## 4 Model Validation  
### Model Performance Verification  
To verify the model's accuracy, we explored several forest fire risk monitoring DCNN models with various hierarchical structures and calculated the image processing time and verification accuracy of each one using the original and enhanced image data sets. The models involved in the comparison included the DCN_Fire model and 13-layer, 15-layer, and 20-layer DCNN models. Figure 4 shows the comparison of flame image verification accuracy. Figure 5 shows the comparison of flame image processing times by the models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Batch Size** & **Verification Accuracy** & **Loss Rate** & **Verification Accuracy** \\ \hline
50 & 0.968 & 0.5 & 0.983 \\
64 & 0.971 & 0.6 & 0.967 \\
48 & 0.972 & 0.4 & 0.964 \\
128 & 0.965 & 0.7 & 0.952 \\
32 & 0.961 & 0.3 & 0.949 \\
16 & 0.949 & 0.2 & 0.947 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The influence of batch size and loss rate on the verification accuracy of DCN_Fire.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Image** & \multicolumn{5}{c}{**Total ROI Detected from 240 Aerial Fire Images 492**} & \multicolumn{5}{c}{**Total ROI Detected from 310 Normal-View Images 683**} \\ \hline Performance & TP & FN & FP & TN & TPR & FPR & TP & FN & FP & TN & TPR & FPR \\  & 329 & 19 & 8 & 136 & 96.1\% & 4.8\% & 463 & 47 & 16 & 157 & 92.2\% & 7.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performances of the location and segmentation methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image Type** & \multicolumn{5}{c}{**Original Image Database**} & \multicolumn{5}{c}{**Enhanced Image Database**} \\ \hline
**Image Type** & \begin{tabular}{c} **UAV Image** \\ **Acquisition** \\ \end{tabular} & \begin{tabular}{c} **Ordinary** \\ **Image** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Image** \\ \end{tabular} & \begin{tabular}{c} **UAV Image** \\ **Acquisition** \\ \end{tabular} & \
\begin{tabular}{c} **Ordinary** \\ **Image** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Image** \\ \end{tabular} \\ \hline No flames & 354 & 762 & 1116 & 748 & 1476 & 2224 \\ With flames & 247 & 497 & 744 & 457 & 1164 & 1621 \\ Total & 601 & 1259 & 1860 & 1205 & 2640 & 3845 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The image databases.  
Although the 20-layer DCNN model achieved a verification accuracy similar to that of the DCN_Fire model (0.983), its processing time was 1.67 longer due to the larger number of layers, increasing the calculation time and cost. Therefore, the DCN_Fire model was superior in terms of processing time and verification accuracy. Meanwhile, the original and enhanced image data sets were used to evaluate the impact of data enhancement on model accuracy. The data revealed that the verification accuracy of the same model using the enhanced image data set was better than that using the original image data set.  
After dividing the image samples into training and verification sets, the DCN_Fire model and the 13-layer and 15-layer DCNN models were trained and their loss values were calculated. Figure 6 shows the training and verification loss curves of the four models.  
The loss curves is shown in Figure 6, it mark the training loss once every 100 iterations and the validity loss once every 500 iterations.  
The data from Figures 4-6 show that the DCN_Fire model had better recognition accuracy than the other models with minimal training and verification losses.  
Figure 4: Flame image verification accuracy comparison of the DCN_Fire model with 13-layer, 15-layer, and 20-layer DCNN models.  
Figure 5: Flame image processing time comparison of the DCN_Fire model and 13-layer, 15-layer, and 20-layer DCNN models.