## 3 Results
### Model Accuracy and Evaluation Analysis  
#### 3.2.1 Analysis of the Model Training Process  
This study optimized parameter configurations and data processing workflows, effectively enhancing the training performance of the ResNet18 model for road collapse disaster risk assessment. During the data preprocessing phase, multi-source factor data, including lithology and plan curvature, were uniformly resampled to a consistent spatial resolution to ensure data compatibility [37]. To meet the input requirements of the ResNet18 model, Python (3.9.5) scripts were utilized to transform single-channel factor data into three-channel color images, aligning with the model's pretrained input format. Subsequently, all factor data were integrated into a comprehensive factor map for model training and testing.  
Model training employed the stochastic gradient descent (SGD) optimizer for parameter optimization, with an initial learning rate of 0.001 and a dynamic learning rate adjustment strategy. For data allocation, 80% of the road collapse disaster points were assigned to the training set, with the remaining 20% allocated to the test set. Through iterative experimentation, the optimal parameter combination was determined, setting the training duration to 100 epochs, with each epoch processing batches of 64 images fed into the model.  
Figure 4 illustrates the loss curve during the ResNet18 model training process, where the horizontal axis represents training epochs (unitless, ranging from 0 to 100), and the vertical axis indicates loss values (unitless, ranging from 0 to 1.8). The loss value is defined as the cross-entropy loss between the model's predicted probabilities and true labels, serving as a metric for evaluating classification performance [38]. The calculation formula is as follows:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(\widehat{y_{i}})+(1-y_{i})\log(1- \widehat{y_{i}})] \tag{6}\]  
where \(L\) represents the loss value, \(N\) denotes the number of samples, \(y_{i}\) is the true label for the \(i\)-th sample, and \(y_{i}\) is the predicted probability for the \(i\)-th sample [39]. The loss curve indicates a rapid decline in loss within the first 10 epochs, followed by stabilization, signifying robust convergence during the training process [40].  
Figure 5 illustrates the accuracy curve of The ResNet18 model for road collapse risk assessment using multi-source remote sensing data, including slope gradient, lithology, vegetation coverage, and annual precipitation. The horizontal axis represents training epochs (unitless, ranging from 0 to 100), while the vertical axis denotes accuracy (expressed as a percentage, ranging from 0% to 100%), calculated by comparing model predictions with ground-truth labels using a test set comprising 20% of the road collapse points. The curve reveals that accuracy rises rapidly within the first 5 epochs, reaching approximately 80%, exhibits slight fluctuations and a brief decline around the 20 th epoch, and subsequently stabilizes at approximately 90% by the 100 th epoch. These approximate values reflect the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Risk Level**} & \multirow{2}{*}{**Area (km2)**} & **Proportion of** & \multirow{2}{*}{**Number of Collapse**} & **Hazard Point** \\  & & **Guangxi’s Total** & & **Hazard Points** \\  & & **Area (\%)** & & **(Points/100 km2)** \\ \hline High-Risk Area & 49,219 & 20.4 & 4266 & 8.7 \\ Relatively High-Risk Area & 56,544 & 23.4 & 1909 & 3.4 \\ Medium-Risk Area & 44,325 & 18.3 & 807 & 1.8 \\ Relatively Low-Risk Area & 29,665 & 12.3 & 324 & 1.1 \\ Low-Risk Area & 61,751 & 25.6 & 180 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics on the area and density of hazard sites for each risk level.  
model's performance trend on the test set; results may vary if computed using other datasets (e.g., training or validation sets), indicating The ResNet18 model's high classification capability and learning efficiency in this task.