Deprem Erken Uyarı Sistemlerinin Akıllı Şehirlerin Dirençliliğine Etkisi: Kavramsal Çerçeve ve Kapsamlı İnceleme

Öz

Deprem Erken Uyarı Sistemleri (DEUS), sismik olayların etkilerini azaltmak ve kentsel afetlere hazırlık düzeyini güçlendirmek amacıyla gelişmiş dijital teknolojilerden yararlanan dirençli akıllı şehirlerin kritik bileşenlerindendir. Bu sistemler; Dağıtık Nesnelerin İnterneti (IoT) cihazlarını, kablosuz sensör ağlarını, yüksek hızlı iletişim altyapılarını ve makine öğrenmesi algoritmalarını entegre ederek sismik hareketleri gerçek zamanlı olarak tespit etmek, analiz etmek ve değerlendirmek üzere tasarlanmıştır. Birbirine bağlı yer hareketi sensörlerinden toplanan veriler, işleme merkezlerine hızlı bir şekilde iletilerek deprem büyüklüğünün, konumunun ve etkilenecek bölgelerde beklenen sarsıntı şiddetinin güçlü yer hareketleri ulaşmadan önce tahmin edilmesini sağlar. DEUS’un akıllı kentsel altyapılara entegre edilmesi; toplu taşımanın durdurulması, asansörlerin kontrol edilmesi, kritik elektrik ve doğalgaz şebekelerinin izole edilmesi, hassas altyapıların korunması ve acil iletişim protokollerinin başlatılması gibi otomatik ve koordineli müdahaleleri mümkün kılabilir. Bu çalışma, DEUS’un akıllı şehir çerçevelerine entegrasyonunu inceleyerek afetlere hazırlık, gerçek zamanlı karar alma ve uyarlanabilir kentsel yönetişimdeki rolüne odaklanmaktadır. Ayrıca veri birlikte çalışabilirliği, iletişim güvenilirliği, sistem bakımı ve paydaşlar arası koordinasyon gibi teknolojik, operasyonel, kurumsal ve yönetsel zorlukları ele almaktadır. DEUS’un birbirine bağlı kentsel sistemlere yerleştirilmesiyle, kamu güvenliğinin artırılması, sosyoekonomik kesintilerin azaltılması ve uyarlanabilir, depreme dirençli kentsel çevrelerin geliştirilmesine katkı sağlanması hedeflenmektedir.

Anahtar Kelimeler

• Akıllı şehirler
• Deprem
• Erken uyarı sistemleri
• Mimarlıkta teknoloji

The Impact of Earthquake Early Warning Systems on Smart Cities' Resilience: Conceptual Framework and Comprehensive Review

Öz

Earthquake Early Warning Systems (EEWS) are critical components of resilient smart cities, employing advanced digital technologies to reduce the impacts of seismic events and strengthen urban disaster preparedness. These systems integrate distributed Internet of Things (IoT) devices, wireless sensor networks, high-speed communication infrastructures, and machine learning algorithms to detect, analyze, and evaluate seismic activity in real time. Data collected from interconnected ground-motion sensors are rapidly transmitted to processing centers, enabling the estimation of earthquake magnitude, location, and expected intensity before strong ground shaking reaches affected areas. When integrated into smart urban infrastructures, EEWS can trigger automated and coordinated responses, including halting public transportation, controlling elevators, isolating critical power and gas networks, protecting vulnerable infrastructure, and initiating emergency communication protocols. This study examines the integration of EEWS into smart city frameworks, focusing on their role in enhancing disaster preparedness, real-time decision-making, and adaptive urban governance. It also discusses technological, operational, organizational, and institutional challenges, including data interoperability, communication reliability, system maintenance, and coordination among stakeholders. By embedding EEWS within interconnected urban systems, the study highlights their potential to enhance public safety, reduce socioeconomic disruption, and support the development of adaptive and earthquake-resilient urban environments.

Anahtar Kelimeler

• Early warning systems
• Earthquake
• Smart cities
• Technology in architecture

Kaynakça

1. Abdalzaher, M. S., Elsayed, H. A., Fouda, M. M., Salim, M. M. (2023). Employing machine learning and IoT for earthquake early warning system in smart cities. Energies, 16(1), 495. https://doi.org/10.3390/en16010495
2. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., Airaksinen, M. (2017). What are the differences between sustainable and smart cities? Cities, 60, 234–245. https://doi.org/10.1016/j.cities.2016.09.009
3. Akın, S. (2024). Caravanserais on Konya-Beyşehir road in the context of urban tourism. Journal of Konya Art, 7, 181–192. https://doi.org/10.51118/konsan.2024.48
4. Akkar, S., Sandıkkaya, M. A., Şenyurt, M., Azari Sisi, A., Ay, B. Ö., Traversa, P., Douglas, J., Cotton, F., Luzi, L., Hernandez, B., Godey, S. (2014). Reference database for seismic ground-motion in Europe (RESORCE). Bulletin of Earthquake Engineering, 12(1), 311-339. https://doi.org/10.1007/s10518-013-9506-8
5. Albino, V., Berardi, U., Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), 3–21. https://doi.org/10.1080/10630732.2014.942092
6. Allen, R. M., Melgar, D. (2019). Earthquake early warning: Advances, scientific challenges, and societal needs. Annual Review of Earth and Planetary Sciences, 47(1), 361–388. https://doi.org/10.1146/annurev-earth-053018-060457
7. Anadolu Ajansı. (2026, 7 Eylül). Türkiye deprem izleme istasyonu sayısında Avrupa'nın en yüksek kapasitesine ulaştı. https://www.aa.com.tr/tr/gundem/turkiye-deprem-izleme-istasyonu-sayisinda-avrupanin-en-yuksek-kapasitesine-ulasti/4050170
8. Ansari, K., Corumluoglu, O., Sharma, S. K. (2017). Numerical simulation of crustal strain in Turkey from continuous GNSS measurements in the interval 2009–2017. Journal of Geodetic Science, 7(1), 113-129. https://doi.org/10.1515/jogs-2017-0013

9. Bassurucu, M., Yildiz, O., Kina, C. (2025). Seismic performance assessment of an RC building due to 2023 Türkiye earthquakes: A case study in Adıyaman, Türkiye. Buildings, 15(4), 521. https://doi.org/10.3390/buildings15040521
10. Batty, M. (2013). The new science of cities. MIT Press.
11. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214(1), 481-518. https://doi.org/10.1140/epjst/e2012-01703-3
12. Bibri, S. E., Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212. https://doi.org/10.1016/j.scs.2017.02.016
13. Boore, D. M., Stewart, J. P., Seyhan, E., Atkinson, G. M. (2014). NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthquake Spectra, 30(3), 1057–1085. https://doi.org/10.1193/070113EQS184M
14. Bosher, L., Chmutina, K. (2017). Disaster risk reduction for the built environment. John Wiley & Sons.
15. Böse, M., Clayton, R., Heaton, T., Hauksson, E., Kanamori, H. (2014). CISN ShakeAlert: An earthquake early warning demonstration system for California. Seismological Research Letters, 85(3), 597–608. https://doi.org/10.1785/0220130094
16. Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
17. Caragliu, A., Del Bo, C., Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82. https://doi.org/10.1080/10630732.2011.601117
18. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 2012 45th Hawaii International Conference on System Sciences (pp. 2289–2297). IEEE. https://doi.org/10.1109/HICSS.2012.615
19. Cremen, G., Galasso, C. (2020). Earthquake early warning: Recent advances and perspectives. Earth-Science Reviews, 205, 103184. https://doi.org/10.1016/j.earscirev.2020.103184
20. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
21. Eren, K., Uzel, T., Gülal, E., Yıldırım, O., Cingöz, A. (2009). Results from a comprehensive GNSS test in the CORS-TR network: Case study. Journal of Surveying Engineering, 135(1), 10-18. https://doi.org/10.1061/(ASCE)0733-9453(2009)135:1(10)
22. Feroz, S., Mehmood, R., Guizani, M. (2021). Data privacy and security challenges in smart cities: An integrated perspective. IEEE Communications Magazine, 59(10), 92–98. https://doi.org/10.1109/MCOM.001.2100063
23. Foulger, G. R., Wilson, M. P., Gluyas, J. G., Julian, B. R., Davies, R. J. (2018). Global review of human-induced earthquakes. Earth-Science Reviews, 178, 438–514. https://doi.org/10.1016/j.earscirev.2017.07.008
24. Ghosh, A., Bose, M., Ray, P. (2020). Earthquake early warning as a tool for improving society's resilience and crisis response. Natural Hazards, 104(1), 1–25. https://doi.org/10.1007/s11069-020-04132-5
25. Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., Williams, P. (2010). Foundations for smarter cities. IBM Journal of Research and Development, 54(4), 1–16. https://doi.org/10.1147/JRD.2010.2048257
26. Hohensinn, R., Aichinger-Rosenberger, M., Wareyka-Glaner, M. F., Ravanelli, M. (2024). Natural hazard monitoring with global navigation satellite systems (GNSS). In Space geodesy for environmental monitoring (pp. 1–123). Elsevier.
27. Jones, L., Olshansky, R. B. (2018). Urban disaster recovery: A measurement framework and its application to the 1906 San Francisco earthquake. International Journal of Disaster Risk Reduction, 27, 292–300. https://doi.org/10.1016/j.ijdrr.2017.08.005
28. Jousset, P., Calò, M., Wassermann, J., Satriano, C. (2018). What to expect when you're expecting earthquake early warning. Geophysical Journal International, 213(2), 1386–1394. https://doi.org/10.1093/gji/ggy053
29. Kalafat, D., Zülfikar, A. C., & Okuyan Akcan, S. (2021). Seismicity of Turkey and real-time seismology applications in determining earthquake hazard. Academic Platform Journal of Natural Hazards and Disaster Management, 2(2), 96-111. https://doi.org/10.52114/apjhad.1039670
30. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
31. Kong, Q., Martin-Short, R., Allen, R. M. (2020). Toward global earthquake early warning with the MyShake smartphone seismic network, Part 1: Simulation platform and detection algorithm. Seismological Research Letters, 91(4), 2206–2217. https://doi.org/10.1785/0220190177
32. Kramer, S. L., Stewart, J. P. (2024). Geotechnical earthquake engineering (2nd ed.). CRC Press. https://doi.org/10.1201/9781003512011
33. Liu, Z., Jiang, H., Li, S., Li, M., Liu, J., Zhang, J. (2023). Implementation and verification of a real-time system for automatic aftershock forecasting in China. Earth Science Informatics, 16, 1891-1907. https://doi.org/10.1007/s12145-023-00960-6
34. Minson, S. E., Baltay, A. S., Cochran, E. S., Hanks, T. C., Page, M. T., McBride, S. K., Milner, K. R., Meier, M.-A. (2019). The limits of earthquake early warning accuracy and best alerting strategy. Scientific Reports, 9, Article 2478. https://doi.org/10.1038/s41598-019-39384-y
35. National Research Council. (2011). National earthquake resilience: Research, implementation, and outreach. The National Academies Press. https://doi.org/10.17226/13092
36. Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., Scorrano, F. (2014). Current trends in smart city initiatives: Some stylised facts. Cities, 38, 25–36. https://doi.org/10.1016/j.cities.2013.12.010
37. Pérez, R., Wang, H. (2022). Governance frameworks for resilient smart cities integrating disaster early warning systems. International Journal of Disaster Risk Reduction, 68, 102743. https://doi.org/10.1016/j.ijdrr.2022.102743
38. Rydin, Y. (2012). The purpose of planning: Creating sustainable towns and cities. Policy Press.
39. Talebi, A., Fard, R. A., Rahimi, H., Aoudia, A. (2022). Estimation of τc, τmaxc and Pd earthquake early warning parameters for northwest of Iran. Pure and Applied Geophysics, 179(3), 935–948. https://doi.org/10.1007/s00024-021-02857-x
40. Tan, M. L., Becker, J. S., Stock, K., Prasanna, R., Brown, A., Kenney, C., Cui, A., Lambie, E. (2022). Understanding the social aspects of earthquake early warning: A literature review. Frontiers in Communication, 7, Article 939242. https://doi.org/10.3389/fcomm.2022.939242
41. Van Westen, C. J. (2013). Remote sensing and GIS for natural hazards assessment and disaster risk management. In Treatise on geomorphology (Vol. 3, pp. 259–298). Elsevier. https://doi.org/10.1016/B978-0-12-374739-6.00115-0
42. Yıldırım, O., Yaprak, S., İnal, C. (2014). Determination of 2011 Van/Turkey earthquake (M=7.2) effects from measurements of CORS-TR network. Geomatics, Natural Hazards and Risk, 5(2), 132-144. https://doi.org/10.1080/19475705.2013.789453
43. Yıldız, Ö., Çabalar, A. F. (2025). Analyzing seismic impact across regions: A comprehensive study of the Pazarcık and Elbistan earthquakes in Türkiye (February 6, 2023). Journal of African Earth Sciences, 223, 105463.
44. Yigitcanlar, T., Kamruzzaman, M., Buys, L., Ioppolo, G., Sabatini-Marques, J., da Costa, E. M., Yun, J. J. (2018). Understanding smart cities: Intertwining development drivers with desired outcomes in a multidimensional framework. Cities, 81, 145–160. https://doi.org/10.1016/j.cities.2018.04.003
45. Zeybek, A., Yıldız, Ö., Sönmezer, Y. B. (2025). Post-earthquake assessment of liquefaction in Gölbaşı-Adıyaman following the 6 February 2023 Kahramanmaraş earthquakes: Field observations, SPT-based analysis, and laboratory testing of soil ejecta. Natural Hazards. Advance online publication. https://doi.org/10.1007/s11069-025-07257-7
46. Zhang, X., Zhang, M., Tian, X. (2021). Real-time earthquake early warning with deep learning: Application to the 2016 M6.0 Central Apennines, Italy, earthquake. Geophysical Research Letters, 48(5), e2020GL089394. https://doi.org/10.1029/2020GL089394
47. Zschau, J., Küppers, A. (Eds.). (2013). Early warning systems for natural disaster reduction. Springer. https://doi.org/10.1007/978-3-642-12233-0