An efficient compressive CNN and transformer hybrid framework for long-term dissolved oxygen prediction in aquaculture

Current methods for predicting water quality do not fully consider the nonlinear coupling relationship between multiple parameters, given the multifaceted nature of water quality factor evolution in aquaculture systems. This results in limited prediction accuracy and generalization ability, making it impossible to predict water quality parameters over an extended period. This paper proposes a multi-factor related long-term dissolved oxygen concentration forecasting framework, leveraging the synergistic integration of efficient compression CNN and Transformer. First, to further improve the short-term feature fusion of multi-sensor data, a CNN is used to learn localized spatial representations from multi-sensor measurement inputs; then, a compressed pooling strategy with binary coding is introduced to mitigate network accuracy degradation during pooling and achieve efficient and accurate feature coding; finally, the feature fusion results are inputted into Transformer to improve the global temporal capture ability of the model, achieving long-term water quality parameter prediction. Empirical evidence demonstrates the superior performance of this approach in long-term dissolved oxygen concentration forecasting. Compared with traditional max and average pooling methods, the proposed compressed pooling strategy improves model convergence speed by 13.59%. Compared with LSTM, GRU, Transformer, CNN-Transformer (Max-Pool), Strided Convolution-Transformer, iTransformer, and Informer models, the Coefficient of Determination ( ) can reach 0.969 ± 0.004 and 0.963 ± 0.003 at prediction step lengths of 96 (4 days) and 168 (7 days). The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were reduced to 0.022 ± 0.004 mg/L and 0.017 ± 0.002 mg/L for the 96-step prediction horizon, and to 0.025 ± 0.001 mg/L and 0.020 ± 0.001 mg/L for the 168-step horizon, respectively. The novel predictive framework engineered in this paper can optimize faster during the parameter learning, enhancing the model training efficiency and improving the accuracy of long-term predictions.