Northwest Pacific Ocean ommastrephidae bartramii winter-spring stock colony abundance prediction method based on gray system

Abstract

The invention discloses a northwest Pacific Ocean ommastrephidae bartramii winter-spring stock colony abundance prediction method based on a gray system. The northwest Pacific Ocean ommastrephidae bartramii winter-spring stock colony abundance prediction method comprises steps of (1) obtaining nino3.4 anomaly, PDO data, sea surface temperature SST and chlorophyll concentration ch1-a, and four sea environment and climate factors, (2) performing gray correlation analysis on four sea environment and weather factors, (3) choosing four factors having a highest association degree according to a gray association analysis result, wherein the four factors include: average sea surface temperature of an egg laying field in march, an interdecadal oscillation index of the pacific ocean in January, nino3.4 anomaly in march and average chlorophyll concentration in march; (4) establishing 8 prediction models based on the gray system according to four chosen factors; and (5) performing effective examination on 8 prediction models to choose a gray association model GM (1,4) structure as the prediction method for the northwest pacific ocean ommastrephidae bartramii winter-spring stock colony abundance. The prediction accuracy of the prediction method of the invention reaches more than 90%.

Description

technical field [0001] The invention relates to a method for forecasting fishing conditions based on a gray system, in particular to a method for forecasting resource abundance of winter and spring populations of Northwest Pacific squid. Background technique [0002] Fishery forecast is a key link in fishery production. Forecasting the abundance of winter and spring populations of the Northwest Pacific softfish (Ommastrephes bartramii) is beneficial to the forecast of the production of the Northwest Pacific softfish (Ommastrephes bartramii) winter and spring populations, the location of the central fishing ground and the fish season. The general life cycle of squid is one year, and its spawning period is from January to April. The success of spawning and hatching in that year directly determines the size of its replenishment and thus the abundance of resources. Badness necessarily led to a change in its resources. The external environmental factors include sea surface tempe...

AI Technical Summary

Problems solved by technology

However, in the current resource abundance forecasting, people often use one environmental factor or several environmental factors that affect a certain forecasting type, and use linear models to forecast. Although these forecasts have achieved better forecast results, usually The accuracy is about 70%, but because the marine ecosystem is usually nonlinear, and the influencing factors are mutual influence and interaction, the linear model may not be able to simulate well; in addition, it often takes a long time to use the linear model statistically Sequence, generally speaking, its samples should be more than 30, so as to meet the requirements of statistics

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