基于CNN-GRU混合模型的养殖工船水体溶解氧预测研究  

Prediction of dissolved oxygen in water of aquaculture ship based on CNN-GRU hybrid model

作　　者：苏辉锋 丁乐声 王绪旺 陈木生 陈潇 SU Huifeng;DING Lesheng;WANG Xuwang;CHEN Musheng;CHEN Xiao(Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang),Zhanjiang 524000,China;Ningbo Institute of Dalian University of Technology,Ningbo 315700,China)

机构地区：[1]南方海洋科学与工程广东省实验室(湛江),广东湛江524000 [2]大连理工大学宁波研究院,浙江宁波315700

出　　处：《南方水产科学》2023年第4期174-180,共7页South China Fisheries Science

基　　金：广东省海洋经济发展(海洋六大产业)专项资金资助项目(GDNRC[2021]42);湛江市海洋装备和海洋生物揭榜挂帅制人才团队专项资金资助项目(2021E05034);南方海洋科学与工程广东省实验室(湛江)项目(ZJW-2019-01)。

摘　　要：溶解氧(Dissolved oxygen,DO)是影响养殖工船水产品健康生长的重要因素,准确预测DO对提高水产品产量和品质具有重要意义。为提高DO预测精度,以卵形鲳鲹(Trachinotus ovatus)养殖试验采集的数据为样本,使用卷积神经网络(Convolutional neural network,CNN)和门控循环单元(Gated recurrent unit,GRU)方法建立养殖工船水体DO预测混合模型,通过Pearsons相关性分析,选用DO、温度、pH和循环水流量4个预测因子进行训练和校准,预测了DO含量。通过与CNN、GRU和长短期记忆(Long short-term memory,LSTM)模型进行对比,所建模型在各项评价指标中的性能均最优,其均方根误差(Root mean square error,RMSE)、平均绝对误差(Mean absolute error,MAE)和决定系数R 2分别为0.119、0.084和0.976。结果表明,所建模型的预测精度最高,可以满足养殖工船实际生产中对DO预测的需求,为养殖工船生产过程中DO的监控和预警提供参考。Dissolved oxygen(DO)content is a critical factor that affects the healthy growth of aquatic products in aquaculture ships.Accurate prediction of DO content is necessary to improve aquatic production and quality.To increase the accuracy of DO prediction,based on the data collected from a Trachinotus ovatus culture experiment,we established a hybrid model for DO prediction in aquaculture ships by applying the convolutional neural network(CNN)and gated recurrent unit(GRU)methods.Based on Pearson correlation analysis,we selected four predictors,namely dissolved oxygen content,temperature,pH value and circulating water flow,which were trained and calibrated to predict the DO content.The model proposed in this paper outper-formed CNN,GRU and long short-term memory(LSTM)models in all evaluation indexes,and its root mean square error(RMSE),mean absolute error(MAE)and determination coefficient R 2 were 0.119,0.084 and 0.976,respectively.The results indicate that the model proposed in this paper has the greatest prediction precision and can meet the demand for DO content prediction in actual production of aquaculture ships,which provides references for monitoring and early warning of DO con-tent in the production process of aquaculture ships.

关 键 词：养殖工船 溶解氧 卷积神经网络 门控循环单元 

分 类 号：S967.9[农业科学—水产养殖]