Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2
(2024) In Remote Sensing 16(21).- Abstract
Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data,... (More)
Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.
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- author
- Wu, Zhengrong ; Yang, Haibo ; Cai, Yingchun ; Yu, Bo ; Liang, Chuangheng ; Duan, Zheng LU and Liang, Qiuhua
- organization
- publishing date
- 2024-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- disaster monitoring, expert knowledge formalization, multi-source heterogeneous data, spatio-temporal knowledge inference
- in
- Remote Sensing
- volume
- 16
- issue
- 21
- article number
- 4056
- publisher
- MDPI AG
- external identifiers
-
- scopus:85208535619
- ISSN
- 2072-4292
- DOI
- 10.3390/rs16214056
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024 by the authors.
- id
- d5a601b5-e477-41f3-a34b-2c00ceeb3dba
- date added to LUP
- 2025-01-15 08:29:23
- date last changed
- 2025-04-04 14:01:22
@article{d5a601b5-e477-41f3-a34b-2c00ceeb3dba,
abstract = {{<p>Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.</p>}},
author = {{Wu, Zhengrong and Yang, Haibo and Cai, Yingchun and Yu, Bo and Liang, Chuangheng and Duan, Zheng and Liang, Qiuhua}},
issn = {{2072-4292}},
keywords = {{disaster monitoring; expert knowledge formalization; multi-source heterogeneous data; spatio-temporal knowledge inference}},
language = {{eng}},
number = {{21}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2}},
url = {{http://dx.doi.org/10.3390/rs16214056}},
doi = {{10.3390/rs16214056}},
volume = {{16}},
year = {{2024}},
}