文章摘要
张畅,陈新军.基于灰色系统的澳洲鲐太平洋群系资源量预测模型[J].上海海洋大学学报,2019,28(1):154-160
基于灰色系统的澳洲鲐太平洋群系资源量预测模型
Forecasting model for spotted mackerel biomass based on grey system theory
投稿时间:2017-06-17  修订日期:2018-10-20
DOI:10.12024/jsou.20170602086
中文关键词: GM模型  澳洲鲐  太平洋群系  产卵场海表面温度  黑潮潮位差
英文关键词: GM model  Scomber australasicus  The Pacific stock  sea surface temperature of spawning ground  tidal range of Kuroshio
基金项目:海洋局公益性行业专项(20155014);上海市科技创新行动计划(5DZ1202200);海洋二号卫星地面应用系统项目(HY2A-HT-YWY-006)
作者单位E-mail
张畅 上海海洋大学 海洋科学学院, 上海 201306  
陈新军 上海海洋大学 海洋科学学院, 上海 201306
农业部大洋渔业开发重点实验室, 上海 201306
国家远洋渔业工程技术研究中心, 上海 201306
农业部大洋渔业资源环境科学观测实验站, 上海 201306 
xjchen@shou.edu.cn 
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中文摘要:
      利用1995-2014年日本中央水产研究所提供的澳洲鲐(Scomber australasicus)太平洋群系资源量与渔获量数据,结合索饵场和产卵场的海表面温度以及黑潮指标等,建立6种基于灰色系统的澳洲鲐资源量预测模型。建立的模型包括不考虑任何环境因子的GM(1,1)模型,分别基于索饵场(140°E~160°E,35°N~50°N)海表面温度SST1、产卵场1(130°E~132°E,30°N~32°N)海表面温度SST2、产卵场2(138°E~141°E,34°N~35°N)海表面温度SST3和黑潮潮位差TR的GM(1,2)模型,以及综合以上4个因子的GM(1,5)模型,6种模型的平均误差分别为6.72%,3.73%,4.41%,4.78%,29.56%,19.38%。研究结果表明,基于索饵场、产卵场海表面温度因子建立的灰色预测模型,对澳洲鲐太平洋群系资源量预报精度较高,可应用于后续渔业生产中。对GM(1,5)模型的灰色参数值a、b分析可知,所有因子中SST2和SST3对模型的制约影响最高,即对资源量的影响最大。产卵场温度与资源量具有较高的相关性,结合最适温度,可推测在该适宜温度范围内资源量随着产卵场温度的升高而增加。可见,全球气候变暖海水温度升高,可能对澳洲鲐资源波动存在积极影响。
英文摘要:
      GM (1, n) is established by the Spotted mackerel (Scomber australasicus) resources of the Pacific group which is supplied by Japan's central fisheries research institute from 1995 to 2012, combined with the sea surface temperature (SST) of spawning and feeding ground and tidal range. There are six models:GM(1,1) without environmental factor; four GM (1,2) models established by the SSTs of the feeding ground(140°E-160°E,35°N-50°N, SST1), spawning ground1(130°E-132°E,30°N-32°N, SST2), spawning ground2(138°E-141°E,34°N-35°N, SST3)and tidal range of Kuroshio; GM (1, 5) established by all the factors. The average error of each model is 6.72%, 3.73%, 4.41%, 4.78%, 29.56% and 19.38% respectively. The results show that the GM models based on feeding and spawning grounds temperature have a high accuracy in the spotted mackerel resource forecast, and can be used into more researches. By analyzing the gray parameters a and b, it is concluded that the SST2 and SST3 are the most restrictive to the model, which means the fluctuation of those factors had the greatest effect on the amount, and the temperature of spawning ground has a high correlation with the resources. When compared with the optimum temperature, it is presumed that the mackerel resources in the appropriate temperature range can increase with the temperature of the spawning ground. The global warming and gradual increase of SST are likely to have a positive effect on the spotted mackerel stock.
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