Method and device for deducing drainage basin variable-frequency flood region composition under influence of reservoir regulation

Abstract

The invention discloses a method and a device for deducing basin variable-frequency flood area composition under influence of reservoir regulation, and the method comprises the steps: considering the influence of reservoir regulation on flood non-consistency, and representing the regulation and storage capacity of a reservoir through a reservoir index; a generalized additive model with a reservoir index as a covariable is adopted to represent inconsistency of hydrometric stations or downstream reservoir inflow flood under the influence of reservoir adjustment, and time-varying distribution parameter estimation is carried out in combination with a maximum likelihood method and a quantile map line adaptation method; aiming at a nonlinear, asymmetric and high-dimensional dependent structure of the basin flood area composition under the influence of reservoir regulation, constructing a basin flood area composition model by adopting a vine structure Copula joint distribution function, and analyzing frequency conversion characteristics of the basin flood area composition under the influence of reservoir regulation; on the basis of water balance constraints, a sequential estimation method and a particle swarm optimization algorithm are combined to calibrate the Copula joint distribution function parameters of the rattan structure, the composition of the variable-frequency flood area of the drainage basin is deduced, and the adaptability of a composition model of the flood area of the drainage basin in a changing environment is improved.

Description

Technical Field

[0001] The 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 reservoir with a large regulation and storage function upstream of the design section or the design reservoir has a flood control task 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 formulated. The "Specifications for Calculation of Design Floods for Water Conservancy and Hydropower Engineering" (SL44-2006) (hereinafter referred to as the "Specifications") recommends the use of the typical flood composition method or the same-frequency flood composition method to deduce the regional composition of the design flood. The "Specifications" stipulate that the design flood process lines of each sub-district in these two flood regional composition methods use the same flood process line as a typical one. The typical flood process line method selects representative major floods as typical from measured data, and amplifies the typical flood process lines of each district according to the multiple ratio of the design section flood peak or flood volume. The same-frequency flood composition method specifies that a certain sub-district will have a flood with the same frequency as the design section, and the corresponding flood will occur in the remaining sub-districts. The "Specifications" also recommends that for areas with a long data series, the regional flood frequency combination method can be used to deduce the design flood affected by the regulation and storage of upstream projects. 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] These three methods are all based on the premise of "restore first and then restore" to deduce the composition of the design flood area. The "restore" here aims to eliminate the non-stationarity of the original sequence by stabilizing the data. With the completion and commissioning of large-scale water conservancy projects in the basin, if the composition of the design flood area continues to be deduced based on the "same frequency" assumption, the composition of the flood area will be over-stabilized, reducing the adaptability and generalization ability of this type of method to the changing environment. In addition, when the composition of the flood area in the basin is complex and there are many divisions, the regional flood frequency combination method has the problem of calculating the dimensionality disaster. 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. It is necessary to carry out the deduction of the variable frequency flood area composition in the basin under the influence of reservoir regulation, aiming to overcome the bottleneck of dimensionality disaster in the calculation of the flood area composition in the basin, which can improve the generalization ability of the deduction method of the design flood area composition. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, 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 a changing environment.

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

[0006] Obtain source data to be derived;

[0007] The source data to be derived is input into the calibrated basin flood area composition model to deduce 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. The 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 map fitting method are combined to estimate the time-varying distribution parameters.

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

[0010] As a further technical solution, the calibration of the basin flood area composition model 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 determined 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, calculating the Kendall rank correlation coefficient γ of each node in the first layer tree structure to identify the correlation between node pairs, and determining the tree structure and its root node by integrating the category of the vine structure and the correlation between the node pairs;

[0015] Step 3, on each edge of the determined first-layer 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-layer tree, generate node values ​​corresponding to the second-layer tree, iterate step 2-step 3, construct the dependency structure of the second-layer 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] 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 calculating the composition of variable-frequency flood areas 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. The 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 map fitting method are combined to estimate the time-varying distribution parameters.

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

[0024] As a further technical solution, a third main module is also included, which is used to calibrate the parameters of the vine structure Copula joint distribution function based on water balance constraints in combination 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 used 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, calculating the Kendall rank correlation coefficient γ of each node in the first layer tree structure to identify the correlation between node pairs, and determining the tree structure and its root node by integrating the category of the vine structure and the correlation between the node pairs;

[0028] Step 3, on each edge of the determined first-layer 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-layer tree, generate node values ​​corresponding to the second-layer tree, iterate step 2-step 3, construct the dependency structure of the second-layer 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 maximum joint probability density function as the objective function.

[0032] According to one aspect of the present invention specification, 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 specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable 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 structure 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; combining the sequential estimation method and the particle swarm optimization algorithm to determine the parameters of the vine structure Copula joint distribution function, overcome 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 composition of the most likely flood areas, and improve 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 is 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 schematic flow chart of a method for deriving 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 the construction process of 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 schematic 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 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 component 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] The present invention considers the inconsistency of flood regulation by reservoirs, applies reservoir index to characterize reservoir regulation capacity, adopts generalized additive model with reservoir index as covariate to characterize the inconsistency of flood inflow of hydrological stations or downstream reservoirs under the influence of reservoir regulation, and combines maximum likelihood method and quantile map fitting method to estimate time-varying distribution parameters; for the nonlinear, asymmetric and high-dimensional dependent structure of flood area composition in the basin under the influence of reservoir regulation, the flood area composition no longer satisfies the same frequency assumption, and analogizes the intricate vine tree structure, and adopts vine structure Copula joint distribution function to construct basin flood area composition model, and analyzes the variable frequency characteristics of basin flood area composition under the influence of reservoir regulation; based on water balance constraints, sequential estimation method and particle swarm optimization algorithm are combined to determine the parameters of vine structure Copula joint distribution function, and the basin variable frequency flood area composition is deduced, so as to improve the adaptability of basin flood area composition model under changing environment. The present invention not only characterizes the variable frequency characteristics of basin flood area composition, but also improves the generalization ability of design flood area composition deduction method.

[0045] The terms "including" and "having" and any variations thereof in the specification 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 comprising 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 independent entities. That is, these functional entities may be implemented in software, or 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 exemplary and do not necessarily include all the contents and operations / steps, nor do they necessarily have to be executed in the order described. For example, some operations / steps may be further decomposed, while some operations / steps may be combined or partially combined, so the actual execution order may change according to 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 in the present invention, all other embodiments obtained by ordinary technicians in this field without 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 the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered 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 deriving the composition of flood areas with variable frequency in a watershed under the influence of reservoir regulation provided by an exemplary embodiment is shown, the method comprising:

[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 the hydrological station or downstream reservoir under the influence of reservoir regulation, and combining the maximum likelihood method and the quantile map fitting method to estimate the 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 output the calibrated basin flood area composition model.

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

[0053] The construction process is implemented 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 provided by an exemplary embodiment is shown. 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 a changing environment.

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

[0056] In step 2.1, a generalized additive model with reservoir index as covariate is used to characterize the inconsistency of flood inflows at 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 are the location, scale, and shape parameters of the time-varying Pearson III distribution, respectively. Here, t represents the time variable. Considering α t It is relatively sensitive and is often assumed to be a constant α that 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 studied section (km 2 ) and the average annual runoff volume (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 maximum likelihood method and the quantile plot fitting method are combined to estimate the time-varying Pearson III distribution parameters. 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 means that the flood sample points obtained by the proposed distribution model have passed 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, the vine structure Copula joint distribution function is used to construct the basin flood area composition model. Vine structure Copula is a high-dimensional probability distribution modeling method based on quantiles. By constructing a tree structure, each node can select a different two-variable Copula function, so as to more accurately capture the complex dependencies between different variables.

[0067] For a q-dimensional variable x 1 , x 2 , …, x q , and their marginal distribution functions are F 1 (x 1 ), F 2 (x 2 ), …, F q (x q ), the vine structure consists of q-1 layers of tree structures, denoted by 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] In the formula: 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 tree layer T p The vine structures cannot be independent of each other, but there should be an intersection node to ensure that the dependencies between the layers 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 vine structure Copula probability density function 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 u is the set of conditional variable numbers of edge u; φ(u) and is the set of relation variable numbers for edge u; For 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 sSerial 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 sequential estimation method and particle swarm optimization algorithm are combined to determine the parameters of the vine structure Copula joint distribution function, and the composition of the variable frequency flood area in the basin is deduced. 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 are the same order logarithms and different order logarithms between two random variables respectively; n is the number of samples of the random variable. The value of |γ| lies in the range of 0 to 1 and is used to determine the strength of the correlation between 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 best binary Copula function.

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

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

[0081] ⑥ Considering the water balance constraint, maximizing the joint probability density function is taken as the objective function, and the particle swarm optimization algorithm is used for optimization solution:

[0082]

[0083] Where: objective function f C (x,y 1 ,y 2 ,y 3,…,y q-1 ) is the upstream water flow X and the water flow in each interval Y e (e=1,2,…,q-1) joint probability density function value; c(·) is the vine copula joint probability density function; u x and v 1 ,v 2 ,…,v q-1 is the cumulative distribution function value of the upstream water volume and the water volume in each interval, [u x ,v 1 ,v 2 ,…,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 is the upstream water volume X and the water volume in each interval Y e The marginal probability density function of z P is the flood volume of the downstream control section corresponding to the recurrence period P. 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 device for deducing the variable-frequency flood area composition of a watershed under the influence of reservoir regulation provided in the present application, the device comprising: a first main module 102, used to obtain source data to be deduced; a second main module 104, used to input the source data to be deduced into a calibrated watershed flood area composition model, and output the watershed flood area composition.

[0085] Please refer to Figure 6 , which shows a block diagram of the composition model of the flood area in the basin under the influence of reservoir regulation provided by the present 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 hydrological stations or downstream reservoirs under the influence of reservoir regulation, and estimates time-varying distribution parameters by combining the maximum likelihood method and the quantile map fitting method;

[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 aims 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, which reduces the adaptability and generalization ability of such methods to the changing environment. 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 status quo, and adopts Figure 6 Several 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 the over-stabilization processing and dimensionality disaster of the method for deducing the composition of flood areas with the same frequency and the method for deducing the composition of the most likely flood areas, 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 used to implement the methods in the above method embodiments as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set, and the principle is basically the same as the principle of the above device embodiments provided by the present invention. As long as the technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above device embodiments, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, they can improve the equipment in the above device embodiments to obtain the corresponding device class embodiments, which are used to implement the methods in other method class embodiments. For example:

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

[0091] The third main module, based on water balance constraints, combines the sequential estimation method and the 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 a changing environment.

[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 composition of the flood area in the watershed under the influence of reservoir regulation. The vine structure Copula joint distribution function aims to bring the non-stationary characteristics caused by reservoir regulation into the calculation of the flood area composition 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, a device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation is provided in the embodiments of the present invention, and the calibration of the watershed flood area composition model also includes: in view of the inconsistency of floods entering hydrological stations or downstream reservoirs under the influence of reservoir regulation, a generalized additive model with the reservoir index as a covariate is used to characterize it; in view of the nonlinear, asymmetric and high-dimensional dependent structure of the flood area composition in the watershed under the influence of reservoir regulation, a vine structure Copula joint distribution function is used 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, an apparatus for calculating the composition of flood areas in a watershed with variable frequency under the influence of reservoir regulation provided by an embodiment of the present invention is introduced. Figure 7 , Figure 7 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. Among them, the device 300 for calculating the composition of a variable-frequency flood area in a watershed under the influence of reservoir regulation is used to implement Figure 1 The function of executing the 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 In the example of FIG. 3 , 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 a non-volatile random access memory (NVRAM). The memory 304 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, wherein the operation instructions may include various operation instructions for implementing various operations.

[0097] The processor 303 controls the operation of the execution device. In a specific application, the various components of the execution device are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are referred to as bus systems in the figure.

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

[0099] The receiver 301 can be used to receive input digital or character information and generate signal input related to the relevant settings and function control of the execution device. The transmitter 302 can be used to output digital or character information through the first interface; the transmitter 302 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the 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 used 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] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 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 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.

[0104] 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.

[0105] 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 The steps for the functions 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 the hydrological station or the downstream reservoir under the influence of reservoir regulation, and combines the maximum likelihood method and the quantile map fitting method to estimate the 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 satisfies 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 vine structure Copula joint distribution function parameters are determined in combination with the sequential estimation method and the particle swarm optimization algorithm, and the variable frequency flood area composition of 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 in that: include: Obtain source data to be derived; Input the source data to be derived into the calibrated basin flood area composition model to deduce 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. The 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 map 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.

2. The method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation according to claim 1 is characterized in that: Calibration of the flood zone component model of the basin includes: 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.

3. The method for estimating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation according to claim 2 is characterized in that: Based on the water balance constraint, the parameters of the vine structure Copula joint distribution function are determined 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, calculating the Kendall rank correlation coefficient γ of each node in the first layer tree structure to identify the correlation between node pairs, and determining the tree structure and its root node by integrating the category of the vine structure and the correlation between the node pairs; Step 3, on each edge of the determined first-layer tree, the maximum likelihood method is used to determine the parameter values ​​of each binary Copula function, and the best 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-layer tree, generate node values ​​corresponding to the second-layer tree, iterate step 2-step 3, construct the dependency structure of the second-layer 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; 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.

4. 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. The 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 map 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.

5. The device for calculating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation according to claim 4, characterized in that: 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, combining the sequential estimation method and the particle swarm optimization algorithm, and output the calibrated basin flood area composition model.

6. The device for calculating the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation according to claim 5, characterized in that: The third main module 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, calculating the Kendall rank correlation coefficient γ of each node in the first layer tree structure to identify the correlation between node pairs, and determining the tree structure and its root node by integrating the category of the vine structure and the correlation between the node pairs; Step 3, on each edge of the determined first-layer tree, the maximum likelihood method is used to determine the parameter values ​​of each binary Copula function, and the best 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-layer tree, generate node values ​​corresponding to the second-layer tree, iterate step 2-step 3, construct the dependency structure of the second-layer 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; 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.

7. 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 described in any one of claims 1 to 3.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the method of any one of claims 1 to 3.

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