ArticleJamba (Potchefstroom, South Africa)2025
Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review.
Article in Jamba (Potchefstroom, South Africa), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Resilience through the integration of governance, lived experience, and knowledge.Jamba (Potchefstroom, South Africa) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of artificial intelligence (AI) technology presents promising opportunities to improve disaster management's effectiveness and efficiency, particularly with the rising risk of natural hazards globally. This study used the Scopus database to offer a bibliometric review of AI applications in disaster management. Publications were chosen based on research scope (natural hazards), source type (journals and conference proceedings), document type (articles, conference papers and reviews) and language (English). VOSviewer and Biblioshiny were utilised to analyse trends and scientific mapping from 848 publications. The finding shows a rapid increase in AI studies for disaster management, with an annual growth rate of 15.61%. The leading source was the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. Amir Mosavi was the most prolific author, with 10 documents. The analysis reveals that China was the most productive country, while the United States was the most cited. Six research clusters were identified through keyword network mapping: (1) disaster monitoring and prediction using IoT networks, (2) AI-based geospatial technology for risk management, (3) decision support systems for disaster emergency management, (4) social media analysis for emergency response, (5) machine learning algorithms for disaster risk reduction, and (6) big data and deep learning for disaster management. Contribution: This research contributes by mapping the application of AI technology in disaster management based on peer-reviewed literature. This helps identify major developments, research hotspots, and gaps.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.