周茜涵, 吴洽儿, 周艳波, 谢恩阁, 马胜伟. 基于优化灰色模型的南海鸢乌贼资源丰度预测[J]. 南方水产科学, 2021, 17(3): 1-7. DOI: 10.12131/20200218
引用本文: 周茜涵, 吴洽儿, 周艳波, 谢恩阁, 马胜伟. 基于优化灰色模型的南海鸢乌贼资源丰度预测[J]. 南方水产科学, 2021, 17(3): 1-7. DOI: 10.12131/20200218
ZHOU Xihan, WU Qia'er, ZHOU Yanbo, XIE Enge, MA Shengwei. Prediction of abundance of Sthenoteuthis oualaniensis in South China Sea based on optimized grey system model[J]. South China Fisheries Science, 2021, 17(3): 1-7. DOI: 10.12131/20200218
Citation: ZHOU Xihan, WU Qia'er, ZHOU Yanbo, XIE Enge, MA Shengwei. Prediction of abundance of Sthenoteuthis oualaniensis in South China Sea based on optimized grey system model[J]. South China Fisheries Science, 2021, 17(3): 1-7. DOI: 10.12131/20200218

基于优化灰色模型的南海鸢乌贼资源丰度预测

Prediction of abundance of Sthenoteuthis oualaniensis in South China Sea based on optimized grey system model

  • 摘要: 鸢乌贼 (Sthenoteuthis oualaniensis) 是南海重要的经济头足类之一,巨大的开发潜力与经济价值使其在我国南海海洋渔业中的地位日益凸显。为深入了解南海鸢乌贼单位捕捞努力量渔获量 (Catch per unit effort, CPUE) 变化情况并作出科学预测,文章选择构建灰色预测模型GM (1,N),结合海洋环境因子,对2013—2019年春夏季南海鸢乌贼生产数据进行拟合预测,使用Simpson公式和Fourier级数改造原模型背景值并对拟合值残差做修正。结果显示,在鸢乌贼CPUE预测上,普通GM (1,N) 模型的平均相对误差为7.78%,优化灰色GM (1,N) 模型为2.54%;在2019年鸢乌贼CPUE预测上,优化灰色GM (1,N) 模型将相对误差从普通GM (1,N) 模型的4.79%降至1.87%。结果表明,优化的灰色模型较普通多因素GM (1,N) 模型的预测精度更高,为准确预测鸢乌贼资源相对丰度提供了一种新思路。

     

    Abstract: Squid (Sthenoteuthis oualaniensis), one of the important economic Cephalopods in the South China Sea, has great development potential and economic value, playing an increasingly significant role in the marine fisheries of the South China Sea. In order to understand the variation of catch per unit effort (CPUE) and make scientific predictions, we constructed a gray prediction model GM (1,N) and combined it with marine environmental factors, then fitted and predicted the production data of the South China Sea squid from spring to summer of 2013−2019. Besides, we modified the background value of the original model by Simpson formula and Fourier series, and corrected the residual error of the simulated value. The results show that the average relative errors of the traditional GM (1,N) model and the optimized grey GM (1,N) model were 7.78% and 2.54%, respectively. For the prediction of squid CPUE in 2019, the optimized grey GM (1,N) model reduced the relative error from 4.79% of the traditional GM (1,N) model to 1.87%. It is showed that the optimized grey system model has a higher prediction accuracy than the traditional GM (1,N) model, which provides a new idea on how to predict the relative abundance of squid resources accurately.

     

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