Prediction and control of water quality in Recirculating Aquaculture System based on hybrid neural network

In the Recirculating Aquaculture Systems (RAS), the control of water quality indices remains essential to survival and growth of aquaculture objects. This requires effect prediction of future water status in advance, which can be adopted to help the generation of following control strategies. However, conventional methods of water quality prediction were mostly dependent on redundant parameters of model, which leads to inefficiency and low accuracy. In addition, the complexity of the RAS multi-units requires intelligent control of the water quality unit. Thus, a prediction and control framework for predicting water quality in RAS is proposed in this paper. Specifically, a hybrid deep learning structure which combines the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Attention mechanism is presented. To begin with, the CNN is utilized to extract local features for different timestamped water quality parameter. After the local features have been extracted, the proposed GRU model replicates the global sequential features of the parameters. The attention mechanism is then applied to focus on more critical features to promote the efficiency and accuracy of prediction. Finally, to demonstrate the efficiency and stability of the prediction and control framework with the mixture of CNN, GRU and Attention (PC-CGA), multiple groups of experiments and evaluations are carried out in a medium size RAS.