A Hybrid Neural Network Model For Predicting The Nitrate Concentration In The Recirculating Aquaculture System
arxiv(2024)
摘要
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.
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