## 4 Results and Discussion
### Comparative Analysis of Residual Values for Water Quality DO Predictions  
With the optimal parameters of the DE-GWO-SVR model set, the model training was carried out according to the process shown in Figure 2. First, through the PPMCC analysis, the feature of PH was excluded, the rest of the features were retained, the division of the training set and test set was done, and then the initialization parameters of the model were set up as shown in Figure 5, and the prediction of DO in the aquaculture water body of the California perch fish was carried out. In order to compare and validate the performance of the DE-GWO-SVR model, the BPNN and its optimized models (GA-BPNN, PSO-BPNN, GWO-BPNN, DE-GWO-BPNN), and the SVR and its optimized models (GA-SVR, PSO-SVR, GWO-SVR) were also selected to predict the DO content, and the results are shown in Figure 6. Here in this paper, each test model was tuned with initial parameters and set the number of training times 500 times against the DE-GWO-SVR model. By comparing the degree of difference between the model-fitted prediction curves and the original data curves, it can be seen that the prediction effect based on SVR and its optimization model is generally better than that based on BPNN and its optimization model.  
Figure 5: Plot of fitness function for hybrid DE-GWO optimization.  
Figure 6: _Cont._  
As can be seen in Figure 6, the SVR model using hybrid DE-GWO optimization is more accurate in predicting the DO compared to the other models, and fits the real data better. The RBF kernel was chosen as the kernel function of the SVR model, which improved the nonlinear fitting ability of the model. In addition, the DE optimization algorithm was introduced to cross-mutate the GWO algorithm for the population, so that the GWO optimization algorithm further improved the global search ability with its own excellence. Therefore, it can accurately predict the values of the nonlinearly correlated. Subsequently, the performance of the DE-GWO-SVR optimization model used in this paper was further verified by calculating the residual values by each model for the prediction of DO in the California perch water. As shown in Figure 6, the residual values obtained from the prediction of dissolved oxygen in California perch water using the DE-GWO-SVR model had an upper quartile of 0.2185 and a lower quartile of \(-\)0.2363, with a small fluctuation range of the residual values. Compared with the SVR model without the optimization algorithm, the upper quartile of the residual values of the DE-GWO-SVR model is lower by 0.1601 and the lower quartile is higher by 0.1535. Compared with the SVR model optimized with the PSO, the upper quartile and the lower quartile of the SVR model are lower by 0.1256 and higher by 0.1859, respectively. Compared with the SVR model optimized using the GWO, the two have the closest and smaller residual ranges (the residual values are distributed near the baseline location). In addition, it can be seen from Figure 6 that the residual value of the results predicted by the BPNN model fluctuates the most. The upper quartile of 0.495 is much higher than the result of the DE-GWO-SVR hybrid optimization prediction model, and the lower quartile of \(-\)0.3892 is also much lower than the result of DE-GWO-SVR. The predictive performance of the BPNN and its optimized model is lower than that of the non-optimized SVR model. Although the residual value of the GA-BPNN model is small in general, the deviation of individual predicted value is too large. Through the above analysis, it can be seen that the DE-GWO hybrid optimization strategy used in this paper can effectively improve the prediction accuracy of the model, and the optimized model has good stability for the prediction of dissolved oxygen in the water of California perch.