Citation:

Abhinav Balasubramanian, 2024. "RAG-Powered Real-Time Intelligence for Crisis Management", ESP Journal of Engineering & Technology Advancements  4(4): 118-126.

Abstract:

Efficient decision-making during crises such as natural disasters, pandemics, and large-scale emergencies requires rapid access to reliable and contextually relevant information. This paper presents a novel Retrieval-Augmented Generation (RAG) framework designed to integrate, synthesize, and analyze diverse data sources for real-time crisis management. By combining structured inputs, such as weather reports and resource inventories, with unstructured data, including social media updates and news articles, the system generates actionable insights tailored to specific emergency scenarios.The framework employs dynamic retrieval algorithms to prioritize high-relevance information under time constraints and leverages generative AI models to produce contextual, scenario-specific reports. Multi-source validation techniques are integrated to ensure the accuracy and trustworthiness of generated outputs. Key applications include optimizing disaster response logistics, streamlining resource allocation, and enhancing public communication during emergencies.The proposed RAG framework demonstrates the transformative potential of AI in addressing global challenges through intelligent data integration and decision support. By empowering emergency responders with real-time, reliable insights, the system enables swift and effective action in dynamic, high-stakes situations, ultimately enhancing public safety and crisis resilience.

References:

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Keywords:

: AI in Emergency Response and Management, Crisis Management, Retrieval-Augmented Generation (RAG), Generative AI, Dynamic Retrieval Algorithms.