## 3 Results and Discussion
### Performance Comparison with Previous Studies  
Previous research has successfully demonstrated the application of ML hybrid models such as PSO-ANFIS and MKSVM-GA in different studies. [PERSON] et al. (2019) [PERSON] et al. (2019) and [PERSON] et al. (2020)[PERSON] et al. (2020) made important contribution in predicting CO\({}_{2}\) diffusion in brine using advanced models like MKSVM-GA and PSO-ANFIS, achieving high R\({}^{2}\) of 0.9960 and 0.9993 for the training dataset, respectively. While their test results were valid, the studies relied on small datasets (92 and 86 data points, respectively), which increases the risk of overfitting. Hybrid models require large datasets to perform reliably when such models are applied to small datasets, they are prone to overfitting [PERSON] et al. (2019). In contrast to hybrid models, our study employed RF, GBR, and XGBoost using 176 datapoints. Although the best R\({}^{2}\) achieved in our study is 0.96 and 0.95 for train and test datasets, respectively, it is suggested that our model can be more reliable due to dependence on large interval, large dataset and close data points which help the ML model to identify the best pattern among the input parameters (pressure, temperature, and salinity) with the output (DC) when compared to [PERSON] et al. (2019) [PERSON] et al. (2019) and [PERSON] et al. (2020) [PERSON] et al. (2020). Additionally, most studies selected viscosity and density as an input parameter, which is important, but salinity has a more direct relationship with the DC [PERSON] et al. (2020). Therefore, our study prioritized salinity as an input parameter with pressure and temperature to capture the pattern and relationship with the output, which is DC. RF, GBR, and XGBoost models displayed exceptional predictive accuracy as shown by their high R\({}^{2}\) values (e.g., 0.95 for RF testing) along with minimal error (0.11 MAE and 0.03 RMSE for RF) among both training and testing data.  
For the evaluation of any ML model, it is essential to analyze different performance metrics such as R\({}^{2}\), RMSE, and MAE. Relying on a single evaluated metric may lead to misleading conclusions. [PERSON] et al. (2025) [PERSON] et al. (2025) also predicted CO\({}_{2}\) DC in the brine case with models including MLP, CFNN, RNN, and GEP with high RMSE values of 3.5452, 5.2872, 4.9287, and 5.5611, respectively, indicating significant errors in prediction performance although their R\({}^{2}\) is very high, as shown in Table 1 [PERSON] et al. (2025). Their input parameters were temperature, pressure, and density. In contrast, the present study achieved considerably lower RMSE values, as shown in Table 7 and Figure 10, indicating improved accuracy and reliability in the predictions.  
Another important advantage of our models is their interpretability and computational efficiency. Regression-based models like RF and GBR offer valuable insights into feature importance through SHAP values, illustrating the importance of each input parameter--such as temperature, salinity, and pressure--impact the DC. In contrast, hybrid models like PSO-ANFIS and MKSVM-GA are mostly difficult to interpret, resource-intensive, and time-consuming. These combined strengths demonstrate that our models provide a balance of accuracy, interpretability, and efficiency for reliable predictions of DC across varied conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Salinity (mol/L)**} & **RF** & **GBR** & **XGBoost** \\  & **DC (10\({}^{-9}\) m\({}^{2}\)/s)** & **DC (10\({}^{-9}\) m\({}^{2}\)/s)** & **DC (10\({}^{-9}\) m\({}^{2}\)/s)** \\ \hline
0 & 2.69 & 2.63 & 2.41 \\
1 & 2.53 & 2.39 & 2.61 \\
2 & 2.19 & 2.1 & 2 \\
4 & 1.48 & 1.37 & 1.29 \\
6 & 1 & 0.91 & 1.07 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of ML models precited DC at different salinities and at constant pressure and temperature.