Development of a retrieval-augmented generation system for contextual QA based on regional disaster management plans

Abstract

Regional disaster management plans are revised annually in accordance with Japan’s national Basic Disaster Management Plan, and the revision process places a substantial burden on local authorities. While previous studies have employed natural language processing techniques to detect omissions or inadequately addressed sections in these plans, fully automated identification remains a challenge. This paper proposes a novel Retrieval-Augmented Generation (RAG) framework tailored to the structure of regional disaster plans, with the aim of generating answers consistent with the descriptions in Regional Disaster Management Plans. We designed a domain-specific chunking method to improve document segmentation and retrieval performance. Experimental results demonstrate that the proposed approach significantly outperforms conventional chunking techniques in both retrieval accuracy and answer generation quality. These findings highlight the potential of RAG to enhance the efficiency and accuracy of plan revisions, contributing to more robust and responsive local disaster preparedness.