Determination method of rain peak coefficient of Chicago rain pattern design under different rainfall levels

Abstract

The present application discloses a method for determining the design rain peak coefficient of Chicago rain patterns under different rainfall levels. In the method disclosed in the present application, after determining the correlation coefficient between the rainfall and the rain peak coefficient, the probability density of the rain peak coefficient under different rainfall levels is determined based on the correlation coefficient and the copula function, and the design rain peak coefficient under different rainfall levels is determined based on the probability distribution. Thus, the influence of rainfall on the rain peak coefficient can be eliminated, making the calculation of the rain peak coefficient more scientific and reasonable.

Description

Technical Field

[0001] The present application relates to the technical field of hydrological and meteorological data processing, and in particular to a method for determining a design rain peak coefficient of a Chicago rain pattern under conditions of different rainfall levels. Background Art

[0002] With the rapid development of cities and the change of global climate, urban flooding has become a major natural disaster affecting the safety and development of cities around the world. The assessment and response to urban flooding risks have become a hot topic today. The birth and promotion of various one- and two-dimensional hydrological and hydrodynamic models have laid a solid foundation for the assessment and response to urban flooding risks. Designing a storm process line is a necessary hydrological input condition for the use of such hydrological and hydrodynamic models. The choice of designing a storm process line will directly affect the simulation results of the water level, flow, and water depth of the drainage system, thereby affecting the construction scale and investment of drainage and flood prevention facilities. Rain type is the key factor in determining the design of a storm process line. The Chicago rain type can be directly derived through the rainfall intensity-duration-frequency curve, and after determining the rainfall process, it is applicable to the calculation of each pipeline. The method is simple and easy to obtain. It is precisely because of these advantages that the Chicago rain type is the most widely used.

[0003] However, in the process of determining the design rain peak coefficient, people often conduct a unified analysis of all the collected rainstorm samples without distinguishing the rainfall amount of each rainfall, which leads to the need to improve the accuracy of determining the design rain peak coefficient. Summary of the invention

[0004] In view of this, the present application provides a method for determining the design rain peak coefficient of the Chicago rain pattern under different rainfall level conditions, which can more accurately determine the design rain peak coefficient.

[0005] In a first aspect, the present application provides a method for determining the rain peak coefficient of a Chicago rain pattern design under different rainfall levels, comprising:

[0006] Obtain the rainfall and rain peak coefficient corresponding to the target rainfall data sequence;

[0007] Determine the correlation coefficient between the rainfall and the rainfall peak coefficient;

[0008] Based on the correlation coefficient, the probability density of the rain peak coefficient under different rainfall levels is determined according to the copula function;

[0009] The designed rain peak coefficient under different rainfall level conditions is determined according to the probability distribution.

[0010] Optionally, the target rainfall data sequence is obtained by adopting an annual maximum value method or an annual diversity method.

[0011] Optionally, the target rainfall data sequence is extremely short-duration heavy rainfall.

[0012] Optionally, the correlation coefficient between the rainfall and the rainfall peak coefficient is determined by a Spearman correlation analysis method.

[0013] Optionally, the probability density of the rain peak coefficient under the different rainfall levels is determined by the following formula:

[0014]

[0015] Where r represents the rain peak coefficient, R represents the upper bound of the rain peak coefficient, h represents rainfall, H1 and H2 represent the endpoints of the design rainfall interval, f(·,·) is the derivative of the joint probability distribution function F(·,·), F(·) represents the marginal probability distribution function, and f(R) represents the probability density of the rain peak coefficient under the condition of rainfall level H1~H2.

[0016] Optionally, the expected value method is used to determine the design rain peak coefficient under different rainfall level conditions.

[0017] Optionally, the designed rain peak coefficient is determined by the following formula:

[0018]

[0019] In the formula, r d To design the rain peak coefficient, f(R) represents the probability density of the rain peak coefficient under the conditions of rainfall levels H1~H2, and R represents the upper limit of the rain peak coefficient.

[0020] In a second aspect, the present application provides a method for determining the peak coefficient of the Chicago rain pattern design under different rainfall levels, including:

[0021] An acquisition module is used to obtain the rainfall and rain peak coefficient corresponding to the target rainfall data sequence;

[0022] A first determination module is used to determine the correlation coefficient between the rainfall and the rain peak coefficient;

[0023] A second determination module is used to determine the probability density of the rain peak coefficient under different rainfall levels according to the copula function based on the correlation coefficient;

[0024] The third determination module is used to determine the design rain peak coefficient under different rainfall level conditions according to the probability distribution.

[0025] In a third aspect, the present application provides a computer-readable storage medium, characterized in that it includes a program, and when the program is run on a computer, the computer executes the method as described above.

[0026] In a fourth aspect, the present application provides an execution device, comprising a processor and a memory, wherein the processor is coupled to the memory;

[0027] The memory is used to store programs;

[0028] The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0029] In the method disclosed in the present application, after determining the correlation coefficient between the rainfall and the rain peak coefficient, based on the correlation coefficient, the probability density of the rain peak coefficient under different rainfall levels is determined according to the copula function, and the design rain peak coefficient under different rainfall levels is determined according to the probability distribution. Thus, the influence of rainfall on the rain peak coefficient can be eliminated, making the calculation of the rain peak coefficient more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0031] Figure 1 An operational flow chart of a method for determining a Chicago rain pattern design rain peak coefficient under different rainfall level conditions provided by an exemplary embodiment is shown.

[0032] Figure 2 A block diagram of determining the Chicago rain pattern design rain peak coefficient under different rainfall level conditions provided by an exemplary embodiment is shown.

[0033] Figure 3 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0035] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between units can be electrical or other similar forms, which are not limited in this application. In addition, the units or sub-units described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed in multiple circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

[0036] Please refer to Figure 1 ,in Figure 1 A flow chart of a method for determining a Chicago rain pattern design rain peak coefficient under different rainfall levels provided by an exemplary embodiment is shown. This method is implemented through steps 102-108.

[0037] In step 102, the rainfall amount and rain peak coefficient corresponding to the target rainfall data sequence are obtained.

[0038] The target rainfall data series may be data related to extremely short-duration heavy rainfall, for example, 2 hours.

[0039] As a typical example, the target rainfall data sequence is obtained by adopting the annual maximum value method or the annual diversity method.

[0040] Specifically, we select the largest rainfall event or multiple events of 5, 10, 15…120 minutes each year, and then use 120 minutes as the time step to slide through these events, and find the 120-minute rainfall data sequence with the largest total rainfall as the sample. If the rainfall event duration is less than 2 hours, the subsequent data will be padded with 0.

[0041] In step 104, the correlation coefficient between the rainfall amount and the rain peak coefficient is determined.

[0042] The correlation coefficient may be determined by the Spearman correlation analysis method, or other methods such as the Pearson correlation analysis method, etc., but is not limited thereto.

[0043] Since the Spearman correlation analysis method is well known to those skilled in the art, it will not be described in detail here.

[0044] In step 106, based on the correlation coefficient, the probability density of the rain peak coefficient under different rainfall levels is determined according to the copula function.

[0045] Here, the Copula function converts the random vectors X1, X2, ..., X N The joint distribution function F(x1,x2,…x n ) and their respective edge functions F1, F2, ..., F n The connection function connected, that is, the function C(u1,u2,...,u n ), that is, satisfying the following:

[0046] F(x1,x2,…x n )=C(F1(x1),F2(x2),…,F n (x n )) (1).

[0047] The implementation of the Copula function can be Frank Copula, Clayton Copula, Gumbel Copula, etc.

[0048] Combined with the general technical knowledge of copula function calculation, we can know that:

[0049] The probability density of the rain peak coefficient under different rainfall levels is determined according to the copula function, including:

[0050] Step 106A1: Use parameter estimation or non-parameter estimation to deduce the marginal probability distribution of rainfall and rainfall peak coefficient in S2.

[0051] Step 106A2: derive the joint probability distribution of rainfall and rain peak coefficient using the copula function, and calculate the probability distribution of rain peak coefficient under different rainfall levels.

[0052] The basic steps for calculating the peak coefficient of Chicago rainfall type design under different rainfall levels are:

[0053]

[0054] Taking the derivative of both sides of formula (2) with respect to R, we get:

[0055]

[0056] In formulas (2) and (3), r represents the rain peak coefficient, R represents the upper bound of the rain peak coefficient, h represents rainfall, H1 and H2 represent the endpoints of the design rainfall interval, f(·,·) is the derivative of the joint probability distribution function F(·,·), F(·) represents the marginal probability distribution function, and f(R) represents the probability density of the rain peak coefficient under the condition of rainfall level H1~H2.

[0057] In step 108, the designed rain peak coefficients under different rainfall levels are determined according to the probability distribution.

[0058] The expected value method can be used to determine the design rain peak coefficient under different rainfall levels.

[0059] The design rain peak coefficient is determined by the following formula,

[0060]

[0061] In the formula, r d To design the rain peak coefficient, f(R) represents the probability density of the rain peak coefficient under the conditions of rainfall levels H1~H2, and R represents the upper limit of the rain peak coefficient.

[0062] The calculation results of the design rain peak coefficient are summarized by using formula (4) and some measured data.

[0063] Table 1:

[0064]

[0065] It can be seen from Table 1 that the peak coefficients under different rainfall levels are significantly different. The peak coefficient currently used in a certain city is 0.35, which is safer for low-return period rainfall, but not conducive to engineering safety for high-return period rainfall.

[0066] Please refer to Figure 2 , which shows a block diagram of the device for determining the Chicago rain pattern design rain peak coefficient under different rainfall levels provided by the present application. The device 200 includes:

[0067] An acquisition module 202 is used to acquire the rainfall and rain peak coefficient corresponding to the target rainfall data sequence;

[0068] A first determination module 204 is used to determine the correlation coefficient between the rainfall and the rain peak coefficient;

[0069] A second determination module 206 is used to determine the probability density of the rain peak coefficient under different rainfall levels according to the copula function based on the correlation coefficient;

[0070] The third determination module 208 is used to determine the design rain peak coefficient under different rainfall level conditions according to the probability distribution.

[0071] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0072] Next, an execution device provided by an embodiment of the present application is introduced. Figure 3 , Figure 3 This is a schematic diagram of a structure of an execution device provided in an embodiment of the present application. The execution device 300 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 functions of the execution device in the corresponding embodiment. Specifically, the execution device 300 includes: a receiver 301, a transmitter 302, a processor 303 and a memory 304 (wherein the number of the processor 303 in the execution device 300 can be one or more, Figure 3 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.

[0073] 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.

[0074] 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.

[0075] The method disclosed in the above embodiment of the present application 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 or software instructions in the processor 303. 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 various methods, steps and logic block diagrams disclosed in the embodiments of the present application. 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 application can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. 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.

[0076] 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.

[0077] In the embodiment of the present application, 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.

[0078] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A method for determining the peak coefficient of Chicago rain pattern design under different rainfall levels, characterized in that: include: Obtain the rainfall and rain peak coefficient corresponding to the target rainfall data sequence; Determine the correlation coefficient between the rainfall and the rainfall peak coefficient; Based on the correlation coefficient, the probability density of the rain peak coefficient under different rainfall levels is determined according to the copula function; the basic steps are: Taking the derivative of both sides with respect to R, we get: In the formula, represents the rain peak coefficient, represents the upper bound of the rain peak coefficient, Indicates rainfall, , represents the end point of the design rainfall interval, is the joint probability distribution function The derivative of represents the marginal probability distribution function, Indicates the rainfall level Probability density of rain peak coefficient under the conditions; Determine the design rain peak coefficient under different rainfall levels according to the probability distribution; The expected value method is used to determine the design rain peak coefficient under different rainfall levels. The design rain peak coefficient is determined by the following formula: ； In the formula, is the design rain peak factor, Indicates the rainfall level The probability density of rain peak coefficient under the condition, Indicates the upper bound of the rain peak coefficient.

2. The method according to claim 1, characterized in that The target rainfall data sequence is obtained by adopting the annual maximum value method or the annual diversity method.

3. The method according to claim 1, characterized in that The target rainfall data series is extremely short-duration heavy rainfall.

4. The method according to claim 1, characterized in that The correlation coefficient between the rainfall and the rainfall peak coefficient is determined by the Spearman correlation analysis method.

5. A device for determining the peak coefficient of Chicago rain pattern design under different rainfall levels, characterized in that: include: An acquisition module is used to obtain the rainfall and rain peak coefficient corresponding to the target rainfall data sequence; A first determination module is used to determine the correlation coefficient between the rainfall and the rain peak coefficient; A second determination module is used to determine the probability density of the rain peak coefficient under different rainfall levels according to the copula function based on the correlation coefficient; A third determination module is used to determine the design rain peak coefficient under different rainfall level conditions according to the probability distribution; The device for determining the Chicago rain pattern design rain peak coefficient under different rainfall level conditions is used to execute the method described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that: The invention comprises a program, which, when being executed on a computer, causes the computer to execute the method according to any one of claims 1 to 4.

7. An execution device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The memory is used to store programs; The processor is configured to execute the program in the memory so that the execution device executes the method according to any one of claims 1 to 4.

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