# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 2 Related Studies  
In this study, the secondary disaster factors refer to common objects or phenomena in life that can cause six kinds of disaster events, namely, fire, flammable objects, explosive objects, toxic substances, trapped persons, and dangerous buildings, especially fire in a natural disaster site, which worsens the other five kinds of secondary disaster events [13]. Therefore, after a natural disaster, the identification of fire and the five secondary kinds of disaster events is the key work that needs to be completed. In this section, we summarize and analyze the current research status of the methods of reducing secondary disasters. In terms of fire monitoring methods, [PERSON] et al. [14] used the YOLOv5 (You Only Live Once v5) algorithm to identify fires and added three convolutional attention modules to the algorithm to improve the key feature extraction capability; at the same time, the C2f module was used to replace the original C2 module to obtain more information. [PERSON] et al. [15], using the convolutional attention module, optimized YOLOv5, substituting BiFPN for Panet and transposition convolution for neighbor interpolation and ulteriorly used MobileNet V3, ShuffleNet V2, and GhostNet to improve the performance of the YOLOv5 model. [PERSON] et al. [16] proposed an integrated device of real-time fire detection and automatic fire extinguishing based on computer vision and developed a unique local fire location technology for fire location combined with fire color characteristics. [PERSON] et al. [17] introduced an improved adaptive lightweight FireViT fire identification method based on MobileViT. To better adapt to the irregular changes in smoke and flame in fire scenarios, deformable convolution and an improved adaptive activation function were introduced in their study to improve the performance of the network model. [PERSON] et al. [18] combined the current frame of a video with the corresponding block in the previous frame to determine whether the phenomenon with flow characteristics is smoke and used it for fire warning. [PERSON] et al. [19] proposed a Bayes-Poisson regression analysis method to study the relationship between fire probability and ground motion intensity during seismic events in Japan from 1995 to 2022. The evaluation indexes of earthquake intensity are peak ground acceleration, peak ground velocity, and Japan Meteorological Agency earthquake intensity. [PERSON] et al. [20] proposed a physical model-based on-site fire spread simulation and smoke visualization method for use after an earthquake. In this study, a fire dynamics simulator was used to build a city-scale fire scene after an earthquake. In terms of explosive detection, [PERSON] et al. [21], based on the recent progress on nanostructured vapor phase explosive gas sensors that operate in dark conditions, highlighted the attractiveness of developing optoelectronic sensors for vapor phase explosive detection and proposed employing photocatalysis principles to enhance the sensitivity. [PERSON] et al. [22] used a simple and efficient self-approach strategy to apply ultrasensitivity and self-revive ZnO-Ag hybrid surface-enhanced Raman scattering sensors for the highly sensitive and selective detection of explosive TNT in both solution and vapor conditions. [PERSON] et al. [23] studied explosive detection methods, standoff spectroscopy-based methods, and LIBS and compared the existing methods of trace detection, providing a review of the world's smallest drone made by Israel, which can detect explosives and drugs from a distance of 2.8 km. In terms of chemical leakage, because chemicals are often toxic or corrosive, the method of monitoring chemicals is relatively complex, and it is usually carried out after an accident according to national standards and the scientific disposal process to further identify the various types. It is often used as a targeted reference for subsequent environmental management.