Disaster Detection and Alert Generation for Smart City Scenario using Deep Learning Technique

Abstract

Disasters, both natural and man-made, cause yearly losses in infrastructure, money, stress, injuries, and lives. The devastating potential of natural catastrophes is unfortunately being amplified by climate change. In this light, IoT-based disaster monitoring and preparedness and response have already been suggested to better identify catastrophes and aid in search-and-rescue efforts in the aftermath of an emergency. As a result, Internet of Things (IoT) devices are deployed to gather information, which is then utilized to recognize risks after catastrophes and pinpoint the location of victims. However, the absence of connection with IoT devices could occur, owing to breaks in communications technologies or networking congestions, therefore a completely IoT-based detection and reaction system is unlikely to be entirely adequate for emergency service in smart buildings. In light of this, we present a new design for a disaster-detection as well as response system that may be used in smart cities. We talk about the fundamental components of our planned smart system and the major obstacles that must be overcome before our smart system can be put into action.

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