# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 4 Results and Discussion
### Model Validation Using Independent Regional Data  
To evaluate the generalization ability and practical applicability of the proposed temperature prediction model, Site05 was used as an independent validation site. Local meteorological and pavement temperature data were collected and compared with the  
Figure 12: Predicted and measured temperature using RF and TL-RF models.  
Figure 13: Comparison of model prediction results at different depths.  
model outputs. The model was initially trained using data from Site01 to Site04 through a TL model enhanced with feature augmentation. This trained model was then applied to predict temperatures at Site05. A comparison between the predicted results and the measured data is shown in Figure 14. The results indicate a high degree of agreement between the predicted and measured values, with most of the data points distributed closely along the \(y=x\) line, demonstrating the model's strong adaptability and generalization capability. Specifically, the \(R^{2}\) at depths of 0 cm, 5 cm, 12 cm, and 30 cm was 0.96, 0.97, 0.96, and 0.94, respectively, while the MAE was 2.1 \({}^{\circ}\)C, 1.88 \({}^{\circ}\)C, 1.92 \({}^{\circ}\)C, and 2.23 \({}^{\circ}\)C, respectively, indicating a high level of overall prediction accuracy. These results confirm the model's high predictive accuracy under unfamiliar climatic conditions, demonstrating its suitability for practical engineering use.  
In addition, a region-based k-fold cross-validation was implemented to further assess the model's cross-regional adaptability. The five study sites were treated as separate folds. In each round, one site served as the target region, while the remaining four provided the source data. A base random forest model was first trained on the combined source datasets and then applied to the target site for initial predictions. These predictions were integrated with the original inputs to create an augmented feature set. Subsequently, 70% of the target site's data were used for model fine-tuning, and the remaining 30% were used for evaluation. Metrics such as prediction error and R2 were computed for each fold. This process was repeated for all five regions, yielding a comprehensive validation scheme. The results, summarized in Table 6, demonstrate the model's consistent accuracy and stability across different climatic zones, underscoring its strong potential for cross-regional pavement temperature prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **RMSE** & **MAE** & **R2** \\ \hline
01 & 3.375 \(\pm\) 0.108 & 2.250 \(\pm\) 0.040 & 0.961 \(\pm\) 0.002 \\
02 & 3.674 \(\pm\) 0.110 & 2.637 \(\pm\) 0.053 & 0.938 \(\pm\) 0.005 \\
03 & 2.575 \(\pm\) 0.033 & 1.799 \(\pm\) 0.014 & 0.966 \(\pm\) 0.001 \\
04 & 2.980 \(\pm\) 0.047 & 2.241 \(\pm\) 0.041 & 0.894 \(\pm\) 0.003 \\
05 & 2.846 \(\pm\) 0.047 & 2.082 \(\pm\) 0.030 & 0.961 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction performance of the TL-RF model using k-fold cross-validation, by region.  
Figure 14: Validation results of the TL-RF model based on Site05.