Volume 6, Issue 2 (4-2024)                   sjis 2024, 6(2): 1-6 | Back to browse issues page


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Artificial Intelligence and Advanced Technologies in Modern Search and Rescue Operations: Opportunities, Challenges, and Future Directions. sjis 2024; 6 (2) :1-6
URL: http://sjis.srpub.org/article-5-227-en.html
Abstract:   (74 Views)
Search and rescue (SAR) operations have undergone substantial transformation with the integration of artificial intelligence (AI) and advanced digital technologies. From predictive analytics and autonomous systems to real-time data fusion and robotics, AI is reshaping emergency response strategies across terrestrial, maritime, and urban disaster environments. This article critically examines the role of AI and emerging technologies in modern SAR operations, focusing on operational efficiency, decision support, ethical considerations, and implementation challenges. Drawing on interdisciplinary literature in emergency management, robotics, and data science, the paper proposes a framework for integrating AI-driven systems into rescue infrastructures while addressing limitations related to reliability, governance, and human oversight. The study concludes that while AI significantly enhances situational awareness and operational speed, effective integration requires regulatory frameworks, robust training, and human-centered design to ensure safety and accountability.
     
Type of Study: Research | Subject: Artificial Intelligence
Received: 2024/02/14 | Revised: 2024/02/14 | Accepted: 2024/04/2 | Published: 2024/04/10

References
1. Adams, S. M., & Friedland, C. J. (2011). A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management. 9th International Workshop on Remote Sensing for Disaster Response.
2. Cummings, M. L. (2017). Artificial intelligence and the future of warfare. Chatham House Report.
3. Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24-32. [DOI:10.1109/MPRV.2017.11]
4. Floridi, L., et al. (2018). AI4People-An ethical framework for a good AI society. Minds and Machines, 28(4), 689-707. [DOI:10.1007/s11023-018-9482-5]
5. Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response. International Journal of Digital Earth, 3(3), 231-241. [DOI:10.1080/17538941003759255]
6. Kankanamge, N., et al. (2020). Artificial intelligence for disaster response: A systematic review. International Journal of Disaster Risk Reduction, 45, 101548.
7. Koester, R. J. (2008). Lost person behavior: A search and rescue guide on where to look. dbS Productions.
8. Kumar, N., Gupta, R., & Kumar, S. (2023). AI-enabled communication technologies for disaster management in 5G networks. IEEE Access, 11, 11245-11260.
9. Liu, W., et al. (2020). Deep learning-based object detection for search and rescue operations. Pattern Recognition Letters, 131, 343-350.
10. Murphy, R. R., Tadokoro, S., & Kleiner, A. (2016). Disaster robotics. In Springer Handbook of Robotics (pp. 1577-1604). Springer. [DOI:10.1007/978-3-319-32552-1_60]
11. Turoff, M., Chumer, M., Van de Walle, B., & Yao, X. (2021). The design of a dynamic emergency response management information system (DERMIS). Journal of Information Technology Theory and Application, 5(4), 1-35.

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