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基于CNN-GRU混合模型的养殖工船水体溶解氧预测研究

苏辉锋 丁乐声 王绪旺 陈木生 陈潇

苏辉锋, 丁乐声, 王绪旺, 陈木生, 陈潇. 基于CNN-GRU混合模型的养殖工船水体溶解氧预测研究[J]. 南方水产科学. doi: 10.12131/20220298
引用本文: 苏辉锋, 丁乐声, 王绪旺, 陈木生, 陈潇. 基于CNN-GRU混合模型的养殖工船水体溶解氧预测研究[J]. 南方水产科学. doi: 10.12131/20220298
SU Huifeng, DING Lesheng, WANG Xuwang, CHEN Musheng, CHEN Xiao. Prediction of dissolved oxygen in water of aquaculture ship based on CNN-GRU hybrid model[J]. South China Fisheries Science. doi: 10.12131/20220298
Citation: SU Huifeng, DING Lesheng, WANG Xuwang, CHEN Musheng, CHEN Xiao. Prediction of dissolved oxygen in water of aquaculture ship based on CNN-GRU hybrid model[J]. South China Fisheries Science. doi: 10.12131/20220298

基于CNN-GRU混合模型的养殖工船水体溶解氧预测研究

doi: 10.12131/20220298
基金项目: 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)
详细信息
    作者简介:

    苏辉锋 (1995—),男,研究实习员,硕士,研究方向为海洋工程装备、渔业设备研发。E-mail: suhuifeng@zjblab.com

    通讯作者:

    陈 潇 (1986—),男,高级工程师,本科,研究方向为海洋工程装备、渔业设备研发。E-mail: chenxiao_ship@163.com

  • 中图分类号: S 967.9

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

  • 摘要: 溶解氧 (Dissolved oxygen, DO) 含量是影响养殖工船水产品健康生长的重要因素,准确预测DO含量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) 和门控制循环神经网络 (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含量、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R2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含量预测的需求,为养殖工船生产过程中DO含量的监控和预警提供参考。
  • 图  1  养殖舱

    Figure  1.  Aquaculture water tank

    图  2  养殖舱监控系统结构简图和实物图

    Figure  2.  Schematic diagram and physical diagram of the aquaculture tank monitoring system

    图  3  LSTM单元示意图

    Figure  3.  Schematic diagram of LSTM unit

    图  4  GRU单元示意图

    Figure  4.  Schematic diagram of GRU unit

    图  5  CNN-GRU网络结构图

    Figure  5.  Structure diagram of CNN-GRU network

    图  6  4种模型预测结果

    Figure  6.  Prediction results of four models

    表  1  水质传感器规格

    Table  1.   Water quality sensor specification

    水质参数Parameter of water quality测量范围Range of measurement精度Precision分辨率Resolution ratio型号Model品牌Brand
    溶解氧质量浓度 DO/(mg·L−1)0~20±2%F.SLDO II哈希 Hach
    温度 Temperature/℃0~50±0.50.1 ℃MPS-400凯米斯 Chemins
    pH0~14±0.1p0.01MPS-400凯米斯 Chemins
    盐度 Salinity/‰0~100±3.5%F.S0.1‰MPS-400凯米斯 Chemins
    下载: 导出CSV

    表  2  4种预测模型的超参数设置

    Table  2.   Hyperparameter setting of four prediction models

    模型 Model参数 Paramenter
    卷积神经网络 CNN卷积核个数:3卷积核大小:32
    长短期记忆 LSTM隐藏层个数:128全连接层神经元个数:128
    门控制循环神经网络 GRU隐藏层个数:128全连接层神经元个数:128
    卷积神经网络-门控制循环神经网络 CNN-GRU卷积核个数:3卷积核大小:32GRU隐藏层个数:128全连接层神经元个数:128
    下载: 导出CSV

    表  3  4种模型的预测性能

    Table  3.   Predictive performance of four models

    模型
    Model
    均方根误差
    RMSE/
    (mg·L−1)
    平均绝对误差
    MAE/
    (mg·L−1)
    决定性
    系数
    R2
    卷积神经网络 CNN0.1730.1320.945
    长短期记忆 LSTM0.1430.120.956
    门控制循环神经网络 GRU0.1380.1140.966
    卷积神经网络-门控制循环
    神经网络
    CNN-GRU
    0.1190.0840.976
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-11-23
  • 修回日期:  2023-03-13
  • 录用日期:  2023-03-31
  • 网络出版日期:  2023-04-28

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