DOI: 10.12688/f1000research.190213.1 ISSN: 2046-1402

Mathematical Modeling in Disaster Risk Reduction Education: A Bibliometric Analysis of Research Trends, Knowledge Structures, and Emerging Pedagogical Directions (2015-2025)

Louis Robert C. Sison, Mike L. Santos, Ronilo P. Antonio
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Background Despite the growing recognition of disaster risk reduction education, the specific contribution of mathematical modeling to risk literacy, preparedness, and resilience-oriented learning remains insufficiently synthesized. This study conducted a bibliometric analysis of research on mathematical modelling in disaster risk reduction education to examine publication trends, knowledge structures, and emerging pedagogical directions from 2015 to 2025. Methods Using Scopus as the data source guided by the PRISMA, 191 documents were identified through a search strategy combining mathematical modeling, disaster risk reduction, natural hazards, and education-related terms. The dataset was analyzed using Bibliometrix/Biblioshiny and VOSviewer through performance analysis, citation analysis, co-citation analysis, keyword co-occurrence analysis, trend-topic analysis, and overlay visualization. Results show that the field is recent, growing, interdisciplinary, and collaborative, with an annual growth rate of 14.13% and publications distributed across 151 sources. Results China and the United States emerged as the most productive countries, while leading sources were largely situated in environmental science, applied modeling, disaster risk, and interdisciplinary research. Citation patterns revealed that influential works are strongly associated with flood forecasting, flash flood susceptibility, landslide mapping, earthquake signal analysis, evacuation modeling, and simulation-based risk assessment. Co-citation analysis showed that the intellectual base of the field is grounded in machine learning, statistical prediction, simulation-based modeling, and disaster-mitigation models. Keyword co-occurrence analysis identified four major thematic clusters: AI-driven forecasting and decision support, mathematical modeling foundations and human-centered disaster applications, flood risk and climate monitoring, and landslide susceptibility modeling. Conclusions Overall, the findings suggest that mathematical modeling can strengthen disaster risk reduction education by promoting risk literacy, data interpretation, predictive reasoning, computational thinking, and evidence-based decision-making. However, a persistent gap remains in translating technical modeling research into classroom instruction, teacher education, curriculum design, and assessment.

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