CN106228456A - A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid - Google Patents
A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid Download PDFInfo
- Publication number
- CN106228456A CN106228456A CN201610580970.2A CN201610580970A CN106228456A CN 106228456 A CN106228456 A CN 106228456A CN 201610580970 A CN201610580970 A CN 201610580970A CN 106228456 A CN106228456 A CN 106228456A
- Authority
- CN
- China
- Prior art keywords
- factor
- ground
- recruitment
- marine environment
- month
- 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.)
- Pending
Links
- 241000238366 Cephalopoda Species 0.000 title claims abstract description 36
- 230000007115 recruitment Effects 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 9
- 239000004459 forage Substances 0.000 claims abstract description 16
- 235000013601 eggs Nutrition 0.000 claims abstract description 15
- 230000001932 seasonal effect Effects 0.000 claims abstract description 6
- 230000007774 longterm Effects 0.000 claims abstract description 4
- 230000002596 correlated effect Effects 0.000 claims 1
- 241000739514 Dosidicus gigas Species 0.000 description 12
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical group OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 238000010219 correlation analysis Methods 0.000 description 4
- 241000251468 Actinopterygii Species 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000010205 computational analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000017448 oviposition Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Animal Husbandry (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Agronomy & Crop Science (AREA)
- Evolutionary Computation (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Artificial Intelligence (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Farming Of Fish And Shellfish (AREA)
Abstract
A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid, it is characterized in that utilizing time sequential value and the CPUE seasonal effect in time series dependency in this year of forage month feeding ground marine environment factor composition, select the correlation factor that squid resources magnitude of recruitment is affected as forage habitat by the marine environment factor in dependency height marine site；Utilize time sequential value and the next year CPUE seasonal effect in time series dependency of month spawning ground marine environment factor composition of laying eggs, select the correlation factor that resource magnitude of recruitment is affected as habitat of laying eggs by the marine environment factor in dependency height marine site；Utilize month spawning ground suitable surface temperature scope of laying eggs to account for the ratio of the gross area, forage month feeding ground suitable surface temperature scope accounts for the ratio of the gross area, uses PS、PFExpress feeding ground, spawning ground habitat suitable degree；With selected envirment factor and PS、PFVarious combination, sets up BP network structure forecast model respectively, selects optimal models, for medium-term and long-term fishery forescast.
Description
Technical field
The method that the present invention relates to fishery forescast Medium-long Term Prediction, especially Peru squid resource magnitude of recruitment Forecasting Methodology.
Background technology
The forecast of resource magnitude of recruitment belongs to the one of fishery forescast Medium-long Term Prediction, forecasts resource magnitude of recruitment accurately
It it is the fishery key that carries out scientific management, reasonable development.Property economic squids in ocean is short life cycle kind, although himself
There is the strongest capacity of self-regulation, within a short period of time marine environment change can be reacted, and quickly adapt to this
Change, but marine environment variation is the most notable on the impact of its resource magnitude of recruitment.There are some researches show, affect ocean property economy soft
The topmost factor of stock of fish magnitude of recruitment is environmental factors.Therefore, to the prediction research of its resource magnitude of recruitment also it is at present
Launch based on this.But the envirment factor that conventional research selects is the most single, the forecasting model of foundation is also simple linear
Model.Peru squid is distributed in southeast Pacific marine site, easily by the shadow of the abnormal environment factor such as EI Nino phenomenon, La Nina
Ring, and change particularly evident between year, drastically influence fish production.For this reason, it may be necessary to research marine environment factor pair ocean property
The impact of economic squid resources magnitude of recruitment, particularly Peru squid, find out squid resources magnitude of recruitment shadow economic to ocean property
Ring the most significantly marine environment factor, set up resource magnitude of recruitment forecasting model on this basis, and analyze its reason.
Summary of the invention
The present invention have studied the impact of marine environment factor pair ocean property economic squid resources magnitude of recruitment, finds out ocean
Property the economic squid resources magnitude of recruitment impact the most significantly marine environment factor, it is therefore an objective to set up a kind of Peru on this basis
The resource magnitude of recruitment Forecasting Methodology of squid, for medium-term and long-term fishery forescast.
Technical scheme includes selecting the marine environment factor and setting up BP network structure forecast model, it is characterized in that
Utilize time sequential value and this average annual daily volume (CPUE) that the marine environment factor in squids forage month feeding ground forms
The dependency of seasonal effect in time series, select dependency height marine site the marine environment factor as forage habitat to squid resources
The correlation factor of magnitude of recruitment impact；Utilize the time sequential value of the squids marine environment factor composition in month spawning ground of laying eggs
With next year CPUE seasonal effect in time series dependency, select dependency height marine site the marine environment factor as habitat of laying eggs to soft
The correlation factor of stock of fish magnitude of recruitment impact；Squids month spawning ground suitable surface temperature scope of laying eggs is utilized to account for the gross area
Ratio (PS), forage month feeding ground suitable surface temperature scope account for the ratio (P of the gross areaF), use PS、PFExpress squids
The suitable degree of feeding ground, resource spawning ground habitat；Relative coefficient (r) uses Pearson came (Pearson) correlation coefficient,
Formula is as follows:
In formula: x, y represent that envirment factor (includes the marine environment factor in spawning ground and feeding ground, and each month respectively
PSAnd PF), CPUE composition series of values；
Utilize selected spawning ground and feeding ground envirment factor and PS、PFDefeated as BP forecast model of various combination
Enter the factor, set up BP network structure forecast model respectively, then select the model that forecast precision is the highest, pre-for medium-term and long-term fishing feelings
Report.
The present invention utilizes the impact of marine environment factor pair ocean property economic squid resources magnitude of recruitment, finds out ocean property
The economic squid resources magnitude of recruitment impact the most significantly marine environment factor, the resource magnitude of recruitment setting up a kind of Peru squid is pre-
Survey method, forecast precision is all more than 90%, and compared with traditional multivariate linear model, forecast precision improves 20%, has aobvious
Write technique effect.
Accompanying drawing explanation
Fig. 1 is southeast Pacific jumbo flying squid GAM model standardization CPUE figure in 2003～2012.
Fig. 2 is the linear relationship chart (a) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 3 is the linear relationship chart (b) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 4 is the linear relationship chart (c) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 5 is the linear relationship chart (d) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 6 is the linear relationship chart (e) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 7 is the linear relationship chart (f) of feature envirment factor and jumbo flying squid catch per unit effort.
Fig. 8 is analog result and the accuracy rate figure of different neural network model.
Detailed description of the invention
Property economic squid resources magnitude of recruitment in ocean is closely related with the habitat in its spawning ground and feeding ground.Therefore,
Squids time sequential value and CPUE in this year time sequence of marine environment factor composition in forage month feeding ground can be calculated
Row dependency, select dependency height marine site the marine environment factor as forage habitat to squid resources magnitude of recruitment
Impact；Calculate time sequential value and time next year CPUE of the squids marine environment factor composition in month spawning ground of laying eggs
The dependency of sequence, select dependency height marine site the marine environment factor as habitat of laying eggs to squid resources magnitude of recruitment
Impact.
It is to weigh squids habitat quality that spawning ground, feeding ground the suitableeest surface temperature scope account for the ratio of the gross area
One of index.Calculate month spawning ground suitable surface temperature scope of laying eggs to account for the ratio of the gross area and (use PSRepresent), the forage moon
Part feeding ground suitable surface temperature scope accounts for the ratio of the gross area and (uses PFRepresent), use PS、PFExpress squid resources spawning ground rope
The suitable degree of bait field habitat.
Relative coefficient uses Pearson correlation coefficient, and formula is as follows:
Wherein x, y represent the series of values that environment, CPUE form respectively.
According to the correlation factor chosen, set up between the significant correlation factor and the CPUE affecting squid resources magnitude of recruitment
Multivariate linear model or BP neural network model.
The temporal resolution moon, spatial resolution 0.5 ° × 0.5 ° and envirment factor SST, SSH and Chl-a is utilized to carry out year
CPUE standardization, the GAM model formation of use is as follows:
Ln (CPUE+c)=factor (year)+factor (month)+s (longitude)+s (latitude)+s
(SST)+s(SSH)+S(Chl-a)+ε
In formula, s is spline smooth function, and constant c is 1, var ε=σ2, E (ε)=0.
Adding envirment factor and the mutual factor one by one, the model selecting DIC value minimum is standardized as jumbo flying squid CPUE
Best model.
Utilizing time factor (year, the moon), steric factor (longitude, latitude), envirment factor (SST, SSH, Chl-a) builds
CPUE standardization GAM model result finds: Nominal CPUE and GAM CPUE variation tendency are essentially identical, and both are maximum
Value occurs in 2004, Nominal CPUE be 7.06t/d, GAM CPUE be 7.82t/d, Nominal CPUE minima occur
It was that 4.03t/d, GAM CPUE minima occurs in 2012 for 3.99t/d in 2007.
Jumbo flying squid resource magnitude of recruitment is closely related with the habitat in its spawning ground and feeding ground.Therefore computational analysis is distinguished
1～the time sequential value of December research marine site every point (0.5 ° × 0.5 °) SST, SSH, Chla composition and this year and next year CPUE
The dependency of the time sequential value of composition, chooses SST, SSH, the Chl-a in dependency height marine site as jumbo flying squid resource magnitude of recruitment
Factor of influence.Wherein, the marine site that SST, SSH, Chl-a are high with CPUE dependency in this year represents that resource is mended by forage habitat
The impact of charge；The marine site that SST, SSH, Chl-a are high with next year CPUE dependency represents that habitat of laying eggs is to resource magnitude of recruitment
Impact.
JIUYUE jumbo flying squid when laying eggs suitable SST be 24～28 DEG C；July jumbo flying squid forage time suitable SST be 17～22
℃.When therefore calculating JIUYUE when laying eggs, forage in July, the suitableeest surface temperature scope accounts for the ratio of the gross area, expresses spawning ground rope
The suitable degree of bait field habitat.
In the range of 1～20 ° of S of December～20 ° of N, 110 ° of W～70 ° of W marine sites, the SST of each 0.5 ° × 0.5 ° of each moon
Make correlation analysis with this year and next year CPUE, find 13 ° of N that SST and this year, CPUE correlation maximum occurred in July,
102 ° of W (Point1) (table 1, Fig. 2), SST and next year CPUE correlation maximum occur in 8 ° of N in June, 103.5 ° of W
(Point2) (table 1, Fig. 4) place；In the range of 1～20 ° of S of December～20 ° of N, 110 ° of W～70 ° of W marine sites, each moon each
The SSH of 0.5 ° × 0.5 ° and this year and next year CPUE makees correlation analysis, finds that SSH occurs with CPUE correlation maximum in this year
At 11 ° of N, 102 ° of W (Point3) (table 1, Fig. 3) places of JIUYUE, SSH and next year CPUE correlation maximum occur in February
12 ° of N, 97.5 ° of W (Point4) (table 1, Fig. 5) places；In the range of 1～20 ° of S of December～20 ° of N, 110 ° of W～70 ° of W marine sites, respectively
The Chla and this year and next year CPUE of the moon each 0.5 ° × 0.5 ° makees correlation analysis, finds that Chl-a is relevant to CPUE in this year
Property maximum occur in 8 ° of S in March, 107 ° of W (Point5) (table 1, Fig. 6) places, Chl-a and next year CPUE correlation maximum
Occur in 10 ° of S in October, 93.5 ° of W (Point4) (table 1, Fig. 7) places.
The crucial sea environment factor of table 1 and resource abundance, the correlation analysis parameter of magnitude of recruitment
Utilize the selected crucial sea environment factor and PS、PFVarious combination as EBP forecasting model input because of
Son, constructs multiple EBP forecasting model, is respectively as follows:
Scheme 1: choose Chl-a, P of SSH, Point5 of SST, Point3 of Point1FTotally 4 factors are as input
Layer, the EBP network of structure 4:5:1, represent the forecasting model utilizing forage environment Key Influential Factors to set up.
Scheme 2: choose SST, Point4 of Point2 SSH, Point6 Chl-a, Ps totally 4 factors as input because of
Son, the network structure of structure 4:5:1, represent the forecasting model utilizing egg-laying environment Key Influential Factors to set up.
Scheme 3: select SST, Point3 of Point1 Yu Point2 and SSH, Point5 of Point4 and Point6's
Chla、PS、PFTotally 8 factors are as the input factor, the network structure of structure 8:9:1, represent and utilize integrated environment key factor to build
Vertical forecasting model.
Utilize matlab to be modeled, calculate the mean square error (Fig. 8) under three kinds of schemes, scheme 2 and the mean square error of scheme 3
Difference is close and is better than scheme 1, and its accuracy rate is about 90%.
Claims (1)
1. a resource magnitude of recruitment Forecasting Methodology for Peru squid, including selecting the marine environment factor and to set up BP network structure pre-
Survey model, it is characterized in that the time sequential value utilizing the marine environment factor in squids forage month feeding ground to form was put down with this year
All dependencys of daily output CPUE seasonal effect in time series, select the marine environment factor in dependency height marine site as forage habitat
Correlation factor on the impact of squid resources magnitude of recruitment；Utilize the squids marine environment factor composition in month spawning ground of laying eggs
Time sequential value and next year CPUE seasonal effect in time series dependency, select the marine environment factor in dependency height marine site as laying eggs
The correlation factor that squid resources magnitude of recruitment is affected by habitat；Squids is utilized to lay eggs the suitable surface temperature in month spawning ground
Scope accounts for ratio P of the gross areaS, forage month feeding ground suitable surface temperature scope account for ratio P of the gross areaF, use PS、PFTable
Reach the suitable degree of feeding ground, squid resources spawning ground habitat；Relative coefficient r uses Pearson came (Pearson) to be correlated with
Coefficient, formula is as follows:
In formula: x, y represent that envirment factor includes the marine environment factor in spawning ground and feeding ground, and the P in each month respectivelySWith
PF, CPUE composition series of values；
Utilize selected spawning ground and feeding ground envirment factor and PS、PFVarious combination as BP forecast model input because of
Son, sets up BP network structure forecast model respectively, then selects the model that forecast precision is the highest, for medium-term and long-term fishery forescast.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610580970.2A CN106228456A (en) | 2016-07-22 | 2016-07-22 | A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610580970.2A CN106228456A (en) | 2016-07-22 | 2016-07-22 | A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106228456A true CN106228456A (en) | 2016-12-14 |
Family
ID=57532176
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610580970.2A Pending CN106228456A (en) | 2016-07-22 | 2016-07-22 | A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106228456A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403243A (en) * | 2017-08-29 | 2017-11-28 | 上海海洋大学 | Morocco's marine site Resources of Cephalopods abundance Forecasting Methodology |
| CN109460860A (en) * | 2018-10-18 | 2019-03-12 | 上海海洋大学 | Argentinian squid Resources Prediction method based on Antarctic Oscillations index |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN110427685A (en) * | 2019-07-29 | 2019-11-08 | 中国水利水电科学研究院 | A kind of animal habitat ground model building method |
| CN110555567A (en) * | 2019-09-10 | 2019-12-10 | 上海彩虹鱼海洋科技股份有限公司 | method, system and device for fish flood prediction |
-
2016
- 2016-07-22 CN CN201610580970.2A patent/CN106228456A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403243A (en) * | 2017-08-29 | 2017-11-28 | 上海海洋大学 | Morocco's marine site Resources of Cephalopods abundance Forecasting Methodology |
| CN109460860A (en) * | 2018-10-18 | 2019-03-12 | 上海海洋大学 | Argentinian squid Resources Prediction method based on Antarctic Oscillations index |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN109767040B (en) * | 2019-01-15 | 2023-09-15 | 上海海洋大学 | Prediction method for central fishing ground of pacific saury based on habitat index |
| CN110427685A (en) * | 2019-07-29 | 2019-11-08 | 中国水利水电科学研究院 | A kind of animal habitat ground model building method |
| CN110555567A (en) * | 2019-09-10 | 2019-12-10 | 上海彩虹鱼海洋科技股份有限公司 | method, system and device for fish flood prediction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106228456A (en) | A kind of resource magnitude of recruitment Forecasting Methodology of Peru squid | |
| WO2018014658A1 (en) | Ommastrephidaeentral fishing ground prediction method | |
| CN106295833B (en) | Pacific ocean Pleurotus giganteus resource replenishment quantity prediction method and application thereof | |
| CN106485093B (en) | Based on the solar irradiance time series synthetic method for improving Markov chain | |
| CN109767040B (en) | Prediction method for central fishing ground of pacific saury based on habitat index | |
| CN106228010A (en) | A kind of North Pacific squid resource magnitude of recruitment Forecasting Methodology | |
| CN112819312A (en) | Method and system for evaluating drought socioeconomic exposure degree under climate change scene | |
| CN107590549A (en) | A kind of Japanese squid winter life group's resource abundance Forecasting Methodology | |
| CN105260820A (en) | Water ecological system risk evaluation method for water-receiving region of water transfer project | |
| Igarashi et al. | Identifying potential habitat distribution of the neon flying squid (Ommastrephes bartramii) off the eastern coast of Japan in winter | |
| Bauwe et al. | Tree-ring growth response of Scots pine (Pinus sylvestris L.) to climate and soil water availability in the lowlands of North-Eastern Germany | |
| CN103049659A (en) | Method for forecasting stock recruitment of Peruvian open-sea Dosidicus gigas and method for applying same | |
| CN106251006A (en) | A kind of Argentina squid resource magnitude of recruitment Forecasting Methodology | |
| CN104834966A (en) | Fishery forecasting method based on ant colony classifying algorithm | |
| Zeldis | Applicability of egg surveys for spawning-stock biomass estimation of snapper, orange roughy, and hoki in New Zealand | |
| Avinash et al. | Population dynamics of Trichiurus lepturus (Linnaeus, 1758) off Veraval | |
| Mithiya et al. | Time series analysis and forecasting of oilseeds production in India: using autoregressive integrated moving average and group method of data handling–neural network | |
| Wolfe et al. | Fish population fluctuation estimates based on fifteen years of reef volunteer diver data for the Monterey Peninsula, California | |
| Kugarajh et al. | Implementing a stochastic bioeconomic model for the North-East Arctic cod fishery | |
| Bujalska | Fluctuations in an island bank vole population in the light of the study on its organization | |
| Oshima et al. | Shift of fishing efforts for Pacific blefin tuna and target shift occurred in Japanese coastal longliners in recent years | |
| Underhill et al. | Pentad-scale distribution maps for bird atlas data | |
| Wang et al. | Application of grey systems in predicting the degree of cotton spider mite infestations | |
| CN107330541A (en) | A kind of west and central Pacific ocean stripped tuna Predicting Center Fishery method | |
| Carter et al. | Structural vector error correction modeling of integrated sportfishery data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161214 |