## 4 Results and Discussion
### Comparative Analysis of Different Optimization Models on DO Prediction Performance  
In this paper, the optimization results of the SVR model and the BPNN model are compared between the conventional optimization algorithms (GA, PSO, GWO) and the proposed hybrid optimization algorithm (DE-GWO). As shown in Figure 7, the horizontal coordinate of each subgraph is the true value, and the vertical coordinate is the predicted value. The dotted line in the figure is the regression line when the predicted value is equal to the true value. Herein, the more scattered points in the figure are concentrated on both  
Figure 6: Prediction results and residual graphs of various optimization algorithms when BPNN and SVR are used as base models to be optimized for DO forecast. (**a**) BPNN, (**b**) SVR, (**c**) GA\(-\)BPNN, (**d**) GA\(-\)SVR, (**e**) PSO\(-\)BPNN, (**f**) PSO\(-\)SVR, (**g**) GWO\(-\)BPNN, (**h**) GWO\(-\)SVR, (**i**) DE\(-\)GWO\(-\)BPNN, (**j**) DE\(-\)GWO\(-\)SVR. sides of the regression line, the closer the predicted value of the model is to the true value. Compared with other models, the scatter-value distribution of the proposed DE-GWO-SVR hybrid optimization model is more concentrated on both sides of the regression line, indicating that its prediction accuracy is higher. Table 2 further lists the DE-GWO-SVR and DE-GWO-BPNN hybrid optimization models and the SVR, GA-SVR, PSO-SVR, GWO-SVR, BPNN, GA-BPNN, PSO-BPNN, and GWO-BPNN models with the corresponding R\({}^{2}\), MSE, MAE, and RMSE evaluation index. From the results, it can be seen that the SVR base model predicts DO better than the BPNN. Due to the fact that the parameter transfer between individual neurons of the BPNN model is linear, and only the activation function can be selected as a nonlinear activation function (e.g., sigmoid and ReLU activation function). The BPNN cannot have good prediction of the nonlinear metrics when the model is trained, whereas the SVR model that has used RBF kernel functions can fit a hyperplane to uplift and re-divide the data when the model is trained, attributes to the advantage of SVR in nonlinear prediction. The performance of the SVR model optimized by the GA, PSO, and GWO is overall better than the corresponding BPNN optimized model, in which the prediction accuracy of the PSO-BPNN and the PSO-SVR model is comparable. The main reason is that when the PSO optimization algorithm is searching for the optimal parameters, there is no priority among the particle swarms. The network structure of the BPNN model can be changed arbitrarily within a certain range, which has a certain probability of jumping out of the local optimum, but it is still not as good as the optimization effect of the GWO-SVR model. In this respect, the SVR  
Figure 7: Comparison of prediction results between DE-GWO-SVR hybrid optimization algorithm and other different optimization algorithms. **(a)** BPNN, **(b)** SVR, **(c)** GA-BPNN, **(d)** GA-SVR, **(e)** PSO-BPNN, **(f)** PSO-SVR, **(g)** GWO-BPNN, **(h)** GWO-SVR, **(i)** DE-GWO-BPNN. was selected as the base model for algorithm optimization. By comparing the performance of the optimization algorithms, it can be seen that the optimization performance of the GA, GWO, and PSO on the prediction results of the BPNN model is improved successively (R\({}^{2}\) values of 0.81, 0.84 and 0.86, respectively), and its optimization effect is improved by 9.88%, 13.09%, and 15.12%, respectively, compared with that of the un-optimized BPNN base model (R\({}^{2}\) = 0.73). The optimization performance of the PSO, GA, and GWO on the SVR model is improved successively (R\({}^{2}\) values of 0.86, 0.89, and 0.91, respectively), which improved their optimization performance by 9.30%, 12.36%, and 14.28%, respectively, compared to the un-optimized SVR base model (R\({}^{2}\) = 0.78).