Harnessing Artificial Intelligence and Big Data for Proactive Disaster Management: Strategies, Challenges, and Future Directions

Yıl 2024, , 57 - 91, 30.10.2024

Murat Şengöz

https://doi.org/10.46373/hafebid.1534925

Öz

Disasters are events that significantly impact people's lives and living spaces globally. Natural disasters can arise from various causes, such as climate change, geological movements, weather events, and human factors. The damage caused by these disasters can affect millions of people and negatively impact societies economically, socially, and environmentally. Disaster management has emerged as a multidisciplinary field aimed at minimizing the damage caused by disasters and making communities more resilient to them. Traditional disaster management strategies include emergency planning, crisis management, pre-disaster preparation, and rapid response during disasters. However, these strategies generally reflect a reactive approach and rely on human resources and existing infrastructure. This article aims to examine the role and impact of innovative technologies such as artificial intelligence and big data in the field of disaster management. While artificial intelligence is known for its ability to analyze complex datasets, discover patterns and relationships, optimize decision-making processes, and predict future events, big data provides the ability to process large amounts of data quickly and efficiently, transforming them into meaningful information. These technologies play a significant role in pre-disaster preparation, crisis management during disasters, and post-disaster recovery processes. The article discusses how artificial intelligence and big data technologies can be used in disaster management, how these technologies can be integrated into disaster risk reduction strategies, and how their effectiveness can be assessed. In conclusion, the integration of artificial intelligence and big data technologies into disaster management offers a more effective and efficient approach to dealing with disasters and can make significant contributions to making communities more resilient to disasters. This article aims to provide a guide to understanding the current state of disaster management and developing more effective strategies.

Anahtar Kelimeler

Disaster Management, Artificial Intelligence, Big Data, Risk Analysis, Pre-disaster Preparedness

Yapay Zekâ ve Büyük Veriyi Proaktif Afet Yönetimi İçin Kullanma: Stratejiler, Zorluklar ve Gelecek Yönelimler

Öz

Afetler, dünya genelinde insanların yaşamını ve yaşam alanlarını ciddi şekilde etkileyen olaylardır. Doğal afetler; iklim değişikliği, jeolojik hareketler, hava olayları ve insan kaynaklı etmenler gibi çeşitli sebeplerden ortaya çıkabilir. Bu afetlerin yol açtığı zararlar, milyonlarca insanı etkileyebilir ve toplumları ekonomik, sosyal ve çevresel açıdan olumsuz yönde etkileyebilir. Afet yönetimi, afetlerin neden olduğu zararları en aza indirmek ve toplumları afetlere karşı daha dirençli hâle getirmek amacıyla multidisipliner bir alan olarak ortaya çıkmıştır. Geleneksel afet yönetimi
stratejileri; acil durum planlaması, kriz yönetimi, afet öncesi hazırlık ve afet sırasında hızlı müdahale gibi süreçleri içermektedir. Ancak bu stratejiler genellikle reaktif bir yaklaşımı yansıtır ve insan kaynaklarına ve mevcut altyapıya dayanır. Bu makale, afet yönetimi alanında yapay zekâ ve büyük veri gibi yenilikçi teknolojilerin rolünü ve etkisini incelemeyi amaçlamaktadır. Yapay zekâ; karmaşık veri kümelerini analiz etme, desenler ve ilişkiler keşfetme, karar alma süreçlerini optimize etme ve gelecekteki olayları tahmin etme yeteneği ile bilinirken, büyük veri, büyük miktarda veriyi hızlı ve etkin bir şekilde işleyerek anlamlı bilgilere dönüştürme imkânı sağlar. Bu teknolojiler, afet öncesi hazırlık, afet sırasında kriz yönetimi ve afet sonrası iyileştirme süreçlerinde
önemli bir rol oynamaktadır. Makalede, yapay zekâ ve büyük veri teknolojilerinin afet yönetiminde nasıl kullanılabileceği, bu teknolojilerin afet risklerini azaltma stratejilerine nasıl entegre edilebileceği ve etkinliğinin nasıl değerlendirilebileceği ele alınmaktadır. Sonuç olarak, yapay zekâ ve büyük veri teknolojilerinin afet yönetimine entegrasyonu, afetlerle başa çıkmada daha etkili ve verimli bir yaklaşım sunmakta olup toplumların afetlere karşı daha dirençli hâle gelmesine önemli katkılar sağlayabilir.

Anahtar Kelimeler

Afet Yönetimi, Yapay Zekâ, Büyük Veri, Risk Analizi, Afet Öncesi Hazırlık.

Kaynakça

• Allen, R. M., & Melgar, D. (2019). Earthquake early warning: Advances, scientific challenges, and societal needs. Annual Review of Earth and Planetary Sciences, 47, 361-388. https://doi.org/10.1146/annurev-earth-053018-060457
• Anderson, J. & Brown, T. (2019). Leveraging Big Data and AI for disaster recovery. Journal of Disaster Management, 7(2), 45-56.
• Boccardo, P., Giulio, A., & Tonolo, F. G. (2020). Digital infrastructure for disaster management: Challenges and opportunities. International Journal of Disaster Risk Reduction, 46, 101526. https://doi.org/10.1016/j.ijdrr.2020.101526
• Brown, A. & Miller, B. (2021). Remote Sensing and Geographic Information Systems in Natural Disaster Management. Journal of Disaster Management, 25(2), 45-62.
• Brown, D., Lee, J., & Smith, T. (2019). Enhancing disaster response with real-time data analysis. Journal of Emergency Management, 18(2), 118-125.
• Chen, C., Wang, L., Liu, H. & Zhang, Y. (2022). Machine Learning Approaches for Natural Disaster Prediction. International Journal of Machine Learning Research, 40(3), 112-129
• Chen, J., Saha, S., Muralidharan, P., & Khare, A. (2021). AI-powered flood forecasting and alerts in India. Communications of the ACM, 64(12), 46-49. https://doi.org/10.1145/3463725
• Chen, L. & Wang, Y. (2020). The Role of Artificial Intelligence in Crisis Response. Journal of Emergency Management, 18(3), 45-58.
• Chen, W., Liu, Q., Wang, H. & Zhang, L. (2020). The role of artificial intelligence in disaster risk reduction: A systematic review. Journal of Risk Research, 22(5), 643-658.
• Choi, S., & Patel, A. (2022). Big data analytics for disaster management: Techniques and applications. International Journal of Data Science, 27(3), 56-73.
• Garcia, D., Martinez, J. & Rodriguez, A. (2020). Hydrological and Hydrometeorological Modeling for Disaster Risk Assessment. Journal of Hydroinformatics, 18(4), 203-217.
• Garcia, R. & Martinez, S. (2023). Advancements in Disaster Management Technologies. Disaster Prevention and Management, 31(4), 512-527.
• Google. (2020). Earthquake Alert System. Retrieved from https://www.google.com/earthquake
• Gupta, R., Sharma, S. & Patel, A. (2021). Advancements in artificial intelligence for disaster management. Journal of Artificial Intelligence Research, 17(2), 89-104.
• Huang, H., Clements, C. B., & Conyers, M. G. (2021). The role of AI and machine learning in wildfire prediction and mitigation: Case studies from California. Environmental Research Letters, 16(7), 074001. https://doi.org/10.1088/1748- 9326/ac0c6d
• Huang, Y., Wang, X. & Liu, Z. (2023). Harnessing big data for disaster risk reduction: A systematic review. Disasters, 28(4), 521-537. IBM. (2018). Watson in Mexico Earthquake. Retrieved from https://www.ibm.com/ watson-mexico
• Johnson, A. (2020). Big data applications in disaster management: A review. Disaster Prevention and Management: International Journal of Disasters, 29(3), 378-391.
• Johnson, A. B. (2021). Disaster Preparedness and Management Strategies. Disaster Recovery Journal, 17(3), 45-60.
• Johnson, E., Smith, J. & Lee, K. (2020). Time Series Analysis Techniques for Natural Disaster Prediction. Journal of Time Series Analysis, 30(1), 78-92.
• Jones, A., & Lee, H. (2020). Big Data and disaster risk management: A review of current trends and future opportunities. Natural Hazards, 97(1), 279-298.
• Jones, H., & Brown, P. (2020). Identifying and mitigating disaster risks: A multidisciplinary approach. Risk Analysis Journal, 42(1), 45-58.
• Jones, H., & Brown, P. (2021). Advanced risk analysis techniques for disaster preparedness. Journal of Risk Management, 23(5), 210-225.
• Jones, R. & Thompson, L. (2020). Real-time data analytics for disaster response: A review. Disaster Management, 37(2), 143-158.
• Kumar, S. & Singh, R. (2020). Real-time Monitoring and Early Warning Systems for Natural Disasters. Journal of Disaster Research, 35(3), 102-115.
• Li, T., & Kim, S. (2023). The role of AI in disaster risk reduction: Current trends and future directions. Journal of Risk and Safety Management, 40(5), 567-581.
• Liu, H., & Li, S. (2020). Data security and privacy concerns in AI-driven disaster management. Cybersecurity Journal, 15(1), 10-18. Los Angeles Fire Department (LAFD). (2019). FireCast: Predicting Fire Risks. Retrieved from https://www.lafd.org/firecast
• Minson, S. E., et al. (2018). Crowdsourced earthquake early warning. Science Advances, 4(3), e1500578. https://doi.org/10.1126/sciadv.1500578
• NASA. (2020). Drone Rapid Mapping in Australia. Retrieved from https://www. nasa.gov/drone-mapping
• Nguyen, T., Adams, R., & Miller, K. (2020). Policy frameworks for integrating AI in disaster management. Journal of Public Policy and Technology, 29(4), 1095- 1118.
• Robinson, J., & Nguyen, H. (2022). Humanitarian crises and disaster management: A global perspective. Humanitarian Affairs Review, 18(3), 156-170.
• Robinson, L. (2017). Social Media Data Analytics for Disaster Management. International Journal of Social Media and Disaster Management, 12(2), 34-47.
• Santos, J. R., & Rappold, A. G. (2021). Ethical considerations in AI-driven disaster management: Lessons from the 2018 Camp Fire. Disaster Medicine and Public
• Health Preparedness, 15(3), 305-312. https://doi.org/10.1017/dmp.2020.167
• Sarabadani, J., Ghaffari, M., & Forouzandeh, S. (2019). Technological access and equity in disaster response. Journal of Technology and Society, 17(4), 662-679.
• Smith, A., & Lee, J. (2018). Predictive models for disaster management using AI.Journal of Artificial Intelligence Research, 15(3), 1-14.
• Smith, J. (2018). Data Collection and Processing Techniques for Natural Disaster Prediction. Journal of Data Science Applications, 15(4), 210-225.
• Smith, J. (2019). Utilizing big data for disaster risk reduction: A comprehensive framework. Disasters, 43(3), 589-607.
• Tollefsen, A. F., et al. (2019). The role of data availability in the performance of early warning systems: A global analysis. Journal of Natural Hazards, 98(2), 565-578. https://doi.org/10.1007/s11069-019-03659-yŞengöz, M.
• Voosen, P. (2019). Google AI beats path to flood forecasting. Science, 364(6439),1228-1229. https://doi.org/10.1126/science.364.6439.1228
• Wang, C., Li, J., Zhang, H. & Chen, X. (2024). Emergency planning in disaster management: Strategies and challenges. International Journal of Emergency Management, 10(2), 87-102.
• Zaytsev, A., & Titov, V. V. (2020). Enhancing tsunami early warning systems using machine learning algorithms: Case studies and system improvements. Journal of Geophysical Research: Oceans, 125(9), e2019JC015819. https:// doi.org/10.1029/2019JC015819
• Zhang, Q., Wang, L., Liu, Y. & Zhou, W. (2021). Machine learning techniques for real-time disaster risk assessment: A case study in wildfire management. Risk Analysis, 41(8), 1608-1623.