Prediction of dissolved oxygen in water of aquaculture ship based on CNN-GRU hybrid model
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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。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 , we used data collected from a Trachinotus ovatus culture experiment as a sample, 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 the actual production of aquaculture ships, which provides references for monitoring and early warning of DO content in the production process of aquaculture ships.
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Key words:
- Aquaculture ship /
- Dissolved oxygen /
- Convolutional neural network /
- Gated recurrent unit
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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
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.S LDO II 哈希 Hach 温度 Temperature/℃ 0~50 ±0.5 0.1 ℃ MPS-400 凯米斯 Chemins pH 0~14 ±0.1p 0.01 MPS-400 凯米斯 Chemins 盐度 Salinity/‰ 0~100 ±3.5%F.S 0.1‰ MPS-400 凯米斯 Chemins 
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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
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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 CNN 0.173 0.132 0.945 长短期记忆 LSTM 0.143 0.12 0.956 门控制循环神经网络 GRU 0.138 0.114 0.966 卷积神经网络-门控制循环
神经网络
CNN-GRU0.119 0.084 0.976
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