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Natural disasters can be unexpected. They are a part of life, and there is no guarantee of when they will happen, how severe they will be, and how many lives will be lost. In the past two decades, humanity has made significant progress by utilising technology. Although they have successfully developed ways to predict or semi-predict when and where natural disasters will occur, they are unable to predict them in areas that typically do not experience many natural disasters. In this ever-changing world, numerous technological advancements have been made to help reduce the death toll when a disaster strikes. This paper aims to provide insights into how artificial intelligence (AI) and machine learning (ML) can be integrated to create faster and more efficient disaster recovery plans, with meticulous attention to detail according to the specific circumstances people find themselves in. We propose a platform that allows individuals to access detailed reports on ongoing disasters. Additionally, this platform enables people to apply for training certifications and volunteer opportunities, where they can learn all the necessary safety measures before being assigned to disaster response cases. This platform holds great promise as it surpasses other similar existing ideas in the field, ultimately saving more lives than ever before.
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Stored only when both papers are in the corpus — 6 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.