Deep Learning and Network Analysis: Classifying and Visualizing Geologic Hazard Reports

Wenjia Li; Liang Wu; Xinde Xu; Zhong Xie; Qinjun Qiu; Hao Liu; Zhen Huang; Jianguo Chen

doi:10.1007/s12583-021-1589-6

Volume 35 Issue 4

Aug 2024

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Article Contents

Journal of Earth Science > 2024 > 35(4): 1289-1303.

Wenjia Li, Liang Wu, Xinde Xu, Zhong Xie, Qinjun Qiu, Hao Liu, Zhen Huang, Jianguo Chen. Deep Learning and Network Analysis: Classifying and Visualizing Geologic Hazard Reports. Journal of Earth Science, 2024, 35(4): 1289-1303. doi: 10.1007/s12583-021-1589-6

Citation:

Wenjia Li, Liang Wu, Xinde Xu, Zhong Xie, Qinjun Qiu, Hao Liu, Zhen Huang, Jianguo Chen. Deep Learning and Network Analysis: Classifying and Visualizing Geologic Hazard Reports. Journal of Earth Science, 2024, 35(4): 1289-1303. doi: 10.1007/s12583-021-1589-6

Citation:

Wenjia Li, Liang Wu, Xinde Xu, Zhong Xie, Qinjun Qiu, Hao Liu, Zhen Huang, Jianguo Chen. Deep Learning and Network Analysis: Classifying and Visualizing Geologic Hazard Reports. Journal of Earth Science, 2024, 35(4): 1289-1303. doi: 10.1007/s12583-021-1589-6

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Deep Learning and Network Analysis: Classifying and Visualizing Geologic Hazard Reports

doi: 10.1007/s12583-021-1589-6

Wenjia Li^1
,,
Liang Wu^{2, 3, 4, 5},
Xinde Xu⁶,
Zhong Xie^{2, 3, 4, 5},
Qinjun Qiu^{2, 3, 4, 5
,
,
,},
Hao Liu¹,
Zhen Huang¹,
Jianguo Chen⁷

1.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2.
Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China
3.
School of Computer Science, China University of Geosciences, Wuhan 430078, China
4.
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
5.
Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China
6.
Wuhan Geomatics Institute, Wuhan 430074, China
7.
Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China

More Information

Corresponding author: Qinjun Qiu, qiuqinjun@cug.edu.cn
Received Date: 06 Sep 2021
Accepted Date: 19 Nov 2021

Available Online: 16 Aug 2024

Issue Publish Date: 30 Aug 2024

Abstract

Abstract

If progress is to be made toward improving geohazard management and emergency decision-making, then lessons need to be learned from past geohazard information. A geologic hazard report provides a useful and reliable source of information about the occurrence of an event, along with detailed information about the condition or factors of the geohazard. Analyzing such reports, however, can be a challenging process because these texts are often presented in unstructured long text formats, and contain rich specialized and detailed information. Automatically text classification is commonly used to mine disaster text data in open domains (e.g., news and microblogs). But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order. These deficiencies are most obviously exposed in long text fields. Therefore, this paper uses the bidirectional encoder representations from Transformers (BERT), to model long text. Then, utilizing a softmax layer to automatically extract text features and classify geohazards without manual features. The latent Dirichlet allocation (LDA) model is used to examine the interdependencies that exist between causal variables to visualize geohazards. The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards. Moreover, it can help users visualize causes, processes, and other geohazards and assist decision-makers in emergency responses.
- geologic hazard,
- network analysis,
- latent dirichlet allocation,
- text classification,
- deep learning

Conflict of Interest
The authors declare that they have no conflict of interest.

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References(44)

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