CN108021786A - A kind of coastal more ground storm tide joint nature strength analysis method - Google Patents
A kind of coastal more ground storm tide joint nature strength analysis method Download PDFInfo
- Publication number
- CN108021786A CN108021786A CN201711367800.7A CN201711367800A CN108021786A CN 108021786 A CN108021786 A CN 108021786A CN 201711367800 A CN201711367800 A CN 201711367800A CN 108021786 A CN108021786 A CN 108021786A
- Authority
- CN
- China
- Prior art keywords
- distribution
- joint
- wave height
- storm tide
- copula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Environmental & Geological Engineering (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention is disclosed according to a kind of coastal more ground storm tide joint nature strength analysis method, is comprised the following steps：(1) statistical analysis is carried out to the multiple regional while affected storm tide to be studied；(2) the joint return period of single regional storm tide water level and wave height is analyzed；(3) the more regional storm tide nature intensity multivariate joint probability distribution models of structure；(4) the more regional storm tide joint nature intensity of analysis；This method with the return period of combining of significant wave height by regarding the peak level in single regional Typhoon Process as storm tide nature intensity index, select stochastic variable of the different regions respective wave height water level joint return period as edge distribution, consider that the influence of the frequency occurs for typhoon, build the Joint Distribution of coastal multiple regional storm tide nature intensity, and then analyze and the probability of Storm Surge Disasters occurs at the same time in different location, prevent and reduce natural disasters to seashore, goods and materials are allocated, and particularly economic sustainable development is of great significance.
Description
Technical field
The present invention relates to a kind of coastal more ground storm tide joint nature strength analysis method.
Background technology
Typhoon is a kind of diastrous weather process, and different typhoons, the scope of its influence area is different, thus causes disaster
It is of different sizes., can be right by the synchronous Journal of Sex Research of the risk analysis caused disaster to different regions different cities, particularly calamity degree
Shoot the arrow at the target in Regional Disaster mitigation, realize accurate prevention and control.
Typhoon storm tide is to the disaster of coastal engineering, and often under the conditions of high water level, impact of the wave to coastal structure, leads
Cause sea wall to burst, and then flood coastal region.The coastal disaster that the storm tide that typhoon triggers produces, with astronomical tide, storm tide
Surge, the relation of wave height it is very close；And storm surge disaster, due to by regional economic development level, the forecasting of typhoon, local occupy
The influence of the consciousness of preventing and reducing natural disasters of the people is very big, and the disaster degree thereby resulted in, under identical storm surge action, also has very
Big difference；And it is current, since the understanding to interaction relationship between variable is very few, rarely have makes on joint probability method
With, and traditional single factors statistical method can only reflect a kind of influence of ocean essential, it is difficult to analyze coastal cities differently
The probability that causes disaster of point.
By the synchronous Journal of Sex Research of the Risk Calculation caused disaster to coastal area different cities, particularly calamity degree, for region
Prevent and reduce natural disasters, can shoot the arrow at the target, realize accurate prevention and control；So, it would be highly desirable to a kind of scheme is proposed to solve the above problems.
The content of the invention
The present invention is directed to defect existing in the prior art, it is proposed that a kind of coastal more ground storm tide joint nature intensity point
Analysis method, using the joint return period of single regional storm tide water level and wave height as storm tide nature intensity index, to difference
The stochastic variable of the respective wave height in area and water level joint return period as edge distribution, and consider that the shadow of the frequency occurs for typhoon
Ring, so as to build the Joint Distribution of multiple regional storm tide intensity, and then analyze and typhoon storm tide calamity occurs at the same time in different location
Harmful probability, can more rightly describe Disaster degree of the coastal area in Typhoon Process, take precautions against natural calamities for coastal region, goods and materials allotment carries
Technical Reference foundation is supplied.
The present invention is realized using following technical solution：A kind of coastal more ground storm tide joint nature intensive analysis side
Method, comprises the following steps：
A, how regional while affected more storm tides are selected to carry out statistical analysis, etesian time of list statistics
Number, selects Poisson distribution to be fitted the year frequency；
B, for the storm tide process for influencing more ground at the same time, the storm tide water level of each department every and the joint weight of wave height are analyzed
It is current, and as the natural intensity index of the storm tide；
C, it is more regional according to the storm tide water level of each department every obtained in step B and the joint return period of wave height, structure
Multiple areas are carried out combined probability analysis by storm tide nature intensity multivariate joint probability distribution model；
D, more regional storm tide joint nature intensity are obtained.
Further, in the step B, the joint return period for analyzing the storm tide water level of each department every and wave height specifically wraps
Include following steps：
B1, the fitting of distribution for carrying out water level and wave height；
The Joint Distribution of B2, analysis water level and wave height；
B3, acquisition peak level combine the return period with corresponding significant wave height.
Further, the more regional storm tide nature intensity multivariate joint probability distribution models of step C structure specifically include with
Lower step：
C1, the joint return period according to the obtained storm tide water levels of each department every of step B and wave height, draw different regions
Peak level and significant wave height the scatter diagram for combining return period sequence, and be respectively adopted according to the characteristics of scatter diagram different
Distribution pattern is fitted each department joint return period index, and selection Optimal Distribution is examined by K-S；The distribution pattern bag
Include Gumbel distributions, the distribution of III types of P-, Weibull distributions, Log-normal distributions and the distribution of logarithm P-III types；
C2, obtain more regional storm tide naturally strength associated return periods.
Further, the step B1 specifically includes following steps：
Peak level and significant wave height in every B11, statistics Typhoon Process；
B12, the edge distribution fitting for carrying out peak level and significant wave height；
B13, carry out distribution model test, chooses optimal edge distribution function.
Further, the step B2 specifically includes following steps：
B21, the optimal Coupla functions of selection build two-dimentional Joint Distribution model of the peak level with corresponding significant wave height；
B22, establish in Typhoon Process, the two-dimentional Poisson distribution composite model of peak level and corresponding significant wave height.
Further, it is theoretical theoretical with multiple malformation based on two dimension copula in step B2 in the step B3
Establish in Typhoon Process after the two-dimentional Poisson overlapped distribution models of peak level and corresponding significant wave height, analysis obtains peak level
With the co-occurrence probability curve of the two-dimentional overlapped distribution models of corresponding significant wave height, and then peak level and corresponding significant wave height are obtained
The joint return period, as storm tide nature intensity index.
Further, the step C2 is specifically included：
C21, using Optimal Distribution types results in step C1 as edge, by the use of copula functions as link construction of function it is each
The multivariable joint probability distribution of joint return period：In formula, x=(x1,
x2,…,xd)；f1(x1),f2(x2),…,fd(xd) it is corresponding to one-dimensional edge distribution F1(x1),F2(x2),…,Fd(xd) density
Function, the copula functions include binary Normal, Frank, Clayton and G-H copula and other polynary Copula letters
Number；
C22 and then progress parameter Estimation and χ2Examine, select optimal copula functions as function is linked and establish mould
Type, is fitted multivariate data；
C23, the multidimensional probability of recombination distribution for establishing more regional storm tide intensity indexs, draw joint probability isopleth, obtain
Much joint return periods of regional storm tide intensity index.
Further, in the step B12, optimal marginal distribution function is fitted in order to obtain, maximum entropy is respectively adopted
Distribution, Gumbel distributions and Log-normal distribution equal distributions, to the peak level and its phase during coastal a certain regional statistics
The significant wave height statistical series answered carry out fitting of distribution.
Further, in the step B13, K-S methods of inspection or root-mean-square error are used when carrying out distribution model test
Method of inspection.
Further, in the step B21, a variety of distribution function structure peak levels and corresponding significant wave are primarily based on
High two-dimentional Joint Distribution model, the distribution function include Gaussian copula, GH copula, Clayton copula
With Frank copula, and edge distribution of the fitting preferably distribution function as peak level and corresponding significant wave height is chosen；
When selection is fitted preferably distribution function, according to related coefficient index method, the parameter value of a variety of two dimension copula functions is obtained, will
Theoretical joint probability and the experience joint probability that two-dimentional copula functions obtain is put respectively is plotted in figure, analyzes a variety of two dimensions point respectively
Minimum deviation the quadratic sum RSME and AIC of cloth, and then choose the two-dimentional Joint Distribution model of optimal copula construction of function.
Compared with prior art, the advantages and positive effects of the present invention are：
The method that this programme is proposed, using the measured data of the tidal level observation station of coastal cities different regions as research pair
As, triggered using each tidal level station in every Typhoon Process of alarm the peak level that occurs and at the same time there is effect wave height sequence as
Example, and different waves-water level joint is established based on two-dimentional copula function theories and Poisson multiple malformation theory respectively
The two-dimentional Poisson multiple malformation model of the peak level of observation station and corresponding significant wave height；By analyzing single regional typhoon
During peak level and significant wave height combine the return period, will be coastal and in this, as the natural intensity index of storm tide
Stochastic variable of the respective storm tide nature intensity index in different regions as edge distribution, while consider the increasing of typhoon initiation
Water and astronomical tide, and the influence of the typhoon frequency, establish overlapped distribution models, and it is several to analyze causing disaster for coastal cities different location
Rate, realizes probability Estimation of the typhoon to large area scope causing disastrous degree, so that according to the forecast intensity of typhoon, carries out in advance anti-
Model, realizes the allotment for goods and materials of taking precautions against natural calamities, achievees the purpose that precisely to prevent and reduce natural disasters, and especially has important meaning to economic sustainable development
Justice.
Brief description of the drawings
Fig. 1 is analysis method flow diagram described in the embodiment of the present invention；
Fig. 2 is the fitting schematic diagram of experience joint probability and copula theory joint probability distributions in the embodiment of the present invention；
Fig. 3 is based on Gaussian copula, tetra- kind two of GH copula, Clayton copula and Frank copula
Tie up peak level and the corresponding significant wave height joint probability density function curve synoptic diagram that copula functions are established；
Fig. 4 answers for Gaussian copula, GH tetra- kinds of two dimensions of copula, Clayton copula and Frank copula
Close the co-occurrence probability curve synoptic diagram of distributed model；
Fig. 5 be Hsinchu station and Hua Lianzhan year typhoon storm tide number Poisson distribution schematic diagrams；
Fig. 6 is the extreme water level at Hsinchu station and Hua Lianzhan and the scatter diagram for combining return period sequence with wave height；
Fig. 7 is joint probability isopleth schematic diagram.
Embodiment
In order to which the above objects, features and advantages of the present invention is more clearly understood, below in conjunction with the accompanying drawings and implement
The present invention will be further described for example.It should be noted that in the case where there is no conflict, in embodiments herein and embodiment
Feature can be mutually combined.
The present embodiment proposes a kind of coastal more ground storm tide joint nature strength analysis method, and typhoon storm tide is to coastal area
Disaster often by peak level and significant wave height with caused, the joint return period of the two is to carry out storm tide intensity to draw
The important indicator divided, stochastic variable of this programme selection coastal single area respective joint return period as edge distribution, is examined
Consider typhoon and the influence of the frequency occurs, so as to build the Joint Distribution of more ground storm tide intensity, and then obtain in different location at the same time
The probability of Storm Surge Disasters occurs, with reference to figure 1, comprises the following steps：
S1, select how regional while affected more storm tides to carry out statistical analysis, and list statistics is etesian
Number, selects Poisson distribution to be fitted the year frequency；
S2, the storm tide process for the more ground of influence at the same time, analyze the joint of the storm tide water level of each department every and wave height
Return period, and as the natural intensity index of the storm tide；
The storm tide water level of each department every obtained in S3, foundation step S2 and the joint return period of wave height, build more ground
Multiple areas are carried out combined probability analysis by area's storm tide nature intensity multivariate joint probability distribution model；
S4, obtain more regional storm tide joint nature intensity.
In step s 2, analyze the storm tide water level of each department every and the joint return period of wave height specifically includes following step
Suddenly：
1) fitting of distribution of water level and wave height is carried out：
It is distributed assuming that the frequency n of a certain coastal typhoon obeys the Poisson that parameter is λ, carries out statistical analysis and χ2
Examine.
It is distributed if n obeys the Poisson that parameter is λ, i.e.,
Optimal marginal distribution function is fitted in order to obtain, and maximum entropy distribution, Gumbel distributions and Log- is respectively adopted
Normal is distributed equal distribution type, and sequence is counted to the peak level during coastal a certain regional statistics and its corresponding significant wave height
Row carry out fitting of distribution, carry out K-S inspections or root-mean-square error examine selection can more excellent fitting peak level and its it is corresponding effectively
The distribution of wave height sequence.
Common one-dimensional statistical model in ocean engineering, if certain Marine Environmental Elements obeys Gumbel distributions, its probability
Distribution function is：
In the formula, μ and σ are respectively location parameter and scale parameter；
If certain Marine Environmental Elements X obeys three-parameter weibull distribution, its probability-distribution function is
In the formula, μ>0, it is location parameter；σ>0, it is form parameter；γ>0, it is scale parameter；
It is two-factor Weibull distribution as μ=0, its probability-distribution function is
If certain Marine Environmental Elements X obeys Three-paramerter Lognormal Distribution, its probability-distribution function is
In the formula, μy、σyRespectively form parameter, scale parameter, corresponding to the average and standard deviation of variable ln (X-a)；a
For location parameter；It is two parameter logistic normal distributions as a=0.
If certain Marine Environmental Elements X obeys Pearson III distribution, its probability-distribution function is
In the formula, μ is location parameter, and 0<μ<xmin(xminFor the minimum value of sample)；α is form parameter；β joins for scale
Number；
If random variable of continuous type X values are in section [a0,+∞], according to principle of maximum entropy, the density function for obtaining X is
In the formula, a0>0 is location parameter, and λ, γ and β represent Lagrange multiplier respectively.
During distribution model test, used method includes K-S methods of inspection and frequency root-mean-square error method of inspection, below it is right
Its method of inspection does specific introduction respectively：
(1) K-S methods of inspection：
Assuming that overall distribution is F0(x), then the hypothesis that K-S is examined is as follows,
Null hypothesis H0:F0(x)=F (x) (8a)
Alternative hvpothesis H1:F0(x)≠F(x) (8b)
Comprise the following steps that.
1. solve empirical distribution function Fn(x)；
2. assume H0Set up, analyze each sample point xiCorresponding theoretical value F (xi)；
3. to each xi, calculate empirical Frequency Fn(xi) and theoretical value F (xi) difference absolute value：|Fn(xi)–F(xi)
|, | Fn(xi+1)–F(xi)|；
4. analyze K-S statistics；
5. given confidence alpha, K-S test statistics D is determined by following formulan(α)：
P{Dn≥Dn(α) }=α (10)
If 6. Dn≥Dn(α), then refuse null hypothesis H0；Otherwise null hypothesis is not refused, it is believed that the theoretical distribution and sample of null hypothesis
This serial empirical distribution function fitting is good, no significant difference.
(2) frequency root-mean-square error is examined：
Analysis obtains all empirical Frequency Fn(xi) and theoretical value F (xi) root-mean-square error, probability distribution pair is judged with this
The fitting of initial data is good and bad, and it is optimal to be worth smaller fitting.The root-mean-square error Q of frequency is defined as：
2) Joint Distribution of water level and wave height is analyzed：
Fitting preferably edge distribution of the distribution as peak level and corresponding significant wave height is chosen, based on Gaussian
The bivariate distribution functions such as copula, GH copula, Clayton copula and Frank copula build peak level with it is corresponding
Significant wave height two-dimentional Joint Distribution model, according to related coefficient index method, try to achieve the parameter of a variety of two dimension copula functions
Value.Theoretical joint probability and the experience joint probability that two-dimentional copula functions are obtained is put respectively is plotted in figure, analyzes respectively a variety of
Minimum deviation the quadratic sum RSME and AIC of Two dimensional Distribution, choose optimal Two dimensional Distribution model, establish peak level has with corresponding
Imitate wave height joint probability density function curve.
Conversion of multiple edge distributions to Joint Distribution is completed based on Sklar theorems, if it is F that F (x, y), which is edge distribution,X
(x) and FY(y) two-dimentional joint distribution function, then certainly exist a binary Copula function C (u, v) and meet to arbitrary
(x,y)∈[–∞,+∞]2, have
F (x, y)=C (u, v)=C (FX(x),FY(y)) (12)
The density function of corresponding two dimension Joint Distribution F (x, y) is
C (u, v) is the density function of binary Copula function C (u, v) in formula.
Root mean square error method examine formula be：
In formula, F (xi,yi) and Fn(xi,yi) (x is represented respectivelyi,yi) place theoretical probability and empirical probability.
AIC (Akaike information criterion) method examine formula be：
AIC=nln (RSME2)+2k (15)
In formula, numbers of the k by estimating parameter in model；RSME is root-mean-square error (see formula (14))；N holds for sample
Amount, AIC values are smaller, illustrate that models fitting is better.
3) obtain peak level and corresponding significant wave height combines the return period：
It is Poisson distribution to consider that the frequency occurs for typhoon, and platform is established based on two-dimentional copula theories and multiple malformation theory
The two-dimentional Poisson overlapped distribution models of peak level and corresponding significant wave height during wind, analysis obtain peak level with should mutually have
The co-occurrence probability curve of the two-dimentional overlapped distribution models of wave height is imitated, and then obtain peak level and corresponding significant wave height combines weight
It is current, as storm tide nature intensity index.
The multivariate compound extreme value distribution formula that load extreme value corresponds in the case of environmental element is
G (u in formula1,u2,…,ud) it is joint density function.
If n obeys the Poisson that parameter is λ and is distributed, such as formula (1), then formula (16) turns to
G (u in formula1,u2,…,ud) it is joint density function.
More regional storm tide nature intensity multivariate joint probability distribution models are built in step S3, typhoon storm tide is to coastal area
Disaster is often that the joint return period of the two is to carry out storm tide intensity division with being produced at the same time with wave height by extreme water level
Important indicator, the respective stochastic variable for combining the return period as edge distribution in the coastal single area of the present embodiment selection, is examined
Consider typhoon and the influence of the frequency occurs, so as to build the Joint Distribution of more ground storm tide intensity, and then analyze in different location at the same time
The probability of Storm Surge Disasters occurs, specifically includes following steps：
(1), according to the obtained storm tide water levels of each department every of step S2 and the joint return period of wave height, as the storm
The natural intensity index of tide, draws the extreme water level of different regions and the scatter diagram for combining return period sequence with wave height, root
The characteristics of according to scatter diagram, be respectively adopted different regions Gumbel distributions, the distribution of III types of P-, Weibull distributions and Log-
Normal distributions, logarithm P-III types distribution (or other distribution patterns) etc. are fitted each department joint return period index,
Selection Optimal Distribution type (formula 9,10) is examined by K-S.
(2) analysis more regional storm tide naturally strength associated return periods：
Index using the joint return period of the storm tide water level of each department every and wave height as its natural intensity, then to more
A area carries out combined probability analysis, studies the co-occurrence of more regional storm tide intensity.
The Optimal Distribution result of selection as edge, utilizes copula function (binary using in above-mentioned steps (1) first
Normal, Frank, Clayton and G-H copula and other polynary Copula functions etc.), construct the more of each joint return period
Dimension dimension joint probability distribution (formula 18), carries out parameter Estimation and χ2Examine (formula 19), select most suitable copula functions conduct
Link the model that function is established, multivariate data is fitted.
Consider the frequency of storm tide, establish the multidimensional probability of recombination distribution (formula of more regional storm tide intensity indexs
17) joint probability isopleth, is drawn, obtains the joint return period of more regional storm tide intensity indexs.
According to polynary Sklar theorems, edge distribution connected using polynary Copula functions to obtain multidimensional probability model, d
Tieing up the density function f (x) being distributed is
In formula, x=(x1,x2,…,xd)；f1(x1),f2(x2),…,fd(xd) it is corresponding to one-dimensional edge distribution F1(x1),
F2(x2),…,Fd(xd) density function.
Introduce an obedience χ2The statistic M of distribution examines fit solution of the fitting function to real data,
A in formulaijThe number of the point of grid R (i, j), B are fallen into for data pointijRepresent model prediction obtain fall into R (i,
J) number of point.If significance is α, region of rejection isHerein Represent
The free degree is (k -1)2χ2The upside α quantiles of distribution.Therefore if statistic M is without falling into region of rejection, binary Copula models
Fitting is good；Otherwise it is fitted poor.
Made a concrete analysis of below by taking return period analysis of causing disaster is combined in the island of Taiwan Hsinchu with flower lotus storm tide as an example.Hua Lian and
Hsinchu is located at the island of Taiwan east and west sides, selects 2005-2013 to influence the typhoon of this two places at the same time, with water level and the joint of wave height
Index of the return period as storm tide intensity, studies the joint occurrence frequency of two places storm tide intensity, so as to for China Taiwan
Preventing and reducing natural disasters for side provides foundation.
Specifically：
1st, Hsinchu station storm tide intensity is analyzed：
Selection influences Hsin-chu area 75 Typhoon Process of 1998 to 2013, obtains the highest in every typhoon
Water level (WL) and the significant wave height (SWH) accordingly occurred.Assuming that it is λ that the frequency n of Hsin-chu area typhoon, which obeys parameter,
The Poisson distributions of=75/16=4.69, statistical analysis, has passed through χ during confidence level α=0.052Examine.
(1) fitting of distribution of water level and wave height
Optimal marginal distribution function is fitted in order to obtain, and maximum entropy distribution, Gumbel distributions and Log- is respectively adopted
Normal be distributed, to Hsinchu area 1998 to 2013 during peak level and its corresponding significant wave height statistical series into
Row fitting of distribution.The results are shown in Table 1 for the parameter Estimation being respectively distributed and the goodness of fit；
The parameter Estimation and goodness of fit result of 1 peak level of table and corresponding significant wave height
As shown in Table 1, peak level and the K-S of maximum entropy distribution and the Log-normal distribution of corresponding significant wave height are examined
StatisticRespectively less than standard statistic Dn(0.05)=0.1544, and Gumbel distributionIt is all higher than Dn(0.05)=
0.1544, in other words maximum entropy distribution and Log-normal distributions can be fitted 1998 to the 2013 each field typhoons in Hsinchu station
Peak level and corresponding significant wave height sequence.(Q values are smaller) is evaluated by minimum deviation sum-of-squares criterion, it is believed that maximum entropy point
Cloth can more preferably be fitted peak level and its corresponding significant wave height sequence.
(2) Joint Distribution of water level and wave height
Edge distribution of the fitting preferably maximum entropy distribution as peak level and corresponding significant wave height is chosen, is based on
Tetra- kinds of bivariate distribution function structures of Gaussian copula, GH copula, Clayton copula and Frank copula are most
High water level and the two-dimentional Joint Distribution model of corresponding significant wave height, according to related coefficient index method, try to achieve four kinds of two dimensions
The parameter value of copula functions such as table 2.
2 four kinds of copula function parameter estimates of table
| Two-dimentional copula functions | Gaussian copula | GH copula | Clayton copula | Frank copula |
| θ | 0.2544 | 1.1820 | 0.3640 | 1.4129 |
Theoretical joint probability and the experience joint probability that four kinds of copula functions are obtained is put respectively is plotted in figure, such as Fig. 2.From
, can be relatively more straight as can be seen that the data point in the case of four kinds of two dimension copula functions is distributed near 45 ° of straight lines in Fig. 2
See ground and find out in 75 Typhoon Process that four kinds of two dimension copula function pairs Hsinchu station occurs during 1998 to 2013 occur
Peak level and corresponding significant wave height fitting effect it is all relatively good.Table 3 is the minimum deviation quadratic sum RSME of Two dimensional Distribution
With AIC analysis results, the peak level and corresponding significant wave height joint probability established based on four kinds of two dimension copula functions are close
It is as shown in Figure 3 to spend function curve；
RMSE the and AIC values of the two dimension of table 3 copula fittings
(3) peak level and corresponding significant wave height combine the return period
It is Poisson distribution to consider that the frequency occurs for typhoon, and platform is established based on two-dimentional copula theories and multiple malformation theory
The two-dimentional Poisson overlapped distribution models of peak level and corresponding significant wave height during wind, analysis obtain peak level with should mutually have
The co-occurrence probability curve of four kinds of two-dimentional overlapped distribution models of wave height is imitated, as shown in Figure 4.
(4) storm tide strength grade divides
First using international hurricane strength grading standard, strength grading is carried out to the intensity of typhoon for influencing Hsinchu.It is existing
Wave height water level is combined into the return period as storm tide nature intensity index, in the world, American Association typhoon warning center (JTWC)
Sa Feier-Simpson's hurricane grade (Saffir-Simpson Hurricane Wind Scale, abbreviation SSHS) is proposed to hurricane
Monsoon intensity is classified (such as table 4).Hurricane strength is divided into one to Pyatyi, the higher highest sustained wind for representing hurricane of series by SSHS
Speed is higher, and the sustained wind velocity that SSHS is used refers to " 1 minute mean wind speed ".
4 SSHS hurricane hierarchical tables of table
As shown in Table 3, the two dimension of the peak level that two-dimentional Gaussian copula functions are established and corresponding significant wave height
RMSE the and AIC values of Joint Distribution are minimum, therefore select two-dimentional Poisson Gaussian copula models 1998 regional to Hsinchu
Peak level in 75 Typhoon Process occurred to 2013 carries out the solution of joint probability with corresponding significant wave height, obtains it
The joint return period (such as table 5).The table gives the grade of Hsinchu area intensity of typhoon at the same time (with the criteria for classifying of table 6).
5 Hsinchu platform wind grade classification of table
The grading standard of 6 typhoon storm tide intensity of table
(5) storm tide intensity and disaster comparison
Cause disaster record of the typhoon to Hsinchu coastal structures is gathered, such as table 7；
7 typhoon of table causes the record that coastal engineering is destroyed in Hsinchu
It was found from the SSHS classification results that table 5 provides：
The intensity for influencing Hsinchu area in (1) 1998 year to 2013 is that C5 grades of typhoon includes：Zeb(1998)、Bilis
(2000)、Haitang(2005)、Sepat(2007)、Jangmi(2008)、Megi(2010)、Songda(2011)、Muifa
(2011), Nanmadol (2011), Jelawat (2012), Usagi (2013) etc. 11, account for the 14.7% of all typhoons, according to
Table 5-7, the typhoon of above-mentioned C5 intensity do not cause engineering project disaster to Hsinchu coastal area；
(2) intensity for influencing Hsinchu area is the typhoon 18 of C4 ranks, accounts for the 24% of typhoon sum：Wherein Sinlaku
(2008) breakwater of Hsinchu seashore 60m long is damaged；Soulik (2003) makes the breakwater at seamount port cause damage；
Meanwhile according to table 7, intensity is divided into each 2 typhoons of C1 and C2, causes destruction to Hsinchu coastal engineering respectively.Therefore,
SSHS stagings are differentiating that obtained result and actual disastrous situation is widely different when influencing Hsinchu area intensity of typhoon.
It was found from the JWSG classification results that table 5 provides：The intensity for influencing Hsinchu area in 1998 to 2013 is IV grades
Typhoon only has 1, i.e. Saola (2012)；Intensity is the typhoon 2 of III level：Including Soulik (2013) and Trami (2013).
This 3 typhoons all cause very big destruction to the engineering structure of Hsinchu seashore, and intensity of typhoon is consistent with actual disastrous situation.But also
See, according to JWSG stage divisions, Fungwong (2008), Sinlaku (2008) and Morakot (2009) are divided into slightly
The condition of a disaster (SL), intensity I, but these three typhoons cause the engineering construction of coastal area different degrees of destruction.
2nd, Hua Lian stations storm tide intensity：
The analysis of Hua Lian stations storm tide intensity and division use and Hsinchu station same procedure, it is not described here in detail.
3rd, the return period caused disaster is combined in Hsinchu with flower lotus storm tide
The storm tide for influencing two places in 2005-2013 at the same time shares 54, etesian number statistics such as table 8.Choosing
The year frequency is fitted with Poisson distributions, the estimate of parameter lambda is 6.Fitting result such as Fig. 5 of Poisson distributions, intends
Conjunction works well.Therefore in two-dimentional probability of recombination model, the Poisson that the definite obedience parameter lambda of the frequency is 6 is distributed.
8 typhoon storm tide number of table counts
| Time | Number | Time | Number | Time | Number |
| 2005 | 7 | 2008 | 6 | 2011 | 5 |
| 2006 | 7 | 2009 | 4 | 2012 | 8 |
| 2007 | 6 | 2010 | 5 | 2013 | 6 |
(1) Hsinchu and the single regional storm tide intensity of flower lotus
For this 54 storm tide processes, obtain Hua Lian and the joint of the storm tide water level of Hsinchu two places every and wave height is reappeared
Phase, as the natural intensity index of the storm tide, its scatter diagram difference is as shown in Figure 6.
From fig. 6 it can be seen that two places it is respective joint the return period contain several especially big values, needed in fitting of distribution into
The especially big value processing of row.Gumbel distributions, the distribution of III types of P-, Weibull distributions and Log-normal distributions are selected, to this two groups of connection
Return period index is closed to be fitted.The result shows that these fitting effects are all bad.Therefore the distribution of logarithm P-III types is selected, to flower lotus
It is fitted respectively with the joint return period sequence of Hsinchu two places storm tide water level and wave height.Such as table 9, table 10, K-S inspection statistics
AmountRespectively less than standard statistic Dn(0.01)=0.1814 it, passed inspection.
Table 9 spends the reproduction value of lotus and Hsinchu storm tide intensity
Table 10 spends the fitting result of lotus and Hsinchu joint return period sequence
(2) Hsinchu and Hua Lian two places storm tide co-occurrence probability
The joint return period T of Yi Hualian and the storm tide water level of Hsinchu two places every and wave heightHL、THCAs its natural intensity
Index, then to the two indexs carry out combined probability analysis, study two places storm tide intensity co-occurrence.First with right
The result of number P- III type distributions utilizes copula functions (binary Normal, Frank, Clayton and G-H as edge
Copula), T is constructedHLAnd THCTwo-dimentional joint probability distribution.Its parameter Estimation and χ2Inspection result is shown in Table 11.Carry out χ2Examine
When, confidence alpha takes 0.05；In addition because sample size is 54, therefore grid number k × k can use 7 × 7 and 8 × 8.
Table 11 spends the binary copula parameter Estimations and χ of lotus and Hsinchu joint return period2Examine
As can be seen from Table 11, the parameter estimation result of all copula functions is all fallen within the range of it；But only
Clayton copula functions have passed through χ when grid k × k takes 7 × 72Examine.Therefore Clayton copula are selected as company
The model that function is established is tied, binary data is fitted.Consider the frequency of storm tide, establish flower lotus and Hsinchu two places wind
The two-dimentional probability of recombination distribution of sudden and violent tide intensity index, joint probability isopleth such as Fig. 7, Hua Lian and Hsinchu two places storm tide intensity refer to
Target combines return period such as table 12.
Table 12 spends lotus, the joint return period of 54, Hsinchu storm tide intensity
And then the joint occurrence frequency of colored lotus and Hsinchu two places storm tide intensity has been obtained, this method feasibility is strong, analysis
Reliable results, effective foundation is provided for preventing and reducing natural disasters for China Taiwan place.
In conclusion the core concept of this programme is：The single regional joint return period is obtained first, and then selection is suitable
Edge distributions of the distribution pattern as multiple area joint return periods, next select suitable Copula functions as connection
The multivariable joint probability distribution of construction of function joint return period, the joint for finally obtaining more regional storm tide intensity indexs are reappeared
Phase, this method provide theoretical foundation for more regions resources allotments under storm tide, and any person skilled in the art may
The equivalent embodiment of equivalent variations, or adjustable corresponding steps are changed or are modified as using the technology contents of the disclosure above
Realize identical purpose, but it is every without departing from technical solution of the present invention content, and the technical spirit according to the present invention is implemented to more than
Any simple modification, equivalent variations and the remodeling that example is made, still fall within the protection domain of technical solution of the present invention.
Claims (10)
1. a kind of coastal more ground storm tide joint nature strength analysis method, it is characterised in that comprise the following steps：
A, how regional while affected more storm tides are selected to carry out statistical analysis, list counts etesian number,
Poisson distribution is selected to be fitted the year frequency；
B, for the storm tide process for influencing more ground at the same time, the joint for analyzing the storm tide water level of each department every and wave height is reappeared
Phase, and as the natural intensity index of the storm tide；
C, according to the storm tide water level of each department every obtained in step B and the joint return period of wave height, more regional storms are built
Multiple areas are carried out combined probability analysis by damp nature intensity multivariate joint probability distribution model；
D, more regional storm tide joint nature intensity are obtained.
2. analysis method according to claim 1, it is characterised in that：In the step B, the storm tidewater of each department every is analyzed
The joint return period of position and wave height specifically includes following steps：
B1, the fitting of distribution for carrying out water level and wave height；
The Joint Distribution of B2, analysis water level and wave height；
B3, acquisition peak level combine the return period with corresponding significant wave height.
3. analysis method according to claim 1, it is characterised in that：The more regional storm tide nature intensity of step C structures
Multivariate joint probability distribution model specifically includes following steps：
C1, the joint return period according to the obtained storm tide water levels of each department every of step B and wave height, draw different regions most
The scatter diagram for combining return period sequence of high water level and significant wave height, and different distributions is respectively adopted according to the characteristics of scatter diagram
Type is fitted each department joint return period index, and selection Optimal Distribution is examined by K-S；The distribution pattern includes
Gumbel distributions, the distribution of III types of P-, Weibull distributions, Log-normal distributions and the distribution of logarithm P-III types；
C2, analysis obtain more regional storm tide naturally strength associated return periods.
4. analysis method according to claim 2, it is characterised in that：The step B1 specifically includes following steps：
Peak level and significant wave height in every B11, statistics Typhoon Process；
B12, the edge distribution fitting for carrying out peak level and significant wave height；
B13, carry out distribution model test, chooses optimal edge distribution function.
5. analysis method according to claim 2, it is characterised in that：The step B2 specifically includes following steps：
B21, the optimal Coupla functions of selection build two-dimentional Joint Distribution model of the peak level with corresponding significant wave height；
B22, establish in Typhoon Process, the two-dimentional Poisson distribution composite model of peak level and corresponding significant wave height.
6. analysis method according to claim 2, it is characterised in that：In the step B3, based on two dimension in step B2
The two-dimentional Poisson that copula theories and multiple malformation theory establish peak level and corresponding significant wave height in Typhoon Process is answered
After closing distributed model, analysis obtains the co-occurrence probability curve of peak level and the two-dimentional overlapped distribution models of corresponding significant wave height,
And then obtain peak level and corresponding significant wave height combines the return period, as storm tide nature intensity index.
7. analysis method according to claim 3, it is characterised in that：The step C2 is specifically included：
C21, using Optimal Distribution types results in step C1 as edge, by the use of copula functions as link construction of function respectively combine
The multivariable joint probability distribution of return period：In formula, x=(x1,x2,…,
xd)；f1(x1),f2(x2),…,fd(xd) it is corresponding to one-dimensional edge distribution F1(x1),F2(x2),…,Fd(xd) density function,
The copula functions include binary Normal, Frank, Clayton and G-H copula and other polynary Copula functions；
C22 and then progress parameter Estimation and χ2Examine, select optimal copula functions as function is linked and establish model, to more
Metadata is fitted；
C23, the multidimensional probability of recombination distribution for establishing more regional storm tide intensity indexs, draw joint probability isopleth, obtain more
The joint return period of regional storm tide intensity index.
8. analysis method according to claim 4, it is characterised in that：Peak level and significant wave are carried out in the step B12
High edge distribution fitting, is respectively adopted different distributions to the peak level during coastal a certain regional statistics and its corresponding
Significant wave height statistical series carry out fitting of distribution, and the distribution includes maximum entropy distribution, Gumbel distributions and Log-normal and divides
Cloth.
9. analysis method according to claim 4, it is characterised in that：In the step B13, when carrying out distribution model test
Using K-S methods of inspection or root-mean-square error method of inspection.
10. analysis method according to claim 5, it is characterised in that：In the step B21, a variety of distribution letters are primarily based on
Number builds two-dimentional Joint Distribution model of the peak level with corresponding significant wave height, and the distribution function includes Gaussian
Copula, GH copula, Clayton copula and Frank copula, and fitting preferably distribution function is chosen as most
The edge distribution of high water level and corresponding significant wave height；When selection is fitted preferably distribution function, according to related coefficient index method, obtain
Obtain the parameter value of a variety of two dimension copula functions, theoretical joint probability and the experience joint probability that two-dimentional copula functions are obtained
Point is plotted in figure respectively, analyzes minimum deviation the quadratic sum RSME and AIC of a variety of Two dimensional Distributions respectively, and then choose optimal copula
The two-dimentional Joint Distribution model of construction of function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711367800.7A CN108021786B (en) | 2017-12-18 | 2017-12-18 | Coastal multi-geowind storm surge combined natural intensity analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711367800.7A CN108021786B (en) | 2017-12-18 | 2017-12-18 | Coastal multi-geowind storm surge combined natural intensity analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108021786A true CN108021786A (en) | 2018-05-11 |
| CN108021786B CN108021786B (en) | 2021-11-09 |
Family
ID=62073981
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711367800.7A Active CN108021786B (en) | 2017-12-18 | 2017-12-18 | Coastal multi-geowind storm surge combined natural intensity analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108021786B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109657409A (en) * | 2019-01-15 | 2019-04-19 | 西南交通大学 | A kind of sea-crossing bridge structural optimization method extremely responded based on stormy waves Joint Distribution |
| CN110134981A (en) * | 2019-02-02 | 2019-08-16 | 中国海洋大学 | A kind of new analysis method of the pipeline endoparticle deposition process based on ratio research |
| CN115270080A (en) * | 2022-09-27 | 2022-11-01 | 中国海洋大学 | Method for quickly generating sea condition time history |
| CN115828637A (en) * | 2023-01-09 | 2023-03-21 | 交通运输部天津水运工程科学研究所 | Method and system for determining extreme parameters by combining multiple factors of offshore wind, wave and tide level |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102063527A (en) * | 2010-12-20 | 2011-05-18 | 中国海洋大学 | Typhoon influence considered method for calculating combined return period of ocean extreme value |
| WO2014205497A9 (en) * | 2013-06-26 | 2015-04-02 | Climate Risk Pty Ltd | Computer implemented frameworks and methodologies for enabling climate change related risk analysis |
| CN104615907A (en) * | 2015-03-11 | 2015-05-13 | 武汉大学 | Method for deriving and designing flood process line based on multi-variable most possible condition combination |
| CN104732104A (en) * | 2015-04-07 | 2015-06-24 | 东南大学 | Method for calculating extreme high water levels in different reappearance periods under insufficient long-term tide level data condition |
| CN107103173A (en) * | 2016-10-31 | 2017-08-29 | 陈柏宇 | A kind of Design Wave projectional technique for embodying the influence of the factor of typhoon three |
-
2017
- 2017-12-18 CN CN201711367800.7A patent/CN108021786B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102063527A (en) * | 2010-12-20 | 2011-05-18 | 中国海洋大学 | Typhoon influence considered method for calculating combined return period of ocean extreme value |
| WO2014205497A9 (en) * | 2013-06-26 | 2015-04-02 | Climate Risk Pty Ltd | Computer implemented frameworks and methodologies for enabling climate change related risk analysis |
| CN104615907A (en) * | 2015-03-11 | 2015-05-13 | 武汉大学 | Method for deriving and designing flood process line based on multi-variable most possible condition combination |
| CN104732104A (en) * | 2015-04-07 | 2015-06-24 | 东南大学 | Method for calculating extreme high water levels in different reappearance periods under insufficient long-term tide level data condition |
| CN107103173A (en) * | 2016-10-31 | 2017-08-29 | 陈柏宇 | A kind of Design Wave projectional technique for embodying the influence of the factor of typhoon three |
Non-Patent Citations (2)
| Title |
|---|
| 董胜 等: ""异地海域年极值风暴增水同现规律的探讨"", 《中国海洋大学学报》 * |
| 陶山山: "多维最大熵模型及其在海岸和海洋工程中的应用研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109657409A (en) * | 2019-01-15 | 2019-04-19 | 西南交通大学 | A kind of sea-crossing bridge structural optimization method extremely responded based on stormy waves Joint Distribution |
| CN110134981A (en) * | 2019-02-02 | 2019-08-16 | 中国海洋大学 | A kind of new analysis method of the pipeline endoparticle deposition process based on ratio research |
| CN110134981B (en) * | 2019-02-02 | 2023-05-26 | 中国海洋大学 | Novel analysis method for particle deposition process in pipeline based on proportion research |
| CN115270080A (en) * | 2022-09-27 | 2022-11-01 | 中国海洋大学 | Method for quickly generating sea condition time history |
| CN115270080B (en) * | 2022-09-27 | 2023-01-31 | 中国海洋大学 | Method for quickly generating sea condition time history |
| CN115828637A (en) * | 2023-01-09 | 2023-03-21 | 交通运输部天津水运工程科学研究所 | Method and system for determining extreme parameters by combining multiple factors of offshore wind, wave and tide level |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108021786B (en) | 2021-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108021786A (en) | A kind of coastal more ground storm tide joint nature strength analysis method | |
| CN109814175A (en) | A kind of satellite-based strong convection monitoring method and its application | |
| CN105425320A (en) | Probabilistic forecasting method and system of coastal gale caused by tropical cyclone | |
| Qi et al. | Multi-factor evaluation indicator method for the risk assessment of atmospheric and oceanic hazard group due to the attack of tropical cyclones | |
| Liu et al. | Numerical study on factors influencing typhoon-induced storm surge distribution in Zhanjiang Harbor | |
| Brinkkemper et al. | Parameterization of wave run-up on beaches in Yucatan, Mexico: A numerical study | |
| Imaduddina et al. | Sea level rise flood zones: Mitigating floods in Surabaya coastal area | |
| CN111222662A (en) | Power grid typhoon flood disaster early warning method and device | |
| CN115239156A (en) | Method for warning influence of urbanization indexes on water system structure | |
| Jeon et al. | Characterization of extreme precipitation within atmospheric river events over California | |
| Panagoulia et al. | Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece | |
| CN109241369B (en) | Rainfall isopleth construction method based on grid stretching method | |
| CN104699979B (en) | Urban lake storehouse algal bloom Study on prediction technology of chaotic series based on complex network | |
| CN111898089B (en) | Method for determining effective wave height and water increasing joint probability of typhoon affecting sea area | |
| Kisi et al. | Wind speed prediction by using different wavelet conjunction models | |
| Kang et al. | Disaster vulnerability assessment in coastal areas of Korea | |
| CN115980885A (en) | Rainfall forecast deviation correction method based on ensemble forecast | |
| Gramstad et al. | Projected changes in the occurrence of extreme and rogue waves in future climate in the North Atlantic | |
| Hisamatsu et al. | Estimation of expected loss by storm surges along Tokyo Bay coast | |
| Roeder et al. | Mapping lightning fatality risk | |
| Raj et al. | Cyclone preparedness strategies for regional power transmission systems in data-scarce coastal regions of India | |
| GUPTA | CLIMATE NETWORKS | |
| Gatti et al. | An Assessment of Severe Storms, Their Impacts and Social Vulnerability in Coastal Areas: A Case Study of General Pueyrredon, Argentina | |
| Zhang et al. | Discussion on evaluating the vulnerability of storm surge hazard bearing bodies in the coastal areas of Wenzhou | |
| Jiang | Dynamic feature extraction of flood disaster data based on improved Gabor wavelet transform. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |