# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 4 Results and Discussion  
### Pavement Temperature Prediction Based on Ensemble Models  
Among the studied meteorological factors, air temperature was found to be the primary factor influencing pavement temperature, while solar radiation, relative humidity, and wind speed showed varying degrees of correlation, depending on the region and depth. Considering these factors, this study selected air temperature, solar radiation, relative humidity, and wind speed as input features, and we used pavement temperature at different depths as the prediction target. An RF model was developed to perform the temperature predictions. Table 4 presents the model performance in each region on the training and testing datasets. The training and testing data, based on samples from the region, were divided at a ratio of 7:3. The results demonstrate that the RF model achieves high overall accuracy in pavement temperature prediction, confirming its effectiveness for this task. To better illustrate the model's performance across regions, the evaluation metrics at different depths were averaged, as shown in Figure 5. The figure indicates that the model maintained stable predictive performance on both the training and testing datasets. However, the prediction accuracy varied among regions due to differences in local meteorological and temperature conditions. For example, the \(R^{2}\) value on the testing set reached 0.96 at Site01, while it was 0.89 at Site04. It should be noted that these R\({}^{2}\) values represent the average goodness of fit across all measured depths, reflecting the overall prediction performance at each site. These  
Figure 4: Correlation analysis between temperature at different depths and meteorological characteristics. results suggest that, although regional performance varies, training and testing within each region yield acceptable prediction accuracy. This can be attributed to the robustness of RF in modeling nonlinear relationships, as well as its tolerance to noise and outliers. Furthermore, the model achieves strong accuracy and generalization without extensive parameter tuning, even under varying regional data distributions. To further evaluate the model's cross-regional adaptability, data from Site01 were used for training, while the RF model was applied to predict pavement temperatures at Site02. The measured and predicted values are shown in Figure 6. Although both sites are located in northern China and share similar climate conditions, the model's prediction accuracy at Site02 was limited. The \(R^{2}\) value for surface temperature prediction was only 0.72, indicating a considerable error and highlighting the model's limitations in cross-regional applications. This result was mainly due to the fact that the model was trained using local features such as temperature, solar radiation, relative humidity, and wind speed, which vary between regions. Without adaptation, the model lacks the ability to account for inter-regional climate differences, leading to reduced accuracy when applied to new locations. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Depth**} & \multicolumn{6}{c}{**Training Datasets**} & \multicolumn{6}{c}{**Testing Datasets**} \\ \cline{3-10}  & & **MAE** & **MSE** & **RMSE** & **R2** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline \multirow{4}{*}{01} & 0 cm & 1.16 & 3.29 & 1.81 & 0.99 & 2.21 & 11.58 & 3.40 & 0.96 \\  & 5 cm & 0.99 & 1.9 & 1.38 & 0.99 & 1.96 & 7.0  
Similarly, the model trained on Site01 data was used to predict pavement temperatures at different depths at Site03 and Site04, as shown in Figure 7. The results indicate that the highest prediction accuracy was achieved only at Site01, with a correlation coefficient of approximately 0.98. In contrast, the prediction performance declined notably at Site03 and Site04, particularly at Site04, where the \(R^{2}\) for surface temperature prediction dropped to just 0.51.