广东海洋大学学报2017，Vol.37Issue(6)：65-73,9.DOI:10.3969/j.issn.1673-9159.2017.06.011
基于BP神经网络的中西太平洋鲣鱼渔场预报模型构建与比较
Comparison of Fishing Ground of Skipjack Based on BP Neural Network in the Western and Central Pacific Ocean
摘要
Abstract
Central-western Pacific Ocean is an important fishing area for skipjack tuna(Katsuwonus pelamis)purse seine fisheries.Accurately forecasting the central fishing ground can help fishery fleets improve their fishing efficiency. According to the production statistical data of skipjack in the central-western Pacific Ocean during 1998 – 2013 and related oceanographic condition, 22 BP neural networks are constructed to compare the forecasting effects between Catch per unit of effort (CPUE) and fishing effort as output factors, respectively. The results showed that the value of minimum residuals in the forecasting models based on fishing effort were all lower than those based on CPUE, which represent that the fishing effort is better considered as the indicator of fishing ground. It is also found that the mean value of fitting residuals decreased as the number of input factors increased, which also show that the selected environmental variables in this study(month, sea surface temperature SST, sea surface height SSH, Nino3.4 index, Chl-a)are the important indicators affecting the distribution of skipjack. The BP neural network with a structure of 7-5-1 was the most suitable for forecasting fishing ground with a highest accuracy and lowest residuals. The significance of longitude, Chl-a, SST, latitude, Nino3.4 index, SSH and month vary from high to low.关键词
中西太平洋/鲣鱼/中心渔场/神经网络Key words
central-west Pacific Ocean/skipjack/central fishing ground/neural networks分类
农业科技引用本文复制引用
陈洋洋,陈新军,郭立新,方舟,汪金涛..基于BP神经网络的中西太平洋鲣鱼渔场预报模型构建与比较[J].广东海洋大学学报,2017,37(6):65-73,9.基金项目
上海市科技创新计划(15DZ1202200) （15DZ1202200）
海洋局公益性行业专项(20155014) （20155014）
