Global Research Landscape and Knowledge Evolution of Early Warning Systems in Natural Disaster Management
Bibliographic record
Abstract
The comprehensive approach to bibliometric analysis examines global research on Early Warning Systems (EWS) in relation to disaster management from 2001 to 2024, with data retrieved from the Web of Science and Scopus databases. The Bibliometrix R package is the tool that is used to examine publication trends, influential sources, country-level contributions, collaboration networks, and thematic evolution to map the intellectual and structural development of the field. It helps to reveal a steady and exponential growth in publications, particularly after 2015, coinciding with the adoption of the Sendai Framework for Disaster Risk Reduction (2015–2030). China takes the lead in research productivity with the largest share of publications followed by Italy, the United States, Australia, and Canada that have higher citation impact and international visibility. Natural Hazards, Landslides, and the International Journal of Disaster Risk Reduction are identified as the top research outlets and the developing conceptual basis in the field is comprised of keywords associated with “early warning system,” “disaster management,” “floods,” “risk assessment,” and, “climate change." The thematic and factorial analyses depicted early warning systems, climate change, and disaster management as well-developed motor themes, while emerging themes in the research literature included machine learning, resilience, and Internet of Things (IoT) based warning models. Additionally, the international collaboration network suggests a dual-core pattern with China leading in South-South partnerships, and Europe anchoring research networks in the West. The authors highlight that EWS research has developed as a multidisciplinary and globally located compendium of early warning system science, that connects technology innovation, environmental science with policy-developed frameworks, to improve resilience to disasters. Findings from this study suggest the importance of strengthening cross-regional collaboration, strengthening open data integration and developing AI- and IoT-enabled early warning systems to build more adaptive and climate-resilient societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".