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Prediction of water quality factors for sea cucumber farming based on Dual-Assisted prediction model

The water environment plays a vital role in the sea cucumber aquaculture ecosystem, and accurate prediction of seawater quality in sea cucumber aquaculture areas is an important research issue to bring into play the role and value of the sea cucumber farming ecosystem. In view of the problems that the current research only considers the influencing factors between multiple water quality, ignores the influence of meteorological and other physical factors on water quality and the complex nonlinear relationship between them. Constructing a dual-assisted prediction model HG-VMD-LSTM for pH and DO in sea cucumber farming water based on deep learning theory. Firstly, use the variational modal decomposition technology VMD to decompose the data, analyze and extract its surface features and further fuse relevant significant water quality factors to form an auxiliary model one. Secondly, use the improved gray wolf optimization algorithm HGWO to analyze the two LSTM layers. The neurons number, learning rate and dropout are optimized to form the second auxiliary model. Finally, the optimal data and parameters formed by the auxiliary model are input into the base prediction model LSTM. The experimental results show that compared with other sub-models, the RMSE, MAE and MAPE of this prediction model are reduced by 22.7%,14.25% and 13.3% respectively, in the prediction of DO. The prediction of pH by this prediction model is reduced by 3.88%,3.87% and 4.28%, respectively.