A hybrid intelligence model for predicting dissolved oxygen in aquaculture water

Abstract

Dissolved oxygen is an important water quality indicator that affects the health of aquatic products in aquaculture, and its monitoring and prediction are of great significance. To improve the prediction accuracy of dissolved oxygen water quality series, a hybrid prediction model based on variational mode decomposition (VMD) and a deep belief network (DBN) optimized by an improved slime mould algorithm (SMA) is proposed in this paper. First, VMD is used to decompose the nonlinear dissolved oxygen time series into several relatively stable intrinsic mode function (IMF) subsequences with different frequency scales. Then, the SMA is improved by applying elite opposition-based learning and nonlinear convergence factors to increase its population diversity and enhance its local search and global convergence capabilities. Finally, the improved SMA is used to optimize the hyperparameters of the DBN, and the aquaculture water quality prediction VMD-ISMA-DBN model is constructed. The model is used to predict each IMF subsequence, and the ISMA optimization algorithm is used to adaptively select the optimal hyperparameters of the DBN model, and the prediction results of each IMF are accumulated to obtain the final prediction result of the dissolved oxygen time series. The dissolved oxygen data of aquaculture water from 8 marine ranches in Shandong Province, China were used to verify the prediction performance of the model. Compared with the stand-alone DBN model, the prediction performance of the model has been significantly improved, MAE and MSE have been reduced by 43.28% and 40.43% respectively, and (R2) has been increased by 8.37%. The results show that the model has higher prediction accuracy than other commonly used intelligent models (ARIMA, RF, TCN, ELM, GRU and LSTM); hence, it can provide a reference for the accurate prediction and intelligent regulation of aquaculture water quality.