Method and device for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation

Abstract

The present invention discloses a method and device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation. The method comprises: considering the impact of reservoir regulation on the inconsistency of floods, applying a reservoir index to characterize the reservoir storage capacity, adopting a generalized additive model with the reservoir index as a covariate to characterize the inconsistency of floods entering a hydrological station or a downstream reservoir under the influence of reservoir regulation, and estimating time-varying distribution parameters by combining a maximum likelihood method and a quantile map fitting method; constructing a watershed flood area composition model by using a vine-structured Copula joint distribution function for the nonlinear, asymmetric, and high-dimensional dependent structure of the composition of the watershed flood area under the influence of reservoir regulation, and analyzing the variable-frequency characteristics of the composition of the watershed flood area under the influence of reservoir regulation; and calibrating the parameters of the vine-structured Copula joint distribution function by combining a sequential estimation method and a particle swarm optimization algorithm based on water balance constraints to deduce the composition of the variable-frequency flood areas in the watershed, thereby improving the adaptability of the watershed flood area composition model under a changing environment.

Description

Technical Field

[0001] The present invention relates to the technical field of basin flood area composition, and in particular to a method and device for deducing basin variable frequency flood area composition under the influence of reservoir regulation. Background Art

[0002] When there is a significant reservoir upstream of the design section, or when the design reservoir has a flood control role downstream, the regional composition of the major flood should be analyzed and the regional composition of the design flood above the design section or above the flood control section should be determined. The "Specifications for Calculation of Design Floods for Water Conservancy and Hydropower Projects" (SL44-2006) (hereinafter referred to as the "Specifications") recommends using either the typical flood composition method or the same-frequency flood composition method to derive the regional composition of the design flood. The "Specifications" stipulate that for both flood regional composition methods, the design flood hydrographs for each sub-area should be based on the same flood hydrograph as a representative event. The typical flood hydrograph method selects a representative major flood from measured data as a representative example and amplifies the typical flood hydrograph for each sub-area according to the multiple of the design section's flood peak or volume. The same-frequency flood composition method assigns a flood frequency of the same frequency to a specific sub-area, with corresponding floods occurring in the remaining sub-areas. The "Specifications" also recommends using the regional flood frequency combination method to derive the design flood affected by upstream flood storage projects in areas with long data series. These methods have been widely used in the fields of water conservancy project planning and design, water resources development and utilization, etc., providing theoretical support for solving various engineering hydrological problems and improving people's understanding of hydrological laws.

[0003] All three methods are based on the premise of "reduction first, then restoration" to deduce the composition of the design flood area. The "reduction" here aims to eliminate the non-stationarity of the original sequence through data stabilization. With the completion and commissioning of large-scale water conservancy projects within the basin, continuing to deduce the design flood area composition based on the "same frequency" assumption will lead to over-stabilization of the flood area composition, reducing the adaptability and generalization ability of such methods to changing environments. In addition, when the basin's flood area composition is complex and has many subregions, the regional flood frequency combination method suffers from the problem of computational dimensionality. Given the nonlinear, asymmetric, and high-dimensional interdependent structure of the basin's flood area composition under the influence of reservoir regulation, the flood area composition no longer satisfies the same frequency assumption. Therefore, it is necessary to conduct work on deducing the composition of the basin's variable-frequency flood area under the influence of reservoir regulation. This aims to overcome the dimensionality bottleneck in calculating the basin's flood area composition and improve the generalization ability of the deduction method for the design flood area composition. Summary of the Invention

[0004] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a method and device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, which can overcome the technical bottlenecks of "over-stabilization" and "dimensionality disaster" of the flood area composition method, and improve the adaptability and generalization ability of the flood area composition deduction method to changing environments.

[0005] According to one aspect of the present invention, a method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation is provided, comprising:

[0006] Obtain the source data to be derived;

[0007] Inputting the source data to be derived into the calibrated basin flood area composition model to derive the basin variable frequency flood area composition; wherein the construction of the basin flood area composition model includes:

[0008] Considering the impact of reservoir regulation on flood inconsistency, the reservoir index is used to characterize the reservoir storage capacity. A generalized additive model with the reservoir index as a covariate is used to characterize the inconsistency of flood inflows at hydrological stations or downstream reservoirs under the influence of reservoir regulation. The maximum likelihood method and quantile plot fitting method are combined to estimate the time-varying distribution parameters.

[0009] The vine structure Copula joint distribution function is used to construct a basin flood area composition model.

[0010] As a further technical solution, the calibration of the flood area composition model of the watershed includes:

[0011] Based on water balance constraints, the parameters of the vine structure Copula joint distribution function are calibrated by combining the sequential estimation method and the particle swarm optimization algorithm, and the calibrated basin flood area composition model is output.

[0012] As a further technical solution, based on water balance constraints, the parameters of the vine structure Copula joint distribution function are calibrated by combining the sequential estimation method and the particle swarm optimization algorithm, including:

[0013] Step 1: Input the probability density function of the time-varying Pearson III distribution of the variable to be derived;

[0014] Step 2: Calculate the Kendall rank correlation coefficient γ of each node in the first-level tree structure to identify the correlation between node pairs, and determine the tree structure and its root node by integrating the categories of the vine structure and the correlation between the node pairs;

[0015] Step 3: On each edge of the first-level tree, the maximum likelihood method is used to determine the parameter values of each binary Copula function, and the Akaike information criterion is used to select the best binary Copula function;

[0016] Step 4: Based on the optimal binary Copula function determined in step 3, sample the dependency structure of the first-level tree to generate node values corresponding to the second-level tree. Iterate steps 2-3 to construct the dependency structure of the second-level tree and select the optimal Copula function.

[0017] Step 5, iteratively execute steps 2 to 4, construct each tree structure in the vine structure and its corresponding Copula function layer by layer, and obtain a complete Vine-Copula function model;

[0018] In step 6, considering the water balance constraint, the particle swarm optimization algorithm is used to optimize the solution with the maximization of the joint probability density function as the objective function.

[0019] According to one aspect of the present invention, there is provided a device for estimating the composition of variable-frequency flood regions in a watershed under the influence of reservoir regulation, comprising:

[0020] The first main module is used to obtain the source data to be derived;

[0021] The second main module is used to input the source data to be derived into the calibrated basin flood area composition model and output the basin flood variable frequency area composition; wherein the construction of the basin flood area composition model includes:

[0022] Considering the impact of reservoir regulation on flood inconsistency, the reservoir index is used to characterize the reservoir storage capacity. A generalized additive model with the reservoir index as a covariate is used to characterize the inconsistency of flood inflows at hydrological stations or downstream reservoirs under the influence of reservoir regulation. The maximum likelihood method and quantile plot fitting method are combined to estimate the time-varying distribution parameters.

[0023] The vine structure Copula joint distribution function is used to construct a basin flood area composition model.

[0024] As a further technical solution, it also includes a third main module, which is used to calibrate the parameters of the vine structure Copula joint distribution function based on water balance constraints, combined with the sequential estimation method and the particle swarm optimization algorithm, and output the calibrated basin flood area composition model.

[0025] As a further technical solution, the third main module is further configured to execute the following instructions:

[0026] Step 1: Input the probability density function of the time-varying Pearson III distribution of the variable to be derived;

[0027] Step 2: Calculate the Kendall rank correlation coefficient γ of each node in the first-level tree structure to identify the correlation between node pairs, and determine the tree structure and its root node by integrating the categories of the vine structure and the correlation between the node pairs;

[0028] Step 3: On each edge of the first-level tree, the maximum likelihood method is used to determine the parameter values of each binary Copula function, and the Akaike information criterion is used to select the best binary Copula function;

[0029] Step 4: Based on the optimal binary Copula function determined in step 3, sample the dependency structure of the first-level tree to generate node values corresponding to the second-level tree. Iterate steps 2-3 to construct the dependency structure of the second-level tree and select the optimal Copula function.

[0030] Step 5, iteratively execute steps 2 to 4, construct each tree structure in the vine structure and its corresponding Copula function layer by layer, and obtain a complete Vine-Copula function model;

[0031] Step 6: Considering the water balance constraint, the particle swarm optimization algorithm is used to optimize the solution with the objective function of maximizing the joint probability density function.

[0032] According to one aspect of the present invention, there is provided a device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, comprising a processor and a memory, wherein the processor is coupled to the memory; the memory is used to store programs; and the processor is used to execute the programs in the memory, so that the device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation executes the described method.

[0033] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions cause the computer to execute the method described above.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention constructs a vine-structured Copula flood area composition model under the influence of reservoir regulation, which can not only characterize the nonlinear, asymmetric and high-dimensional dependent structure of the basin flood area composition under the influence of reservoir regulation, but also analyze the variable frequency characteristics of the basin flood area composition under the influence of reservoir regulation; combines the sequential estimation method and the particle swarm optimization algorithm to calibrate the parameters of the vine-structured Copula joint distribution function, overcomes the technical bottlenecks of the over-stabilization processing and dimensionality curse of the method for deducing the composition of flood areas with the same frequency and the method for deducing the most likely flood area composition, and improves the adaptability and generalization ability of the flood area composition model under a changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. 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 paying any creative work.

[0037] Figure 1A flow chart of a method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided by an exemplary embodiment is shown.

[0038] Figure 2 A schematic diagram of a process for constructing a basin flood area composition model under the influence of reservoir regulation provided by an exemplary embodiment is shown.

[0039] Figure 3 A schematic diagram of a model parameter calibration process combining a sequential estimation method and a particle swarm optimization algorithm provided by an exemplary embodiment is shown.

[0040] Figure 4 A logical principle diagram of a method for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided by an exemplary embodiment is shown.

[0041] Figure 5 A block diagram of a device for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided by an exemplary embodiment is shown.

[0042] Figure 6 A block diagram of a basin flood area composition model under the influence of reservoir regulation provided by an exemplary embodiment is shown.

[0043] Figure 7 A block diagram of an execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0044] This paper considers the non-uniform impact of reservoir regulation on floods, uses a reservoir index to characterize reservoir storage capacity, and employs a generalized additive model with the reservoir index as a covariate to characterize the non-uniformity of flood inflows at hydrological stations or downstream reservoirs under reservoir regulation. The maximum likelihood method and quantile plot fitting method are then combined to estimate time-varying distribution parameters. Given the nonlinear, asymmetric, and high-dimensional dependency structure of the flood area composition in a watershed under reservoir regulation, the flood area composition no longer satisfies the homogeneous frequency assumption. By analogy with the complex vine-like structure, a vine-structured Copula joint distribution function is used to construct a watershed flood area composition model, analyzing the variable-frequency characteristics of the flood area composition under reservoir regulation. Based on water balance constraints, the parameters of the vine-structured Copula joint distribution function are combined with a sequential estimation method and a particle swarm optimization algorithm to calibrate the parameters of the vine-structured Copula joint distribution function. The variable-frequency flood area composition of the watershed is deduced, improving the adaptability of the watershed flood area composition model under changing environments. This paper not only characterizes the variable-frequency characteristics of the flood area composition in a watershed, but also improves the generalization capability of the design flood area composition derivation method.

[0045] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0046] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be executed in the order described. For example, some operations / steps may be further decomposed, while others may be combined or partially combined, so the actual execution order may vary depending on the actual situation.

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0048] Please refer to Figure 1 ,in Figure 1 A flow chart of a method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided by an exemplary embodiment is shown. The method includes:

[0049] Step 1: Obtain the source data to be derived, including upstream control station flood, interval flood and downstream control station flood.

[0050] Step 2: input the source data to be derived into the calibrated basin flood area composition model, and output the basin flood area composition.

[0051] Among them, the calibration of the basin flood area composition model includes: considering the impact of reservoir regulation on flood inconsistency, applying the reservoir index to characterize the reservoir storage capacity, using a generalized additive model with the reservoir index as a covariate to characterize the inconsistency of floods entering hydrological stations or downstream reservoirs under the influence of reservoir regulation, and combining the maximum likelihood method and the quantile map fitting method to estimate time-varying distribution parameters; using the vine structure Copula joint distribution function to construct the basin flood area composition model; based on water balance constraints, combining the sequential estimation method and the particle swarm optimization algorithm to calibrate the parameters of the vine structure Copula joint distribution function, and outputting the calibrated basin flood area composition model.

[0052] Please refer to Figure 2 , Figure 2 A schematic diagram of the process of constructing a basin flood area composition model under the influence of reservoir regulation provided by an embodiment is shown.

[0053] The construction process is achieved through steps 2.1 to 2.2.

[0054] Please refer to Figure 3 , Figure 3 A schematic diagram of a model parameter calibration process combining a sequential estimation method and a particle swarm optimization algorithm is shown in an exemplary embodiment. After the model is constructed, the model parameters are calibrated in step 2.3 to improve the adaptability and generalization ability of the flood area component model under changing environments.

[0055] Please refer to Figure 4 , Figure 4 The following is a logical principle diagram of a method for deriving the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, provided by an embodiment. The source data used for derivation is calculated through the derivation logic, and the final flood area composition is output.

[0056] In step 2.1, a generalized additive model with the reservoir index as a covariate is used to characterize the inconsistency of flood inflows to hydrological stations or downstream reservoirs under the influence of reservoir regulation. The generalized additive model is expressed as follows:

[0057]

[0058] Ω=[ω 10 ,ω 11 ,ω 20 ,ω 21 ,ω 30 ]

[0059] RI=[RI i ,QRI k ]

[0060]

[0061] Where: a0t , β t and α t They are the location, scale and shape parameters of the time-varying Pearson III distribution, where t represents the time variable, considering α t More sensitive, often assumed to be a constant α, which does not change with the environment; g j (·) is the link function of the jth time-varying distribution parameter (j = 1, 2, 3); Ω is the parameter set of the generalized linear model; M is the number of large reservoirs above the study section; A i and C i are the catchment areas of the i-th reservoir (km 2 ) and total storage capacity (m 3 ), i=0,1,2...,M;A T and C T The water catchment area of the research section (km 2 ) and the average annual runoff (m 3 ); k is the flood interval number that constitutes the downstream control section; A k and C k are the catchment areas of the reservoirs built below the kth interval (km 2 ) and flood control storage capacity (m 3 );RI i is the coefficient of the i-th reservoir; QRI k is the coefficient of the reservoir built in the kth flood interval; RI is the reservoir coefficient set.

[0062] The probability density function expression of the time-varying Pearson III distribution with the reservoir coefficient RI as the covariate is:

[0063]

[0064] Where: y t is the measured flood at time t; f(·|·,·) is the probability density function; Γ(·) is the gamma distribution density function.

[0065] The time-varying Pearson III distribution parameters are estimated by combining the maximum likelihood method and the quantile plot fitting method. If the point distance between the Pearson III distribution and the empirical frequency is close to the 45-degree reference line on the quantile plot, it indicates that the flood sample points obtained by the proposed distribution model pass the test and the next step of analysis and calculation can be carried out, that is, the time-varying Pearson III distribution parameters are obtained by the quantile plot fitting method.

[0066] In step 2.2, a vine-structured Copula joint distribution function was used to construct a model for the flood area composition of the basin. Vine-structured Copula is a quantile-based high-dimensional probability distribution modeling method. By constructing a tree structure, a different bivariate Copula function can be selected at each node, allowing for more accurate capture of the complex dependencies between different variables.

[0067] For q-dimensional variables x1, x2, ..., x q , whose marginal distribution functions are F1(x1), F2(x2),…, F q (x q ), the vine structure consists of q-1 layers of tree structures, denoted as T p , each layer of the tree is composed of N p nodes and U p For an undirected graph consisting of edges, for the tree structure T p =(N p , U p ):

[0068]

[0069] Where: p = 1, 2,…, q-1.

[0070] In the tree structure T p+1 For any pair of adjacent nodes, the corresponding edge in the previous layer of tree T p The vine structures cannot be independent of each other, but there should be an intersection node to ensure that the dependency relationship between each layer of the vine structure can be transmitted and maintained, thereby maintaining the coherence of the entire structure and the correct construction of the dependency network. The probability density function of the vine structure Copula can be expressed as:

[0071]

[0072] Where: f V (·,…,·) is the probability density function of the vine structure Copula; y is the tree T y The layer number of ψ u is the set of conditional variable numbers of edge u; φ(u) and is the set of relation variable numbers for edge u; is the variable x φ(u) and The conditional distribution function of and are the probability density functions of the corresponding variable conditional distributions; s is the random variable x s Serial number; f s (x s ) is a random variable x s The marginal distribution function of .

[0073] In step 2.3, based on the water balance constraint, the parameters of the Vine Structure Copula joint distribution function are calibrated using the sequential estimation method and the particle swarm optimization algorithm to deduce the composition of the variable-frequency flood areas in the basin. The calculation process is as follows:

[0074] ① Input the probability density function of the time-varying Pearson III distribution of the variable to be derived (flood peak or flood volume).

[0075] ② Calculate the Kendall rank correlation coefficient γ of each node in the first-level tree structure to identify the correlation between node pairs, and determine the tree structure and its root node based on the type of vine structure and the correlation between node pairs.

[0076]

[0077] Where: N T With N Y where |γ| is the number of identical and different order pairs between two random variables, respectively; n is the number of samples of the random variables. The value of |γ| lies between 0 and 1 and is used to determine the strength of the correlation between the random variables.

[0078] ③ On each edge of the first-level tree, the maximum likelihood method is used to determine the parameter values of each binary Copula function, and the Akaike information criterion (AIC) is used to select the optimal binary Copula function.

[0079] ④ Based on the optimal binary Copula function determined in step 3, sample the dependency structure of the first-level tree to generate node values corresponding to the second-level tree. Iterate steps 2-3 to construct the dependency structure of the second-level tree and select the optimal Copula function.

[0080] ⑤ Iterate steps 2 to 4 to build each tree structure and its corresponding Copula function in the vine structure layer by layer to obtain a complete Vine-Copula function model.

[0081] ⑥ Considering the water balance constraint, the particle swarm optimization algorithm is used to optimize the solution with the maximization of the joint probability density function as the objective function:

[0082]

[0083] Where: objective function f C (x,y1,y2,y3,…,y q-1 ) is the upstream water volume X and the water volume of each interval Y e The joint probability density function value of (e=1,2,…,q-1); c(·) is the joint probability density function of vine copula; ux and v1,v2,…,v q-1 is the cumulative distribution function value of upstream water volume and water volume in each interval, [u x ,v1,v2,…,v q-1 ] is the decision variable of the particle swarm optimization algorithm, that is, the frequency combination of each partition; f X (x) and The upstream water volume X and the water volume of each interval Y e The marginal probability density function of z P is the flood volume at the downstream control section corresponding to the recurrence period P. The composition of the most likely flood area is deduced from the optimal frequency combination obtained and the marginal distribution of each partition.

[0084] Please refer to Figure 5 , which shows a block diagram of the apparatus for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided in this application, the apparatus comprising: a first main module 102, for acquiring source data to be deduced; a second main module 104, for inputting the source data to be deduced into a calibrated watershed flood area composition model, and outputting the composition of the watershed flood areas.

[0085] Please refer to Figure 6 , which shows a block diagram of the composition model of the basin flood area under the influence of reservoir regulation provided by this application, the model includes:

[0086] The first training module 202 uses a generalized additive model with a reservoir index as a covariate to characterize the inconsistency of flood inflows at a hydrological station or downstream reservoir under the influence of reservoir regulation, and combines the maximum likelihood method and the quantile plot fitting method to estimate time-varying distribution parameters;

[0087] The second training module 204 uses the vine structure Copula joint distribution function to build a basin flood area composition model.

[0088] The non-stationary converter device for flood forecasting in a river basin under the influence of reservoir regulation provided by the embodiment of the present invention is aimed at the fact that with the completion and operation of large-scale water conservancy projects in the river basin, if the design flood area composition is still deduced based on the "same frequency" assumption, the flood area composition will be over-stationary, reducing the adaptability and generalization ability of such methods to changing environments. In addition, due to the nonlinear, asymmetric and high-dimensional dependent structure of the flood area composition in the river basin under the influence of reservoir regulation, the flood area composition no longer meets the same frequency assumption. Figure 6Several modules in it can not only characterize the nonlinear, asymmetric and high-dimensional dependent structure of the composition of flood areas in the basin under the influence of reservoir regulation, but also analyze the variable frequency characteristics of the composition of flood areas in the basin under the influence of reservoir regulation, overcome the technical bottlenecks of over-stabilization processing and dimensionality disaster in the derivation method of flood areas with the same frequency and the derivation method of the most likely flood area composition, and improve the adaptability and generalization ability of the flood area composition model under a changing environment.

[0089] It should be noted that the device embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but are also used to implement the methods in other method embodiments provided by the present invention. The only difference is the setting of corresponding functional modules. The principles thereof are basically the same as those of the above-mentioned device embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned device embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, they can improve the equipment in the above-mentioned device embodiments to obtain corresponding device-type embodiments for implementing the methods in other method-type embodiments. For example:

[0090] Based on the contents of the above device embodiment, as a preferred embodiment, the device for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided in the embodiment of the present invention further includes:

[0091] The third main module, based on water balance constraints, combines the sequential estimation method and particle swarm optimization algorithm to calibrate the parameters of the vine structure Copula joint distribution function, and outputs the calibrated basin flood area composition model to improve the adaptability and generalization ability of the flood area composition model under changing environments.

[0092] Based on the contents of the above-mentioned device embodiments, as a preferred embodiment, in the device for deducing the variable-frequency flood area composition of the watershed under the influence of reservoir regulation provided in the embodiments of the present invention, an intricate vine tree structure is analogized and the vine structure Copula joint distribution function is used to construct the model to analyze the variable-frequency characteristics of the flood area composition of the watershed under the influence of reservoir regulation. The vine structure Copula joint distribution function is intended to bring the non-stationary characteristics caused by reservoir regulation into the flood area composition calculation through a generalized additive model with the reservoir index as a covariate and a vine structure joint distribution function, so as to adapt the watershed flood area composition model.

[0093] Based on the contents of the above-mentioned device embodiments, as a preferred embodiment, the device for inferring the variable-frequency flood area composition of the watershed under the influence of reservoir regulation provided in the embodiments of the present invention, the calibration of the watershed flood area composition model also includes: for the inconsistency of floods entering the hydrological station or downstream reservoir under the influence of reservoir regulation, a generalized additive model with the reservoir index as the covariate is adopted to characterize it; for the nonlinear, asymmetric and high-dimensional dependent structure of the flood area composition of the watershed under the influence of reservoir regulation, a vine structure Copula joint distribution function is adopted to capture it.

[0094] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0095] Next, we will introduce a device for calculating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation provided by an embodiment of the present invention. Figure 7 , Figure 7 This is a schematic diagram of a structure of a device for calculating the composition of a variable-frequency flood area in a watershed under the influence of reservoir regulation provided in an embodiment of the present invention. The device 300 for calculating the composition of a variable-frequency flood area in a watershed under the influence of reservoir regulation can be specifically manifested as an autonomous driving vehicle, a mobile phone, a tablet, a laptop computer, a desktop computer, a monitoring data processing device, etc., which is not limited here. Figure 1 The function of the execution device in the corresponding embodiment. Specifically, the device 300 for calculating the composition of the variable frequency flood area in the watershed under the influence of reservoir regulation includes: a receiver 301, a transmitter 302, a processor 303 and a memory 304 (wherein the number of processors 303 in the device 300 for calculating the composition of the variable frequency flood area in the watershed under the influence of reservoir regulation can be one or more, Figure 7 (taking one processor as an example), the processor 303 may include an application processor 3031 and a communication processor 3032. In some embodiments of the present application, the receiver 301, the transmitter 302, the processor 303 and the memory 304 may be connected via a bus or other means.

[0096] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 303. A portion of the memory 304 may also include non-volatile random access memory (NVRAM). The memory 304 stores processor and operation instructions, executable modules, or data structures, or subsets or extended sets thereof. The operation instructions may include various operation instructions for implementing various operations.

[0097] Processor 303 controls the operation of the execution device. In specific applications, the various components of the execution device are coupled together via a bus system. In addition to a data bus, the bus system may also include a power bus, a control bus, and a status signal bus. However, for clarity, all of these buses are referred to as a bus system in the figure.

[0098] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 303. Processor 303 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits or software instructions in processor 303. The above processor 303 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor, or a microcontroller. It can also include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The processor 303 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 304, and processor 303 reads the information in memory 304 and performs the steps of the above method in conjunction with its hardware.

[0099] Receiver 301 can be used to receive input digital or character information and generate signal input related to executing device-related settings and function control. Transmitter 302 can be used to output digital or character information through the first interface. Transmitter 302 can also be used to send instructions to the disk pack through the first interface to modify data in the disk pack. Transmitter 302 can also include a display device such as a display screen.

[0100] In the embodiment of the present invention, the processor 303 is configured to execute Figure 1 The specific manner in which the application processor 3031 in the processor 303 performs the above steps is the same as that in the present application. Figure 1The corresponding method embodiments are based on the same concept, and the technical effects they bring are the same as those in this application. Figure 1 The corresponding method embodiments are the same. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0101] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the steps of the method for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation.

[0102] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0104] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0106] In summary, the present invention takes into account the impact of reservoir regulation on the inconsistency of floods, applies the reservoir index to characterize the reservoir storage capacity, adopts a generalized additive model with the reservoir index as a covariate to characterize the inconsistency of floods entering hydrological stations or downstream reservoirs under the influence of reservoir regulation, and combines the maximum likelihood method and the quantile map fitting method to estimate time-varying distribution parameters; in view of the nonlinear, asymmetric and high-dimensional dependent structure of the flood area composition in the basin under the influence of reservoir regulation, the flood area composition no longer meets the same frequency assumption, and by analogy with the intricate vine tree structure, the vine structure Copula joint distribution function is used to construct a basin flood area composition model; based on the water balance constraint, the parameters of the vine structure Copula joint distribution function are calibrated in combination with the sequential estimation method and the particle swarm optimization algorithm, and the variable frequency flood area composition in the basin is deduced.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, characterized by: include: Obtain the source data to be derived; Inputting the source data to be derived into the calibrated basin flood area composition model to derive the basin variable frequency flood area composition; wherein the construction of the basin flood area composition model includes: Considering the impact of reservoir regulation on flood inconsistency, the reservoir index is used to characterize the reservoir storage capacity. A generalized additive model with the reservoir index as a covariate is used to characterize the inconsistency of flood inflows at hydrological stations or downstream reservoirs under the influence of reservoir regulation. The maximum likelihood method and quantile plot fitting method are combined to estimate the time-varying distribution parameters. The vine structure Copula joint distribution function is used to construct the basin flood area composition model; The calibration of the flood zone component model of the basin includes: Based on the water balance constraint, the parameters of the vine structure Copula joint distribution function are calibrated by combining the sequential estimation method and the particle swarm optimization algorithm, and the calibrated basin flood area composition model is output; wherein, based on the water balance constraint, the parameters of the vine structure Copula joint distribution function are calibrated by combining the sequential estimation method and the particle swarm optimization algorithm, including: Step 1: Input the probability density function of the time-varying Pearson III distribution of the variable to be derived; Step 2: Calculate the Kendall rank correlation coefficient γ of each node in the first-level tree structure to identify the correlation between node pairs, and determine the tree structure and its root node by integrating the categories of the vine structure and the correlation between the node pairs; Step 3: On each edge of the first-level tree, the maximum likelihood method is used to determine the parameter values of each binary Copula function, and the optimal binary Copula function is selected based on the Akaike Information Criterion. Step 4: Based on the optimal binary Copula function determined in step 3, sample the dependency structure of the first-level tree to generate node values corresponding to the second-level tree. Iterate steps 2-3 to construct the dependency structure of the second-level tree and select the optimal Copula function. Step 5, iteratively execute steps 2 to 4, construct each tree structure in the vine structure and its corresponding Copula function layer by layer, and obtain a complete Vine-Copula function model; In step 6, considering the water balance constraint, the particle swarm optimization algorithm is used to optimize the solution with the maximization of the joint probability density function as the objective function.

2. A device for calculating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, characterized in that: include: The first main module is used to obtain the source data to be derived; The second main module is used to input the source data to be derived into the calibrated basin flood area composition model and output the basin flood variable frequency area composition; wherein the construction of the basin flood area composition model includes: Considering the impact of reservoir regulation on flood inconsistency, the reservoir index is used to characterize the reservoir storage capacity. A generalized additive model with the reservoir index as a covariate is used to characterize the inconsistency of flood inflows at hydrological stations or downstream reservoirs under the influence of reservoir regulation. The maximum likelihood method and quantile plot fitting method are combined to estimate the time-varying distribution parameters. The vine structure Copula joint distribution function is used to construct the basin flood area composition model; The third main module is used to calibrate the parameters of the vine structure Copula joint distribution function based on water balance constraints, combining the sequential estimation method and the particle swarm optimization algorithm, and output the calibrated basin flood area composition model; it is also used to execute the following instructions: Step 1: Input the probability density function of the time-varying Pearson III distribution of the variable to be derived; Step 2: Calculate the Kendall rank correlation coefficient γ of each node in the first-level tree structure to identify the correlation between node pairs, and determine the tree structure and its root node by integrating the categories of the vine structure and the correlation between the node pairs; Step 3: On each edge of the first-level tree, the maximum likelihood method is used to determine the parameter values of each binary Copula function, and the optimal binary Copula function is selected based on the Akaike Information Criterion. Step 4: Based on the optimal binary Copula function determined in step 3, sample the dependency structure of the first-level tree to generate node values corresponding to the second-level tree. Iterate steps 2-3 to construct the dependency structure of the second-level tree and select the optimal Copula function. Step 5, iteratively execute steps 2 to 4, construct each tree structure in the vine structure and its corresponding Copula function layer by layer, and obtain a complete Vine-Copula function model; In step 6, considering the water balance constraint, the particle swarm optimization algorithm is used to optimize the solution with the maximization of the joint probability density function as the objective function.

3. A device for calculating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation, characterized in that: It includes a processor and a memory, the processor is coupled to the memory; the memory is used to store programs; the processor is used to execute the programs in the memory, so that the device for deducing the composition of variable-frequency flood areas in the basin under the influence of reservoir regulation executes the method as claimed in claim 1.

4. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the method of claim 1 .

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