginal distribution functions for river flow, tidal level and precipitation events based on historical observation data; The joint probability distribution construction module uses the Copula function to connect the constructed univariate marginal distribution functions of river flow, tidal level and precipitation events to simulate the joint probability distribution function of at least two variables; A coupling module is used to construct a mechanism model, coupling the joint probability distribution function of at least two variables with the mechanism model to obtain a probability-mechanism coupling model; Composite flood scenario design and analysis module, which is used to determine the joint probability boundary conditions based on the joint probability distribution function of the variables, input the joint probability boundary conditions into the probability-mechanism coupling model, simulate the composite flood scenarios under different return periods, and compare the composite flood scenarios under different return periods with the single variable flood scenarios, and analyze the flooded area data under the composite flood scenarios; The flood disaster assessment module is used to use the inundated areas under different flood scenarios and the corresponding precipitation events, river flow and tidal level data to establish a relationship model between the inundated area ratio and the precipitation events, river flow and tidal level ratio under different risk levels, and quantify the contribution of each variable to the change in the inundated area.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images