Afet Yönetimi Döngüsünde Yapay Zekâ Uygulamaları: Olanaklar, Sınırlılıklar ve Riskler

Öz

Afet ve acil durum yönetimi, toplumların afetlere karşı dayanıklılığını artırmayı ve olası can ile mal kayıplarını en düşük seviyede tutmayı amaçlayan stratejik bir süreçtir. Dijital çağın getirdiği dönüşüm, fiziksel ve sanal sistemler arasındaki sınırları büyük ölçüde ortadan kaldırmış; bu durum, afet yönetiminde teknolojik araçların kullanımını zorunlu ve belirleyici bir unsur hâline getirmiştir. Özellikle Yapay Zekâ (YZ) teknolojilerinin yükselişi, afetlerin erken tespiti, etkilerinin azaltılması ve müdahale süreçlerinin iyileştirilmesi açısından yenilikçi bir paradigma sunmaktadır. Bu çalışma, nitel bir araştırma yaklaşımıyla YZ tabanlı sistemlerin farklı afet türlerine yönelik uygulama alanlarını analiz etmekte ve bu teknolojilerin afet yönetimi sürecine dâhil aktörlere sağladığı stratejik, operasyonel ve analitik katkıları incelemektedir. Araştırma sonuçları, YZ’nin afet yönetiminde karar verme süreçlerini rasyonelleştirdiğini, veri odaklı analizlerle öngörü gücünü artırdığını ve kaynakların daha etkin kullanılmasına katkı sunduğunu göstermektedir. Bununla birlikte, etik, hukuki ve güvenlik boyutlarında yeterli düzenlemelerin yapılmaması durumunda, YZ teknolojilerinin yeni türden insan kaynaklı krizleri tetikleyebileceği tespit edilmiştir. Dolayısıyla, afet ve acil durum yönetiminde YZ’nin entegrasyonu yalnızca teknik bir yenilik olarak değil, aynı zamanda etik yönetişim, toplumsal güvenlik ve sürdürülebilirlik eksenlerinde yeniden düşünülmesi gereken bütüncül bir dönüşüm süreci olarak ele alınmalıdır.Araştırma sonuçları, YZ’nin afet yönetiminde karar verme süreçlerini rasyonelleştirdiğini, veri odaklı analizlerle öngörü gücünü artırdığını ve kaynakların daha etkin kullanılmasına katkı sunduğunu göstermektedir. Bununla birlikte, etik, hukuki ve güvenlik boyutlarında yeterli düzenlemelerin yapılmaması durumunda, YZ teknolojilerinin yeni türden insan kaynaklı krizleri tetikleyebileceği tespit edilmiştir. Dolayısıyla, afet ve acil durum yönetiminde YZ’nin entegrasyonu yalnızca teknik bir yenilik olarak değil, aynı zamanda etik yönetişim, toplumsal güvenlik ve sürdürülebilirlik eksenlerinde yeniden düşünülmesi gereken bütüncül bir dönüşüm süreci olarak ele alınmalıdır.

Anahtar Kelimeler

• Afet Yönetimi Döngüsü
• Etik
• Yapay Zekâ
• Yönetişim

Destekleyen Kurum

ÇAlışma için herhangi bir kurumdan destek alınmamıştır.

Etik Beyan

Çalışma etik beyan gerektirmemektedir.

Artificial Intelligence Applications in the Disaster Management Cycle: Opportunities, Limitations, and Risks

Öz

Disaster and emergency management is a strategic process aimed at enhancing societal resilience against disasters and minimizing potential losses of life and property. The transformation brought about by the digital age has largely dissolved the boundaries between physical and virtual systems, making the use of technological tools an indispensable and defining element in disaster management. In particular, the rise of Artificial Intelligence (AI) technologies offers an innovative paradigm for the early detection of disasters, the mitigation of their impacts, and the improvement of response processes. Adopting a qualitative research approach, this study analyzes the application areas of AI-based systems in combating various types of disasters and examines the strategic, operational, and analytical contributions these technologies provide to the actors involved in disaster management. The findings indicate that AI enhances disaster management by rationalizing decision-making processes, strengthening predictive capacity through data-driven analyses, and promoting more efficient resource utilization. However, the study also reveals that insufficient ethical, legal, and security frameworks may lead AI technologies to trigger new forms of human-induced crises. Therefore, the integration of AI into disaster and emergency management should not be regarded merely as a technical innovation but as a comprehensive transformation process that must be reconsidered within the axes of ethical governance, human security, and sustainability.The findings indicate that AI enhances disaster management by rationalizing decision-making processes, strengthening predictive capacity through data-driven analyses, and promoting more efficient resource utilization. However, the study also reveals that insufficient ethical, legal, and security frameworks may lead AI technologies to trigger new forms of human-induced crises. Therefore, the integration of AI into disaster and emergency management should not be regarded merely as a technical innovation but as a comprehensive transformation process that must be reconsidered within the axes of ethical governance, human security, and sustainability.

Anahtar Kelimeler

• Artificial Intelligence
• Disaster Management Cycle
• Ethics
• Governance

Destekleyen Kurum

No support was received from any institution for the study.

Etik Beyan

The study does not require an ethical declaration.

Kaynakça

1. Abid, S. K., Sulaiman, N., Chan, S. W., Nazir, U., Abid, M., Han, H., Ariza-Montes, A., Vega-Muñoz, A. (2021). Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management. Sustainability, 13(22), 12560. https://doi.org/10.3390/su132212560
2. Akter, S., Wamba, S. F. (2019). Big data and disaster management: a systematic review and agenda for future research, Annals of Operations Research, 1(283), 939-959.
3. Altay, N., Green, W.G. (2006). OR/MS research in disaster operations management, European Journal of Operational Research, 175, 475-493.
4. Angın, C. (2024). Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. Türk Deprem Araştırma Dergisi, 6(2), 610-627.
5. Balsanoa, C., Burra, P., Duvouxc, C., Alisi, A., Piscaglia, F., Gerussi, A. (2023). Artificial Intelligence and liver: Opportunities and barriers. Digestive and Liver Disease, 55, 1455-1461. https://doi.org/10.1016/j.dld.2023.08.048
6. Barth, T.J., Arnold, E. (1999). Artificial Intelligence and Administrative Discretion. The American Review of Public Administration, 29, 332-351.
7. Beduschi, A. (2022). Harnessing the potential of artificial intelligence for humanitarian action: Opportunities and risks. International Review of the Red Cross, 104 (919), 1149–1169.
8. Cacal, N. (26 Eylül 2023). The Problem with AI and Techno-Solutionism: A Case for Positive Sum Design. Medium, https://medium.com/@nicolecacal/the-problem-with-ai-and-techno-solutionism-a-case-for-positive-sum-design-fed7adc40b9e (E.T. 20/08/2025).

9. Carreno, M. L., Cardona, O. D., Barbat, A. H. (2007). A disaster risk management performance index. Nat Hazards 41, 1–20. https://doi.org/10.1007/s11069-006-9008-y
10. Chae, Y.-J., Lee, H.-W., Kim, J.-H., Hwang, S.-W., Park, Y.-Y. (2023). Design of a Mixed Reality System for Simulating Indoor Disaster Rescue. Applied Sciences, 13(7), 4418.
11. Cheatham, B., Javanmardian, K., Samandari, H. (2019). Confronting the risks of artificial intelligence. McKinsey Quarterly, pp. 1-9.
12. Chen, C. (2023). The future is now: artificial intelligence and anticipatory humanitarian action. https://blogs.icrc.org/law-and-policy/2021/08/19/artificial-intelligence-anticipatory-humanitarian/ (E.T.: 01/08/2025).
13. Çelik, A., Sevli, O. (2022). Makine Öğrenmesi Tekniklerini Kullanarak Trafik Kazalarının Sonucunu Tahmin Etme, Türk Doğa ve Fen Dergisi, Cilt 11, Sayı 3, Sayfa 79-83.
14. Desouza, K. C. (2018). Delivering Artificial Intelligence in Government: Challenges and Opportunities. IBM Center for The Business of Government. https://www.businessofgovernment.org/sites/default/files/Delivering%20Artificial%20Intelligence%20in%20Government.pdf (E.T.: 01/08/2025).
15. Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. In: O’Sullivan, Barry., Wooldridge, Michael. (Eds)., Springer Nature Switzerland.
16. Ebers, M. (2019). Chapter 2: Regulating AI and Robotics: Ethical and Legal Challenges, Ebers, Martin., Navarro, Susana Navas. (Eds.), Algorithms and Law, Cambridge, Cambridge University Press, http://dx.doi.org/10.2139/ssrn.3392379
17. Fan, X., Zhang, X., Wang, X., Yu, X. (2023). A deep reinforcement learning model for resilient road network recovery under earthquake or flooding hazards. Journal of Infrastructure Preservation Resilince 4, 8. pp. 1-19.
18. FEMA. (1997). Comparative Emergency Management. https://training.fema.gov/hiedu/docs/cem/comparative%20em%20course%20-%20session%203%20%20the%20global%20historical%20context%20of%20emergency%20management.doc
19. García-Nieto, J. N. (23 Kasım 2021). Towards techno-colonialism, https://www.atalayar.com/en/opinion/juan-n-garcia-nieto/towards-techno-colonialism/20211122154842135940.html (E.T.: 01/08/2025).
20. Ghani, T., jordaGordon, G. (2022). Predictable Disasters AI and the Future of Crisis Response. https://www.brookings.edu/wp-content/uploads/2021/12/Chapter-Six_Breakthrough.pdf (E.T.: 01/08/2025).
21. Guizzo, E. (2020). Types of Robots. https://robots.ieee.org/learn/types-of-robots/. (E.T.: 11/10/2022).
22. Imran, M., Qazi, U., Ofli, F., Peterson, S., Alam, F. (2022). AI for Disaster Rapid Damage Assessment from Microblogs. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12517-12523.
23. Jordan, M. I. (2019). Artificial Intelligence-The Revolution Hasn’t Happened Yet. Harvard Data Science Review, 1(1), pp.1-8. https://doi.org/10.1162/99608f92.f06c6e61
24. Joyce, K. E., Wright, C. K., Samsonov, S. V., Ambrosia, V. G. (2009). Remote sensing and the disaster management cycle. G. Jedlovec (Ed.), Advances in Geoscience and Remote Sensing içinde (s. 317-346). Rijeka, Croatia: In Tech.
25. Jung, D., Tran Tuan, V., Quoc Tran, D., Park, M., Park, S. (2020). Conceptual framework of an intelligent decision support system for smart city disaster management. Applied Sciences, 10(2), 666.
26. Keim, M., Abrahams, J. (2012). Health and Disaster. (Ed.) Wisner, B., Gaillard, J. C., ve Kelman, I. The Routledge Handbook of Hazards and Disaster Risk Reduction içinde (s. 530-543). New York and London. Routledge.
27. Kuglitsch, M., Albayrak, A., Aquino, R., Craddock, A., Edward-Gill, J., Kanwar, R., Koul, A., Ma, J., Marti, A., Menon, M., Pelivan, I., Toreti, A., Venguswamy, R., Ward, T., Xoplaki, E., Rea, A., Luterbacher, J. (2022). Artificial Intelligence for Disaster Risk Reduction: Opportunities, challenges, and prospects. https://public.wmo.int/en/resources/bulletin/artificial-intelligence-disaster-risk-reduction-opportunities-challenges-and, (E.T.: 01/08/2025).
28. Linardos, V., Drakaki, M., Tzionas, P., Karnavas, Y. L. (2022). Machine Learning in Disaster Management: Recent Developments in Methods and Applications. Mach. Learn. Knowl. Extr. 4, 446–473. https://doi.org/10.3390/make4020020
29. Malawani, A. D., Nurmandi, A., Purnomo, E. P., Rahman, T. (2020). Social media in aid of post disaster management. Transforming Government: People, Process and Policy, 14(2), 237-260.
30. Merriam-Webster. (2022). Disaster. Merriam-Webster.com Dictionary. https://www.merriam-webster.com/dictionary/disaster, (E.T.: 01/08/2025).
31. Moreno, C., Aquino, R., Ibarreche, J., Pérez, I., Castellanos, E., Álvarez, E., Rentería, R., Anguiano, L., Edwards, A., Lepper, P., Edwards, R. M., Clark, B. (2019). RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications. Sensors, 19(1), 127. MDPI AG.
32. Munawar, H. S., Mojtahedi, M., Hammad, A. W. A., Kouzani, A., Mahmud, M. A. (2022). Disruptive technologies as a solution for disaster risk management: A review, Science of Total Environment. 806 (2022), 151351.
33. Murphy, R. R. (2014). Disaster Robotics. The MIT Press: Massachusetts.
34. Noji, E. K. (2000). The public health consequences of disasters. Prehospital and disaster medicine, 15(4), 21-31.
35. Pandey, N., Natarajan, S. (2016). How social media can contribute during disaster events? Case study of Chennai floods 2015, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, pp. 1352-1356.
36. Phillips, B. D., Neal, D. M., Webb, G. R. (2017). Introduction to Emergency Management, Second Edition, CRC Press Taylor ve Francis Group, Boca Raton: FL.
37. Polonski, S. (2018). AI Solutionism. https://towardsdatascience.com/risks-of-ai-solutionism-dangers-of-machine-learning-and-artificial-intelligence-in-politics-and-government-728b7577a243 (E.T.: 01/08/2025).
38. Quarantelli, E. L., (2000). Disaster Planning, Emergency Management, and Civil Protection: The Historical Development and Current Characteristics of Organized Efforts to Prevent and Respond to Disasters. University of Delaware Disaster Research Center, Newark, Delaware https://udspace.udel.edu/bitstream/handle/19716/673/PP301.pdf?sequence=1veisAllowed=y
39. Ramchurn, S. D., Farinelli, A., Macarthur, K. S., Jennings, N. R. (2010). Decentralized coordination in robocup rescue. The Computer Journal, 53(9), 1447–1461.
40. Ramchurn, S. D., Wu, F., Jiang, W., Fischer, J.E., Reece, S, Roberts., S, Rodden, T., Greenhalgh, C., Jennings, N. R. (2016). Human-agent collaboration for disaster response. Auton Agents Multi-Agent Syst, 30(1):82–111.
41. Reynard, D., Shirgaokar, M. (2019). Harnessing the power of machine learning: Can Twitter data be useful in guiding resource allocation decisions during a natural disaster? Transportation research part D: Transport and environment, 77, 449-463.
42. Riccardi, M. T. (2016). The power of crowdsourcing in disaster response operations. International Journal of Disaster Risk Reduction, 20, 123–128.
43. Russell, S. Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson Series, Fourth Edition.
44. Schwab, A. K., Sandler, D., Brower, D. J. (2017). Hazard Mitigation and Preparedness: An Introductory Text for Emergency Management and Planning Professionals Second Edition, FL: CRC Press Taylor ve Francis Group. Boca Raton,
45. Shrestha, Y. R., Ben-Menahem, S. M., von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66-83.
46. Shubhendu, S., Vijay, J. F. (2013). Applicability of Artificial Intelligence in Different Fields of Life. International Journal of Scientific Engineering and Research (IJSER), Volume 1 Issue 1, pp. 28-35.
47. Sufi, F. K. (2024). A systematic review on the dimensions of open-source disaster intelligence using GPT. Journal of Economy and Technology, 2, 62-78.
48. Sun, W., Bocchini, P., Davison, B. D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards 103, 2631–2689. https://doi.org/10.1007/s11069-020-04124-3
49. Tierney, K. Oliver-Smith, A. (2012). Social dimensions of disaster recovery. International Journal of Mass Emergencies and Disasters, Vol. 30, No. 2, pp. 123–146.
50. Twigg, J. (2015). Disaster Risk Reduction, Good Practice Review 9, Humanitarian Policy Group Overseas Development Institute, London: UK. http://bvpad.indeci.gob.pe/doc/pdf/esp/doc2601/doc2601-contenido.pdf (E.T.: 01/08/2025).
51. Van Wart, M., Kapucu, N. (2011). Crisis Management Competencies The case of emergency managers in the USA. Public Management Review, Vol. 13, Issue 4, 489–511.
52. Varol, N., Buluş Kırıkkaya, E. (2017). Afetler Karşısında Toplumun Dirençliliği. Resilience, 1(1), 1-9. Wisner, B., Blaikie, P., Cannon, T., ve Davis, I. (2004). At risk: Natural hazards, people’s vulnerability, and disaster (2nd ed.). London: Routledge.
53. Xia, M., Wu, J., Yao, J., Zhu, H., Gong, A., Yang, J., Hu, L., Mo, F. (2023). A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake. International Journal of Disaster Risk Science, 14(6), 947–962. https://doi.org/10.1007/s13753-023-00526-6
54. Yüksel, M. E. (2018). Agent-based evacuation modeling with multiple exits using NeuroEvolution of Augmenting Topologies. Advanced Engineering Informatics, 35, 30-55.
55. Zhang, B. (2021, October 7). Public Opinion Toward Artificial Intelligence, pp. 1-21. https://doi.org/10.31219/osf.io/284sm (E.T.: 10/08/2025).