CN107832935A - Method and device for determining design value of hydrological variable - Google Patents

Abstract

The invention provides a method for determining a hydrologic variable design value, which comprises the following steps: obtaining quantiles corresponding to sample points after time-varying parameter estimation is carried out on each variable parameter Pearson III type curve model to be detected in at least two variable parameter Pearson III type curve models to be detected; determining one optimal variable parameter Pearson III type curve model from at least two variable parameter Pearson III type curve models to be detected after time-varying parameter estimation according to quantiles corresponding to each sample point of each variable parameter Pearson III type curve model to be detected after time-varying parameter estimation; and determining a hydrologic variable design value at a preset moment under the condition of a preset hydrologic frequency according to the optimal variable parameter Pearson III type curve model. The embodiment of the invention can solve the hydrologic variable design value of a certain preset hydrologic frequency at different moments, and is suitable for hydrologic frequency calculation of a non-uniform hydrologic sequence in a changing environment.

Description

Method and device for determining design value of hydrological variable

technical field

The invention relates to the field of hydrology, in particular to a method and a device for determining design values of hydrological variables in a watershed.

Background technique

The Pearson type III curve is an asymmetric unimodal positively skewed curve with a finite end and an infinite end, which is widely used in the field of engineering hydrological calculations in my country. Its probability density function is:

In the formula: Γ(α) is the gamma function of α, and α, β, r are three parameters. When the three parameters are determined, the density function is determined accordingly. Suppose the mean of the population of hydrological random variables is The coefficient of variation is C _v , and the coefficient of skewness is C _S , so the three parameters in the probability density function of the Pearson III curve, α, β, r, have the following relationship with the three statistical parameters of the hydrological random variable population:

It can be seen that when the mean The coefficient of variation C _v , coefficient of skewness C _S and other three parameters are determined, then the three parameters α, β, r in the probability density function of the Pearson III curve can be determined, so that the corresponding The design hydrological value x _p of a certain design frequency P.

The defect of the above-mentioned method is that the design hydrological value obtained by the above-mentioned method is a constant value, which does not take into account the difference of the design hydrological value due to the change of time and environment, that is, when the climate change and the underlying surface change are relatively For watersheds that are heavily influenced by human activities, the accuracy of the design hydrological values obtained through the above methods is low, and there is a large gap between them and the actual situation.

Contents of the invention

The technical problem to be solved by the embodiments of the present invention is to provide a method and device for determining the design value of hydrological variables, so as to solve the corresponding hydrological design values at different times.

In order to solve the above technical problems, the method for determining the design value of hydrological variables provided by the embodiment of the present invention includes:

Obtaining the quantile corresponding to each sample point of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected after performing time-varying parameter estimation;

According to the quantiles corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected, at least two variable parameter Pearson type III curves to be detected after the time-varying parameter estimation are performed One of the optimal Pearson type III curve models is determined in the model;

According to the optimal variable parameter Pearson type III curve model, the design value of the hydrological variable at the predetermined moment is determined under the predetermined hydrological frequency condition of the watershed.

Preferably, the step of obtaining the quantile corresponding to each sample point of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected after performing time-varying parameter estimation includes :

Establishing at least two variable parameter Pearson type III curve models to be detected, and using the maximum likelihood parameter estimation method to estimate the time-varying parameters of each variable parameter Pearson type III curve model to be detected;

Obtain the quantile corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be tested.

Preferably, according to the quantiles corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson curve model to be detected, at least two variable parameter Pearson curve models to be detected after the time-varying parameter estimation are performed. The steps for determining one of the optimal variable parameter Pearson type III curve models in the type III curve model include:

According to the quantiles corresponding to each sample point after the time-varying parameter estimation of each Pearson type III curve model with variable parameters to be detected, the cumulative standard normal distribution corresponding to each Pearson type III curve model with variable parameters to be detected is obtained The inverse function value of the distribution function at each quantile;

According to the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model to be detected at each quantile, from at least two variable parameter Pearson curves to be detected after the time-varying parameter estimation At least one variable parameter Pearson type III curve model is identified in the type III curve model;

Perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variable parameter Pearson type III curve models from at least one available variable parameter Pearson type III curve model according to the fitting test results .

Preferably, according to the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson III curve model at each quantile, from at least two variable parameters to be detected after the time-varying parameter estimation The step of determining at least one variable parameter Pearson type III curve model in the parameter Pearson type III curve model includes:

Judging whether the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model to be detected at each quantile satisfies that the mean value is within the first predetermined interval, the variance is within the second predetermined interval and The skewness coefficient is located in the third predetermined interval;

If it is satisfied, the variable parameter Pearson type III curve model to be detected is determined as an available variable parameter Pearson type III curve model.

Preferably, a fitting test is performed on each available variable parameter Pearson type III curve model, and one of the optimal variable parameter Pearson III curve models is determined from at least one available variable parameter Pearson type III curve model according to the fitting test result The steps of the type curve model include:

Acquiring the number of estimated parameters included in the time-varying parameters of each Pearson Type III curve model with variable parameters, and the maximum value of the log likelihood function corresponding to the Pearson Type III curve model with variable parameters;

According to the number of estimated parameters contained in the time-varying parameters of each variable parameter Pearson type III curve model and the maximum value of the logarithmic likelihood function, obtain the corresponding value of each variable parameter Pearson type III curve model the difference;

One of the available variable parameter Pearson type III curve models with the smallest difference value among at least two available variable parameter Pearson type III curve models is determined as the optimal variable parameter Pearson type III curve model.

Preferably, according to the number of estimated parameters contained in the time-varying parameters of each variable parameter Pearson type III curve model and the maximum value of the logarithmic likelihood function, each variable parameter Pearson type III The steps of the difference corresponding to the curve model include:

By formula:

RE=2*NUM-2*LLF

The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters.

Preferably, according to the optimal variable parameter Pearson type III curve model, the step of determining the hydrological variable design value of the watershed at a predetermined time under predetermined hydrological frequency conditions includes:

By formula:

Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time.

According to another aspect of the embodiments of the present invention, the embodiments of the present invention also provide a device for determining the design value of hydrological variables, including:

The acquisition module is used to obtain the quantile corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected;

The first determination module is used to obtain at least two samples to be detected after time-varying parameter estimation according to the quantiles corresponding to each sample point after each variable-parameter Pearson curve model to be detected is estimated. One of the optimal variable parameter Pearson type Ⅲ curve models is determined in the variable parameter Pearson type Ⅲ curve model;

The second determining module is used to determine the design value of the hydrological variable at a predetermined moment in the watershed under the predetermined hydrological frequency condition according to the optimal variable parameter Pearson type III curve model.

Preferably, the acquisition module includes:

Establishing a unit for establishing at least two Pearson type III curve models with variable parameters to be detected, and estimating the time-varying parameters of each Pearson type III curve model with variable parameters to be detected by using the maximum likelihood parameter estimation method;

The first obtaining unit is used to obtain quantiles corresponding to each sample point after time-varying parameter estimation for each variable-parameter Pearson type III curve model to be detected.

Preferably, the first determination module includes:

The second acquisition unit is used to obtain each variable parameter Pearson type III curve model to be detected according to the quantile corresponding to each sample point after time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected The inverse function value of the corresponding standard normal distribution cumulative distribution function at each quantile;

The second determination unit is used to obtain the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model at each quantile from at least two values after the time-varying parameter estimation. Determine at least one available Pearson type III curve model among the variable parameter Pearson type III curve models to be tested;

The third determining unit is used to perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variables from at least one available variable parameter Pearson type III curve model according to the fitting test result Parametric Pearson type III curve model.

Preferably, the second determination unit includes:

The judging subunit is used to judge whether the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson III curve model at each quantile satisfies that the mean value is within the first predetermined interval and the variance is within the first predetermined interval. within the second predetermined interval and the skewness coefficient is located within the third predetermined interval;

The first determining subunit is configured to determine the variable parameter Pearson type III curve model to be detected as an available variable parameter Pearson type III curve model if it is satisfied.

Preferably, the third determination unit includes:

The first obtaining subunit is used to obtain the number of estimated parameters contained in the time-varying parameters of each Pearson type III curve model with variable parameters, and the logarithm corresponding to the Pearson type III curve model with variable parameters. The maximum value of the natural function;

The second acquisition subunit is used to obtain each available variable parameter according to the number of estimated parameters contained in the time-varying parameters of each available variable parameter Pearson III curve model and the maximum value of the logarithmic likelihood function. The difference corresponding to the parameter Pearson type III curve model;

The second determining subunit is used to determine one of the available variable parameter Pearson type III curve models with the smallest difference among at least two available variable parameter Pearson type III curve models as the optimal variable parameter Pearson type III curve Model.

Preferably, the second acquisition subunit includes:

By formula:

RE=2*NUM-2*LLF

The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters.

Preferably, the second determination module includes:

By formula:

Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time.

According to another aspect of the embodiment of the present invention, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned determination of the design value of the hydrological variable of the watershed is realized steps in the method.

Compared with the prior art, the method and device for determining the design value of hydrological variables provided by the embodiments of the present invention have at least the following beneficial effects:

The above method in the embodiment of the present invention can solve the hydrological variable design value of a certain predetermined hydrological frequency at different times, and is suitable for calculating the hydrological frequency of non-uniform hydrological sequences under changing environments.

Description of drawings

Fig. 1 is a schematic flow chart of a method for determining a hydrological variable design value according to an embodiment of the present invention;

Fig. 2 is a schematic flow chart of step 1 in the embodiment of the present invention;

Fig. 3 is a schematic flow chart of step 2 in the embodiment of the present invention;

Fig. 4 is a schematic flow chart of step 23 in the embodiment of the present invention;

Fig. 5 is a schematic structural diagram of a device for determining a design value of a hydrological variable according to an embodiment of the present invention.

Detailed ways

In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments. In the following description, specific details, such as specific configurations and components, are provided only to assist in a comprehensive understanding of the embodiments of the present invention. Accordingly, those of ordinary skill in the art should recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

It should be understood that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present invention. Thus, appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

Having described preferred embodiments of embodiments of the present invention, additional changes and modifications to these embodiments can be made by those skilled in the art once the basic inventive concept is appreciated. Therefore, the appended claims are intended to be construed to cover the preferred embodiment and all changes and modifications which fall within the scope of the embodiments of the present invention.

Finally, it should also be noted that in this text, relational terms such as first and second etc. are only used to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations, any such actual relationship or order exists. Moreover, the terms "comprises", "comprises" or any other variation thereof are intended to cover a non-exclusive inclusion.

Embodiments of the present invention provide a method for determining design values of hydrological variables, including:

Step 1. Obtain the quantile corresponding to each sample point after the time-varying parameter estimation of each of the at least two variable parameter Pearson type III curve models to be detected.

Specifically, step 1 includes:

Step 11, establishing at least two variable parameter Pearson type III curve models to be detected, and using the maximum likelihood parameter estimation method to estimate the time-varying parameters of each variable parameter Pearson type III curve model to be detected;

Step 12, obtaining quantiles corresponding to each sample point after time-varying parameter estimation for each variable parameter Pearson type III curve model to be tested.

The expression of the probability density function of the variable parameter Pearson type III curve model to be detected established in step 11 is:

Among them, α(t), β(t) and r(t) are functions of time t, and Γ(α(t)) is the gamma function of α(t). In the embodiment of the present invention, α(t)=exp{k ₀ +k ₁ t+k ₂ t ² }, β(t)=exp{k ₃ +k ₄ t+k ₅ t ² }, r(t )=exp{k ₆ +k ₇ t+k ₈ t ² }. In the embodiment of the present invention, after the values of k ₀ , k ₁ , k ₂ , k ₃ , k ₄ , k ₅ , k ₆ , k ₇ and k ₈ are determined, the above α(t), β(t ) and the function expressions of r(t) are determined. In order to determine the specific values of the above k ₀ to k ₈ , in the embodiment of the present invention, the maximum likelihood parameter estimation method is used to estimate the values of the parameters k ₀ to k ₈ .

Specifically, the steps of estimating the values of parameters _k0 to _k8 by using the maximum likelihood parameter estimation method include:

Obtain multiple hydrological measured samples X ₁ , X ₂ , X ₃ , ..., X _n of the watershed within a predetermined time period, and the n hydrological measured samples obey the variable parameter Pearson type III distribution, and each hydrological measured sample corresponds to At a moment within a predetermined period of time, the likelihood functions of the hydrological measured samples X ₁ to X _n can be obtained:

Among them, n is the number of multiple hydrological measured samples, and x _t refers to the tth hydrological measured sample among the multiple hydrological measured sample data.

Taking the logarithm of the above formula, the log likelihood function is obtained as:

By performing maximum likelihood parameter estimation on the parameters of the above-mentioned logarithmic likelihood function, time-varying parameters and probability density function expressions of at least two Pearson type III curve models with variable parameters to be detected can be obtained.

Step 2, according to the quantiles corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson curve model to be detected, from at least two variable parameter Pearson curve models to be detected after the time-varying parameter estimation One of the optimal variable parameter Pearson type Ⅲ curve models was determined in the type Ⅲ curve model.

Preferably, step 2 includes:

Step 21, according to the quantiles corresponding to each sample point after the time-varying parameter estimation of each Pearson type III curve model with variable parameters to be detected, obtain the standard positive value corresponding to each Pearson type III curve model with variable parameters to be detected The inverse function value of the state distribution cumulative distribution function at each quantile;

Step 22, according to the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model to be detected at each quantile, from at least two variable parameters to be detected after the time-varying parameter estimation At least one variable parameter Pearson type III curve model is identified in the parametric Pearson type III curve model;

Step 23: Perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variable parameter Pearson III curve models from at least one available variable parameter Pearson type III curve model according to the fitting test results type curve model.

In step 21, for each variable parameter Pearson type III curve model to be detected, the step of quantiles corresponding to each sample point after performing time-varying parameter estimation includes: obtaining the variable parameter Pearson type III curve model to be detected in The distribution function after time-varying parameter estimation, from which the above-mentioned quantiles are obtained. Specifically, the quantiles corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be tested are:

u _ij ＝F(X _j |θ _i (t))

Among them, i refers to the i-th variable parameter Pearson type III curve model to be detected among at least two variable parameter Pearson type III curve models to be detected, and j refers to the jth hydrological measured sample among the n hydrological measured samples , θ _i (t)={α(t), β(t), r(t)}, θ _i (t) represents the time-varying parameters of each variable-parameter Pearson type III curve model to be detected.

Among them, the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson type III curve model at each quantile is:

r _ij ＝Θ ^-1 (u _ij )

where Θ ^-1 represents the inverse function of the cumulative distribution function of the standard normal distribution.

In step 21, the quantiles of each sample point corresponding to each variable parameter Pearson type III curve model to be detected include n, and the corresponding inverse function values are n. Among them, a hydrological measured sample corresponds to a quantile and an inverse function value.

Preferably, step 22 includes:

Step 221, judging whether the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson III curve model at each quantile satisfies that the mean value is within the first predetermined interval and the variance is within the second predetermined interval. interval and the skewness coefficient is located within the third predetermined interval;

Step 222, if it is satisfied, then determine the variable parameter Pearson type III curve model to be detected as an available variable parameter Pearson type III curve model.

Specifically, the first predetermined interval is between -1 and 1, the second predetermined interval is between 0.5 and 1.5, and the third predetermined interval is between -2 and 2.

In step 221, the mean value, variance and skewness coefficient of the inverse function values of n standard normal distribution cumulative distribution functions corresponding to each variable parameter Pearson type III curve model at each quantile place are required to satisfy the conditions, the variable parameter Pearson type III curve model to be detected can be determined as the available variable parameter Pearson type III curve model.

Preferably in the present invention, step 23 includes:

Step 231, obtain the number of estimated parameters contained in the time-varying parameters of each variable parameter Pearson type III curve model, and the maximum value of the logarithmic likelihood function corresponding to the variable parameter Pearson type III curve model value;

Step 232, according to the number of estimated parameters contained in the time-varying parameters of each available variable parameter Pearson type III curve model and the maximum value of the logarithmic likelihood function, obtain each available variable parameter Pearson type III curve model The difference corresponding to the curve model;

In step 233, one of the available variable parameter Pearson type III curve models with the smallest difference among at least two available variable parameter Pearson type III curve models is determined as the optimal variable parameter Pearson type III curve model.

In step 231, the number of estimated parameters included in the time-varying parameters of each variable-parameter Pearson Type III curve model is the above-mentioned k ₀ , k ₁ , k ₂ , k ₃ , k ₄ , k ₅ , The number of parameters whose values are not equal to 0 among the 9 parameters including k ₆ , k ₇ and k ₈ . The maximum value of the log-likelihood function corresponding to the Pearson III curve model with variable parameters is the log-likelihood function obtained by estimating the parameters whose values are not equal to 0 using the maximum likelihood estimation method. maximum value.

Preferably, according to the number of estimated parameters contained in the time-varying parameters of each variable parameter Pearson type III curve model and the maximum value of the logarithmic likelihood function, each variable parameter Pearson type III The steps of the difference corresponding to the curve model include:

By formula:

RE=2*NUM-2*LLF

The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters.

For example, if there are 8 available variable parameter Pearson type III curve models determined above, it is necessary to determine an optimal variable parameter Pearson type III curve model among the eight available variable parameter Pearson type III curve models. , according to the value of RE in the 8 available variable parameter Pearson type III curve models, the optimal variable parameter Pearson type III curve model is determined. Among the 8 available variable parameter Pearson type III curve models, one of the available variable parameter Pearson type III curve models with the smallest difference is determined as the optimal variable parameter Pearson type III curve model.

Step 3, according to the optimal variable parameter Pearson type III curve model, determine the hydrological variable design value of the watershed at a predetermined time under the predetermined hydrological frequency condition.

Preferably, according to the optimal variable parameter Pearson type III curve model, the step of determining the hydrological variable design value of the watershed at a predetermined time under predetermined hydrological frequency conditions includes:

By formula:

Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time.

Through the method of the present invention, the hydrological variable design value of a predetermined hydrological frequency can be solved at different times, which is suitable for hydrological frequency calculation of non-uniform hydrological sequences under changing environments, and can improve calculation accuracy.

According to another aspect of the embodiments of the present invention, the embodiments of the present invention also provide a device for determining the design value of hydrological variables, including:

Obtaining module 1, used to obtain the quantile corresponding to each sample point of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected after time-varying parameter estimation;

The first determination module 2 is used to obtain the quantiles corresponding to each sample point after the time-varying parameter estimation for each variable-parameter Pearson curve model to be detected, from at least two parameters to be detected after the time-varying parameter estimation. One of the optimal variable parameter Pearson type Ⅲ curve models was determined from the detection of variable parameter Pearson type Ⅲ curve models;

The second determining module 3 is configured to determine the design value of the hydrological variable at a predetermined moment in the watershed under the predetermined hydrological frequency condition according to the optimal variable parameter Pearson type III curve model.

Preferably, the acquisition module includes:

Establishing a unit for establishing at least two Pearson type III curve models with variable parameters to be detected, and estimating the time-varying parameters of each Pearson type III curve model with variable parameters to be detected by using the maximum likelihood parameter estimation method;

The first obtaining unit is used to obtain quantiles corresponding to each sample point after time-varying parameter estimation of each variable-parameter Pearson type III curve model to be detected.

Preferably, the first determination module includes:

The second acquisition unit is used to obtain each variable parameter Pearson type III curve model to be detected according to the quantile corresponding to each sample point after time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected The inverse function value of the corresponding standard normal distribution cumulative distribution function at each quantile;

The second determination unit is used to obtain the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model at each quantile from at least two values after the time-varying parameter estimation. Determine at least one available variable parameter Pearson type III curve model among the variable parameter Pearson type III curve models to be detected;

The third determining unit is used to perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variables from at least one available variable parameter Pearson type III curve model according to the fitting test result Parametric Pearson type III curve model.

Preferably, the second determination unit includes:

The judging subunit is used to judge whether the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson III curve model at each quantile satisfies that the mean value is within the first predetermined interval and the variance is within the first predetermined interval. within the second predetermined interval and the skewness coefficient is located within the third predetermined interval;

The first determining subunit is configured to determine the variable parameter Pearson type III curve model to be detected as an available variable parameter Pearson type III curve model if it is satisfied.

Preferably, the third determination unit includes:

The first obtaining subunit is used to obtain the number of estimated parameters contained in the time-varying parameters of each Pearson type III curve model with variable parameters, and the logarithm corresponding to the Pearson type III curve model with variable parameters. The maximum value of the natural function;

The second acquisition subunit is used to obtain each available variable parameter according to the number of estimated parameters contained in the time-varying parameters of each available variable parameter Pearson III curve model and the maximum value of the logarithmic likelihood function. The difference corresponding to the parameter Pearson type III curve model;

The second determining subunit is used to determine one of the available variable parameter Pearson type III curve models with the smallest difference among at least two available variable parameter Pearson type III curve models as the optimal variable parameter Pearson type III curve Model.

Preferably, the second acquisition subunit includes:

By formula:

RE=2*NUM-2*LLF

The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters.

Preferably, the second determination module includes:

By formula:

Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time.

The device for determining the design value of hydrological variables provided by the embodiment of the present invention is a device corresponding to the above-mentioned method, and all the implementation modes in the above-mentioned method are applicable to the embodiment of the device, and can also achieve the same technical effect. The hydrological variable design value of a certain predetermined hydrological frequency can be solved at different times, and the above solution method can improve the solution accuracy.

According to another aspect of the embodiment of the present invention, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned method for determining the design value of a hydrological variable is implemented. A step of.

The above description is a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, these improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (15)

1. A method for determining design values of hydrological variables, characterized in that, comprising: Obtaining the quantile corresponding to each sample point of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected after performing time-varying parameter estimation; According to the quantiles corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected, at least two variable parameter Pearson type III curves to be detected after the time-varying parameter estimation are performed One of the optimal variable parameter Pearson type Ⅲ curve models is determined in the model; According to the optimal variable parameter Pearson type III curve model, the design value of the hydrological variable at the predetermined moment is determined under the predetermined hydrological frequency condition of the watershed. 2. the determination method of hydrological variable design value according to claim 1, is characterized in that, obtains at least two variable parameter Pearson type curve models to be detected in each variable parameter Pearson type III curve model to be detected in carrying out The quantile steps corresponding to each sample point after time-varying parameter estimation include: Establishing at least two variable parameter Pearson type III curve models to be detected, and using the maximum likelihood parameter estimation method to estimate the time-varying parameters of each variable parameter Pearson type III curve model to be detected; Obtain the quantile corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be tested. 3. the determination method of hydrological variable design value according to claim 1, is characterized in that, according to each to-be-detected variable parameter Pearson type III curve model after carrying out time-varying parameter estimation, the quantile corresponding to each sample point , the steps of determining one of the optimal variable parameter Pearson type III curve models from at least two variable parameter Pearson type III curve models to be tested after time-varying parameter estimation include: According to the quantiles corresponding to each sample point after the time-varying parameter estimation of each Pearson type III curve model with variable parameters to be detected, the cumulative standard normal distribution corresponding to each Pearson type III curve model with variable parameters to be detected is obtained The inverse function value of the distribution function at each quantile; According to the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model to be detected at each quantile, from at least two variable parameter Pearson curves to be detected after the time-varying parameter estimation At least one variable parameter Pearson type III curve model is identified in the type III curve model; Perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variable parameter Pearson type III curve models from at least one available variable parameter Pearson type III curve model according to the fitting test results . 4. the determining method of hydrological variable design value according to claim 3 is characterized in that, according to the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson type III curve model to be detected at each quantile place Inverse function value, the step of determining at least one available variable parameter Pearson type III curve model from at least two variable parameter Pearson type III curve models to be detected after time-varying parameter estimation includes: Judging whether the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model to be detected at each quantile satisfies that the mean value is within the first predetermined interval, the variance is within the second predetermined interval and The skewness coefficient is located in the third predetermined interval; If it is satisfied, the variable parameter Pearson type III curve model to be detected is determined as an available variable parameter Pearson type III curve model. 5. the determination method of hydrological variable design value according to claim 3, is characterized in that, carries out fitting detection to each available variable parameter Pearson type III curve model, and according to fitting detection result from at least one available variable parameter The steps for determining one of the optimal variable parameter Pearson type III curve models in the Pearson type III curve model include: Acquiring the number of estimated parameters included in the time-varying parameters of each variable parameter Pearson type III curve model, and the maximum value of the logarithmic likelihood function corresponding to the variable parameter Pearson type III curve model; According to the number of estimated parameters contained in the time-varying parameters of each variable parameter Pearson type III curve model and the maximum value of the logarithmic likelihood function, obtain the corresponding value of each variable parameter Pearson type III curve model the difference; One of the available variable parameter Pearson type III curve models with the smallest difference value among at least two available variable parameter Pearson type III curve models is determined as the optimal variable parameter Pearson type III curve model. 6. the determination method of hydrological variable design value according to claim 5, is characterized in that, according to the quantity of estimated parameter and described logarithm included in the time-varying parameter of each available variable parameter Pearson type III curve model The maximum value of the likelihood function, the steps of obtaining the difference corresponding to each available variable parameter Pearson type III curve model include: By formula: RE=2*NUM-2*LLF The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters. 7. The determination method of hydrological variable design value according to claim 1, is characterized in that, according to described optimal variable parameter Pearson type III curve model, determine the hydrological variable of watershed under predetermined hydrological frequency condition, at predetermined moment The steps to design values include: By formula: <mrow><msub><mi>x</mi><mi>p</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><mi>g</mi><mi>a</mi><mi>min</mi><mi>v</mi><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mi>P</mi><mo>,</mo><mover><mi>&amp;alpha;</mi><mo>&amp;OverBar;</mo></mover><mo>(</mo><mi>t</mi><mo>)</mo><mo>,</mo><mfrac><mn>1</mn><mrow><mover><mi>&amp;beta;</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mfrac><mo>)</mo></mrow><mo>+</mo><mover><mi>r</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow> Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time. 8. A device for determining a design value of a hydrological variable, characterized in that it comprises: The acquisition module is used to obtain the quantile corresponding to each sample point after the time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected in at least two variable parameter Pearson type III curve models to be detected; The first determination module is used to obtain at least two samples to be detected after time-varying parameter estimation according to the quantiles corresponding to each sample point after each variable-parameter Pearson curve model to be detected is estimated. One of the optimal variable parameter Pearson type Ⅲ curve models is determined in the variable parameter Pearson type Ⅲ curve model; The second determining module is used to determine the design value of the hydrological variable at a predetermined moment in the watershed under the predetermined hydrological frequency condition according to the optimal variable parameter Pearson type III curve model. 9. The determining device of hydrological variable design value according to claim 8, is characterized in that, acquisition module comprises: Establishing a unit for establishing at least two Pearson type III curve models with variable parameters to be detected, and estimating the time-varying parameters of each Pearson type III curve model with variable parameters to be detected by using the maximum likelihood parameter estimation method; The first obtaining unit is used to obtain quantiles corresponding to each sample point after time-varying parameter estimation for each variable-parameter Pearson type III curve model to be detected. 10. The determining device of hydrological variable design value according to claim 8, is characterized in that, the first determining module comprises: The second acquisition unit is used to obtain each variable parameter Pearson type III curve model to be detected according to the quantile corresponding to each sample point after time-varying parameter estimation of each variable parameter Pearson type III curve model to be detected The inverse function value of the corresponding standard normal distribution cumulative distribution function at each quantile; The second determination unit is used to obtain the inverse function value of the cumulative distribution function of the standard normal distribution corresponding to each variable parameter Pearson type III curve model at each quantile from at least two values after the time-varying parameter estimation. Determine at least one available variable parameter Pearson type III curve model among the variable parameter Pearson type III curve models to be detected; The third determining unit is used to perform a fitting test on each available variable parameter Pearson type III curve model, and determine one of the optimal variables from at least one available variable parameter Pearson type III curve model according to the fitting test result Parametric Pearson type III curve model. 11. The determining device of hydrological variable design value according to claim 10, is characterized in that, the second determining unit comprises: The judging subunit is used to judge whether the inverse function value of the standard normal distribution cumulative distribution function corresponding to each variable parameter Pearson III curve model at each quantile satisfies that the mean value is within the first predetermined interval and the variance is within the first predetermined interval. within the second predetermined interval and the skewness coefficient is located within the third predetermined interval; The first determining subunit is configured to determine the variable parameter Pearson type III curve model to be detected as an available variable parameter Pearson type III curve model if it is satisfied. 12. The determining device of hydrological variable design value according to claim 10, is characterized in that, the 3rd determining unit comprises: The first obtaining subunit is used to obtain the number of estimated parameters contained in the time-varying parameters of each Pearson type III curve model with variable parameters, and the logarithm corresponding to the Pearson type III curve model with variable parameters. The maximum value of the natural function; The second acquisition subunit is used to obtain each available variable parameter according to the number of estimated parameters contained in the time-varying parameters of each available variable parameter Pearson III curve model and the maximum value of the logarithmic likelihood function. The difference corresponding to the parameter Pearson type III curve model; The second determination subunit is configured to determine one of the available variable parameter Pearson Type III curve models with the smallest difference among at least two available variable parameter Pearson Type III curve models as the optimal Pearson Type III curve model. 13. The determining device of hydrological variable design value according to claim 12, is characterized in that, the second acquisition subunit comprises: By formula: RE=2*NUM-2*LLF The difference RE is obtained, wherein LLF is the maximum value of the log-likelihood function, and NUM is the number of estimated parameters in the time-varying parameters. 14. The determining device of hydrological variable design value according to claim 8, is characterized in that, the second determining module comprises: By formula: <mrow><msub><mi>x</mi><mi>p</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><mi>g</mi><mi>a</mi><mi>min</mi><mi>v</mi><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mi>P</mi><mo>,</mo><mover><mi>&amp;alpha;</mi><mo>&amp;OverBar;</mo></mover><mo>(</mo><mi>t</mi><mo>)</mo><mo>,</mo><mfrac><mn>1</mn><mrow><mover><mi>&amp;beta;</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mfrac><mo>)</mo></mrow><mo>+</mo><mover><mi>r</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow> Obtain the hydrological variable design value x _p (t) of the watershed at a predetermined time under predetermined hydrological frequency conditions, where, is the shape parameter of the optimal variable parameter Pearson type III curve model, is the scale parameter of the optimal variable parameter Pearson type III curve model, is the position parameter of the optimal variable parameter Pearson type III curve model, Indicates the (1-P) quantile of the gamma distribution, where P is the predetermined hydrological frequency and t is the predetermined time. 15. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, the method for determining the design value of a hydrological variable as claimed in any one of claims 1 to 7 is realized. step.