Afet Yönetiminin Dijital Dönüşümü: Afet Bilişimi, Büyük Sosyal Veri ve Yapay Zekânın Bütünleşik Rolü

Öz

Bu çalışmada, afet yönetiminin dijital dönüşümünde etkili olan “afet bilişimi”, “büyük sosyal veri” ve “yapay zekâ” yaklaşımlarının kuramsal temellerinin incelenmesi, birbirleriyle etkileşim biçimlerinin değerlendirilmesi ve afet yönetimine sundukları katkıların tartışılması amaçlanmıştır. Web 2.0 teknolojilerinin gelişimiyle artış gösteren kullanıcı kaynaklı verilerin afet bilişiminin dönüşümünde önemli bir bileşen hâline geldiği ve aynı zamanda büyük sosyal veri olgusunun gelişimine zemin hazırladığı görülmektedir. Mevcut literatür, sosyal medya platformlarından elde edilen büyük ve çeşitli veri setlerinin afet öncesi risk farkındalığının geliştirilmesi, afet sırasında durumsal farkındalığın artırılması ve afet sonrasında iyileştirme faaliyetlerinin desteklenmesi açısından önemli fırsatlar sunabileceğine işaret etmektedir. Son yıllarda bu verilerin işlenmesinde makine öğrenmesi, derin öğrenme ve doğal dil işleme gibi yapay zekâ tekniklerinin kullanımının, afet yönetiminde veri odaklı ve tahmine dayalı yaklaşımların gelişimini desteklediği değerlendirilmektedir. Bu çalışma, afet bilişimi çerçevesinde büyük sosyal veri ve yapay zekâ entegrasyonunun, afet yönetiminin çeşitli aşamalarında bilgiye dayalı karar alma süreçlerini güçlendirebileceğini göstermektedir. Sonuç olarak bu üç yaklaşımın kesişiminin, modern afet yönetimi uygulamalarında toplumsal dayanıklılığı artırmaya yönelik bütüncül bir yaklaşım oluşturma potansiyeli taşıdığı öngörülmektedir.

Anahtar Kelimeler

• Afet Bilişimi
• Afet Yönetimi
• Büyük Sosyal Veri
• Dijital Dönüşüm
• Yapay Zekâ

Destekleyen Kurum

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Proje Numarası

324K713

Etik Beyan

Çalışma yazarları arasında herhangi bir çıkar çatışması bulunmamaktadır.

Teşekkür

Bu araştırma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 3005 destek programı kapsamında [324K713] numaralı proje ile desteklenmiştir. Teşekkür ederiz.

The Digital Transformation of Disaster Management: The Integrated Role of Disaster Informatics, Big Social Data and Artificial Intelligence

Öz

This study aims to examine the theoretical foundations of “disaster informatics,” “big social data,” and “artificial intelligence” approaches, which are instrumental in the digital transformation of disaster management, evaluate their interactions, and discuss their contributions to the field. It is observed that the proliferation of user-generated data, driven by the advancement of Web 2.0 technologies, has become a crucial component in the transformation of disaster informatics, concurrently paving the way for the phenomenon of "big social data." The current literature indicates that large and diverse datasets derived from social media platforms can offer significant opportunities for enhancing pre-disaster risk awareness, increasing situational awareness during a disaster, and supporting post-disaster recovery efforts. In recent years, the application of artificial intelligence techniques such as machine learning, deep learning, and natural language processing in analyzing this data has been considered to support the development of data-driven and predictive approaches in disaster management. This study demonstrates that integrating big social data and artificial intelligence within the framework of disaster informatics can strengthen informed decision-making processes across various stages of disaster management. Consequently, it is anticipated that the intersection of these three approaches holds the potential to establish a holistic approach aimed at enhancing societal resilience in modern disaster management practices.

Anahtar Kelimeler

• Artificial Intelligence
• Big Social Data
• Disaster Informatics
• Disaster Management
• Digital Transformation

Destekleyen Kurum

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Proje Numarası

324K713

Etik Beyan

There is no conflict of interest among the authors of the study.

Teşekkür

This research was supported by the The Scientific and Technological Research Council of Türkiye (TÜBİTAK) under grant number [324K713] as part of its 3005 support program. We are grateful for this support.

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