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May 20, 2026Open Access

AI-Driven Disaster Aid Prioritization System for Efficient Resource Allocation and Emergency Response

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Authors

SKSathishkumar KVSVasudevan SSKSampathkumar K

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Overview

Randomized trial demonstrates improved resource allocation in disaster scenarios, suggesting more effective emergency response.

Key Points

  • The aim is to improve disaster aid prioritization using machine learning for quicker and more efficient resource allocation.
  • Developed a Disaster Aid Prioritization System utilizing machine learning techniques.
  • Analyzed factors such as population size, damage level, medical emergencies, and resource availability.
  • Classified regions into High, Medium, and Low urgency for aid deployment.
  • Automating the prioritization process improved response times significantly.
  • The system reduced dependency on human judgment for resource distribution.
  • Scalable and cost-efficient integration with real-time data was achieved.

Cite This Study

K et al. (2026) studied this question.

synapsesocial.com/papers/6a0d50aff03e14405aa9cabfhttps://doi.org/10.5281/zenodo.20265192
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