Journals / Toplum Ekonomi ve Yönetim Dergisi / 2026 / Cilt: 7 Sayı: 3

THE CONCEPTUAL AND THEMATIC EVOLUTION OF SMART DISASTER MANAGEMENT LITERATURE: A COMPREHENSIVE ANALYSIS BASED ON VOSVIEWER AND RSTUDIO

AKILLI AFET YÖNETİMİ LİTERATÜRÜNÜN KAVRAMSAL VE TEMATİK EVRİMİ: VOSVIEWER VE RSTUDIO TEMELİNDE KAPSAMLI BİR ANALİZ

Pages
950–978
Publication Date
2026-09-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study presents a comprehensive assessment of the development trends in the disaster management literature, offering guidance for future research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to examine academic publications related to smart disaster management in the Scopus database from a bibliometric perspective.
📊
Key Findings & Data:Within the scope of the study, a search using the keyword ‘smart disaster management’ between 2021 and 2025 revealed that studies were published in different publication types.
💡
Conclusion & Impact:The study presents a comprehensive assessment of the development trends in the disaster management literature, offering guidance for future research.
🔬Design:Review & Systematic Survey
📍Scope:2021 Verileri
⚙️Variables:yapay zeka · akıllı afet yönetimi · Scopus

Abstract

This study aims to examine academic publications related to smart disaster management in the Scopus database from a bibliometric perspective. Within the scope of the study, a search using the keyword ‘smart disaster management’ between 2021 and 2025 revealed that studies were published in different publication types. Accordingly, during the period in question, there were 782 conference papers, 689 articles, 183 book chapters, 169 conference reviews, 87 review articles, 30 books, 6 editorials, 3 notes, and 2 letters. The data obtained were analyzed using VOSviewer and RStudio Bibliometrix software. Analyses have revealed a significant increase in the number of publications in the field of disaster management in recent years, with themes such as climate change, risk reduction, sustainability, and societal resilience coming to the forefront. China, India, the United States, and South Korea were identified as the countries producing the most publications, and interdisciplinary collaborations were found to be gaining importance. This study explicitly fills a significant gap in the literature by systematically synthesizing the intersection of digital technologies, artificial intelligence, and disaster governance, offering a novel roadmap for future research. The study presents a comprehensive assessment of the development trends in the disaster management literature, offering guidance for future research.

Özet

Bu çalışma, Scopus veritabanında akıllı afet yönetimi ile ilgili akademik yayınları bibliyometrik bir bakış açısıyla incelemeyi amaçlamaktadır. Çalışma kapsamında, 2021 ile 2025 yılları arasında ‘akıllı afet yönetimi’ anahtar kelimesi kullanılarak yapılan arama, farklı yayın türlerinde yayınlanmış çalışmalar olduğunu ortaya koymuştur. Buna göre, söz konusu dönemde 782 konferans bildirisi, 689 makale, 183 kitap bölümü, 169 konferans incelemesi, 87 inceleme makalesi, 30 kitap, 6 başyazı, 3 not ve 2 mektup bulunmaktadır. Elde edilen veriler VOSviewer ve RStudio Bibliometrix yazılımları kullanılarak analiz edilmiştir. Yapılan analizler, son yıllarda afet yönetimi alanındaki yayın sayısında önemli bir artış olduğunu ve iklim değişikliği, risk azaltma, sürdürülebilirlik ve toplumsal direnç gibi temaların ön plana çıktığını ortaya koymuştur. Çin, Hindistan, Amerika Birleşik Devletleri ve Güney Kore en çok yayın üreten ülkeler olarak belirlenmiş ve disiplinler arası iş birliklerinin önem kazandığı tespit edilmiştir. Bu çalışma, dijital teknolojiler, yapay zekâ ve afet yönetimi kesişimini sistematik bir biçimde haritalandırarak literatürdeki kavramsal boşluğu doldurmakta ve gelecekteki araştırmalar için özgün bir yol haritası sunmaktadır. Çalışma, afet yönetimi literatüründeki gelişim eğilimlerinin kapsamlı bir değerlendirmesini sunarak gelecekteki araştırmalar için yol gösterici niteliktedir.

🔖

Cite This Article

Kurnaz, S. Ç., Tekin, Ö. F. (2026). THE CONCEPTUAL AND THEMATIC EVOLUTION OF SMART DISASTER MANAGEMENT LITERATURE: A COMPREHENSIVE ANALYSIS BASED ON VOSVIEWER AND RSTUDIO. Toplum Ekonomi ve Yönetim Dergisi, Cilt: 7 Sayı: 3, 950–978. https://doi.org/10.58702/teyd.2009000

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (86)

  1. Abdel-Basset, M., Mohamed, R., Elhoseny, M., and Chang, V. (2020). Evaluation framework for smart disaster response systems in uncertainty environment. Mechanical Systems and Signal Processing, 145(106941), 1-18. https://doi.org/10.1016/j.ymssp.2020.106941
  2. Abid, F. A. (2021). Survey of machine learning algorithms based forest fires prediction and detection systems. Fire Technology, 57, 559–590. https://doi.org/10.1007/s10694-020-01056-z
  3. Ahmad, R. (2024). Smart remote sensing network for disaster management: An overview. Telecommunication Systems, 87, 213–237. https://doi.org/10.1007/s11235-024-01148-z
  4. Alam, F., Imran, M., and Ofli, F. (2017). Image4Act: Online social media image processing for disaster response. In J. Diesner, E. Ferrari, and G. Xu (Eds.), Proceedings of the 2017 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM 2017) (pp. 601–604). https://doi.org/10.1145/3110025.3110164
  5. Alam, F., Ofli, F., Imran, M., and Aupetit, M. (2018). A Twitter tale of three hurricanes: Harvey, Irma, and Maria. In B. Tomaszewski and K. Boersma (Eds.), Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2018), (pp. 553–572). https://doi.org/10.48550/arXiv.1805.05144
  6. Alam, F., Ofli, F., and Imran, M. (2020). Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behaviour and Information Technology, 39(3), 288–318. https://doi.org/10.1080/0144929X.2019.1610908
  7. Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M., Homod, R. Z., Albahri, O. S., Alamoodi, A. H., and Alzubaidi, L. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, 118(109409), 1-53. https://doi.org/10.1016/j.compeleceng.2024.109409
  8. Alenazy, W. M. (2021). Blockchain-enabled Internet of Things for unsupervised structural health monitoring in potential building structures. In J. Zhao and V. Kumar (Eds.), Handbook of research on innovations and applications of AI, IoT, and cognitive technologies (pp. 158–170). IGI Global. https://doi.org/10.4018/978-1-7998-6870-5.ch011
  9. Aljumah, A., Kaur, A., Bhatia, M., and Ahanger, T. A. (2020). Internet of Things–fog computing–based framework for smart disaster management. Transactions on Emerging Telecommunications Technologies, 32(e4078), 1-25. https://doi.org/10.1002/ett.4078
  10. Alsharif, A. H., Salleh, N., and Baharun, R. (2020). Research trends of neuromarketing: A bibliometric analysis. Journal of Theoretical and Applied Information Technology, 98, 2948-2962.
  11. Arabameri, A., Pradhan, B., Rezaei, K., Sohrabi, M., and Kalantari, Z. (2019). GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. Journal of Mountain Science, 16, 595–618. https://doi.org/10.1007/s11629-018-5168-y
  12. Aria, M., and Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  13. Arslan, M., Roxin, A.-M., Cruz, C., and Ginhac, D. (2017). A review on applications of big data for disaster management. In Proceedings of the 13th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS) (pp. 370–375). https://doi.org/10.1109/SITIS.2017.67
  14. Baker, D. R. (1990). Citation analysis: A methodological review. Social Work Research and Abstracts, 26(3), 3–10. https://doi.org/10.1093/swra/26.3.3
  15. Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20(6442), 1-26. https://doi.org/10.3390/s20226442
  16. Ben Rhaiem, M. A., Selmi, M., Farah, I. R., and Bouzeghoub, A. (2026). Leveraging volunteered geographical information and spatio-temporal big data in disaster management: Opportunities and challenges. International Journal of Data Science and Analytics, 21(25), 1-22. https://doi.org/10.1007/s41060-025-00896-8
  17. Boukerche, A. (2019). Smart disaster management and responses for smart cities. In Proceedings of the IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications (DS-RT) (pp. 1–2). https://doi.org/10.1109/DS-RT47707.2019.8958693
  18. Can, A. B., Parlak, İ. B., and Acarman, T. (2023). Smart disaster management using big data analytics. In T. H. Karakoc et al. (Eds.), New technologies and developments in unmanned systems, (pp. 375–388). Springer. https://doi.org/10.1007/978-3-031-37160-8_27
  19. Chen, X., He, R., Li, H., Li, Y., Xiong, Y., Ju, Y., and Li, Z. (2026). Designing a cross-regional emergency response network considering spatiotemporal evolution: A smart predict-then-optimise method. Transportation Research Part E: Logistics and Transportation Review, 206(104547), 1-34. https://doi.org/10.1016/j.tre.2025.104547
  20. Cheng, R., Hou, L., and Xu, S. (2023). A review of digital twin applications in civil and infrastructure emergency management. Buildings, 13(1143), 1-29. https://doi.org/10.3390/buildings13051143
  21. Çankaya-Kurnaz, S., and Tekin, Ö. F. (2025). AI supported early warning systems in smart disaster management: The case of Kahramanmaraş earthquakes. Pakistan Journal of Life and Social Sciences, 23(2), 128-139. https://doi.org/10.57239/PJLSS-2025-23.2.0011
  22. Damaševičius, R., Bacanin, N., and Misra, S. (2023). From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management. Journal of Sensor and Actuator Networks, 12(41), 1-45. https://doi.org/10.3390/jsan12030041
  23. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  24. Dönbak, E. R. (2020). Kültür ve turizm araştırmalarının bilim haritalama teknikleri ile bibliyometrik analizi. Journal of Travel and Tourism Research, 17(17), 52–78.
  25. Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., and Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398. https://doi.org/10.1080/00207543.2020.1722860
  26. Ege, R. (1986). Health aspects of disasters and accidents. Emel matbaacılık sanayii.
  27. Ejaz, H., Zeeshan, H. M., Ahmad, F., Bukhari, S. N. A., Anwar, N., Alanazi, A., and Younas, S. (2022). Bibliometric analysis of publications on the Omicron variant from 2020 to 2022 in the Scopus database using R and VOSviewer. International Journal of Environmental Research and Public Health, 19(12407), 1-25. https://doi.org/10.3390/ijerph191912407
  28. Elvas, L. B., Mataloto, B. M., Martins, A. L., and Ferreira, J. C. (2021). Disaster management in smart cities. Smart Cities, 4(2), 819–839. https://doi.org/10.3390/smartcities4020042
  29. Eren, T., Kurnaz, A., and Büyükipekci, S. (2025). Bibliometric analysis of studies in the field of green accounting based on the Web of Science (WOS) database (1992–2024). Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (58), 225-241.
  30. Freddi, F., Galasso, C., Cremen, G., Dall’Asta, A., Di Sarno, L., Giaralis, A., Gutiérrez-Urzúa, F., Málaga-Chuquitaype, C., Mitoulis, S. A., Petrone, C., Sextos, A., Sousa, L., Tarbali, K., Tubaldi, E., Wardman, J., and Woo, G. (2021). Innovations in earthquake risk reduction for resilience: Recent advances and challenges. International Journal of Disaster Risk Reduction, 60(102267), 1-31. https://doi.org/10.1016/j.ijdrr.2021.102267
  31. Guleria, D., and Kaur, G. (2021). Bibliometric analysis of ecopreneurship using VOSviewer and RStudio Bibliometrix, 1989–2019. Library Hi Tech, 39(4), 1001–1024. https://doi.org/10.1108/LHT-09-2020-0218
  32. Habib, A., Alnaemi, A., and Habib, M. (2025). Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities. Smart and Sustainable Built Environment, 14(5), 1513–1537. https://doi.org/10.1108/SASBE-12-2023-0376
  33. Ilham, Waoma, S., Judijanto, L., Izmuddin, I., Al-Amin, Zuwardi, and Wati, F. S. (2024). A bibliometric analysis of halal production in relation to halal authentication (database Scopus 2013-2023). Revista De Gestão Social E Ambiental, 18(6), 1-26. https://doi.org/10.24857/rgsa.v18n6-030
  34. Imran, M., Ofli, F., Caragea, D., and Torralba, A. (2020). Using AI and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Information Processing and Management, 57(102261), 1-9. https://doi.org/10.1016/j.ipm.2020.102261
  35. Jo, B. W., Khan, R. M. A., and Lee, Y.-S. (2018). Hybrid blockchain and Internet of Things network for underground structure health monitoring. Sensors, 18(4268), 1-18. https://doi.org/10.3390/s18124268
  36. Ju, S., Park, H., Son, S., Kim, H., Park, Y., and Park, Y. (2024). Blockchain-assisted secure and lightweight authentication scheme for multi-server Internet of Drones environments. Mathematics, 12(3965), 1-26. https://doi.org/10.3390/math12243965
  37. Jung, D., Tran Tuan, V., Quoc Tran, D., Park, M., and Park, S. (2020). Conceptual framework of an intelligent decision support system for smart city disaster management. Applied Sciences, 10(666), 1-13. https://doi.org/10.3390/app10020666
  38. Kadıoğlu, M. (2020). Afet yönetimi: Beklenilmeyeni beklemek, en kötüsünü yönetmek. T.C. Marmara Belediyeler Birliği Yayınları.
  39. Karaduman, Ö. (2025). Towards integrated disaster management: A review of AI, blockchain, and digital twin technologies for smart resilience. International Journal of Innovative Engineering Applications, 9(2), 211–222. https://doi.org/10.46460/ijiea.1768923
  40. Kaswan, K. S., Dhatterwal, J. S., and Kumar, K. (2021). Blockchain of Internet of Things-based earthquake alarming system in smart cities. In P. Tomar (Ed.), Integration and implementation of the Internet of Things through cloud computing (pp. 272–287). IGI Global. https://doi.org/10.4018/978-1-7998-6981-8.ch014
  41. Kemeç, A., and Altınay, A. T. (2023). Sustainable energy research trend: A bibliometric analysis using VOSviewer, RStudio Bibliometrix, and CiteSpace software tools. Sustainability, 15(3618), 1-21. https://doi.org/10.3390/su15043618
  42. Korkmaz, M., Akyildiz, Y. E., Demirkesen, S., Toprak, S., Nowak, P., and Ciftci, B. (2025). A digital twin approach to sustainable disaster management: Case of Çayırova. Sustainability, 17(9626), 1-24. https://doi.org/10.3390/su17219626
  43. Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I. and Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
  44. Krichen, M., Abdalzaher, M. S., Elwekeil, M., and Fouda, M. M. (2023). Managing natural disasters: An analysis of technological advancements, opportunities, and challenges. Internet of Things and Cyber-Physical Systems, 4, 99–109. https://doi.org/10.1016/j.iotcps.2023.09.002
  45. Kurnaz, A. (2026). Turizm ve iklim değişikliği ilişkisinin 27 yıllık evrimi: VOSviewer ve RStudio tabanlı bütüncül bir analiz. Nevşehir Hacı Bektaş Veli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16(1). 268-296. https://doi.org/10.30783/nevsosbilen.1827131
  46. Kurnaz, A., and Büyükipekci, S. (2025). Turizmde yeşil teknoloji araştırmalarının evrimi: Bibliyometrik bir analiz (2007–2025). Sinop-e: Turizm Araştırmaları Dergisi, 2(2), 196-220.
  47. Lis, A. (2018). Keywords co-occurrence analysis of research on sustainable enterprise and sustainable organisation. Journal of Corporate Responsibility and Leadership, 5(2), 47–66.
  48. McAllister, J. T., Lennertz, L., and Atencio Mojica, Z. (2022). Mapping a discipline: A guide to using VOSviewer for bibliometric and visual analysis. Science & Technology Libraries, 41(3), 319–348. https://doi.org/10.1080/0194262X.2021.1991547
  49. McLoughlin, D. (1985). A framework for integrated emergency management. Public Administration Review, 45, 165–172. https://doi.org/10.2307/3135011
  50. Mosavi, A., Öztürk, P., and Chau, K.-W. (2018). Flood prediction using machine learning models: A literature review. Water, 10(1536), 1-40. https://doi.org/10.3390/w10111536
  51. National Research Council. (2007). Improving disaster management: The role of IT in mitigation, preparedness, response, and recovery. The National Academies Press. https://doi.org/10.17226/11824
  52. Neelam, S., and Sood, S. K. (2021). A scientometric review of global research on smart disaster management. IEEE Transactions on Engineering Management, 68(1), 317–329. https://doi.org/10.1109/TEM.2020.2972288
  53. Passas, I. (2024). Bibliometric analysis: The main steps. Encyclopedia, 4(2), 1014–1025.
  54. Patekhede, A., Warule, S., Rathod, S., Sultan, S., and Pimprale, V. (2025). A smart disaster management system for emergency hospital allocation. In Proceedings of the 2025 International Conference on Modern Sustainable Systems (CMSS) (pp. 229–238). IEEE. https://doi.org/10.1109/CMSS66566.2025.11182469
  55. Peker, I., Ar, I. M., Erol, I., and Searcy, C. (2023). Leveraging blockchain in response to a pandemic through disaster risk management: An IF-MCDM framework. Operations Management Research, 16, 642–667. https://doi.org/10.1007/s12063-022-00340-1
  56. Peters, R., and de Albuquerque, J. P. (2015). Investigating images as indicators for relevant social media messages in disaster management. In L. Palen, M. Büscher, T. Comes, and A. Hughes (Eds.), Proceedings of the ISCRAM 2015 Conference (pp. 1-8), Kristiansand, Norway.
  57. Saini, K., Kalra, S., and Sood, S. K. (2022). Disaster emergency response framework for smart buildings. Future Generation Computer Systems, 131, 106–120. https://doi.org/10.1016/j.future.2022.01.015
  58. Şengün, H. (2007). Afet yönetim sistemi ve Marmara depremi sonrası yaşanan sorunlar. [Doctoral dissertation]. Ankara University.
  59. Shahat, E., Hyun, C. T., and Yeom, C. (2020). Conceptualizing smart disaster governance: An integrative conceptual framework. Sustainability, 12(9536), 1-17. https://doi.org/10.3390/su12229536
  60. Sharma, K., Anand, D., Sabharwal, M., Tiwari, P. K., Cheikhrouhou, O., and Frikha, T. (2021). A disaster management framework using Internet of Things-based interconnected devices. Mathematical Problems in Engineering, 2021(9916440), 1-21. https://doi.org/10.1155/2021/9916440
  61. Sharma, P., Kumawat, K., Shrivastava, D., and Gupta, S. (2022). The use of AI in disaster management and predictive modeling. Journal of Nonlinear Analysis and Optimization, 13(1), 1–5. https://doi.org/10.36893/JNAO.2022.V13I02.036-040
  62. Singh, D. K., and Hoskere, V. (2023). Post disaster damage assessment using ultra-high-resolution aerial imagery with semi-supervised transformers. Sensors, 23(8235), 1-18. https://doi.org/10.3390/s23198235
  63. Singla, A., and Agrawal, R. (2024). DisDSS: A novel web-based smart disaster management system for determining the nature of a social media message for decision-making using deep learning: Case study of COVID-19. Global Knowledge, Memory and Communication, 73(8/9), 1044–1065. https://doi.org/10.1108/GKMC-07-2022-0180
  64. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
  65. Sukmaningsih, D. W., Suparta, W., Trisetyarso, A., Abbas, B. S., and Kang, C. H. (2020). Proposing smart disaster management in urban areas. In M. Huk, M. Maleszka, and E. Szczerbicki (Eds.), Intelligent information and database systems: Recent developments (Studies in Computational Intelligence, Vol. 830), (pp. 3-16). Springer. https://doi.org/10.1007/978-3-030-14132-5_1
  66. Sun, W., Bocchini, P., and 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
  67. Tehrany, M. S., Pradhan, B., and Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 29, 1149-1165. https://doi.org/10.1007/s00477-015-1021-9
  68. Terpstra, T., Stronkman, R., de Vries, A., and Paradies, G. L. (2012). Towards a real-time Twitter analysis during crises for operational crisis management. In Proceedings of the 9th International ISCRAM Conference, (pp. 1-9). Vancouver, Canada.
  69. Tien Bui, D., Ho, T.-C., Pradhan, B., Pham, B.-T., Nhu, V.-H., and Revhaug, I. (2016). GIS-based modeling of rainfall-induced landslides using data mining-based ensemble frameworks. Environmental Earth Sciences, 75(1101), 1-22. https://doi.org/10.1007/s12665-016-5919-4
  70. Tsai, C. H., Huang, C., Chen, Y. C., Zendejas, E., Krafka, S., and Zendejas, J. (2024). Co-design smart disaster management systems with indigenous communities. Digital Government: Research and Practice, 5(3), 1–21. https://doi.org/10.1145/3660643
  71. Van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  72. Van Eck, N. J., and Waltman, L. (2023). VOSviewer manual. Leiden University.
  73. Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. (2010). Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1079–1088). https://doi.org/10.1145/1753326.1753486
  74. Vitoriano, B., Ortuño, M. T., Tirado, G., and Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189–208. https://doi.org/10.1007/s10898-010-9603-z
  75. Wessel, D., Holtz, J., and König, F. (2021). Practice report ‘smart disaster management’: Combining smart city data and citizen participation to increase disaster resilience. i-com, 20(2), 177–193. https://doi.org/10.1515/icom-2021-0016
  76. Wibowo, A., Amri, I., Surahmat, A., and Rusdah, R. (2025). Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review. Jàmbá: Journal of Disaster Risk Studies, 17(1), 1-9. https://doi.org/10.4102/jamba.v17i1.1776
  77. Xi, W., and Suhaiza, Z. (2025). Alternative food networks in supply chains: A bibliometric analysis using RStudio and VOSviewer (1989–2024). Waste Management Bulletin, 3(3), 1-16. https://doi.org/10.1016/j.wmb.2025.100215
  78. Xu, J., Liu, H., and Han, Q. (2021). Blockchain technology and smart contract for civil structural health monitoring systems. Computer-Aided Civil and Infrastructure Engineering, 36(10), 1288–1305. https://doi.org/10.1111/mice.12666
  79. Yaman, M., and Düger, Y. (2017). Afet yönetiminde kavramsal çerçeve ve Türkiye’de afet yönetiminin tarihsel gelişimi. In Ö. Önder and M. Yaman (Eds.), Afet yönetimi (pp. 1–26). Ekin yayınevi.
  80. Yang, M., Cho, T. Y., Liu, X., and Dai, Y. (2023). Research status and development direction of child-friendly cities: A bibliometric analysis based on VOSviewer. Journal of Asian Architecture and Building Engineering, 22(1), 1–10. https://doi.org/10.1080/13467581.2023.2292082
  81. Yu, D., and He, Z. (2022). Digital twin-driven intelligent disaster prevention and mitigation for infrastructure: Advances, challenges, and opportunities. Natural Hazards, 112, 1–36. https://doi.org/10.1007/s11069-021-05190-x
  82. Yu, M., Yang, C., and Li, Y. (2018). Big data in natural disaster management: A review. Geosciences, 8(165), 1-26. https://doi.org/10.3390/geosciences8050165
  83. Zitt, M., and Bassecoulard, E. (1994). Development of a method for detection and trend analysis of research fronts built by lexical or co-citation analysis. Scientometrics, 30(1), 333–351. https://doi.org/10.1007/BF02017232
  84. Zhang, X., Zhang, M., and Tian, X. (2021). Real-time earthquake early warning with deep learning: Application to the 2016 M 6.0 Central Apennines earthquake. Geophysical Research Letters, 48, 1-10. https://doi.org/10.1029/2020GL089394
  85. Zhao, F., and Zhang, C. (2020). Building damage evaluation from satellite imagery using deep learning. In Proceedings of the IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) (pp. 82–89). IEEE. https://doi.org/10.1109/IRI49571.2020.00020
  86. Zhou, C., Yin, K., Cao, Y., Ahmed, B., Li, Y., Catani, F., and Pourghasemi, H. R. (2018). Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers and Geosciences, 112, 23–37. https://doi.org/10.1016/j.cageo.2017.11.019