Precision prediction of aquaculture water quality: a spatiotemporal model integrating optimized-LSTM and radial basis function neural networks

Aquaculture water quality parameters are influenced by multiple factors, exhibiting significant temporal and spatial variations. Current prediction methods for these parameters primarily focus on time series predictions at specific observation points, which do not comprehensively characterize the spatiotemporal distribution dynamics of pond water quality parameters. To address this limitation, this study proposes a novel model which incorporates a Self-Attention (SA) mechanism to enhance the capture of long-term dependencies within the data. Furthermore, an enhanced Sparrow Search Algorithm (ESSA) is implemented to optimize the hyperparameters of the long short-term memory (LSTM) network, thereby improving the time series prediction of water quality parameters. Building upon these predictions, the Radial Basis Function (RBF) algorithm is utilized for spatial prediction. The proposed spatiotemporal prediction model, which combines ESSA-SA-LSTM and RBF, demonstrates superior performance by reducing the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) of dissolved oxygen (DO) and water temperature spatiotemporal predictions, outperforming existing comparative algorithms. The model presented in this study significantly enhances the accuracy of spatiotemporal predictions for water quality parameters, playing a crucial role in ensuring the safe production and management of aquatic environments in aquaculture.