基于知识图谱检索增强生成的滑坡监测预警系统

吴润泽; 李浩; 梅红波; 王成彬; 朱敏毅; 王红群; 张亮; 胡光鸿; 马明杰; 望致文; 张隆隆; 黄旻; 李振华

doi:10.3799/dqkx.2025.127

Volume 50 Issue 10

Oct. 2025

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Article Navigation > Earth Science > 2025 > 50(10): 4125-4136

Wu Runze, Li Hao, Mei Hongbo, Wang Chengbin, Zhu Minyi, Wang Hongqun, Zhang Liang, Hu Guanghong, Ma Mingjie, Wang Zhiwen, Zhang Longlong, Huang Min, Li Zhenhua, 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Science, 50(10): 4125-4136. doi: 10.3799/dqkx.2025.127

Citation:

Wu Runze, Li Hao, Mei Hongbo, Wang Chengbin, Zhu Minyi, Wang Hongqun, Zhang Liang, Hu Guanghong, Ma Mingjie, Wang Zhiwen, Zhang Longlong, Huang Min, Li Zhenhua, 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Science, 50(10): 4125-4136. doi: 10.3799/dqkx.2025.127

Citation:

Wu Runze, Li Hao, Mei Hongbo, Wang Chengbin, Zhu Minyi, Wang Hongqun, Zhang Liang, Hu Guanghong, Ma Mingjie, Wang Zhiwen, Zhang Longlong, Huang Min, Li Zhenhua, 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Science, 50(10): 4125-4136. doi: 10.3799/dqkx.2025.127

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A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph

doi: 10.3799/dqkx.2025.127

1.
Wuhan Center of China Geological Survey, Wuhan 430205, China
2.
Hubei Key Laboratory of Operation Safety of High Dam and Large Reservoir (China Three Gorges Corporation), Yichang 443133, China
3.
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
4.
School of Computer, China University of Geosciences, Wuhan 430078, China
5.
School of Information Engineering, Xinjiang Institute of Technology, Akesu 843100, China

Received Date: 2025-03-04
Publish Date: 2025-10-25

Abstract

Abstract

Aiming at the current problem of knowledge extraction and utilization from text reports in landslide prevention and control work, this study explores a set of methods for constructing and applying a knowledge graph based on geological disaster data and materials, promoting the transformation of geological disaster prevention from data management to knowledge management. Knowledge extraction and knowledge graph construction were conducted on basic data from 182 reports, 22 specifications, 5 literatures, 4 668 landslide points, and 313 governance projects, as well as three years of historical disaster data, current landslide monitoring data, and forecasted weather data. Retrieval-augmented generation (RAG) technology was adopted to integrate and output multi-source knowledge. A knowledge graph system with 12 797 entities, 34 873 relationships, and 9 658 chunks was established, enabling 10-day early warning for 4 518 landslides. Knowledge graph technology effectively extracts knowledge from text reports, and RAG technology efficiently integrates multiple knowledge sources, improving the accuracy of knowledge question-answering.
- Three Gorges reservoir area,
- landslide monitoring and early warning,
- knowledge graph,
- large language model,
- retrieval-augmented generation,
- engineering geology

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References(45)

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