水产学报2018，Vol.42Issue(5)：704-710,7.DOI:10.11964/jfc.20170410811
基于海表温因子的太平洋褶柔鱼冬生群资源丰度预测模型比较
A comparative study on forecasting model of the stock abundance index for the winter-spawning cohort of Todarodes pacificus in the Pacific Ocean based on the factor of SST
摘要
Abstract
Todarodes pacificus is one of important resources of the ocean economic Ommastrephidae in the world. In order to forecast the stock abundance of winter-spawning cohort, the catch per unit effort (CPUE) as abundance index from T.pacificus stock assessment report of Japan in 2013 is used to establish the forecasting model in this study. The correlation analysis between sea surface temperature (SST) in the spawning areas of 28°N-40°N and 125°E-140°E and CPUE from January to March during 2000—2010 was carried out respectively to select the sig-nificantly affecting factors in statistics. The multivariate linear model and BP neural network model forecasting abundance index of T.pacificus winter-spawning population were established and compared,and the actual CPUE in 2011 and 2012 was used for validation. The results showed that the spawning areas with high correlation coeffi-cient between CPUE and SST in Jan. to Mar. are S1 (30.5° N, 136.5° E) and S2 (31.5° N, 136.5° E) in January, the correlation coefficient are 0.71 and 0.70 respectively; S3 (30.5° N, 137.5° E) and S4 (30.5° N, 135.5° E)in Febru-ary, and the correlation coefficient are 0.87 and 0.84, respectively; S5 (37.5° N, 129.5° E) and S6 (37.5° N, 130.5° E) in March, and the correlation coefficient are 0.72 and 0.70, respectively. Total of five forecasting models includ-ing multivariate linear model and BP neural network model with different structure are established and compared. The BP 6-4-1 neural network model is the best, and the average prediction accuracy of the CPUE value during 2011—2012 attained 98%. This study suggests that the model can be used as the forecasting model of the stock abundance for T.pacificus winter-spawning cohort.关键词
太平洋褶柔鱼/冬生群/资源丰度/预测模型/BP神经网络Key words
Todarodes pacificus/winter-spawning group/abundance index/prediction model/BP neural network分类
农业科技引用本文复制引用
张硕,李莉,陈新军..基于海表温因子的太平洋褶柔鱼冬生群资源丰度预测模型比较[J].水产学报,2018,42(5):704-710,7.基金项目
国家自然科学基金(41476129 （）
41276156) （）
海洋局公益性行业专项(20155014) （20155014）
National Natural Science Foundation of China(41476129,41276156) （41476129,41276156）
Public Science and Technology Research Funds Project of Ocean(20155014) （20155014）
