This study was groundbreaking in its application of neural network models for nitrate management in the Recirculating Aquaculture System (RAS). A hybrid neural network model was proposed, which accurately predicted daily nitrate concentration and its trends using six water quality parameters. We conducted a 105-day aquaculture experiment, during which we collected 450 samples from five sets of RAS to train our model (C-L-A model) which incorporates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-Attention. Furthermore, we obtained 90 samples from a standalone RAS as the testing data to evaluate the performance of the model in practical applications. The experimental results proved that the C-L-A model accurately predicted nitrate concentration in RAS and maintained good performance even with a reduced proportion of training data. We recommend using water quality parameters from the past 7 days to forecast future nitrate concentration, as this timeframe allows the model to achieve maximum generalization capability. Additionally, we compared the performance of the C-L-A model with three basic neural network models (CNN, LSTM, self-Attention) as well as three hybrid neural network models (CNN-LSTM, CNN-Attention, LSTM-Attention). The results demonstrated that the C-L-A model (R2=0.956) significantly outperformed the other neural network models (R2=0.901-0.927). Our study suggests that the utilization of neural network models, specifically the C-L-A model, could potentially assist the RAS industry in conserving resources for daily nitrate monitoring.

翻译：这项研究开创性地应用神经网络模型进行循环水养殖系统（RAS）中的硝酸盐管理，提出了一种混合神经网络模型，该模型利用六项水质参数精准预测每日硝酸盐浓度及其变化趋势。我们开展了为期105天的养殖实验，从五组RAS中收集450个样本用于训练模型（C-L-A模型），该模型融合了卷积神经网络（CNN）、长短期记忆网络（LSTM）和自注意力机制（self-Attention）。此外，我们从独立运行的RAS中获取90个样本作为测试数据，以评估模型在实际应用中的性能。实验结果表明，C-L-A模型能精准预测RAS中的硝酸盐浓度，即使在训练数据比例减少的情况下仍保持良好性能。我们建议使用过去7天的水质参数来预测未来硝酸盐浓度，因为该时间窗口能使模型获得最大泛化能力。同时，我们将C-L-A模型与三种基本神经网络模型（CNN、LSTM、自注意力机制）及三种混合神经网络模型（CNN-LSTM、CNN-Attention、LSTM-Attention）进行了性能对比。结果显示，C-L-A模型（R²=0.956）显著优于其他神经网络模型（R²=0.901-0.927）。我们的研究表明，采用神经网络模型（特别是C-L-A模型）有望帮助RAS行业节约日常硝酸盐监测的资源投入。