## 3 Results and Discussion
### Visual Validation and Trend Analysis  
To assess the predictive fidelity of the developed models, scatter plots were constructed to compare actual and predicted DC values (Figure 7). A model's accuracy is reflected by the proximity of data points to the 1:1 diagonal line; the closer the alignment, the stronger  
Figure 6: Comparative plots showcasing the performance of RF, GBR, and XGBoost models in terms of (**a**) RMSE, (**b**) MAE, and (**c**) R\({}^{2}\) metrics across training and testing dataset.  
the prediction. Among the models, the Random Forest (RF) exhibited the tightest clustering around the diagonal for both training and testing datasets, indicating minimal prediction bias and high generalization capability. The Gradient Boosted Regression (GBR) model also demonstrated strong performance, though with slightly greater deviation in the test data. XGBoost, while performing adequately in the training set, exhibited a more pronounced spread in the test set, suggesting reduced robustness compared to RF and GBR.  
To further evaluate model performance across multiple error and accuracy dimensions, radar charts were employed, as shown in Figure 8. These charts facilitate a comprehensive comparison by representing each performance metric--R\({}^{2}\), RMSE, and MAE--on a separate axis. In the radar plots, metrics with lower error values (MAE, RMSE) are positioned closer to the center, while higher R\({}^{2}\) values are located further outward, indicating superior predictive strength. The RF model consistently outperformed both GBR and XGBoost across all metrics for both training and test sets, reaffirming its robustness and superior generalization. GBR followed closely, while XGBoost displayed relatively lower predictive consistency.  
Figure 7: Predicted vs. actual DC for (**a**) RF, (**b**) GBR, and (**c**) XGBoost models.  
Molecular dynamics studies, such as [PERSON] et al. (2022) [27], highlight the effect of salinity on DC of CO\({}_{2}\) in brine at different pressures and temperatures. At temperature 323 K and pressure 100 MPa, the CO\({}_{2}\) DC decreased from 3.8327 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s in pure water to 3.1553 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (17.68% reduction) at 1 mol/L NaCl, 2.49 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (35.08% reduction) at 3 mol/L, and 1.34 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (64.92% reduction) at 6 mol/L, due to increased salinity and solute-ion interactions. Similar trends were observed in our study: increasing the salinity at constant pressure (10 MPa) and temperature (323 K) resulted in a decrease in DC of CO\({}_{2}\) as shown in Table 6. Figure 9 represents the relationship trends of predicted DC at different salinities and at constant temperature and pressure using ML algorithms. For example, at temperature 310 K and pressure 10 MPa, the CO\({}_{2}\) DC decreased from 2.69 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s in pure water to 2.53 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (5.94% reduction) at 1 mol/L NaCl, 1.48 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (44.98% reduction) at 4 mol/L, and 1 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s (62.82% reduction) at 6 mol/L, due to increased salinity and solute-ion interactions.  
Figure 8: Radar chart comparing RF, GBR, and XGBoost models on RMSE, MAE, and R\({}^{2}\) metrics for training and testing datasets, highlighting their prediction accuracy.  
Figure 9: Represents the relationship trends of predicted DC (10\({}^{-9}\) m\({}^{2}\)/s) at different salinities (mol/L) at constant temperature (310 K) and pressure (10 MPa).