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, 2023, 19(4): 174-180. 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, 2023, 19(4): 174-180. 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, 2023, 19(4): 174-180. 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, 2023, 19(4): 174-180. 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

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) 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。
    Abstract: 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 outperformed 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 R2 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 content in the production process of aquaculture ships.
  • 鲮(Cirrhinus molitorella),属鲤科(Cyprinidae)、野鲮亚科(Labeoninae),是两广地区的四大家鱼(鳙、草、鲢、鲮)之一,在广东省的淡水养殖业中占有重要的地位。四大家鱼繁殖成功以来,普遍存在的近亲繁殖使得亲鱼性成熟提前、个体越变越小、鱼苗病害增多、生长减慢等问题出现[1],养殖群体遗传多样性下降。因此,收集保存野生原种,了解其遗传多样性状况并从中筛选出具有优良性状的个体或群体进行苗种生产对于水产养殖业的持续稳定发展具有重要意义。

    AFLP技术首先由荷兰科学家ZABEAU和VOS提出[2-3],此项技术兼具了RAPD和RFLP的优点,既有前者的简便灵敏和高效性也有后者的可靠性,是迄今为止最有效的分子标记之一。广东鲮鱼原种场2000和2003年先后2次从西江流域肇庆段采捞野生鲮鱼苗,作为鲮原种保存。饲养过程中发现该批鲮原种存在体色及生长速率差异。为了比较该批原种2个不同体色的子群体间的遗传差异,我们采用AFLP方法对其进行了研究。

    2005年7月,从广东鲮鱼原种场取保种样品,对体色不同的2个子群体(体色青,q; 体色淡黄,h)各取29尾,共58尾,分别编号为q1~29、h1~29。剪尾鳍保存于95%的乙醇中,4 h后更换1次95%的乙醇,保存用于总DNA抽提。

    参照VOS等[3]方法并稍作修改进行AFLP分析,研究中使用的接头及引物序列见表 1

    表  1  AFLP分析的寡核苷酸接头和引物序列
    Table  1.  The oligo nucleotide adaptors and primers used for the AFLP analysis
    接头或引物
    adaptor or Primer
    序列(5′→3′)
    sequence(5′→3′)
    adaptor EcoRI-1 CTC GTA GAC TGC GTA CC
    EcoRI-2 AAT TGG TAC GCA GTC TAC
    MseI-1 GAC GAT GAG TCC TGA G
    MseI-2 TAC TCA GGA CTC AT
    primers of pre-amplication EcoRI+1 GTA GAC TGC GTA CCA ATT CA
    MseI+1 GAC GAT GAG TCC TGA GTA AC
    primers of selective amplification E-NNN GAC TGC GTA CCA ATT C NNN
    NNN AAG,AGC,AGT,ATC,ACG
    M-NN(N) GAT GAG TCC TGA GTA A NN(N)
    NN(N) CAA,ACG,CTC,CAG,CGT
    注: ‘E-NNN’和‘M-NN(N)’各自代表一条引物序列,其中的一个‘N’代表一个选择性碱基。‘NNN’为引物‘E-NNN’的选择性碱基,而‘NN(N)’为引物‘M-NN(N)’的选择性碱基
    Note: ‘E-NNN’ and ‘M-NN(N)’ represented a primer sequence respectively,and an ‘N’ represented one selective nucleotide in the primer.‘NNN’was the selective nucleotides for the primer‘E-NNN’,whereas‘NN(N)’for the primer‘M-NN(N)’.
    下载: 导出CSV 
    | 显示表格

    采用本实验室改进的Tris-饱和酚法进行鲮总DNA抽提[4]。在德国Biometra核酸定量仪上测定基因组DNA的吸光值,测定核酸浓度。选取光密度OD260/OD280比值在1.8~2.0之间的基因组DNA,将其浓度稀释至50 ng · μL-1,-20℃保存备用。

    实验方法及操作过程参照夏军红[5]博士论文并稍做修改进行。所做修改如下,在DNA样品限制性消化中,DNA用量为250 ng,+1/+1 PCR预扩增中,+1引物用量为100 ng,+3/+3 PCR选择性扩增中,+3引物用量为150 ng。

    +3/+3循环结束后,1.5% Agrose胶检测扩增结果。等体积2×上样缓冲液终止反应,点样,6%变性聚丙烯酰胺凝胶电泳分离,银染法显色(Premega银染试剂盒操作说明书),数码相机拍照记录。

    银染之后得到的谱带应用软件Labimage(Ver 2.6,by Kapelan)分析,相对于分子量标记100 bp DNA ladder确定等位基因大小。数据统计所有清晰可见的条带,有带记为1,无带记为0,获得0~1矩阵。利用Popgene(Ver.3.1)进行数据分析[6]

    从25对AFLP引物组合(表 1)中选取6对扩增条带丰富、带型清晰、有差异性条带的AFLP引物用于进一步分析。引物组合为E1M3、E1M4、E2M4、E4M3、E5M1、E5M2。以上6对AFLP引物对子群体h和子群体q共58个个体进行PCR扩增,共产生173条扩增条带,分子量范围在50~400 bp之间,平均每对引物产生29条扩增条带,其中多态性条带数为72条,多态位点比例为41.5%。子群体q、h的多态位点数分别为61和70,多态性片段的比例分别为35.11%、40.43%。在6对引物产生的173条扩增条带中,有1条编号为E2M4-1的条带在子群体h中的频率远远高于在子群体q中的出现频率,该条带在2个子群体间的出现频率差别很大,子群体h中的出现频率为72.4%,而子群体q中的出现频率仅为20.6%。图 1为引物E2M4扩增的AFLP图谱,从图谱中可以看出鲮个体间的差异。标注位点为子群体h的高频位点。

    图  1  引物组合E2M4对原种群体的扩增图谱
    q1~29. 子群体q; h1~29. 子群体h; M. 100 bp分子量标准
    Figure  1.  AFLP-PCR pattern of primer pair E2M4 on original populationq
    1~29. sub-stock q; h1~29. sub-stock h; M. 100 bp DNA ladder

    对所取鲮原种群体进行遗传多样性分析结果,鲮原种群体的遗传多样性指数为0.1254,其子群体h与子群体q的遗传多样性指数分别为0.1367和0.0998,子群体h的遗传多样性指数远高于子群体q。

    本研究采用AFLP方法对广东鲮鱼原种场的鲮原种群体进行了遗传多样性分析。对鲮原种群体2个子群体遗传多样性分析表明,子群体h的遗传多样性水平高于子群体q(0.1367>0.0998),2个子群体间的遗传分化较为明显。这一结果与笔者此前采用RAPD方法研究的结果[4]一致。广东鲮鱼原种场在对该批鲮原种的饲养观察中已发现,子群体h的生长性能优于子群体q,因而获得子群体h相对于子群体q的特异性分子标记对于培育具有生长优势的鲮优良品系具有重要意义。AFLP标记能够提供大量且高密度的信息位点,因此,有可能在表型不同的2个鲮原种子群体间筛选出具有群体特异性的分子标记。夏军红等[5]采用AFLP方法从36对引物组合中筛选到一对长江江豚性连锁分子标记的引物组合,找到一个与性别相关的标记性位点。本研究从25对引物组合中选取了6对扩增中显示出有差异条带的引物组合对该批鲮原种的2个子群体进行扩增,获得了1条在子群体h中的出现频率绝对高于子群体q的条带E2M4-1(72.4%>20.6%)。体色是由多基因控制的性状,条带E2M4-1有可能与控制体色的某基因位点呈不完全连锁。进一步扩大筛选范围,采用更多引物组合做进一步研究有希望获得2个子群体间的特异性分子标记。

  • 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 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% (满量程百分比)LDO II哈希 Hach
    温度 Temperature/℃0~50±0.50.1 ℃MPS-400凯米斯 Chemins
    pH0~14±0.10.01MPS-400凯米斯 Chemins
    盐度 Salinity/‰0~100±3.5% (满量程百分比)0.1‰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
    门控循环单元 GRU隐藏层个数:128
    全连接层神经元个数:128
    卷积神经网络-门控循环单元
    CNN-GRU
    卷积核个数:3
    卷积核大小:32
    GRU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 LSTM0.1430.1200.956
    门控循环单元 GRU0.1380.1140.966
    卷积神经网络-门控
    循环单元 CNN-GRU
    0.1190.0840.976
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-11-22
  • 修回日期:  2023-03-12
  • 录用日期:  2023-03-30
  • 网络出版日期:  2023-04-26
  • 刊出日期:  2023-08-04

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