## 4 Results  
### Comparison of Data Inputs  
To test the availability of image data, we show the evaluation results of seven models on three different datasets (Basic, Image, and Integrated) in Figure 5. The Basic dataset  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MSE** & **MAE** \\ \hline ResNet-50 & 0.78 & 0.118 & 0.171 \\ DenseNet-121 & 0.81 & 0.091 & 0.135 \\ Inception-v4 & 0.79 & 0.091 & 0.158 \\ Xception & 0.82 & 0.079 & 0.127 \\ Swin Transformer & 0.85 & 0.067 & 0.105 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of three pre-trained models on experimental data.  
includes only geological parameters, the Image dataset consists solely of image features, and the Integrated dataset combines geological parameters with image features. On the Image dataset, all models show higher R\({}^{2}\) values and a lower RMSE compared to the Basic dataset. This trend is also observed when comparing the Basic and Integrated datasets. In other words, the figure clearly illustrates that the predictive capability of image features surpasses those of the Basic dataset.  
To rigorously validate the effectiveness of image features, we conducted significance tests, and the results are shown in Table 3. For the significance tests, if the variables from two samples are normally distributed, a two-sample t-test was used; otherwise, the Wilcoxon test was applied.  
First, the significance test results for R\({}^{2}\) show significant differences across all three comparisons (\(p\)-value of 0.046875 for each). This result indicates that the integrated dataset captures the complex variations in stress better than using a single dataset alone. The significance test results for RMSE further support this conclusion--models using the Integrated dataset show significant improvements compared to the Basic dataset (\(p<0.05\)), confirming the critical role of image features in stress prediction. Interestingly, the significance test results for MBE and SI demonstrate the effectiveness of image features from both positive and negative perspectives. For comparisons between Basic and Image, as well as Basic and Integrated, significant differences in MBE and SI were observed (\(p<0.05\)), suggesting that image features play a crucial role in reducing the model prediction uncertainty. However, there is no significant difference between the image data and the integrated data, indicating  
Figure 5: Comparison of model performance across different datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Basic vs. Image** & **Basic vs. Integrated** & **Image vs. Integrated** \\ \hline R\({}^{2}\) & 0.046875 (**) & 0.046875 (**) & 0.046875 (**) \\ RMSE & 0.037659(**) & 0.002724 (**) & 0.564496 \\ MBE & 0.000256 (**) & 0.000284 (**) & 0.956401 \\ SI & 0.037659 (**) & 0.002724 (**) & 0.564496 \\ AIC & 0.014681 (**) & 0.003096 (**) & 0.000465 (**) \\ BIC & 0.009939 (**) & 0.000001 (**) & 0.000001 (**) \\ \hline \hline \end{tabular} Note: ** denotes a \(p\)-value less than 0.01, and ** denotes a \(p\)-value less than 0.001.  
\end{table}
Table 3: Significance test of model performance with different data inputs.  
that the superior performance of the integrated dataset is largely derived from the image information contained in it. In fact, this reflects the validity and usability of image data. Finally, the significance test results for AIC and BIC, which are the criteria for model selection, also show a consistent trend--datasets integrating image features result in more concise and predictive models.  
In summary, image features provide a richer and more precise information basis for stress prediction, making them a viable and highly promising predictor. Integrating image features into the model input can significantly improve the model's goodness-of-fit and predictive capability.