# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 4 Experimental Results
### Secondary Disaster Event Classification Based on CNN-CDMV Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample Scene Content** & **1. Welding** & **2. Welding** & **3. Welding** & **4. Welding** & **5. Welding** & **6. Fire** \\ \hline \multirow{8}{*}{**Sample Image**} & \multirow{8}{*}{1 Sunset} & \multirow{8}{*}{0.36911443} & \multirow{8}{*}{0.012486734} & \multirow{8}{*}{0.063333414} & \multirow{8}{*}{0.002232042} & \multirow{8}{*}{0.003821609} & \multirow{8}{*}{0.574047600} \\  & & & & & & & \\  & 3\& 0.33613798 & 0.320645720 & 0.384551320 & 0.426589700 & 0.252431400 & 0.157781400 \\  & 3 Strong light & 0.00691490 & 0.060043160 & 0.000182866 & 0.571178260 & 0.731777900 & 0.00000658 \\  & 4 Weak light & 0.22312464 & 0.193970070 & 0.000842153 & 0.00000049 & 0.002747495 & 0.000950727 \\  & 5 Fire & 0.06470809 & 0.412854250 & 0.551090240 & 0.00000038 & 0.009221609 & 0.267219540 \\  & Maximum value & 0.36911443 & 0.412854250 & 0.551090240 & 0.571178260 & 0.731777900 & 0.574047600 \\  & Result & Sunset & Fire & Fire & Strong light & Strong light & Sunset \\ \hline \multirow{8}{*}{**CondV**} & \multirow{8}{*}{1 Sunset model} & \multirow{8}{*}{0.21327727} & \multirow{8}{*}{0.027238317} & \multirow{8}{*}{0.182076130} & \multirow{8}{*}{0.39404900} & \multirow{8}{*}{0.218830810} & \multirow{8}{*}{0.000606871} \\  & 2 Welding model & 0.47740590 & 0.660151060 & 0.357431860 & 0.788906160 & 0.735142200 & 0.184744900 \\ \cline{1-1}  & 3 Strong light model & 0.16498170 & 0.274327730 & 0.279471500 & 0.341554600 & 0.004985074 & 0.250122960 \\ \cline{1-1}  & 4 Weak light model & 0.20199550 & 0.335800800 & 0.123342960 & 0.112039335 & 0.066756300 & 0.133769810 \\ \cline{1-1}  & 5 Fire model & 0.42620650 & 0.358033660 & 0.000187691 & 0.264854940 & 0.014515885 & 0.653731300 \\ \cline{1-1}  & Maximum value & 0.47740590 & 0.660151060 & 0.457431860 & 0.788906160 & 0.735142200 & 0.653731300 \\ \cline{1-1}  & Result & Welding & Welding & Welding & Welding & Welding & Fire \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ResNet-CDMV model identification results. Table 4 shows the secondary disaster factors' AP (Average Precision) and mAP (Mean Average Precision) values with ResNet-CDMV, and the results are compared with Faster-RCNN (Toward Real-Time Object Detection with Region Proposal Networks) [38], SSD (Single Shot MultiBox Detector) [39], CornerNet [40], CenterNet [41], and YOLOv7 [42]. Based on the importance of secondary disaster factor recognition for disaster event monitoring and early warning, the AP values of each factor in Table 4 that are produced by the'softmax' results are greater than 0.5, and each disaster factor's test set has 100 images. Among the target detection algorithms, YOLOv7 has the best identification effect, correctly identifying 473 disaster factors among the total 600 samples of various types, with an average accuracy of 78.83%. Our ResNet-CDMV algorithm correctly identified 522 secondary disaster factors images, with an average accuracy of 87%, and compared with YOLOv7, the average accuracy improved by 8.17%. In addition, a single 10-fold cross-validation ResNet-50 model mAP was compared with our ResNet-CDMV results, and the mAP of our algorithm improved by 6.5% compared with the single model.