CN109460867A - Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index - Google Patents
Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index Download PDFInfo
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Abstract
The Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index that the invention discloses a kind of comprising following steps: the ocean Nino index ONI value monthly in the Eastern Pacific sea area of the Chilean scad distribution of N before obtaining；Utilize Time series analysis method, correlation analysis is carried out to the ONI value of Chilean scad resource abundance ln (CPUE) and preceding N monthly, the moon ONI value of statistically relevant P < 0.05 is obtained, those month ONI value is as the climatic factor for influencing Chilean scad resource abundance；P value on multiple Chilean scad resource abundance prediction models and counting statistics is established using multiple linear equation；In above-mentioned multiple Chilean scad resource abundance prediction models, select statistically the smallest model of P value as optimal models.
Description
Technical field
The present invention relates to fish resource abundance medium- and long-term forecasting technical fields, are based on ocean Nino index more particularly to one kind
Chilean scad resource abundance medium- and long-term forecasting method.
Background technique
Chilean bamboo fish is important fish resources at the middle and upper levels, is typical straddling fish stocks at the middle and upper levels, is sociability type,
Normal dense clusters.10~300m of the depth of water is inhabited, there is diel vertical migration habit.It is mainly distributed on entire southeast Pacific, is wrapped
Include Chile, Peru and Ecuador exclusive economic zone and the ocean waters adjoined.Research thinks, Chilean 2 age of bamboo fish fish length
About 21cm, 3 age fishes are about 26cm, and 4 age fishes are about 31cm, and 5 age fishes are about 35cm, 9 age fish about 49cm.Fertility is medium,
A length of 200~250mm is pitched in sexal maturity for the first time, and group lays eggs in batches, year magnitude of recruitment it is very high, individual growth is rapid.Annual August arrives
March in next year is the egg-laying season of Chilean bamboo fish.Chilean bamboo fish fishery starts from nineteen fifty, mainly by Chile in its exclusive economy
It is caught in area, catch occupies the overwhelming majority (75%) of global bamboo fish catch.The Chile in southeast Pacific high sea
The fishery of bamboo fish then starts from 1978, is mainly produced by the fleet of the former Soviet Union.Into after the nineties, with the former Soviet Union
Disintegrate and other a variety of causes, the development and utilization of Chilean bamboo fish experienced significant fluctuations.Chilean bamboo fish crop is in nineteen ninety-five
Just decline rapidly only 1,420,000 tons of annual output, is returned again later until entering within 1999 low ebb after reaching 4,950,000 tons of peak
It rises, 2,500,000 tons of levels was restored to by 2001, stablize later at 2,000,000 tons or more.China starts from the exploitation of Chilean bamboo fish
2000,2003 Nian Hou other countries, including Belize, the Cook Islands, the Faeroe Islands, the European Community, South Korea, Russia, Wu Ke
The states such as blue and Vanuatu also joined the fishery one after another.
FAO expert was once compared research to pelagic fishes harvest fluctuation according to weather Long-term Fluctuation trend, it is believed that gas
The Long-term Fluctuation of time will directly affect the yield of pelagic fishes, and the alteration trend of the two reaches unanimity, and to Chilean bamboo
Yield after fish 2000 is predicted, it is believed that the Climate Fluctuations period is 65 years and 55 years, prediction result and actual conditions
It is very close.Chang Yongbo (2016) starts deep-sea fishing Co., Ltd large size according in April, 2002 in December, 2006 Shanghai and drags
Net processing fleet is standardized conversion to CPUE with GLM model, in conjunction with seawater surface temperature in the means of production of southeast Pacific
Sea Surface Temperature, SST data are spent, fish fishing ground is laughed at using Matlab software for Chilean bamboo and establishes spatial discrimination
Rate is 1 ° × 1 ° Bp nerve net fishery forescast network model, has studied four kinds of forecasting model structures 6-3-1,6-6-1,6-9-1 and 6-
The forecast result of 13-1, the results showed that, network structure is that the BP neural network that 6-9-1 hidden neuron quantity is 9 is relatively suitble to use
Make the fishery forescast model in southeast Pacific high sea Chile bamboo fish fishing ground.Wang Jintao etc. (2014) according to 2003-2009 I
Fleet, state catches the Fishing log data of Chilean bamboo fish, the sea surface temperature obtained in conjunction with ocean remote sensing in southeast Pacific sea area
(SST) and the marine environment factor such as sea level height (SSH), using principal component and BP neural network method to Chilean bamboo fish center
Fishing ground forecasting model is studied, and result of study shows through PCA treated BP neural network model that principal component established
Forecast accuracy be respectively 67% and 60%.The studies above shows that at present each scholar is to the center fishing of Chilean bamboo fish both at home and abroad
Field prediction model has carried out good research, but is then blank how to carry out look-ahead its resource abundance with climatic factor.
Summary of the invention
The present invention is in view of the problems of the existing technology and insufficient, provides a kind of Chilean bamboo pod based on ocean Nino index
Fish resource abundance medium- and long-term forecasting method.
The present invention is to solve above-mentioned technical problem by following technical proposals:
The present invention provides a kind of Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index, special
Point is comprising following steps:
The ocean Nino index ONI value monthly in the Eastern Pacific sea area of N Chile scad distribution before S1, acquisition；
S2, using Time series analysis method, to Chilean scad resource abundance ln (CPUE) and the ONI of preceding N monthly
Value carries out correlation analysis, obtains the moon ONI value of statistically relevant P < 0.05, those month ONI value is as the Chilean bamboo of influence
The climatic factor of pod fish resource abundance；
S3, P value on multiple Chilean scad resource abundance prediction models and counting statistics is established using multiple linear equation,
Its formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is single ship annual output, and a is normal
Number, b1, b2, b3 ..., bn be equation coefficient；X1, x2, x3 ..., xn be influence resource abundance moon ONI value；
S4, in above-mentioned multiple Chilean scad resource abundance prediction models, select statistically the smallest model of P value as
Optimal models.
On the basis of common knowledge of the art, above-mentioned each optimum condition, can any combination to get each preferable reality of the present invention
Example.
The positive effect of the present invention is that:
(1) prediction of Chilean bamboo fish resource abundance is carried out using ocean Nino index ONI；
(2) select each moon ONI value of preceding in Jan-Sept, 4 as the climatic prediction factor；
(3) Chilean bamboo fish resource abundance prediction model are as follows: ln (CPUE)=3.6226-1.0200*ONIT-4,2+
1.9411*ONIT-4,3-0.2959*ONIT-4,4, ONIT-4,2、ONIT-4,3、ONIT-4,4Be respectively 2 months 4 years before the Nino index of ocean,
The ONI value in March and April.
Detailed description of the invention
Fig. 1 is the flow chart of the Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index.
Fig. 2 is 2003-2011 Chile bamboo fish resource abundance ln (CPUE) variation diagram between year.
Fig. 3 is Chilean bamboo fish resource abundance ln (CPUE) actual value of 2003-2011 and predicted value change profile figure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, the present embodiment provides long-term in a kind of Chilean scad resource abundance based on ocean Nino index
Prediction technique comprising following steps:
Step 101, the ocean Nino index ONI monthly for obtaining the Eastern Pacific sea area that the Chilean scad of preceding N is distributed
Value；
Step 102, using Time series analysis method, monthly to Chilean scad resource abundance ln (CPUE) and preceding N
ONI value carry out correlation analysis, obtain the moon ONI value of statistically relevant P < 0.05, those month ONI value is as influencing
The climatic factor of Chilean scad resource abundance；
Step 103 establishes multiple Chilean scad resource abundance prediction models and counting statistics using multiple linear equation
Upper P value, formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is that single ship is produced per year
Amount, a are constant, b1, b2, b3 ..., the coefficient that bn is equation；X1, x2, x3 ..., xn be influence resource abundance moon ONI
Value；
Step 104, in above-mentioned multiple Chilean scad resource abundance prediction models, the selection statistically the smallest mould of P value
Type is as optimal models.
A specific example is named to illustrate the present invention, so that those skilled in the art better understood when this
The technical solution of invention.
1, material and method
(1) data source
Chilean bamboo fish is distributed in the sea area of Eastern Pacific, which is influenced by EI Nino, spawning ground and rope
Bait field is influenced by rising intensity of flow caused by peru current.In the sea area, EI Nino (El) it is one important
Climatic factor, EI Nino event refers in equator, the phenomenon that the Eastern Pacific sea a wide range of persistent anomaly of table is partially warm, judges
There is also certain differences in the world for standard.3 area's sea surface temperature anomalies index of NINO is generally reached 0.5 DEG C or more for continuous 6 months to determine
Justice is an EI Nino event, and 3 months sliding averages of 3.4 area's sea surface temperature anomalies of NINO are then reached 0.5 DEG C or more by the U.S.
It is defined as an EI Nino event.More fully to reflect in equator, the integral status of Eastern Pacific, state, China Meteorological Administration
Family's weather center is in business mainly using the sea surface temperature anomalies index in the complex zone NINO (area NINO 1+2+3+4) as judgement Earl
The foundation of Nino event, index are as follows: the complex zone NINO sea surface temperature anomalies index continues 6 months or more >=0.5 DEG C as an Earl
Nino event；If area's index continues 5 months >=0.5 DEG C, and the sum of 5 months index >=4.0 DEG C, it is also defined as an Earl
Nino event.
Ocean Nino index OceanicIndex (ONI) is from American National climatic prediction center
(http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_
V5.php) website, time period are in January, 1999 in December, 2017 (table 1).ONI is continuous trimestral average value.
Chilean production of the bamboo fish resource abundance index (CPUE unit is ton/ship) from Chinese ocean large size trawler
Amount, time are -2011 years 2003 (table 2).
Table in December, -2017 in January, 1 2000 ocean Nino index (ONI) monthly returns
(2) research method and step
Since Chilean bamboo fish resource abundance has a daily output of index with large-scale trawler, the index is because of production statistics etc.
Factor can generate error, therefore to its Resources Richness Rate Index by taking natural logrithm ln (CPUE) to be standardized.
Using Time series analysis method, correlation is carried out to ln (CPUE) value and the AAO value of the 1999-2011 1-12 month
Analysis obtains statistically relevant moon AAO value (statistically P < 0.05), the AAO value of these months is as the Chilean bamboo fish of influence
The climatic factor of resource abundance.
Multiple Chilean bamboo fish resource abundance prediction models, formula are as follows: ln (CPUE) are established using multiple linear equation
=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn.In formula, CPUE is single ship annual output, and a is constant, b1, b2, b3 ...,
Bn is the coefficient of equation；X1, x2, x3 ..., xn be influence resource abundance moon AAO value.In above-mentioned multiple Chilean bamboo fish moneys
In the abundance prediction model of source, select statistically the smallest model of P value as optimal models.
2, result of study
(1) resource abundance ln (CPUE) changes between year
As shown in Figure 2, Chilean bamboo fish resource abundance ln (CPUE) changes between significant year is presented, at 2006-2009
It is horizontal in high stock number；And 2003-2004, to be then within 2011 low stock number horizontal.
(2) the ONI value of resource abundance ln (CPUE) is influenced
Resource abundance ln (CPUE) and the correlation analysis of the ONI value of preceding 4 years each moons think, resource abundance ln (CPUE)
It is significant (P < 0.05) with the first ONI of in Jan-Sept, 4 value correlations, it presents and is positively correlated, related coefficient is respectively 0.7773,
0.8318,0.8979,0.8979,0.8017,0.7923,0.8351,0.8062,0.7870.Wherein 3-4 month ONI with first 4 years
Being worth correlation is extremely significant (P < 0.01).
Resource abundance ln (CPUE) thinks with first 3 years, the correlation analysis of the ONI value of 2 years first, preceding 1 year each moon, resource
Each moon ONI value correlation of abundance ln (CPUE) and preceding 1-3 is not significant (P ﹥ 0.05).
(3) model of resource abundance prediction is established
1) one of prediction model
4 years in the past 3-4 months ONI value (ONIT-4,3, ONIT-4,4) it is used as predictive factor, it is rich to establish Chilean bamboo fish resource
Spend prediction model are as follows:
Ln (CPUE)=3.5831+0.1995*ONIT-4,3+0.2519*ONIT-4,4
Its F value is 14.5305 (P=0.0050 < 0.05).
The statistical form of its actual value and predicted value such as table 3.
The Chilean bamboo fish resource abundance actual value of table 3 and predicted value and its residual error
2) the two of prediction model
4 years in the past 2-4 months ONI value (ONIT-4,2, ONIT-4,3, ONIT-4,4) it is used as predictive factor, establish Chilean bamboo fish
Resource abundance prediction model are as follows:
Ln (CPUE)=3.6226-1.0200*ONIT-4,2+1.9411*ONIT-4,3-0.2959*ONIT-4,4
Its F value is 32.6188 (P=0.0010 < 0.01).
The statistical form of its actual value and predicted value such as table 4.
The Chilean bamboo fish resource abundance actual value of table 4 and predicted value and its residual error
3) the three of prediction model
4 years in the past 3-5 months ONI value (ONIT-4,3, ONIT-4,4, ONIT-2,5) it is used as predictive factor, establish Chilean bamboo fish
Resource abundance prediction model are as follows:
Ln (CPUE)=3.5814+0.3585*ONIT-4,3-0.1304*ONIT-4,4+0.2087*ONIT-2,5
Its F value is 8.4166 (P=0.0212 < 0.05).
The statistical form of its actual value and predicted value such as table 5.
The Chilean bamboo fish resource abundance actual value of table 5 and predicted value and its residual error
4) the four of prediction model
4 years in the past 1-4 months ONI value (ONIT-4,1, ONIT-4,2, ONIT-4,3, ONIT-4,4) it is used as predictive factor, establish intelligence
Sharp bamboo fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6229+0.0813*ONIT-4,1-1.1582*ONIT-4,2+1.9614*ONIT-4,3-0.2553*
ONIT-4,4
Its F value is 19.8598 (P=0.0067 < 0.01).
The statistical form of its actual value and predicted value such as table 5.
The Chilean bamboo fish resource abundance actual value of table 5 and predicted value and its residual error
5) the five of prediction model
4 years in the past 2-5 months ONI value (ONIT-4,2, ONIT-4,3, ONIT-4,4, ONIT-2,5) it is used as predictive factor, establish intelligence
Sharp bamboo fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6252-1.0624*ONIT-4,2+1.9219*ONIT-4,3-0.0984*ONIT-4,4-0.1203*
ONIT-2,5
Its F value is 20.3293 (P=0.0064 < 0.01).
The statistical form of its actual value and predicted value such as table 5, model profile figure is shown in Fig. 2.
The Chilean bamboo fish resource abundance actual value of table 5 and predicted value and its residual error
6) the six of prediction model
4 years in the past 1-5 months ONI value (ONIT-4,1, ONIT-4,2, ONIT-4,3, ONIT-4,4, ONIT-2,5) as prediction because
Son establishes Chilean bamboo fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6399+0.7112*ONIT-4,1-2.4679*ONIT-4,2+2.0103*ONIT-4,3+1.1765*
ONIT-4,4-0.6804*ONIT-2,5
Its F value is 17.6050 (P=0.0197 < 0.05).
The statistical form of its actual value and predicted value such as table 6.
The Chilean bamboo fish resource abundance actual value of table 6 and predicted value and its residual error
By above-mentioned six model comparative analysis, it can be concluded that, this research selects first 4 years 2-4 month ONI values pre- as weather
Survey the factor, Chilean bamboo fish resource abundance prediction model are as follows: ln (CPUE)=3.6226-1.0200*ONIT-4,2+1.9411*
ONIT-4,3-0.2959*ONIT-4,4.The resource abundance variation tendency of its actual value and predicted value is as shown in Figure 3.
Although specific embodiments of the present invention have been described above, it will be appreciated by those of skill in the art that these
It is merely illustrative of, protection scope of the present invention is defined by the appended claims.Those skilled in the art is not carrying on the back
Under the premise of from the principle and substance of the present invention, many changes and modifications may be made, but these are changed
Protection scope of the present invention is each fallen with modification.
Claims (4)
1. a kind of Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index, which is characterized in that it is wrapped
Include following steps:
The ocean Nino index ONI value monthly in the Eastern Pacific sea area of N Chile scad distribution before S1, acquisition；
S2, using Time series analysis method, to Chilean scad resource abundance ln (CPUE) and the ONI value of preceding N monthly into
Row correlation analysis obtains the moon ONI value of statistically relevant P < 0.05, those month ONI value is as the Chilean scad of influence
The climatic factor of resource abundance；
S3, P value on multiple Chilean scad resource abundance prediction models and counting statistics is established using multiple linear equation, it is public
Formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is single ship annual output, and a is constant,
B1, b2, b3 ..., bn be equation coefficient；X1, x2, x3 ..., xn be influence resource abundance moon ONI value；
S4, in above-mentioned multiple Chilean scad resource abundance prediction models, select statistically the smallest model of P value as optimal
Model.
2. Chile's scad resource abundance medium- and long-term forecasting method as described in claim 1, which is characterized in that in step S2
In, according to the correlation analysis of Chilean scad resource abundance ln (CPUE) and the ONI value of preceding 4 years each moons, Chilean scad money
Source abundance ln (CPUE) is significant (P < 0.05) with the first ONI of in Jan-Sept, 4 value correlations, presents and is positively correlated, related coefficient point
Not Wei 0.7773,0.8318,0.8979,0.8979,0.8017,0.7923,0.8351,0.8062,0.7870, wherein with preceding 4
The 3-4 month ONI value correlation in year is extremely significant (P < 0.01)；
According to Chilean scad resource abundance ln (CPUE) and first 3 years, the correlation analysis of the ONI value of 2 years first, preceding 1 year each moon,
Chilean scad resource abundance ln (CPUE) and each moon ONI value correlation of preceding 1-3 be not significant (P ﹥ 0.05).
3. Chile's scad resource abundance medium- and long-term forecasting method as claimed in claim 2, which is characterized in that in step S3
In,
1) one of prediction model
4 years in the past 3-4 months ONI value (ONIT-4,3, ONIT-4,4) it is used as predictive factor, establish Chilean bamboo fish resource abundance prediction
Model are as follows:
Ln (CPUE)=3.5831+0.1995*ONIT-4,3+0.2519*ONIT-4,4
Its F value is 14.5305, P=0.0050 < 0.05；
2) the two of prediction model
4 years in the past 2-4 months ONI value (ONIT-4,2, ONIT-4,3, ONIT-4,4) it is used as predictive factor, establish Chilean bamboo fish resource
Abundance prediction model are as follows:
Ln (CPUE)=3.6226-1.0200*ONIT-4,2+1.9411*ONIT-4,3-0.2959*ONIT-4,4
Its F value is 32.6188, P=0.0010 < 0.01；
3) the three of prediction model
4 years in the past 3-5 months ONI value (ONIT-4,3, ONIT-4,4, ONIT-2,5) it is used as predictive factor, establish Chilean bamboo fish resource
Abundance prediction model are as follows:
Ln (CPUE)=3.5814+0.3585*ONIT-4,3-0.1304*ONIT-4,4+0.2087*ONIT-2,5
Its F value is 8.4166, P=0.0212 < 0.05；
4) the four of prediction model
4 years in the past 1-4 months ONI value (ONIT-4,1, ONIT-4,2, ONIT-4,3, ONIT-4,4) it is used as predictive factor, establish Chilean bamboo
Fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6229+0.0813*ONIT-4,1-1.1582*ONIT-4,2+1.9614*ONIT-4,3-0.2553*ONIT-4,4
Its F value is 19.8598, P=0.0067 < 0.01；
5) the five of prediction model
4 years in the past 2-5 months ONI value (ONIT-4,2, ONIT-4,3, ONIT-4,4, ONIT-2,5) it is used as predictive factor, establish Chilean bamboo
Fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6252-1.0624*ONIT-4,2+1.9219*ONIT-4,3- 0.0984*ONIt-4,4-0.1203*
ONIT-2,5
Its F value is 20.3293, P=0.0064 < 0.01；
6) the six of prediction model
4 years in the past 1-5 months ONI value (ONIT-4,1, ONIT-4,2, ONIT-4,3, ONIT-4,4, ONIt-2,5) it is used as predictive factor, it builds
Vertical Chile's bamboo fish resource abundance prediction model are as follows:
Ln (CPUE)=3.6399+0.7112*ONIT-4,1-2.4679*ONIT-4,2+2.0103*ONIT-4,3+1.1765*
ONIT-4,4-0.6804*ONIT-2,5
Its F value is 17.6050, P=0.0197 < 0.05.
4. Chile's scad resource abundance medium- and long-term forecasting method as claimed in claim 3, which is characterized in that in step S4
In, select each moon ONI value of preceding in Jan-Sept, 4 as the climatic prediction factor, Chilean bamboo fish resource abundance prediction model are as follows: ln
(CPUE)=3.6226-1.0200*ONIT-4,2+1.9411*ONIT-4,3-0.2959*ONIT-4,4, ONIT-4,2、ONIT-4,3、
ONIT-4,4It is respectively 2 months 4 years before the Nino index of ocean, the ONI value in March and April.
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