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当前位置: 首页 > 文章 > 基于特征交互与卷积网络的渔场预测模型 江苏农业学报 2021,37 (6) 1501-1509
Position: Home > Articles > Fishing ground prediction model based on feature interaction and convolutional network Jiangsu Journal of Agricultural Sciences 2021,37 (6) 1501-1509

基于特征交互与卷积网络的渔场预测模型

作  者：
袁红春;王敏;刘慧;陈冠奇
单  位：
上海海洋大学信息学院;农业农村部渔业信息重点实验室
关键词：
长鳍金枪鱼;Cross网络;卷积神经网络;特征交互
摘  要：
长鳍金枪鱼是南太平洋渔业生产中主要的捕捞对象,准确预测其渔场分布对提高渔业捕捞效率具有重要意义。针对传统渔场预测方法预测精度低的问题,本研究提出一种基于特征交互与卷积网络的渔场预测模型——CNN-Cross。该模型引入Embedding层对数据进行处理,解决了One-Hot Encoding(独热编码)带来的特征稀疏性问题以及手动特征工程对结果的影响。同时,引入Cross网络提取特征之间的交互信息,消除了单特征对目标拟合不足的问题,并且结合CNN网络对Embedding层生成的二维特征图进行高阶隐藏信息提取,最后将两部分网络提取到的特征融合,输出分类结果。使用渔业数据对模型预测效果进行验证,结果表明,模型预测南太平洋渔场总召回率达到87.4%,中心渔场召回率达到89.4%。表明,将特征交互网络与卷积神经网络相结合可以明显提高渔场预报精度,且精度能够较好地满足现实渔业作业需求。
译  名：
Fishing ground prediction model based on feature interaction and convolutional network
作  者：
YUAN Hong-chun;WANG Min;LIU Hui;CHEN Guan-qi;College of Information Technology, Shanghai Ocean University;Key Laboratory of Fisheries Information,Ministry of Agriculture and Rural Affairs;
关键词：
Thunnus alalunga;;Cross network;;convolutional neural network;;feature interaction
摘  要：
Thunnus alalunga is the main fishing target of fishery production in the South Pacific Ocean. It is of great significance to accurately predict the fishery distribution of T. alalunga for improving fishery efficiency. In view of lack of accuracy of traditional fishery prediction methods, this paper proposed a fishery prediction model based on feature interaction and convolutional network—CNN-Cross. In this model, the Embedding layer was introduced to process the data, which solved the problem of feature sparsity caused by One-Hot Encoding and the influence of manual feature engineering on the result. At the same time, the Cross network was introduced to extract interactive information between different features to eliminate the problem of insufficient target fitting by single feature, and the two-dimensional feature map generated by the Embedding layer was extracted with the CNN network for high-order hidden information extraction. Finally, the features extracted by two networks were fused and the classification results were output. The effect of the model was verified by fishery data. The results showed that the predicted total recall rate of the South Pacific fishery reached 87.4%, and that of the central fishing ground reached 89.4%. The research results show that the combination of feature interaction network and convolutional neural network can obviously improve the accuracy of fishery forecast, and the accuracy can better meet the needs of practical fishery operations.

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