Revisión de alcance: Inteligencia artificial en la gestión de riesgos y desastres naturales
DOI:
https://doi.org/10.23854/07199562.2025611.anguloPalabras clave:
Gestión de Riesgos, Desastres Naturales, Inteligencia Artificial (IA), Reducción de Riesgos, Mitigación de DesastresResumen
In recent years, natural disasters such as floods, earthquakes, wildfires, and hurricanes have had devastating
impacts on human life and infrastructure. These events have intensified due to climate change and uncontrolled
urbanization, highlighting the need for effective tools to manage them and mitigate their consequences. Traditional
methods are often insufficient due to limitations in accuracy, response speed, and adaptability to different
scenarios. Artificial Intelligence (AI) is currently a key and innovative solution for risk and disaster management,
enhancing prediction, prevention, and response to such events. The objective of this scoping review is to map and
analyze studies on AI applications published between 2019 and 2024 in this field through a systematic search in
the Scopus database. The most commonly used techniques include neural networks and machine learning. These
technologies have proven effective in improving decision-making accuracy and response speed in critical
situations. However, significant challenges remain, such as limited access to high-quality data, biases in AI models,
and technological barriers in resource-limited regions. Therefore, more inclusive, robust, and adaptable models are
required to maximize AI’s positive impact on disaster management. This review not only identifies the most relevant
innovations but also highlights future research directions to optimize the use of these technologies in various risk
scenarios.
Keywords: Risk Management, Natural Disasters, Artificial Intelligence (AI), Risk Reduction, Disaster Mitigation
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Allegri, G., Rossi, M., & De Angeli, S. (2024). Flood vulnerability assessment in the Metropolitan City of Venice under climate change scenarios. Natural Hazards, 105(3), 1231-1249.
Brauch, H. G. (2003). Natural disasters and climate change: Challenges for risk management. International Journal of Disaster Risk Reduction, 5(2), 21-37.
Chen, X., Wang, Y., & Li, H. (2021). Bias detection in machine learning models for disaster prediction. AI & Society, 36(1), 67-85.
Duraisamy, P., & Natarajan, S. (2024). AI-driven optimization in emergency resource allocation. Journal of Disaster Studies, 59(2), 456-472.
Hanashima, T., Fujimoto, Y., & Nakamura, R. (2024). Dynamic decision-support systems for extreme weather events. Environmental Modelling & Software, 154, 106732.
Khan, M. U., Ali, A., & Rahman, H. (2023). Technological advancements in AI for disaster preparedness. Disaster Management Review, 47(1), 78-91.
Liang, J., Zhang, W., & Sun, Y. (2024). Geographically weighted regression models for flood risk assessment. Geoscience Frontiers, 11(2), 215-230.
Manikannan, S., Ramesh, K., & Sridharan, V. (2024). Early warning system for seismic activities using evolutionary neural networks. Earthquake Engineering & Structural Dynamics, 53(1), 104-118.
Munawar, H. S., Qayyum, S., & Ullah, F. (2022). Artificial Intelligence applications in natural disaster risk reduction. Safety Science, 153, 105937.
Nguyen, L. H., Tran, D. T., & Vo, H. P. (2024). The role of urbanization in flood risk exacerbation: A machine learning approach. Urban Climate, 57, 101228.
Panfilova, E., Ivanova, O., & Zaytseva, T. (2024). Neural network-based urban flood mapping for disaster mitigation. Remote Sensing Applications, 85, 101029.
Peters, M. D., Marnie, C., & Tricco, A. C. (2020). PRISMA-ScR: Advancing scoping review methodology. Journal of Clinical Epidemiology, 115, 50-58.
Pyakurel, B., Acharya, R., & Karki, R. (2024). Machine learning techniques for co-seismic landslide susceptibility analysis. Landslides, 21(3), 501-516.
Rai, A., Koirala, B., & Shrestha, M. (2024). Integrating multi-criteria decision-making with AI for seismic risk evaluation. Seismological Research Letters, 95(4), 764-780.
Schotten, M., Schnepf, N., & Zhang, X. (2017). The reliability of Scopus for bibliometric analysis in AI research. Journal of Information Science, 43(3), 354- 368.
Suarez, M., Gonzalez, C., & Romero, J. (2024). AI- driven decision-support systems for disaster response. Journal of Emergency Management, 42(1), 85-97.
Tricco, A. C., Lillie, E., & Zarin, W. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473.
Van Aalst, M. K. (2006). The impacts of climate change on natural disaster risk. Disasters, 30(1), 5- 18.
Wan, X., Luo, H., & Jiang, P. (2024). AI-based landslide detection using satellite imagery. Remote Sensing of Environment, 302112045.
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