# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 2 Related Studies
It is difficult for existing technical means to achieve timely monitoring and early warning at the scene of secondary disasters [10; 24; 25]. In terms of dangerous building detection, [PERSON] et al. [26] built a solid wall, applied horizontal shear force onto the surface to make the wall deform and lean, simulated the static process in the wall when an earthquake occurred, simulated the acceleration outside the wall in the process of wall collapse using the experimental method, and studied the triggering boundary conditions of the fall of external protective components. This study provided an empirical model for the falling of wall attachments. [PERSON] et al. [27] studied the seismic performance of glass walls under different loads during earthquake occurrence, adopted quasi-static and dynamic in-plane loading methods to simulate the load boundary conditions of glass walls during earthquake occurrence, and carried out experimental research in a homemade full-size all-tempered insulating glass curtain wall system. The experimental results show that the stress concentration at the diagonal part of the glass contact with the frame can lead to the breakage of the glass plate. [PERSON] et al. [28] proposed to adopt the concentrated mass shear model of multi-story structures and specific criteria for the falling of external non-structural parts. Based on the uncertainty of the earthquake and vibration process, the incremental dynamic analysis method was used to predict the distribution probability of falling objects during the design life of building clusters, and the urban seismic elastic-plastic analysis method was used to obtain the floor velocity of the floor where falling objects landed. Based on this, the distribution law of falling objects landing is obtained by jointly using the model of flat throwing motion. In terms of injured personnel rescue, [PERSON] et al. [29] highlighted the social systems that enable community-to-community support as well as potential opportunities for providing external aid to support communities more efficiently. They suggested that community-to-community support is critical in the first weeks after a disaster and highlighted the fact that the roles that different support networks play at different stages of disaster response are critical not only to improve people's and institutions' abilities to recover from particular disruptions but also in broader efforts to strengthen community resilience in the face of climate change. [PERSON] et al. [30], in relation to a small-area deployment scenario, proposed a small-area UAV deployment to improve the Broyden-Fletcher-Goldfarb-Shanno algorithm via improving the iterative step size and search direction to solve the high computational complexity of the traditional Broyden-Fletcher-Goldfarb-Shanno algorithm. In a large-area deployment scenario, to address the problem of the premature convergence of the standard genetic algorithm, the large-area UAV deployment elitist strategy genetic algorithm was proposed through the improvement of selection, crossover, and mutation operations. [PERSON] et al. 's [31] review provided a comprehensive survey of the widely used communication technologies applied for setting up an emergency communication network to mitigate the post-disaster aftermath; their review also delivered an overview of the integration of new technologies with the existing standards for improving the performance of the disaster communication networks. Finally, they proposed some promising solutions to overcome the limitations of existing emergency communication technologies to improve the overall network performance. The situational knowledge metadata included information about event characterization characteristics, emergency prevention preparation, and the time, resource, information, and business continuity constraints of event disposal. The scenario construction of electric power emergency communication support for natural disaster sites was realized with time and space decomposition [32; 33].