AI-Driven Post-Earthquake Emergency Material Demand Prediction: Integrating RAG With Reasoning Large Language Model

Meng Huang · Shuai Liu · Yuanwang Zhang · Wenbo Shao

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Facility Location and Emergency Management Disaster Management and Resilience Disaster Response and Management

Abstract

The supply of emergency rescue materials plays a pivotal role in post-earthquake relief operations. However, varying disaster scenarios generate differentiated demands for emergency resources, where factors such as seasonal impacts, geographic environment of affected areas, population density, and requirements of emergency response protocols significantly influence the categories and quantities of required supplies. Current research predominantly focuses on essential materials, while determinations of scenario-specific material demands rely heavily on expert empirical inference, resulting in discrepancies between model predictions and actual requirements. This study proposes a reasoning-enhanced large language model (LLM) framework integrated with Retrieval-Augmented Generation (RAG) technology for post-earthquake emergency material demand prediction. By simulating expert decision-making processes, we construct an emergency knowledge base amalgamating standardized protocols, historical seismic case data, and disaster scenario characteristics extracted from digital resources. The framework employs RAG to enhance domain-specific knowledge integration within the reasoning model, utilizing Chain-of-Thought generation to produce differentiated prediction schemes that specify material categories and per-capita demand metrics. Through dynamic updating of population statistics and material requirements via post-disaster network information monitoring, the system achieves real-time demand prediction based on evolving victim counts and individualized allocation parameters. Validation through expert evaluations using the 2013 Ya’an Lushan earthquake and simulated disaster scenarios demonstrates effectiveness, with successful practical implementation observed in the January 7, 2025, Dingri M6.8 earthquake case.

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2024Enhancing emergency decision-making with knowledge graphs and large language models 2025LLM-Augmented Linear Transformer–CNN for Enhanced Stock Price Prediction 2024Enhanced earthquake impact analysis based on social media texts via large language model 2023Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques 2018Surface vs. Air Shipment of Humanitarian Goods under Demand Uncertainty 2016A new approach on seismic mortality estimations based on average population density 2024Unraveling the prerequisites of citizen engagement intentions initiated by mobile government social media during emergencies: An integrative dual SEM-ANN analysis

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