CN110533245A - A kind of sliding squid fishing ground amount prediction technique of Argentina based on Hai Biaowen - Google Patents
A kind of sliding squid fishing ground amount prediction technique of Argentina based on Hai Biaowen Download PDFInfo
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Abstract
The sliding squid fishing ground amount prediction technique of Argentina that the invention discloses a kind of based on Hai Biaowen, comprising: region and the corresponding Resources Richness Rate Index LogCatch and sea surface temperature SST for dividing region of counting statistics step S100, are divided to sea area to be measured；Step S200, coefficient R is calculated according to the region corresponding Resources Richness Rate Index LogCatch and sea surface temperature SST after division, the sea area for selecting correlation high is as survey region；Step S300, equation is established according to related coefficient, acquire resource abundance prediction model, and optimal models are selected according to statistics, sliding squid fishing ground amount is predicted according to optimal models, the sliding squid fishing ground abundance of Falkland sea area Argentina is predicted using feeding ground and spawning ground SST, optimal models are obtained, maximizes and improves precision of prediction, effectively solve the technical problem in background technique.
Description
Technical field
The present invention relates to marine resources research technical fields, specifically, the present invention relates to a kind of based on Hai Biaowen Ah
Slide squid fishing ground amount prediction technique in the root court of a feudal ruler.
Background technique
The sliding squid of Argentina is one of most important economic siphonopods in the world, has the spies such as life cycle is short, growth is fast
Point, resource is extremely abundant and is highly prone to the influence of environment and goes out border difference now, and integrated distribution is big in the southwest of 22 °~54 ° S
Western continental shelf and Lu Po, wherein especially abundant with 35 °~52 ° S resources.
Falkland sea area is located in Brazil Current and Falkland cold current intersection, and primary productivity is high, bait is extremely abundant, is
One of sliding squid key operation fishing ground of Argentina, and be uniquely to be fished for a long time, on a large scale to foreign fishing boat granting squid in the world
The fishing ground of licensing.
Existing research shows that the sliding squid fishing ground amount of Argentina and Hai Biaowen are closely related, and Wang Jintao etc. (2014) utilizes phase
The analysis of closing property, selecting influences the key area SST of its resource magnitude of recruitment in breeding area, by establishing multivariate linear model and BP mind
The sliding squid fishing ground magnitude of recruitment of Argentina is predicted through network model.Wang Yanfeng etc. (2019) utilizes grey correlation analysis, leads to
The grey absolute correlation degree of the time sequential value and egg-laying season (the 6-8 month) spawning ground Hai Biaowen time sequential value that calculate CPUE is crossed,
It chooses key sea area SST and establishes the sliding squid fishing ground abundance prediction model of Argentina.
The studies above show at present domestic and foreign scholars squid spawning ground SST sliding on Argentina influence its resource magnitude of recruitment into
And it influences its resource abundance and has carried out good research, and establish corresponding prediction model, but these researchs have focused largely on
High sea, and Falkland sea area is as one of sliding squid key operation fishing ground of Argentina, the world, the sliding squid fishing ground amount in Falkland sea area
Prediction technique it is very few, and prediction technique in the prior art mostly only selection spawning ground or feeding ground sea-surface temperature
It is individually analyzed, often accuracy is not high for obtained result.
Summary of the invention
In order to find more efficiently implementation, the sliding squid money of Argentina that the present invention provides a kind of based on Hai Biaowen
Prediction technique is measured in source, using the sea surface temperature SST in feeding ground and spawning ground to the sliding squid fishing ground abundance of Falkland sea area Argentina
It is predicted, obtains optimal models, maximized and improve precision of prediction, effectively solve the technical problem in background technique.
To achieve the above object, sliding squid fishing ground amount prediction side, Argentina that the invention discloses a kind of based on Hai Biaowen
Method, comprising:
Step S100, region and the corresponding Resources Richness Rate Index for dividing region of counting statistics are divided to sea area to be measured
LogCatch and sea surface temperature SST；
Step S200, it is calculated according to the region corresponding Resources Richness Rate Index LogCatch and sea surface temperature SST after division
Coefficient R, the sea area for selecting correlation high is as survey region；
Step S300, equation is established according to related coefficient, acquires resource abundance prediction model, and optimal according to statistics selection
Model predicts sliding squid fishing ground amount according to optimal models.
Further, in the step S100, sea area to be measured divides region detailed process are as follows: the oviposition in selection oviposition month
And forage month feeding ground, selected spawning ground and feeding ground are divided into 1 ° × 1 ° of element mesh according to longitude and latitude
Lattice.
Further, the Catch in the corresponding Resources Richness Rate Index for dividing region in the step S100 indicates to correspond to
Divide the catch in region.
Further, in the step S200, the calculating process of related coefficient are as follows:
R=Cov (LogCatch, SST)/(Var [LogCatch] Var [SST])1/2；
Wherein Cov (LogCatch, SST) indicates the association side between Resources Richness Rate Index LogCatch and sea surface temperature SST
Difference, Var [LogCatch] indicate Resources Richness Rate Index LogCatch variance, Var [SST]) indicate sea surface temperature SST side
Difference.
Further, in the step S200, the high region of correlation includes the region that coefficient R is greater than 0.5.
Further, the alternative condition of optimal models is R in the step S3002Maximum region.
Further, in the step S300, the solution procedure of the prediction model are as follows:
(Y1, Y2 ..., Y15)=(X1, X2 ..., X15) (β 1, β 2 ..., β 15)+(e1, e2 ..., e15)；
Wherein Yi indicates Resources Richness Rate Index Log (Catch)；
β i is equation coefficient, and Xi is the sea surface temperature SST of survey region, wherein i=1,2,3 ... n, n positive integer.
Compared with prior art, a kind of sliding squid fishing ground amount prediction technique of Argentina based on Hai Biaowen of the invention has
It is following the utility model has the advantages that
Application scheme carries out the sliding squid fishing ground abundance of Falkland sea area Argentina using feeding ground and spawning ground SST pre-
It surveys, correlation analysis is carried out between amount of fishing respectively to feeding ground and spawning ground, the sea area SST for selecting correlation high is as pre-
The factor is surveyed, and final choice goes out optimal prediction model, improves the accuracy of subsequent prediction result, is conducive to accurately grasp sea
Midocean slides the case where squid fishing ground abundance.
The additional aspect of the present invention and advantage will be set forth in part in the description, these will become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is prediction technique workflow schematic diagram of the present invention；
Fig. 2 is prediction result of the invention and actual result contrast schematic diagram.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Referring to Fig. 1, the sliding squid fishing ground amount prediction side, Argentina that the embodiment of the invention discloses a kind of based on Hai Biaowen
Method, comprising:
Step S100, region and the corresponding Resources Richness Rate Index for dividing region of counting statistics are divided to sea area to be measured
LogCatch and sea surface temperature SST；
Wherein, sea area to be measured divides region detailed process are as follows: the spawning ground in selection oviposition month and the forage in forage month
, selected spawning ground and feeding ground are divided into 1 ° × 1 ° of unit grid according to longitude and latitude.
The Catch in the corresponding Resources Richness Rate Index for dividing region in the step S100 indicates corresponding division region
Catch.
Step S200, it is calculated according to the region corresponding Resources Richness Rate Index LogCatch and sea surface temperature SST after division
Coefficient R, the sea area for selecting correlation high is as survey region；
In the step S200, the calculating process of related coefficient are as follows:
R=Cov (LogCatch, SST)/(Var [LogCatch] Var [SST])1/2；
Wherein Cov (LogCatch, SST) indicates the association side between Resources Richness Rate Index LogCatch and sea surface temperature SST
Difference, Var [LogCatch] indicate Resources Richness Rate Index LogCatch variance, Var [SST]) indicate sea surface temperature SST side
Difference.
Step S300, equation is established according to related coefficient, acquires resource abundance prediction model, and optimal according to statistics selection
Model predicts sliding squid fishing ground amount according to optimal models.
In the step S200, the high region of correlation includes the region that coefficient R is greater than 0.5.
The alternative condition of optimal models is R in the step S3002Maximum region.
In the step S300, the solution procedure of the prediction model are as follows:
(Y1, Y2 ..., Y15)=(X1, X2 ..., X15) (β 1, β 2 ..., β 15)+(e1, e2 ..., e15)；
Wherein Yi indicates Resources Richness Rate Index Log (Catch)；
β i is equation coefficient, and Xi is the sea surface temperature SST of survey region, wherein i=1,2,3 ... n, n positive integer.
Embodiment
To illustrate simulation and forecast situation, now carried out using the cunning squid fishing ground situation in Falkland sea area as data pre-
Survey, the mode combined respectively for spawning ground SST, feeding ground SST, spawning ground SST and feeding ground SST as predictive factor into
Row prediction, analyzes prediction result.
Selection geospatial area is 55.5 ° of W~65.5 ° W, 44.5 ° of S~52.5 ° S, and spatial resolution is 1 ° of longitude and latitude
×1°。
With reference to former years amount of fishing historical data:
Using catch as resource abundance index, because catch is different with the SST order of magnitude, therefore to its Resources Richness Rate Index
By taking logarithm log (catch) to be standardized, the catch (LogCatch) after conversion is used as Resources Richness Rate Index, simultaneously
Index as next year resource magnitude of recruitment.
Using canonical correlation analysis method, lay eggs month to spawning ground (55.5 ° of W~65.5 ° W, 42.5 ° of S~49.5 ° S)
(the 5-7 month) SST and feeding ground (55.5 ° of W~65.5 ° W, 44.5 ° of S~52.5 ° S) forage month (the 1-6 month) SST be divided into 1 ° ×
1 ° of unit grid and log (catch) carries out correlation analysis, such as 55.5 ° of feeding ground in January W, 42.5 ° of S cell grid table temperature
It is calculated with the relative coefficient of LogCatch as follows:
SST (55.5 ° of W, 42.5 ° of S)=
[17.279,15.201,15.337,18.043,13.854,15.698,14.095,17.582,15.251,
16.272,17.110,14.630,15.864,16.134,15.775]
LogCatch=
[9.504,11.546,7.450,8.979,11.358,11.992,11.577,3.761,9.402,11.282,
11.374,11.868,12.632,12.787,7.765]
Related coefficient, R=-0.42508 are acquired according to the formula of related coefficient
The related coefficient calculating of other grids is same as above, using the sea area of correlation high (R > 0.5) as survey region, and by area
SST in domain is as impact factor.
The SST of three key areas has been determined by related coefficient, for establishing equation, has utilized oviposition region
(Area1:55.5 ° of W~57.5 ° W, 42.5 ° of S~44.5 ° S) SST and LogCatch establish a linear equation, and process is as follows:
(Y1, Y2 ..., Y15)=(X1, X2 ..., X15) (β 1, β 2 ..., β 15)+(e1, e2 ..., e15)
(11.546,7.450 ..., 7.7647)=(7.803,7.600 ..., 7.070) (β 1, β 2 ..., β 15)+
(e1,e2,……,e15)
Here e indicates random error vector, meets E (e)=0, cov (e, e)=Σ
Y=5.018X1-28.986, R can be solved2Value is R2=0.4306 (P=0.006448 < 0.05).
Wherein, Y is log (Catch), and β is the coefficient of equation, and X is the SST value that survey region influences resource abundance.Upper
It states in the sliding squid fishing ground abundance prediction model of multiple Argentina, selects statistically R2It is worth maximum model as optimal models.
Concrete outcome are as follows:
By above-mentioned analytic process, oviposition month (5-7 month) lay eggs region (55.5 ° of W~65.5 ° W, 42.5 ° of S~
49.5 ° of S), each 1 ° × 1 ° of SST of each moon and resource magnitude of recruitment make correlation analysis, and discovery has continuous sheet of area in July
Domain and resource magnitude of recruitment are positively correlated in significant, (Area1) distribution of region one be 55.5 ° of W~57.5 ° W, 42.5 ° of S~
44.5°S；In forage month (the 1-7 month) forage region (55.5 ° of W~65.5 ° W, 44.5 ° of S~52.5 ° S), each 1 ° of each moon
× 1 ° of SST and Resources Richness Rate Index makees correlation analysis, and discovery has continuous sheet of region and current year in February and March respectively
For resource abundance in significant negative correlation, (Area2) distribution of region two is 57.5 ° of W~62.5 ° W, 42.5 ° of S~51.5 ° S, area
(Area3) distribution of domain three is 55.5 ° of W~59.5 ° W, 42.5 ° of S~46.5 ° S, to obtain three estimation ranges and correspondence
Prediction model:
Referring specifically to following table
First, with the spawning ground 2002-2015 (July) key area (55.5 ° of W~57.5 ° W, 42.5 ° of S~44.5 °
S) SST establishes the sliding squid fishing ground abundance prediction model of Argentina are as follows: Y=5.018X1-28.986, R as predictive factor2Value
For R2=0.4306 (P=0.006448 < 0.05).
Specifically prediction result and actual result comparison are as follows:
Second, with the feeding ground 2002-2016 (2 months) key area (57.5 ° of W~62.5 ° W, 42.5 ° of S~51.5 °
S), feeding ground (March) key area (55.5 ° of W~59.5 ° W, 42.5 ° of S~46.5 ° S) SST as predictive factor, establishes Ah root
Slide squid fishing ground abundance prediction model in the court of a feudal ruler are as follows: Y=-1.0454X1-0.8838X2+36.1243, R2Value is R2=0.3203 (P
=0.03912 < 0.05).
Specifically prediction result and actual result comparison are as follows:
Third is total to three pieces key sea area SST as predictive factor, with 2002-2015 in conjunction with spawning ground and feeding ground
Resource abundance prediction model is established between the sliding squid amount of fishing of Falkland sea area Argentina are as follows: Y=3.4639X1-0.6635X2-
0.4373X3-2.1328, R2Value is R2=0.4311 (P=0.03458 < 0.05).
Specifically prediction result and actual result comparison are as follows:
By the comparative analysis of above three model, it can be concluded that, the combination spawning ground chosen herein and feeding ground SST are as pre-
The forecast result of model for surveying the factor is best, and the resource abundance variation tendency of actual value and predicted value is as shown in Figure 2.
In application scheme, using feeding ground and spawning ground SST to the sliding squid fishing ground abundance of Falkland sea area Argentina
Predicted, the sea area SST for selecting correlation high as predictive factor, to feeding ground and spawning ground respectively between amount of fishing into
Row correlation analysis, the prediction model prediction effect in conjunction with spawning ground and feeding ground SST are best.
The above is only some embodiments of the invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (7)
1. a kind of sliding squid fishing ground amount prediction technique of Argentina based on Hai Biaowen characterized by comprising
Step S100, to sea area to be measured divide the corresponding Resources Richness Rate Index LogCatch for dividing region of region and counting statistics and
Sea surface temperature SST；
Step S200, related to sea surface temperature SST calculating according to the corresponding Resources Richness Rate Index LogCatch in region after division
Coefficients R, the sea area for selecting correlation high is as survey region；
Step S300, equation is established according to related coefficient, acquires resource abundance prediction model, and optimal mould is selected according to statistics
Type predicts sliding squid fishing ground amount according to optimal models.
2. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
In the step S100, sea area to be measured divides region detailed process are as follows: the spawning ground in selection oviposition month and the rope in forage month
Selected spawning ground and feeding ground are divided into 1 ° × 1 ° of unit grid according to longitude and latitude by bait field.
3. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
The Catch in the corresponding Resources Richness Rate Index for dividing region in the step S100 indicates the corresponding catch for dividing region.
4. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
In the step S200, the calculating process of related coefficient are as follows:
R=Cov (LogCatch, SST)/(Var [LogCatch] Var [SST])1/2；
Wherein Cov (LogCatch, SST) indicates the covariance between Resources Richness Rate Index LogCatch and sea surface temperature SST,
Var [LogCatch] indicate Resources Richness Rate Index LogCatch variance, Var [SST]) indicate sea surface temperature SST variance.
5. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
In the step S200, the high region of correlation includes the region that coefficient R is greater than 0.5.
6. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
The alternative condition of optimal models is coefficient R in the step S3002Maximum region.
7. the sliding squid fishing ground amount prediction technique of a kind of Argentina based on Hai Biaowen as described in claim 1, which is characterized in that
In the step S300, the solution procedure of the prediction model are as follows:
(Y1, Y2 ..., Y15)=(X1, X2 ..., X15) (β 1, β 2 ..., β 15)+(e1, e2 ..., e15)；Wherein Yi
It indicates Resources Richness Rate Index Log (Catch)；
βiFor equation coefficient, XiFor the sea surface temperature SST of survey region, wherein i=1,2,3 ... n, n positive integer.
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