摘要
长鳍金枪鱼(Thunnus alalunga)作为大洋中上层洄游性鱼类,因其经济价值高、分布范围广而成为各渔业国家的主要捕捞对象之一。结合南太平洋长鳍金枪鱼渔业捕捞作业背景,提出一种新的面向渔业应用的产量预测方法。依据2000-2015年南太平洋长鳍金枪鱼的延绳钓渔获数据、空间因子以及海表温度、海面高度和叶绿素a质量浓度等关键影响因子数据,利用可拓神经网络模型对金枪鱼进行产量预测,并采用粒子群算法(PSO)进行权值优化。结果显示:总召回率达到68%,较传统方法有所提高,对高产区预测有较大优势,召回率达到74.2%,但对中产区的预测效果明显低于高产区和低产区。研究表明,利用粒子群可拓的方法可解决可拓神经网络中经典域不易确定的问题,对丰富渔场预测方法和合理捕捞作业具有一定的指导作用。
As a migratory fish in the middle and upper ocean,Thunnus alalunga has become one of the main fishing targets in fishing countries because of its high economic value and wide distribution range.Based on the background of Thunnus alalunga fishery in the South Pacific,this paper proposes a new yield prediction method for fishery applications.According to the longline fishery data of Thunnus alalunga,spatial factors and sea surface temperature,sea surface height and chlorophyll a mass concentration in the South Pacific from 2000 to 2015,the extension neural network model was used to predict the yield of tuna,and Particle Swarm Optimization(PSO)was used for weight optimization.The results show that the total recall rate reaches 68%,which is higher than that predicated with the traditional method,and it has a great advantage in the prediction of high-yield areas where the recall rate reaches 74.2%,but the prediction effect on the middle-yield areas is significantly poorer than that in the high-yield areas and the low-yield areas.The research shows that the particle swarm extension method can solve the problem that the classical domain in the extension neural network is difficult to determine,and it has a certain guiding effect on enriching the fishery prediction method and reasonable fishing operation.
作者
袁红春
胡光亮
陈冠奇
张天蛟
YUAN Hongchun;HU Guangliang;CHEN Guanqi;ZHANG Tianjiao(College of Information Technology,Shanghai Ocean University,Shanghai 201306,China)
出处
《渔业现代化》
CSCD
2019年第6期96-103,共8页
Fishery Modernization
基金
国家自然科学基金资助项目(41776142)
上海市科学技术委员会支撑项(1439190400)
上海市青年科技英才扬帆计划资助项目(YF1407700)
关键词
渔情预测
粒子群可拓
长鳍金枪鱼
南太平洋
fishing situation prediction
particle swarm extension
Thunnus alalunga
South Pacific
