Artificial intelligence and machine learning for disaster prediction: a scientometric analysis of highly cited papers

This study conducts an analysis of artificial intelligence (AI) and machine learning (ML) applications in natural disaster prediction using a scientometric approach. The Web of Science Core Collection served as the primary data source, yielding 38,456 records spanning from 2003 to 2022. The analysis concentrated on highly influential research, defined by papers garnering 100 or more citations, resulting in a final set of 1,637 publications. VOSviewer software facilitated the exploration of collaboration patterns among authors, institutions, and countries, along with the identification of emerging research topics and the most impactful articles. These highly cited papers were distributed across various sources (625). A total of 443,502 citations were counted, with an average of 270.92 citations per document. Interestingly, the average annual citation growth rate exhibited a negative trend (-1.02%), suggesting a potential shift in citation patterns over time. The average document age of 6.9 years indicates that the majority of the research is relatively recent. Collaboration emerges as a prominent feature within the field, with an average of 5.09 co-authors per document and 46.55% of collaborations being international. This underscores the collaborative nature inherent in research within this domain. Scholarly articles (1263) represent the predominant document type, followed by reviews (323), indicative of the field’s solid foundation in peer-reviewed literature. The study’s findings hold significant implications for future research and practical applications, identifying gaps in the literature and underscoring the necessity for further exploration in developing AI and ML models tailored to specific types of natural disasters, as well as assessing these models in real-world scenarios. International collaboration and interdisciplinary approaches are highlighted as pivotal components in advancing this critical field. While providing valuable insights, this approach acknowledges limitations associated with its focus on highly cited papers and a single database. Future research could address these limitations by incorporating additional databases, employing broader search criteria, and utilising alternative methodologies to attain a more comprehensive understanding of the evolving research landscape.