## 5 Conclusions  
In this paper a DE-GWO-SVR model based on second-order hybrid optimization is proposed for accurate and reliable prediction of DO in California perch aquaculture water bodies. Based on the data preprocessing and prediction model optimized with the objective, adopts PPMCC correlation analysis and regression analysis, realizes the dimensionality reduction of the water quality multi-parameter data, and improves the quality of the data. The structure of with hybrid optimization of the DE-GWO and combined weight coefficient optimization with parameter initialization and validation of fitness function value solves the dilemma of traditional single optimization algorithm, which can easily fall into local optimum. The DE-GWO-SVR model constructed in this paper can effectively characterize water quality factors with multivariate, coupling, and nonlinear characteristics, but also can avoid the limitations of traditional methods in terms of small samples, high dimensionality and overfitting. Compared with the SVR, BPNN, and their optimization models, the method in this paper not only improves the accuracy of model prediction, but also the training set of the fitness function has a good convergence effect, and it has achieved a more reliable application effect in water quality DO content prediction, which provides a new idea for high-precision and high-efficiency prediction of water quality factors in complex aquaculture water bodies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model\(\backslash\)Indictors** & **R\({}^{2}\)** & **MSE** & **MAE** & **RMSE** \\ \hline BPNN & 0.73 & 0.484 & 0.5434 & 0.6961 \\ GA-BPNN & 0.81 & 0.334 & 0.3971 & 0.5785 \\ PSO-BPNN & 0.86 & 0.254 & 0.3889 & 0.5043 \\ GWO-BPNN & 0.84 & 0.294 & 0.4051 & 0.5427 \\ DE-GWO-BPNN & 0.86 & 0.245 & 0.3391 & 0.4952 \\ SVR & 0.78 & 0.396 & 0.4792 & 0.6294 \\ GA-SVR & 0.89 & 0.202 & 0.2696 & 0.4502 \\ PSO-SVR & 0.86 & 0.253 & 0.4221 & 0.5040 \\ GWO-SVR & 0.91 & 0.163 & 0.3076 & 0.4038 \\ DE-GWO-SVR & 0.94 & 0.108 & 0.2629 & 0.3293 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of error assessment of DE-GWO hybrid optimization with other optimization algorithms.  
**Author Contributions:** Conceptualization, X.B., Y.J. and L.Z.; Methodology, X.B., Y.J., L.X. and F.Q.; Software, L.C., W.Z., L.X. and X.L.; Validation, X.B., Y.J. and X.L.; Formal Analysis, X.B., Y.J. and L.Z.; Investigation, L.C., Y.J., W.Z. and X.L.; Data Curation, X.B., Y.J. and B.L.; Writing--Original Draft Preparation, X.B., Y.J. and F.Q.; Writing--Review and Editing, X.B., Y.J. and F.Q.; Visualization, X.B., L.Z. and L.X.; Supervision, R.W., B.L. and F.Q.; Project Administration, B.L. and F.Q.; Funding Acquisition, R.W. and F.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by Natural Science Foundation of Fujian Province, China [grant numbers 20230]01079]; Educational Scientific Research Project for Middle-aged and Young teachers of Fujian Provincial Department of Education (science and technology category), China [grant numbers JAT220066].  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author (The data are not publicly available due to privacy).  
**Conflicts of Interest:** The authors declare no conflict of interest.