## 3 Results
### Split_Comparison (SC) Model  
The complexity of gas-liquid two-phase flow results in significant errors in individual flow predictions. To address this issue, this research introduces a Split Comparison (SC) model. This model comprises the optimal model GBR, designated as the preset measurement model GBR_A. Flow values of the test set are predicted using the optimal model as the benchmark. The test data are subsequently divided into two categories: large-flow data exceeding a predefined threshold and small-flow data below the threshold. In this study, to ensure a balanced representation of large and small-flow data, and considering the data distribution variances observed in Figure 9, a threshold of 5 m\({}^{3}\)/h is employed. The original dataset is then split into 93 large-flow and 93 small-flow data points. The large-flow data exceeding the threshold are utilized as training data for the large-flow model (GBR_B), while the small-flow data are used to train the small-flow model (GBR_C). GBR_B and GBR_C are utilized to predict the respective data segments in the test set, and the final result is derived by integrating the predictions from GBR_A, GBR_B, and GBR_C. The block diagram of this process is presented in Figure 10.  
The measured data are generated by combining the results of GBR_A, GBR_B, and GBR_C. The SC model output represents the result with the minimum absolute error among the current datasets.  
GBR_A models are trained and tested using the training dataset and the test dataset, respectively. The predictions of GBR_A on the test dataset are divided into test_H and test_S based on the threshold output. Subsequently, re-predictions are conducted using GBR_B and GBR_C. The test results of the three models are summarized in Table 2.  
Figure 9: Distribution of prediction results using different models.  
The model evaluation results indicate that the GBR_B model achieves superior prediction accuracy for larger flow rates, whereas the GBR_C model exhibits less accuracy for smaller flow rates. This is possibly due to the fact that the GBR_B model is better suited for predicting larger flows, whereas changes in flow state resulting from minor flow variations are less evident. Despite this, the GBR_C model still outperforms the overall predictions of GBR_A, and its results are relatively more consistent.  
By comparing and computing the prediction outcomes of the three models, the selected prediction values based on absolute error are utilized to generate the final output of the SC model after assessment in Figure 11.  
Table 3 presents a comparison of the precision between the GBR model and the SC model implementation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **RMSE (m3/h)** & **MAE (m3/h)** & \(R^{2}\) & **MAPE\%** \\ \hline GBR\_A & 1.23 & 0.86 & 0.91 & 16.7\% \\ GBR\_B & 0.94 & 0.77 & 0.87 & 8.2\% \\ GBR\_C & 0.55 & 0.44 & 0.74 & 16.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance measures of different models.  
Figure 11: The test results obtained using the SC model.  
Figure 10: Block diagram of the split comparison modeling system.  
As depicted in Figure 10 and Table 3, the SC model demonstrates enhanced corrective capabilities over the GBR model through its partitioning approach. The MAPE decreased from 17.2% to 8.0%, \(R^{2}\) increased by 0.07, RMSE decreased by 0.58 m\({}^{3}\)/h, and MAE decreased by 0.4 m\({}^{3}\)/h. These results demonstrate that the SC model notably enhances the prediction accuracy of the GBR model.