Leveraging the DeepSeek large model: A framework for AI-assisted disaster prevention, mitigation, and emergency response systems

Abstract

We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response, leveraging the DeepSeek large language model (LLM) to advance intelligent decision-making in geohazard management. We systematically analyze the technical pathways for deploying LLMs in disaster scenarios, emphasizing three breakthrough directions: (1) knowledge graph-driven dynamic risk modeling, (2) reinforcement learning-optimized emergency decision systems, and (3) secure local deployment architectures. The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition, enabling cost-effective processing of multi-source data spanning historical disaster records, real-time IoT sensor feeds, and socio-environmental parameters. A modular system architecture is designed to achieve three critical objectives: (a) automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships, (b) scenario-adaptive resource allocation using risk simulations, and (c) preserving emergency coordination via federated learning across distributed response nodes. The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles (Findable, Accessible, Interoperable, Reusable) for geoscientific data governance. This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.

Keywords

AI large language models / DeepSeek / System framework research / Natural disaster prevention and control / Emergency assistance

CRediT authorship contribution statement

Chenchen Xie: Writing-original draft. Huiran Gao: Visualization, Formal analysis. Yuandong Huang: Formal analysis. Zhiwen Xue: Investigation, Formal analysis. Chong Xu: Writing-review & editing, Investigation, Formal analysis, Conceptualization. Kebin Dai: Formal analysis.

Author agreement and acknowledgment

All the authors who contributed to the study have approved the final version. We thank the anonymous reviewer and the editor, whose constructive and helpful comments improved this manuscript.This research was funded by the ​Chongqing Water Resources Bureau, China ​(Project No. ​CQS24C00836).

Declaration of competing interest

The authors declared that they have no conflicts of interest in this work. Professor Chong Xu is the deputy EIC of Earthquake Research Advances and is not involved in the peer review process.

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