## 5 Results and Discussion
### Model Comparison and Test
#### 5.3.2 Model Test  
In order to effectively evaluate the robustness of the model's performance, the generalization ability and overfitting phenomenon of the model are tested by adjusting the number of training samples. This study divides the samples into five different proportions to assess the generalization ability and actual impact of the validated optimal model, aiming to boost the credibility and scientific value of the research findings. Figure 14 illustrates the predictive effect of the model with varying numbers of training samples, e.g., 140, 160, 180, 200, and 220 (using PM10 as an example). Combined with the prediction performance of the training set and the test set as shown by the learning curve in Figure 15, the prediction performance of both sets changes with the increase in the number of training samples. If the training error decreases with an increase in the number of samples, but the verification error does not decrease (or even increases), the model appears to be overfitting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**PM**} & \multicolumn{4}{c}{**Evaluating Indicator**} \\ \cline{3-5}  & & \(\mathbf{R^{2}}\) & **RMSE** & **STD** \\ \hline \multirow{2}{*}{LSSVM} & PM2.5 & 0.482 & 13.484 & 4.197 \\  & PM10 & 0.466 & 15.751 & 3.618 \\ \hline \multirow{2}{*}{GA-LSSVM} & PM2.5 & 0.872 & 8.592 & 13.503 \\  & PM10 & 0.913 & 7.476 & 14.606 \\ \hline \multirow{2}{*}{PSO-LSSVM} & PM2.5 & 0.805 & 9.189 & 10.082 \\  & PM10 & 0.813 & 11.110 & 9.937 \\ \hline \multirow{2}{*}{GWO-LSSVM} & PM2.5 & 0.808 & 9.319 & 12.033 \\  & PM10 & 0.859 & 8.974 & 11.161 \\ \hline \multirow{2}{*}{ISSA-LSSVM} & PM2.5 & 0.763 & 10.956 & 7.630 \\  & PM10 & 0.835 & 9.283 & 10.816 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of prediction results from different models.  
Figure 13: Test sample prediction result error analysis Taylor diagram.  
Figure 14: Model test result diagram.  
Therefore, as seen in Figure 14c, when the number of training samples is 180, the dataset is allocated a 7:3 ratio. The results of the prediction set of the model indicate that the correlation coefficient and the error value are optimal. It can be concluded that the model's fitting effect and robustness are at their best in this state.  
It is important to highlight that the model comparison and testing discussed above demonstrate the effectiveness of the algorithm presented in this paper. First, the dust concentration prediction index set is quantitatively refined using the random forest (RF) method, which helps eliminate redundant factors and enhances the model's prediction speed. Next, a combined prediction model for dust concentration in opencast mines is developed using a GA-LSSVM approach, where the GA algorithm optimizes the LSSVM model, resulting in strong predictive performance with low error and high accuracy. However, the algorithm does have some limitations that require further enhancement. These limitations include its suitability for modeling long sequence data, its robustness and generalization capabilities when handling high-dimensional data, and the necessity for a substantial amount of data for model training, which adds complexity and makes it sensitive to hyperparameter selection. Additionally, while the genetic optimization algorithm used in the model is flexible, its outcomes are probabilistic rather than deterministic, and the optimization process may lead to local convergence. In the future, we will keep enhancing the algorithm model to significantly boost the accuracy and reliability of predictions.