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A Bibliometric Analysis of Geological Hazards Monitoring Technologies
Summary
Researchers conducted a bibliometric analysis of 12,123 geological hazard monitoring publications from 1976 to 2023 using VOSviewer and CiteSpace to identify research trends and hot topics. Current hotspots include deep learning, data fusion, and GNSS-based early warning systems, with this study providing a meta-level roadmap for future geological hazard monitoring research.
This study systematically analyzed research trends and hot issues in the field of geological hazard prediction using bibliometric analysis methods. A total of 12,123 related articles published from 1976 to 2023 were retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Co-occurrence analysis and burst detection were conducted on the literature using the VOSviewer and CiteSpace tools to identify the research trends in geological hazard monitoring technologies. The results reveal that “data fusion”, “landslide identification”, “deep learning”, and “risk early warning” are currently the main research hot spots. Additionally, the combined application of Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) technologies, as well as GNSS and Long Short-Term Memory (LSTM) models, were identified as important directions for future research. The bibliometric perspective offers a systematic theoretical framework and technical guidance for future research, thereby facilitating the sustainable advancement of safety, security, and disaster management.
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