CN112149893A - Design flood value prediction method and device - Google Patents

Design flood value prediction method and device Download PDF

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CN112149893A
CN112149893A CN202010978962.XA CN202010978962A CN112149893A CN 112149893 A CN112149893 A CN 112149893A CN 202010978962 A CN202010978962 A CN 202010978962A CN 112149893 A CN112149893 A CN 112149893A
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宋昕熠
鲁帆
赵勇
王浩
周毓彦
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention provides a method and a device for predicting a designed flood value, which relate to the technical field of hydrologic analysis, and the method comprises the following steps: acquiring an annual maximum peak flow information sequence of a target watershed; acquiring a likelihood function of Pearson III type distribution according to the annual maximum peak flow information sequence; obtaining a punishment likelihood function according to the likelihood function of the Pearson III type distribution; predicting the design flood value of the target watershed according to the punishment likelihood function; outputting the predicted design flood value. The invention can reduce the error of predicting the designed flood value.

Description

Design flood value prediction method and device
Technical Field
The invention relates to the technical field of hydrological analysis, in particular to a prediction method and a prediction device for designing a flood value.
Background
Since the 20 th century, with the change of climate, the global temperature of the world has a tendency to rise remarkably, and the frequency of extreme hydrological meteorological events has also increased. Human activities cause significant changes in the watershed environment, such as: the impervious area is obviously increased along with the expansion of cities and towns, and the production confluence process is accelerated; the change of the ranges of grassland, forest land and cultivated land changes the production convergence capacity in the drainage basin; the construction of a large number of hydraulic engineering influences the confluence process of a basin.
The annual trend of river basin flood is a new challenge to the design of water conservancy projects. For a long time, the economy and the safety of the hydraulic engineering need to be balanced in the planning and designing stage, unnecessary engineering investment is increased by overestimating the designed flood value, and the engineering is exposed to higher risks by underestimating the designed value, so that the scale of one engineering is often determined by predicting the flood value. The traditional design method assumes that the flood sequence obeys consistency and independent equal distribution assumption, and fails to consider the characteristic that the extreme hydrological and meteorological events show more remarkable change trends at present, because the planning life of hydraulic engineering is long, the conclusion obtained by the traditional statistical inference method may not be applied in the future environment, and the non-consistency distribution model with the parameter having the change characteristic is gradually applied in the last twenty years. The parameters of the non-uniform distribution model can be expressed as functions of time terms or other covariates, and under the condition that the future change trend of the covariates can be clarified, the application of the non-uniform distribution model can obtain a better fitting result.
Many different distribution models are widely used in hydrological frequency analysis, and the pearson type III distribution is widely used. Under the consistency assumption, the distributed parameter estimators can be obtained based on a moment method and a maximum likelihood method, but the moment method cannot be applied to the non-consistency assumption, and an unreasonable solution can be obtained when the non-uniform Pearson III-type distribution is estimated by the maximum likelihood method. When the maximum likelihood method is used for estimating the non-uniform pearson type III distribution, a calculation result needs to be obtained based on a numerical method, but in the optimization process based on the numerical method, an incorrect optimal solution is often obtained because the optimization process is interfered. It can be seen that the error of the current prediction design flood value is too large.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting a design flood value, which aim to solve the problem of predicting the design flood value.
In a first aspect, an embodiment of the present invention provides a method for predicting a designed flood value, including:
acquiring an annual maximum peak flow information sequence of a target watershed;
acquiring a likelihood function of Pearson III type distribution according to the annual maximum peak flow information sequence;
obtaining a punishment likelihood function according to the likelihood function of the Pearson III type distribution;
predicting the design flood value of the target watershed according to the punishment likelihood function;
outputting the predicted design flood value.
In a second aspect, an embodiment of the present invention provides a device for predicting a designed flood value, including:
the first acquisition module is used for acquiring a yearly maximum peak flow information sequence of the target watershed;
the second acquisition module is used for acquiring a likelihood function of Pearson III type distribution according to the annual maximum flood peak flow information sequence;
the third acquisition module is used for acquiring a punishment likelihood function according to the likelihood function of the Pearson III type distribution;
the fourth acquisition module is used for predicting the design flood value of the target watershed according to the punishment likelihood function;
and the output module is used for outputting the predicted design flood value.
In the embodiment of the invention, the annual maximum peak flow information sequence of the target basin is obtained; acquiring a likelihood function of Pearson III type distribution according to the annual maximum peak flow information sequence; obtaining a punishment likelihood function according to the likelihood function of the Pearson III type distribution; and according to the punishment likelihood function, predicting the design flood value of the target watershed and outputting the predicted design flood value. Therefore, the design flood value is predicted according to the punishment likelihood function obtained by the likelihood function of the Pearson III-type distribution, so that the error of predicting the design flood value can be reduced.
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Fig. 1 is a flow chart of a method for designing a flood value prediction according to an embodiment of the present invention;
FIG. 2 is a graph of change in penalty function for different coefficient values;
fig. 3 is a structure of a flood value prediction device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any inventive effort, shall fall within the scope of protection of the present application.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced otherwise than as shown or described herein and the terms "first" and "second" used in the description may generally refer to one type of object and may not necessarily refer to the same number of objects, e.g., the first object may be one or more.
Referring to fig. 1, fig. 1 is a flowchart of a method for predicting a flood value according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
step 101, acquiring a yearly maximum peak flow information sequence of a target watershed.
And 102, acquiring a likelihood function of the Pearson III type distribution according to the annual maximum flood peak flow information sequence.
The Pearson III distribution is a probability distribution curve of a single peak of asymmetric distribution with a finite end and an infinite end. Under the consistency assumption, the distributed parameter estimators can be obtained based on the moment method, but the moment method cannot be applied in the non-consistency condition. Therefore, under the non-uniform condition, the Pearson type III distribution is widely applied to the technical field of hydrological analysis.
And 103, acquiring a punishment likelihood function according to the likelihood function of the Pearson III type distribution.
And 104, predicting the design flood value of the target watershed according to the penalty likelihood function.
And 105, outputting the predicted design flood value.
The predicted design flood value output in the above steps can be used as important reference data for hydraulic engineering construction, for example, according to the predicted design flood value, whether the design of the flood-control engineering building meets the design requirements is checked.
In the embodiment of the invention, the punishment likelihood function is obtained according to the likelihood function of the Pearson III type distribution to predict the designed flood value through the steps, so that the error of predicting the designed flood value can be reduced.
As an optional implementation manner, the obtaining of the annual maximum peak flow information sequence of the target watershed includes:
and selecting the annual maximum value of the flood flow from the actually measured flood sequences of the target watershed to form an annual maximum flood peak flow information sequence.
Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in a basin, and also customarily represents the amount of water passing through a certain section of a river within a certain period of time, namely runoff. Therefore, the runoff rate is the amount of water passing through a certain cross section of the target basin per unit time, and is one of the most important hydrological factors on land. The daily runoff is the unit time of runoff volume which is day, namely the total water volume passing through a certain section of a target basin every day. The main factors that affect runoff are climatic factors, including rainfall intensity and evaporation, and underlying surface factors, including geometric factors, natural geographic location factors, and human activity factors. It can be seen that the total amount of water passing through a certain section of the target basin per day is different due to different influencing factors.
And summarizing actual measured flood information of the target watershed to form a flood data set of each year. And in the flood data set of each year, selecting the maximum value of flood flow in each year to form an annual maximum flood peak flow information sequence.
In this embodiment, since the maximum value of the flood flow in each year is selected as the annual maximum peak flow information sequence, the accuracy of the acquired annual maximum peak flow information can be improved.
Optionally, the obtaining a likelihood function of pearson type III distribution according to the annual maximum peak flow information sequence includes:
according to the annual maximum peak flow information sequence, a probability density function of Pearson type III distribution is obtained by calculating as follows:
Figure BDA0002686870290000041
wherein, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year;
obtaining a likelihood function of the pearson type III distribution from the probability density function by calculating:
Figure BDA0002686870290000042
wherein L (alpha (t), beta (t), r (t)) is a likelihood function, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year.
In the probability density function of the pearson type III distribution, t is a covariate, α (t) shape parameter, β (t) scale parameter, and r (t) position parameter are all expressed as functions of the covariate t. Covariates are generally expressed as time, in terms of timeThe shape parameter, the dimension parameter and the position parameter are also in a changed state. As the annual maximum peak flow information sequence belongs to the annual maximum value sequence, the Pearson III type distribution can present a positive bias characteristic, namely corresponding to beta (t) > 0, and xt-r(t)≥0。
And a group of parameter values of the maximum value obtained by the likelihood function of the Pearson III type distribution are the maximum likelihood estimation result of the Pearson III type distribution parameters.
In the embodiment, as the estimation result of the function parameters under the non-consistency condition that the likelihood function of the pearson type III distribution can be determined, the obtained parameter estimation result is more in line with the actual situation, so that the reliability of the design flood value prediction result is improved.
Optionally, the obtaining a penalty likelihood function according to the likelihood function of the pearson type III distribution includes:
obtaining a penalty function by calculating;
Figure BDA0002686870290000051
wherein, pi (x)t-r (t)) is a penalty function, t is a covariate, r (t) is a position parameter, xtA, c and B are coefficients in a penalty function respectively for a yearly maximum flood peak flow information sequence;
obtaining a penalty likelihood function according to the likelihood function of the Pearson III type distribution and the penalty function by calculating:
Figure BDA0002686870290000052
wherein PL (alpha (t), beta (t), r (t)) is a penalty likelihood function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, f (x) is a position parametert) Is a probability density function, pi (x)t-r (t)) is a penalty function.
In the above penalty function, the coefficients a and c in the penalty function reflect the penalty function as (x)t-r(t) Penalty degree when x is approximately equal to 0, B is the action threshold of the penalty function, when x istWhen r (t) is less than or equal to B, the penalty function will take effect and limit the value of the position parameter in the distribution.
And carrying out combined multiplication on the penalty function and the likelihood function of the Pearson III type distribution to obtain the penalty likelihood function.
The penalty function pi (x)t-r (t)) ranges between 0 and 1, when xtWhen-r (t) > B, there is π (x)t-r (t)) -1, when the penalty likelihood function is identical to the likelihood function of the pearson type III distribution described above. When x istWhen-r (t) is less than or equal to B, penalty function pi (x)t-r (t)) is between 0 and 1, thereby reducing the value of the penalty likelihood function.
Referring to FIG. 2, FIG. 2 is a diagram illustrating the variation of penalty function with different coefficient values, as shown in FIG. 2, FIG. 2 shows the x of penalty function when the coefficients a and c in penalty function take different valuest-r(t)∈[0,0.05]The variation in the interval. It can be seen that the larger the values of a and c are, the larger the penalty function pair (x)tThe greater the penalty, i.e. (x), when-r (t)) ≈ 0tThe smaller the probability that-r (t)) ≈ 0. In general, the values of a and c recommend a > 50, and c is an odd number greater than or equal to 1.
The principle of obtaining the optimal parameter estimation by adopting the maximum likelihood method is that the maximum parameter is taken as the optimal parameter estimation result through the likelihood function of the Pearson III type distribution, therefore, the penalty function is introduced into the likelihood function to ensure that x is xt-r (t) falls within the interval [0, B]The probability of between decreases. To avoid (x)tIn the case of-r (t)) ≈ 0, the action threshold B of the penalty function should not be too large, and is usually B ∈ [0.03,0.1]。
Therefore, the coefficients in the penalty function may be a equal to 100, B equal to 0.05, and c equal to 3, in this scenario, when x is equal tot-r(t)∈[0,0.03]When, there is π (x)t-r (t)) ≈ 0, where the penalty likelihood functions PL (α (t), β (t), r (t)) ≈ 0, thereby avoiding the occurrence of (x) when parameter optimization is performed using the maximum likelihood methodt-r (t)) ≈ 0.
In this embodiment, the penalty likelihood function is obtained according to the likelihood function of the pearson type III distribution, so that the accuracy of the optimal parameter can be improved.
Optionally, the predicting the design flood value of the target watershed according to the penalty likelihood function includes:
obtaining a penalty likelihood function estimation parameter corresponding to a penalty likelihood function maximum value according to the penalty likelihood function;
and according to the estimation parameters of the penalty likelihood function, calculating and designing a flood value by the following steps:
Figure BDA0002686870290000061
wherein Q ispFor designing flood value, gamnv represents inverse function of gamma distribution cumulative distribution function, p is transcendental probability, t is covariate, beta (t) is penalty likelihood function estimation scale parameter, and r (t) is penalty likelihood function estimation position parameter.
Based on the maximum likelihood principle, when the penalty likelihood function takes a maximum value, the corresponding parameter is the penalty likelihood function estimation parameter, and further, the predicted design flood value corresponding to the annual maximum flood peak flow information sequence under the exceeding probability p is obtained according to the penalty likelihood function estimation parameter. The maximum likelihood principle refers to that a parameter value which enables a function to reach the maximum is selected in a possible value range of parameters and is used as an estimated value of the parameters.
The advantages and disadvantages of the distribution model can be verified by adopting an information evaluation criterion, the commonly used method mainly comprises an information amount criterion (AIC) and a Bayesian Information Criterion (BIC), and when a plurality of groups of distribution models are selected, the group of models with the minimum AIC/BIC values is the optimal model:
AIC＝-2LLF+2numPar
BIC＝-2LLF+numPar×log(n)
where LLF is the maximum log-likelihood estimation function value, for example, in the penalty likelihood function, LLF is a value obtained by taking the logarithm of the maximum value of the penalty likelihood function, n is the length of the annual maximum peak flow information sequence, numPar is the number of parameters to be estimated in the distribution, and for the pearson type III distribution under the consistency condition, when the scale and shape parameters of the non-consistency pearson type III distribution are fixed values, and the position parameter is a linear function of time t, that is, r (t) is at + b, numPar is 4. For the flood sequence with obvious change trend, the Pearson type III distribution under the consistency condition and the non-uniform Pearson type III distribution based on the punishment likelihood function can be respectively adopted to fit the annual maximum flood peak flow information sequence, and the theoretical rationality and feasibility of the punishment likelihood function are verified based on the AIC/BIC detection result or other model diagnosis methods.
In this embodiment, the accuracy of the flood prediction value can be improved by using the parameter corresponding to the maximum value of the penalty likelihood function as the estimation parameter of the penalty likelihood function.
Referring to fig. 3, fig. 3 is a structural diagram of a designed flood value prediction apparatus according to an embodiment of the present invention, as shown in fig. 3, the designed flood value prediction apparatus includes:
a first obtaining module 301, configured to obtain an annual maximum peak flow information sequence of a target watershed;
a second obtaining module 302, configured to obtain a likelihood function of pearson type III distribution according to the annual maximum peak flow information sequence;
a third obtaining module 303, configured to obtain a penalty likelihood function according to the likelihood function of the pearson III-type distribution;
a fourth obtaining module 304, configured to predict a designed flood value of the target drainage basin according to the penalty likelihood function;
an output module 305 for outputting the predicted design flood value.
Optionally, the first obtaining module is configured to select an annual maximum value of flood flow from actually measured flood sequences of the target watershed to form an annual maximum flood peak flow information sequence.
Optionally, the second obtaining module is configured to obtain a probability density function of pearson type III distribution according to the annual maximum peak flow information sequence by calculating as follows:
Figure BDA0002686870290000081
wherein, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year;
obtaining a likelihood function of the pearson type III distribution from the probability density function by calculating:
Figure BDA0002686870290000082
wherein L (alpha (t), beta (t), r (t)) is a likelihood function, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year.
Optionally, the third obtaining module is configured to obtain a penalty function through the following calculation;
Figure BDA0002686870290000083
wherein, pi (x)t-r (t)) is a penalty function, t is a covariate, r (t) is a position parameter, xtA, c and B are coefficients in a penalty function respectively for a yearly maximum flood peak flow information sequence;
obtaining a penalty likelihood function according to the likelihood function of the Pearson III type distribution and the penalty function by calculating:
Figure BDA0002686870290000084
wherein PL (alpha (t), beta (t), r (t)) is a penalty likelihood function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, f (x) is a position parametert) Is a probability density function, pi (x)t-r (t)) isA penalty function.
Optionally, the fourth obtaining module is configured to obtain a penalty likelihood function estimation parameter corresponding to the maximum value of the penalty likelihood function according to the penalty likelihood function;
and according to the estimation parameters of the penalty likelihood function, calculating and designing a flood value by the following steps:
Figure BDA0002686870290000085
wherein Q ispIn order to design a flood value, gamnv represents an inverse function of a gamma cumulative distribution function, p is an override probability, t is a covariate, beta (t) is a penalty likelihood function estimation scale parameter, and r (t) is a penalty likelihood function estimation position parameter.
The designed flood value prediction device provided by the embodiment of the invention can realize each process in the method embodiment of fig. 1, and is not described herein again in order to avoid repetition.
It should be noted that the design flood value prediction apparatus in the embodiment of the present invention may be an apparatus, or may be a component, an integrated circuit, or a chip in an electronic device.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order, depending on the functionality involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the above embodiment method can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application may be substantially or partially embodied in the form of a software product stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk), and including instructions for enabling a terminal (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims (10)

1. A method of predicting design flood values, comprising:
acquiring an annual maximum peak flow information sequence of a target watershed;
acquiring a likelihood function of Pearson III type distribution according to the annual maximum peak flow information sequence;
obtaining a punishment likelihood function according to the likelihood function of the Pearson III type distribution;
predicting the design flood value of the target watershed according to the punishment likelihood function;
outputting the predicted design flood value.
2. The method of predicting a flood value according to claim 1, wherein the obtaining of the annual maximum peak flow information sequence of the target basin comprises:
and selecting the annual maximum value of the flood flow from the actually measured flood sequences of the target watershed to form an annual maximum flood peak flow information sequence.
3. The method of claim 1, wherein the obtaining the likelihood function of the pearson type III distribution from the sequence of annual maximum peak flow information comprises:
according to the annual maximum peak flow information sequence, a probability density function of Pearson type III distribution is obtained by calculating as follows:
Figure FDA0002686870280000011
wherein, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year;
obtaining a likelihood function of the pearson type III distribution from the probability density function by calculating:
Figure FDA0002686870280000012
wherein L (alpha (t), beta (t), r (t)) is a likelihood function, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year.
4. The method of claim 1, wherein the deriving a penalty likelihood function from the likelihood function of the pearson type III distribution comprises:
obtaining a penalty function by calculating;
Figure FDA0002686870280000021
wherein, pi (x)t-r (t)) is a penalty function, t is a covariate, r (t) is a position parameter, xtA, c and B are coefficients in a penalty function respectively;
and obtaining a penalty likelihood function according to the likelihood function of the Pearson III type distribution and the penalty function by calculating the following steps:
Figure FDA0002686870280000022
wherein PL (alpha (t), beta (t), r (t)) is a penalty likelihood function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, f (x) is a position parametert) Is a probability density function, pi (x)t-r (t)) is a penalty function.
5. The method of claim 1, wherein predicting the design flood value for the target basin based on the penalty likelihood function comprises:
obtaining a penalty likelihood function estimation parameter corresponding to a penalty likelihood function maximum value according to the penalty likelihood function;
and according to the estimation parameters of the penalty likelihood function, calculating and designing a flood value by the following steps:
Figure FDA0002686870280000023
wherein Q ispIn order to design a flood value, gamnv represents an inverse function of a gamma distribution cumulative distribution function, p is an override probability, t is a covariate, beta (t) is a penalty likelihood function estimation scale parameter, and r (t) is a penalty likelihood function estimation position parameter.
6. A designed flood value prediction device, comprising:
the first acquisition module is used for acquiring a yearly maximum peak flow information sequence of the target watershed;
the second acquisition module is used for acquiring a likelihood function of Pearson III type distribution according to the annual maximum flood peak flow information sequence;
the third acquisition module is used for acquiring a punishment likelihood function according to the likelihood function of the Pearson III type distribution;
the fourth acquisition module is used for predicting the design flood value of the target watershed according to the punishment likelihood function;
and the output module is used for outputting the predicted design flood value.
7. The designed flood value prediction device according to claim 6, wherein the first obtaining module is configured to select an annual maximum value of the flood flow rate in the actually measured flood sequence of the target basin to form an annual maximum peak flow rate information sequence.
8. The design flood value prediction device of claim 6, wherein the second obtaining module is configured to obtain the probability density function of the Pearson type III distribution according to the annual maximum flood peak flow information sequence by calculating:
Figure FDA0002686870280000031
wherein, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year;
obtaining a likelihood function of the pearson type III distribution from the probability density function by calculating:
Figure FDA0002686870280000032
wherein L (alpha (t), beta (t), r (t)) is a likelihood function, f (x)t) Is a probability density function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, xtThe maximum flood peak flow information sequence of the year.
9. The design flood value prediction device of claim 6, wherein the third capture module is configured to capture a penalty function by calculating;
Figure FDA0002686870280000033
wherein, pi (x)t-r (t)) is a penalty function, t is a covariate, r (t) is a position parameter, xtA, c and B are coefficients in a penalty function respectively;
and obtaining a penalty likelihood function according to the likelihood function of the Pearson III type distribution and the penalty function by calculating the following steps:
Figure FDA0002686870280000034
wherein PL (alpha (t), beta (t), r (t)) is a penalty likelihood function, t is a covariate, alpha (t) is a shape parameter, beta (t) is a scale parameter, r (t) is a position parameter, f (x) is a position parametert) Is a probability density function, pi (x)t-r (t)) is a penalty function.
10. The flood value prediction device according to claim 6, wherein the fourth obtaining module is configured to obtain a penalty likelihood function estimation parameter corresponding to a penalty likelihood function maximum according to the penalty likelihood function;
and according to the estimation parameters of the penalty likelihood function, calculating and designing a flood value by the following steps:
Figure FDA0002686870280000041
wherein Q ispIn order to design a flood value, gamnv represents an inverse function of a gamma cumulative distribution function, p is an override probability, t is a covariate, beta (t) is a penalty likelihood function estimation scale parameter, and r (t) is a penalty likelihood function estimation position parameter.
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