An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

Abstract

In intensive aquaculture systems, accurate water temperature prediction is crucial for aquaculture efficiency. Traditional prediction models often have limitations in dealing with strongly coupled, nonlinear, and time-varying water temperature data. A novel hybrid model for temperature prediction is proposed to improve prediction generalization ability and robustness. The model integrates advanced data processing and prediction techniques. Firstly, the VMD method is utilized to achieve effective data decomposition and noise reduction. Secondly, the CNN algorithm is applied to achieve feature extraction of the data. Finally, the bi-directional LSTM and self-concerned combination are used to obtain the final prediction results. The experimental results show that the MAE, RMSE, MSE, MAPE, and R2 of the VMD-CNN-BILSTM-SA combination prediction model proposed in this paper are 0.016, 0.143, 0.020, 0.035, and 0.978, respectively. Compared with other deep learning models, the BiLSTM model presented in this paper improves the R2 by 13.2% compared with LSTM and 13.7% over the GRU model. This study can be applied in fishery farming, which can reduce the risk of farming and promote the modernization of fishery.