Abstract
Internet data performs a crucial part in calamity response during disaster events. The disaster might be natural or man-made, Internet community have proven to be the most productive transmission and combine among afflicted communities. Research has manifested utilizing methods for machine learning to recognize the applicable posts on web, net and other sources for disaster reciprocation. Communication breakdowns and association failures are common issues during disaster response, leading to delayed or inefficient aid distribution. In this paper, we are going to evaluate the effect caused due to calamities. This research is categorized into two proportions: Response after the disaster, identifying the damage caused, and allowing for faster response. The findings of this study may help experimenters in the future identify relevant areas for disaster assessment.
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