# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 4 Results and Discussion
### Integrated Prediction Across All Regions via Enhanced Transfer Learning  
To improve the model's generalization capability and enhance its prediction accuracy in multi-region scenarios, a transfer learning framework based on multiple target regions was developed, building upon the single-region transfer approach. In this framework, the Site01 region was designated as the source domain, while the combined data from Site02, Site03, and Site04 formed the target domain. Using RF as the base learner, TF was performed through feature enhancement. This strategy enabled the model to capture shared patterns across regions and achieve integrated prediction of pavement temperature variations in diverse climatic environments. The prediction results of the RF and the TL-RF models at different depths were compared, as shown in Figure 12. As shown in the figure, the prediction performance of the TL-RF model was better than that of the RF, with more data points near the \(y=x\) line.  
Figure 10: Predicted and measured temperature at Site04.  
The comparative evaluation metrics of the models at different depths are presented in Figure 13. The results indicate that, after data fusion from multiple target regions, the TL-RF model achieved lower MSE and MAE values, along with a significant improvement in \(R^{2}\). Specifically, for surface temperature prediction, the TL-RF model showed significant improvements over the RF model. The RMSE decreased from 6.7 to 3.86, the MAE from 4.91 to 2.78, and the \(R^{2}\) improved from 0.75 to 0.92. Furthermore, a comparison of prediction performance at different depths revealed that both models exhibited improved accuracy with increasing depth. The prediction accuracy at the road surface was the lowest, primarily because surface temperature is more susceptible to fluctuations caused by meteorological conditions. Notably, the TL-RF model substantially reduced prediction errors at the surface level, demonstrating enhanced adaptability to varying environmental factors across regions. These results further validate the effectiveness of feature enhancement and multi-region data fusion in temperature prediction. In conclusion, the transfer learning strategy based on feature enhancement not only enhances the model's adaptability and robustness across different regions but also provides a practical pathway for developing cross-regional models  
Figure 11: Model evaluation at Site02 using a TL-RF model trained on Site01 data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**5 cm**} & \multicolumn{3}{c}{**12 cm**} & \multicolumn{3}{c}{**30 cm**} \\ \cline{3-10}  & & \multirow{2}{*}{**RF**} & & \multirow{2}{*}{**TL-RF**} & & \multirow{2}{*}{**Relative Change**} & & & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**TL-RF**} & **Relative Change** \\ \cline{3-10}  & & & & & & & & & & & \\ \hline \multirow{4}{*}{02} & MSE & 18.24 & 12.13 & 33.5 & 14.06 &  
in engineering applications, such as pavement temperature monitoring, road maintenance planning, intelligent transportation systems, and climate-responsive infrastructure design.