Hybrid deep learning framework for real-time DO prediction in aquaculture.

Dissolved oxygen (DO) is a vital parameter in regulating water quality and sustaining the health of aquatic organisms in aquaculture environments. Therefore, estimation and control of DO levels are essential in aquaculture operations. However, traditional chemical and photochemical approaches are limited by inaccuracies, environmental interferences, time consumption, and the inability to provide real-time data. Recently, artificial intelligence techniques have been studied for DO estimation. However, off-the-shelf models, such as Random Forest (RF) and Back Propagation (BP) have demonstrated poor performance due to intricate interactions in aquatic ecosystems, which leads to complex data patterns. This study proposes a water quality estimation model by combining a convolutional neural network (CNN), self-attention (SA), and bidirectional simple recurrent unit (BiSRU). One-dimensional convolution in CNN was employed to extract effective features and input into the SA mechanism to assign weights and emphasise crucial information. The model's accuracy is improved by incorporating BiSRU. This model evaluated the DO levels of the intensive aquaculture base in Nansha, Guangzhou City, Guangdong Province, China. The proposed CNN-SA-BiSRU achieved MSE, MAE, RMSE, and R of 0.0022, 0.0341, 0.0471, and 0.9765, respectively. The results of the experiments showed that the proposed model had a high level of accuracy in estimating the outcomes with minimal fluctuations in estimation errors. Moreover, accuracy for short-term prediction was significantly improved, surpassing the performance of existing methods. The highly accurate results indicate the potential of the proposed methodology for DO-level monitoring in aquaculture and its usage in the fishery industry.