Finding Survivors In Flood Affected Areas During Response Operations By Deep Learning Approach
Mohammad Nasim1, GV Ramaraju2
1Mohammad Nasim, Department of CSE, Lingayas Vidyapeeth, Faridabad (Haryana), India.
2GV Ramaraju, Lingayas Vidyapeeth, Faridabad (Haryana), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 329-334 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6115048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explains an approach of finding survivors in flood affected areas during response operations by Deep-Learning technique. Most of the flood disasters affect a large scale livelihood by damaging physical assets, infrastructures and creating local environmental disturbance. We don’t have much control to avoid this natural disaster but we can plan it in efficient way at the time of rescue operations on the affected areas and move the people to a safer place where time is an important factor. Here we are assuming that the local transport is completely damaged, there is no internet or network through which we can connect with the people in the affected areas and many people are stuck into it. It means all connections to connect a person has been damaged by the flood. We have more data than any other time in recent history following a debacle on account of automations and accessible developed satellite system that can take picture of disasters as aerial, but we are still studying on ways to process this raw data so that it is valuable for relief efforts. Deep learning technique [11] is what enables Artificial intelligence to identify patterns in images, videos, sounds, and other information utilizing a neural system that reflects our own dim issues. This study relates to identify moving human patterns in a video that has been captured from a flying drone in the affected area through pattern recognition techniques and that can produce a quality analytical output. This technique can be used in various disasters to find the moving human pattern or any particular moving object pattern.
Keywords: Deep Learning, Drone, Flood, Rescue, Disaster
Scope of the Article: Deep Learning