## 4 Discussion
### Uncertainty Analysis  
In the uncertainty quantification section, the performance of each experimental design (A-E) in DBM reconstruction tasks was compared from multiple perspectives. The results demonstrate that the introduction of an advanced uncertainty quantification module significantly enhanced the model's reliability and detail recovery capability. Specifically, Experiment A, which only utilized a fixed dropout strategy, somewhat alleviated overfitting but failed to effectively capture predictive uncertainty. As a result, the error variance, entropy, and ECE in both Region 1 and Region 2 were notably high, indicating large prediction fluctuations and excessive smoothing. Experiments B and C, which incorporated hierarchical progressive dropout and activation strength-driven adaptive dropout, respectively, showed some improvements. These methods reduced certain metrics but still had limitations in processing local details and expressing overall uncertainty. Experiment D introduced a dynamic gating network, which adaptively controlled the feature channels using learned gating coefficients. This approach effectively reduced expected calibration errors (ECEs), making the model more sensitive to key features in complex scenarios. However, only Experiment E, by combining MC-Dropout with heteroscedastic noise modeling, established a multi-stage uncertainty quantification framework. This method not only provided probabilistic interpretations of the predictions but also achieved the lowest error variance and ECEs in both Region 1 and Region 2 while maintaining relatively low entropy values. From the data presented in Table 3 and Figure 5, it is evident that Experiment E reduced the error variance in Region 1 from 1480.9277 to 793.4338, marking a 46.5% decrease. In Region 2, the error variance was reduced from 614.3796 to 242.1961, marking a 60.5% decrease. This result indicates that Experiment E exhibits significant advantages in reducing reconstruction errors, improving structural fidelity, and suppressing artifacts. To accurately assess the stability and reliability of the model across different regions, uncertainty heatmaps, confidence heatmaps, and variance heatmaps [23] are introduced as key tools for evaluating model performance. The confidence heatmap reflects the model's certainty in predictions across various regions, effectively distinguishing between high-confidence and low-confidence areas. Meanwhile, the uncertainty heatmap quantifies prediction dispersion through multiple evaluations, ensuring consistency and reliability within specific regions. The variance heatmap visually displays prediction dispersion, revealing model stability issues in complex or noisy areas. These heatmaps collectively provide comprehensive insights into prediction volatility, stability, and confidence [56], crucial for evaluating model performance in complex terrain reconstruction tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Region** & **Error Variance (m2)** & **Entropy (Nats)** & **ECE (\%)** \\ \hline A & 1 & 1480.9277 & 1.368 & 30.6711 \\ B & 1 & 1467.23027 & 0.6491 & 11.4844 \\ C & 1 & 1353.9114 & 0.6027 & 25.2278 \\ D & 1 & 1179.5446 & 0.611 & 8.201 \\ E & 1 & 793.4338 & 0.6062 & 7.9098 \\ A & 2 & 614.3796 The confidence heatmap quantifies model certainty by simulating multiple predictions through 50 MC-Dropout sampling iterations, calculating the maximum predicted probability for each pixel. These probability values (unitless) are displayed on a 0-1 scale, using a discrete color scale with 0.2 intervals designed to provide a fine visual resolution of the probability values, allowing for the accurate identification of critical areas of prediction confidence. Cooler hues (blue spectrum) indicate high confidence, where probability values approach 1, while warmer tones (red spectrum) denote lower confidence associated with greater uncertainty.