Araştırma Makalesi
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Artificial Intelligence in Disaster Management: Approaches, Methods and Applications

Yıl 2024, Cilt: 6 Sayı: 2, 610 - 627
https://doi.org/10.46464/tdad.1532261

Öz

In disaster management, new methods, techniques, and approaches are being developed every day. Among these new methods and techniques, artificial intelligence has gained significant importance, making its presence felt in nearly every field today. This study addresses disaster management and its approaches in general, with a specific focus on artificial intelligence in disaster management. The aim of the study is to reveal the importance and potential of artificial intelligence in disaster management with current developments and examples from around the world. The main claim of the study is that with artificial intelligence, there has been a transformation from traditional disaster management understanding to artificial intelligence-supported technological disaster management understanding. In the study, it was concluded that artificial intelligence has a strong potential in disaster management, is adaptable to every stage of disaster management, its use is becoming increasingly widespread and brings an up-to-date perspective to disaster management.

Kaynakça

  • AIDR, 2024. AIDR: Artificial Intelligence for Digital Response, Erişim adresi: https://aidr.qcri.org/.
  • Alexa L., Pîslaru M., Avasilcai S., 2022. From Industry 4.0 to Industry 5.0: An Overview of European Union Enterprises, (In: Sustainability and Innovation in Manufacturing Enterprises, Editor: A. Draghici, L. Ivascu), 221-231.
  • Bali R., 2024. Disaster Management Cycle, Asian Journal of Geographical Research, 7(1), 85-93.
  • Basher R., 2006. Global Early Warning Systems for Natural Hazards: Systematic and People-Centred, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2167-2182.
  • Bennett S., 2012. Innovative Thinking in Risk, Crisis and Disaster Management, Gower Publishing Limited, Farnham, 287 p.
  • Bentzen J., 2019. Acts of God? Religiosity and Natural Disasters Across Subnational World Districts, The Economic Journal, 129(622), 2295–2321.
  • Bharti U., Deepali B., Batra H., Lalit S., Lalit S., Gangwani A., 2020. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 10-12, June 2020, NewJersey-USA, Erişim Adresi: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9137944.
  • Bhattacharya T., 2012. Disaster Science and Management. Tata McGraw Hill Education Private Limited, New Delhi, India, 184 p.
  • Büyükkaracığan N., 2016. Türkiye'de Yerel Yönetimlerde Kriz ve Afet Yönetim Çalışmalarının Mevzuat Açısından Değerlendirilmesi, Selçuk Üniversitesi Sosyal ve Teknik Araştırmalar Dergisi, 12, 195-219.
  • Carayannis E., Joanna Morawska J., 2022. The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities, Journal of the Knowledge Economy, 13, 3445-3471.
  • Chaudhary M., Piracha A., 2021. Natural Disasters - Origins, Impacts, Management, Encyclopedia, 1(4), 1101-1131.
  • Coetzee C., 2010. The Development, Implementation and Transformation of the Disaster Management Cycle. Master's Thesis, North-West University Master's Thesis. Potchefstroom, 131 p.
  • Couvat E., 2024. A Historical Overview of AI Winter Cycles, Erişim Adresi: https://www.perplexity.ai/page/History-of-AI-A8daV1D9Qr2STQ6tgLEOtgi.
  • DeepGlobe, 2018. DeepGlobe-CVPR18, Erişim adresi: http://deepglobe.org/challenge.html.
  • EarthX, 2020. EarthX: The First Earthquake Monitoring System Driven by AI. Erişim adresi: https://en.ustc.edu.cn/info/1007/1158.htm.
  • European Commission, 2021. Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, Publications Office of the European Union, https://doi.org/10.2777/308407.
  • Germain M.-L., Gernier R.S., 2021. Expertise at Work: Current and Emerging Trends, Palgrave Macmillan, Cham, 254 p.
  • Holdsworth J., Scapicchio M., 2024. What is Deep Learning? Erişim adresi: https://www.ibm.com/topics/deep-learning.
  • IBM Data and AI Team, 2023. AI Versus Machine Learning Versus Deep Learning Versus Neural Networks: What’s the Difference? Erişim adresi: https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks.
  • Karaca M., 2023. Yapay Zekâ Tabanlı Stratejik Afet Yönetimi: Verilerin Tam Kullanımı. Afet ve Risk Dergisi, 6(4), 1312-133.
  • Küçük K., Bayılmış C., Sönmez A., Kaçar S., 2019. IoT Teknolojilerini Kullanan Afet Sonrası Yönetim Sistemi, Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 7(2), 298-305.
  • Lamsal R., Kumar T., 2020. Artificial Intelligence and Early Warning Systems, (In: AI and Robotics in Disaster Studies, Editor: T. Kumar, S. Keshav, Palgrave Macmillan, Singapore, 267p.), 13-32.
  • Larson E., 2021. How AI Changed: In a Very Big Way, Around the Year 2000 (Podcast), Erişim adresi: https://mindmatters.ai/2021/12/how-ai-changed-in-a-very-big-way-around-the-year-2000/.
  • Li N., Sun N., Cao C., Hou S., Gong Y., 2022. Review on Visualization Technology in Simulation Training System for Major Natural Disasters, Natural Hazards, 112, 1851-1882.
  • McEntire D., Colleen Gilmore C., Peters E., 2010. Addressing Vulnerability Through an Integrated Approach, International Journal of Disaster Resilience in the Built Environment, 1(1), 50-64.
  • Mei X., Lee H.-C., Diao K.-Y., Bin L., Liu C., 2020. Artificial Intelligence Enabled Rapid Diagnosis of Patients with COVID-19, Nature Medicine, 26, 1224-1228.
  • Morçöl G., 2012. A Complexity Theory for Public Policy. Routledge, New York, 324 p.
  • Munakata T., 1994. Commercial and Industrial AI. Communications of the ACM, 37(3), 23-26, https://doi.org/10.1145/175247.175248.
  • Murugaiah S., 2021. Artificial Intelligence's Impact on Our Everyday Lives, (In: Learning Outcomes of Classroom Research, Editor: J. Karthikeyan, S.H. Ting, N. Yu-Jin, Nuovo Publication, New Delhi, 463 p.), 1-11.
  • Muthukrishnan N., Maleki F., Ovens K., Reinhold C., Forghani B., Forghani R., 2020. Brief History of Artificial Intelligence, Neuroimaging Clinics of North America, 30(4), 393-399.
  • Ouerhani N., Maalel A., Ghéze H., 2020. SPeCECA: A Smart Pervasive Chatbot for Emergency Case Assistance Based On Cloud Computing, Cluster Computing, 23, 2471–2482.
  • Partigöç N.S., 2022. Afet Risk Yönetiminde Yapay Zekâ Kullanımının Rolü, Bilişim Teknolojileri Dergisi, 15(4), 401- 411, https://doi.org/10.17671/gazibtd.1067831.
  • Rajaraman V., 2014. JohnMcCarthy: Father of artificial intelligence, Resonance Journal of Science Education, 19, 198-207.
  • Rana G., Sharma R., 2019. Emerging Human Resource Management Practices in Industry 4.0, Strategic HR Review, 18(4), 176-181.
  • Samuel A.L., 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210-229.
  • Sarker I., 2021. Machine Learning: Algorithms, Real-World Applications and Research Directions, SN Computer Science, 2(160), 1-21.
  • Simões-Marques M., Correia A., Nunes I., 2020. Design of Disaster Management Intelligent System: A Review of the Applied UCD Methods, Advances in Human Factors and Systems Interaction Conference, 16-20 July 2020, Florida-USA, Erişim Adresi: https://link.springer.com/book/10.1007/978-3-030-51369-6.
  • Singh A., 2020. Introduction: Enhancing Capacity to Manage, (In: AI and Robotics in Disaster Studies, Editor: T. Kumar, K. Sud, Singapore, 263 p.), 1-10.
  • Sparkes M., 2023. DeepMind AI Predicts the Weather, New Scientist, 260(3465), 9.
  • Sun W., Bocchini P., Davison B., 2020. Applications of Artificial Intelligence for Disaster Management, Natural Hazards, 103, 2631-2689.
  • Şahin Ş., Üçgül İ., 2019. Türkiye’de Afet Yönetimi ve İş Sağlığı Güvenliği, Afet ve Risk Dergisi, 2(1), 43-63.
  • Şen G., 2021. An Overview of Disaster Resilience, Turkish Journal of Health Science and Life, 4(3), 106-115.
  • Taye M., 2023. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions, Computers, 12(5), 1-26.
  • Turing A.M., 1950. Computing Machinery and Intelligence, Mind, LIX(236), 433-460, https://doi.org/10.1093/mind/LIX.236.433.
  • Weichselgartner J., Bertens J., 2000. Natural disasters: Acts of God, Nature or Society? On the Social Relation to Natural Hazards (In: Risk Analysis II, Editor: C. Brebbia, Southampton, 584 p.), 85-94.
  • Weizenbaum J., 1966. ELIZA: A Computer Program for the Study of Natural Language Communication Between Man and Machine, Communications of the ACM, 9(1), 36-45.
  • Wooldridge M., 1996. A Brief History of Artificial Intelligence: What It is, Where We Are and Where We Are Going?, Flatiron Books, New York, 272 p.
  • Yang Y., Guo H., Chen L., Xiao L., Mingyun G., Pan W., 2020. Multiattribute Decision Making for the Assessment of Disaster Resilience in the Three Gorges Reservoir Area, Ecology & Society, 25(2), 1-14.

Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar

Yıl 2024, Cilt: 6 Sayı: 2, 610 - 627
https://doi.org/10.46464/tdad.1532261

Öz

Afet yönetiminde her geçen gün yeni yöntemler, teknikler ve yaklaşımlar geliştirilmektedir. Bu yeni yöntem ve teknikler arasında günümüzde hemen hemen her alanda kendini gösteren yapay zekâ önemli bir konuma erişmiştir. Çalışma buradan hareketle genel olarak afet yönetimi ve afet yönetimindeki yaklaşımları, daha spesifik olarak da afet yönetiminde yapay zekâyı ele almaktadır. Çalışmanın amacı afet yönetiminde yapay zekânın önemini, potansiyelini, dünyadaki güncel gelişmeler ve örnekler eşliğinde ortaya koymaktır. Çalışmanın temel iddiası, yapay zekâ ile birlikte geleneksel afet yönetimi anlayışından yapay zekâ destekli teknolojik afet yönetimi anlayışına doğru bir dönüşüm yaşandığı şeklindedir. Çalışmada afet yönetiminde yapay zekânın güçlü bir potansiyele sahip olduğu, afet yönetiminin her aşamasına uyarlanabilir olduğu, kullanımının giderek yaygınlaştığı ve afet yönetimine güncel bir bakış açısı getirdiği sonucuna ulaşılmıştır.

Etik Beyan

Çalışma etik kurul izni gerektirmemektedir.

Teşekkür

Derginin var olmasına, yayın hayatını sürdürmesine etki ve katkı sunan herkese Teşekkürlerimi sunarım.

Kaynakça

  • AIDR, 2024. AIDR: Artificial Intelligence for Digital Response, Erişim adresi: https://aidr.qcri.org/.
  • Alexa L., Pîslaru M., Avasilcai S., 2022. From Industry 4.0 to Industry 5.0: An Overview of European Union Enterprises, (In: Sustainability and Innovation in Manufacturing Enterprises, Editor: A. Draghici, L. Ivascu), 221-231.
  • Bali R., 2024. Disaster Management Cycle, Asian Journal of Geographical Research, 7(1), 85-93.
  • Basher R., 2006. Global Early Warning Systems for Natural Hazards: Systematic and People-Centred, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2167-2182.
  • Bennett S., 2012. Innovative Thinking in Risk, Crisis and Disaster Management, Gower Publishing Limited, Farnham, 287 p.
  • Bentzen J., 2019. Acts of God? Religiosity and Natural Disasters Across Subnational World Districts, The Economic Journal, 129(622), 2295–2321.
  • Bharti U., Deepali B., Batra H., Lalit S., Lalit S., Gangwani A., 2020. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 10-12, June 2020, NewJersey-USA, Erişim Adresi: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9137944.
  • Bhattacharya T., 2012. Disaster Science and Management. Tata McGraw Hill Education Private Limited, New Delhi, India, 184 p.
  • Büyükkaracığan N., 2016. Türkiye'de Yerel Yönetimlerde Kriz ve Afet Yönetim Çalışmalarının Mevzuat Açısından Değerlendirilmesi, Selçuk Üniversitesi Sosyal ve Teknik Araştırmalar Dergisi, 12, 195-219.
  • Carayannis E., Joanna Morawska J., 2022. The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities, Journal of the Knowledge Economy, 13, 3445-3471.
  • Chaudhary M., Piracha A., 2021. Natural Disasters - Origins, Impacts, Management, Encyclopedia, 1(4), 1101-1131.
  • Coetzee C., 2010. The Development, Implementation and Transformation of the Disaster Management Cycle. Master's Thesis, North-West University Master's Thesis. Potchefstroom, 131 p.
  • Couvat E., 2024. A Historical Overview of AI Winter Cycles, Erişim Adresi: https://www.perplexity.ai/page/History-of-AI-A8daV1D9Qr2STQ6tgLEOtgi.
  • DeepGlobe, 2018. DeepGlobe-CVPR18, Erişim adresi: http://deepglobe.org/challenge.html.
  • EarthX, 2020. EarthX: The First Earthquake Monitoring System Driven by AI. Erişim adresi: https://en.ustc.edu.cn/info/1007/1158.htm.
  • European Commission, 2021. Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, Publications Office of the European Union, https://doi.org/10.2777/308407.
  • Germain M.-L., Gernier R.S., 2021. Expertise at Work: Current and Emerging Trends, Palgrave Macmillan, Cham, 254 p.
  • Holdsworth J., Scapicchio M., 2024. What is Deep Learning? Erişim adresi: https://www.ibm.com/topics/deep-learning.
  • IBM Data and AI Team, 2023. AI Versus Machine Learning Versus Deep Learning Versus Neural Networks: What’s the Difference? Erişim adresi: https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks.
  • Karaca M., 2023. Yapay Zekâ Tabanlı Stratejik Afet Yönetimi: Verilerin Tam Kullanımı. Afet ve Risk Dergisi, 6(4), 1312-133.
  • Küçük K., Bayılmış C., Sönmez A., Kaçar S., 2019. IoT Teknolojilerini Kullanan Afet Sonrası Yönetim Sistemi, Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 7(2), 298-305.
  • Lamsal R., Kumar T., 2020. Artificial Intelligence and Early Warning Systems, (In: AI and Robotics in Disaster Studies, Editor: T. Kumar, S. Keshav, Palgrave Macmillan, Singapore, 267p.), 13-32.
  • Larson E., 2021. How AI Changed: In a Very Big Way, Around the Year 2000 (Podcast), Erişim adresi: https://mindmatters.ai/2021/12/how-ai-changed-in-a-very-big-way-around-the-year-2000/.
  • Li N., Sun N., Cao C., Hou S., Gong Y., 2022. Review on Visualization Technology in Simulation Training System for Major Natural Disasters, Natural Hazards, 112, 1851-1882.
  • McEntire D., Colleen Gilmore C., Peters E., 2010. Addressing Vulnerability Through an Integrated Approach, International Journal of Disaster Resilience in the Built Environment, 1(1), 50-64.
  • Mei X., Lee H.-C., Diao K.-Y., Bin L., Liu C., 2020. Artificial Intelligence Enabled Rapid Diagnosis of Patients with COVID-19, Nature Medicine, 26, 1224-1228.
  • Morçöl G., 2012. A Complexity Theory for Public Policy. Routledge, New York, 324 p.
  • Munakata T., 1994. Commercial and Industrial AI. Communications of the ACM, 37(3), 23-26, https://doi.org/10.1145/175247.175248.
  • Murugaiah S., 2021. Artificial Intelligence's Impact on Our Everyday Lives, (In: Learning Outcomes of Classroom Research, Editor: J. Karthikeyan, S.H. Ting, N. Yu-Jin, Nuovo Publication, New Delhi, 463 p.), 1-11.
  • Muthukrishnan N., Maleki F., Ovens K., Reinhold C., Forghani B., Forghani R., 2020. Brief History of Artificial Intelligence, Neuroimaging Clinics of North America, 30(4), 393-399.
  • Ouerhani N., Maalel A., Ghéze H., 2020. SPeCECA: A Smart Pervasive Chatbot for Emergency Case Assistance Based On Cloud Computing, Cluster Computing, 23, 2471–2482.
  • Partigöç N.S., 2022. Afet Risk Yönetiminde Yapay Zekâ Kullanımının Rolü, Bilişim Teknolojileri Dergisi, 15(4), 401- 411, https://doi.org/10.17671/gazibtd.1067831.
  • Rajaraman V., 2014. JohnMcCarthy: Father of artificial intelligence, Resonance Journal of Science Education, 19, 198-207.
  • Rana G., Sharma R., 2019. Emerging Human Resource Management Practices in Industry 4.0, Strategic HR Review, 18(4), 176-181.
  • Samuel A.L., 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210-229.
  • Sarker I., 2021. Machine Learning: Algorithms, Real-World Applications and Research Directions, SN Computer Science, 2(160), 1-21.
  • Simões-Marques M., Correia A., Nunes I., 2020. Design of Disaster Management Intelligent System: A Review of the Applied UCD Methods, Advances in Human Factors and Systems Interaction Conference, 16-20 July 2020, Florida-USA, Erişim Adresi: https://link.springer.com/book/10.1007/978-3-030-51369-6.
  • Singh A., 2020. Introduction: Enhancing Capacity to Manage, (In: AI and Robotics in Disaster Studies, Editor: T. Kumar, K. Sud, Singapore, 263 p.), 1-10.
  • Sparkes M., 2023. DeepMind AI Predicts the Weather, New Scientist, 260(3465), 9.
  • Sun W., Bocchini P., Davison B., 2020. Applications of Artificial Intelligence for Disaster Management, Natural Hazards, 103, 2631-2689.
  • Şahin Ş., Üçgül İ., 2019. Türkiye’de Afet Yönetimi ve İş Sağlığı Güvenliği, Afet ve Risk Dergisi, 2(1), 43-63.
  • Şen G., 2021. An Overview of Disaster Resilience, Turkish Journal of Health Science and Life, 4(3), 106-115.
  • Taye M., 2023. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions, Computers, 12(5), 1-26.
  • Turing A.M., 1950. Computing Machinery and Intelligence, Mind, LIX(236), 433-460, https://doi.org/10.1093/mind/LIX.236.433.
  • Weichselgartner J., Bertens J., 2000. Natural disasters: Acts of God, Nature or Society? On the Social Relation to Natural Hazards (In: Risk Analysis II, Editor: C. Brebbia, Southampton, 584 p.), 85-94.
  • Weizenbaum J., 1966. ELIZA: A Computer Program for the Study of Natural Language Communication Between Man and Machine, Communications of the ACM, 9(1), 36-45.
  • Wooldridge M., 1996. A Brief History of Artificial Intelligence: What It is, Where We Are and Where We Are Going?, Flatiron Books, New York, 272 p.
  • Yang Y., Guo H., Chen L., Xiao L., Mingyun G., Pan W., 2020. Multiattribute Decision Making for the Assessment of Disaster Resilience in the Three Gorges Reservoir Area, Ecology & Society, 25(2), 1-14.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Politika ve Yönetim (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Cem Angın 0000-0002-2813-5586

Erken Görünüm Tarihi 5 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 13 Ağustos 2024
Kabul Tarihi 18 Kasım 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 2

Kaynak Göster

APA Angın, C. (2024). Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. Türk Deprem Araştırma Dergisi, 6(2), 610-627. https://doi.org/10.46464/tdad.1532261
AMA Angın C. Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. TDAD. Aralık 2024;6(2):610-627. doi:10.46464/tdad.1532261
Chicago Angın, Cem. “Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler Ve Uygulamalar”. Türk Deprem Araştırma Dergisi 6, sy. 2 (Aralık 2024): 610-27. https://doi.org/10.46464/tdad.1532261.
EndNote Angın C (01 Aralık 2024) Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. Türk Deprem Araştırma Dergisi 6 2 610–627.
IEEE C. Angın, “Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar”, TDAD, c. 6, sy. 2, ss. 610–627, 2024, doi: 10.46464/tdad.1532261.
ISNAD Angın, Cem. “Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler Ve Uygulamalar”. Türk Deprem Araştırma Dergisi 6/2 (Aralık 2024), 610-627. https://doi.org/10.46464/tdad.1532261.
JAMA Angın C. Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. TDAD. 2024;6:610–627.
MLA Angın, Cem. “Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler Ve Uygulamalar”. Türk Deprem Araştırma Dergisi, c. 6, sy. 2, 2024, ss. 610-27, doi:10.46464/tdad.1532261.
Vancouver Angın C. Afet Yönetiminde Yapay Zekâ: Yaklaşımlar, Yöntemler ve Uygulamalar. TDAD. 2024;6(2):610-27.

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