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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
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Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review
Arief Wibowo1, Ikhwan Amri2, Asep Surahmat3
1Department of Computer Science, Faculty of Information Technology, Universitas Budi Luhur, Jakarta, Indonesia.
Jamba (Potchefstroom, South Africa)
|May 13, 2025
Summary
Artificial intelligence (AI) is rapidly advancing disaster management, with studies growing 15.61% annually. Key applications include AI-based geospatial technology and machine learning for risk reduction.
Area of Science:
- Environmental Science
- Computer Science
- Information Science
Background:
- Global natural hazard risks are increasing, necessitating improved disaster management strategies.
- Artificial intelligence (AI) offers significant potential to enhance disaster management effectiveness and efficiency.
Purpose of the Study:
- To conduct a bibliometric review of AI applications in disaster management using Scopus database.
- To identify trends, research hotspots, and knowledge gaps in AI for natural hazard management.
Main Methods:
- Bibliometric analysis of 848 publications from the Scopus database.
- Utilized VOSviewer and Biblioshiny for trend analysis and scientific mapping.
- Selected publications based on natural hazards scope, journal/conference sources, article/paper/review document types, and English language.
Main Results:
- A significant annual growth rate of 15.61% in AI studies for disaster management was observed.
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives was the leading source.
- China led in productivity, while the United States received the most citations. Six key research clusters were identified.
Conclusions:
- AI technology is a rapidly growing field in disaster management, with diverse applications.
- Identified research clusters highlight key areas such as IoT for monitoring, geospatial technology, decision support systems, social media analysis, machine learning, and big data/deep learning.
- This mapping provides valuable insights for future research and development in AI-driven disaster management.
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