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

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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,how-ever,can be a challenging process because these texts are often presented in unstructured long text for-mats,and contain rich specialized and detailed information.Automatically text classification is com-monly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limita-tions 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 inter-pretation of text-based geohazards.Moreover,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.

geologic hazardnetwork analysislatent dirichlet allocationtext classificationdeep learning

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

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School of Geography and Information Engineering,China University of Geosciences,Wuhan 430078,China

Key Laboratory of Geological Survey and Evaluation of Ministry of Education,China University of Geosciences,Wuhan 430078,China

School of Computer Science,China University of Geosciences,Wuhan 430078,China

Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen 518034,China

Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,China Three Gorges University,Yichang 443002,China

Wuhan Geomatics Institute,Wuhan 430074,China

Faculty of Earth Resources,China University of Geosciences,Wuhan 430074,China

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Natural science Foundation of ChinaNational Key Research and Development ProgramOpen Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural ResourcesNatural Science Foundation of Hubei Province of ChinaOpen Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric EngineeringOpening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of EducationFundamental Research Funds for the Central Universities

423014922022YFB3904200KF-2022-07-0142022CFB6402022SDSJ04GLAB 2023ZR01

2024

10.1007/s12583-021-1589-6

地球科学学刊(英文版)
中国地质大学

地球科学学刊(英文版)

CSTPCD
影响因子：0.724
ISSN：1674-487X
年,卷(期)：2024.35(4)
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