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CAN IMAGE PROCESSING BE USED AS AI/ML METHOD IN INTEGRATED DISASTER MANAGEMENT?

Yıl 2022, Cilt: 7 Sayı: IMISC2021 Special Issue, 116 - 131, 30.03.2022

Çiğdem Tarhan , Ahmet Selçuk Özgür , İlknur Teke , Murat Komesli

https://doi.org/10.54452/jrb.1025382
Cited By: 1

Öz

In Turkey, cities with a high building density and population have risks dur-ing earthquakes and other natural disasters. It is a difficult process to quickly determine the location of damaged houses after an emergency (earthquake, etc.) and to determine the regional density. At the same time, there are prob-lems in the process of directing resources to disaster areas according to the existing damage situations of public and non-governmental organizations. The scope of the study is to perform artificial intelligence-based damage as-sessment in a fast and effective manner with the application in the proposed system. Thus, damaged structures will be recorded in the database with loca-tion information and support will be given to all processes that will take place after the disaster.

Anahtar Kelimeler

Disaster Management Artificial Intelligence Image Processing Mobile App.

Kaynakça

  • BBC News, https://www.bbc.com/news/world-europe-54749509, last accessed 2021/05/07.
  • Erkal, T., Değerliyurt, M.: Türkiye’de afet yönetimi. Doğu Coğrafya Dergisi 14 (22), 147-164 (2011).
  • Fujita ve ark., “Damage Detection from Aerial Images via Convolutional Neural Networks”, 2017.
  • Google Colaboratory, https://colab.research.google.com/notebooks/welcome.ipynb?hl=tr, last accessed 2021/05/07.
  • Hoskere V, ve ark., “Towards Automated Post-Earthquake Inspections with Deep Learning-based Condition-Aware Models”, The 7th World Conference on Structural Control and Monitoring, 7WCSCM, July 22-25, 2018, Qingdao, China.
  • Ji, Min, Lanfa Liu, and Manfred Buchroithner. 2018. "Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake" Remote Sensing 10, no. 11: 1689. https://doi.org/10.3390/rs10111689
  • Kalantar, Bahareh, Naonori Ueda, Husam A.H. Al-Najjar, and Alfian A. Halin 2020. "Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images" Remote Sensing 12, no. 21: 3529. https://doi.org/10.3390/rs12213529.
  • Liou J.C., Duclervil S.R. A survey on the effectiveness of the secure software development life cycle models. In: Daimi K., Francia III G. (eds) INNOVATIONS IN CYBERSECURITY EDUCATION. Springer, Cham. https://doi.org/10.1007/978-3-030-50244-7_11 (2020).
  • Macit, İ.: Bütünleşik afet yönetim sistemleri için karar destek sistemi geliştirilmesi: mobil uygulama örneği. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi 2(1):23-41 (2018).
  • Memiş, L., Babaoğlu, C.: Afet yönetimi ve teknoloji.In: Yaman M., Çakır E. (eds.) FARKLI BOYUTLARIYLA AFET YÖNETIMI, Ankara: Nobel, pp. 163-178. (2020).
  • Moe, T.L., Pathranarakul, P.: An integrated approach to natural disaster management public project management and its critical success factors. Disaster Prevention and Management Vol. 15 No. 3, pp. 396-413. (2006) DOI 10.1108/09653560610669882. Nahata D. vd., “Post-Earthquake Assessment of Buildings Using Deep Learning”, 2019.
  • Nunavath, V., Goodwin, M.: The role of artificial intelligence in social media big data analytics for disaster management - initial results of a systematic literature review. 2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM), pp. 1-4 (2018). doi: 10.1109/ICT-DM.2018.8636388.
  • Onat, N.C., Kucukvar, M., Halog, A., Cloutier, S.: Systems thinking for life cycle sustainability assessment: a review of recent developments, applications, and future perspectives. Sustainability 9, 706 (2017) https://doi.org/10.3390/su9050706.
  • Robertson, B.W., Johnson, M., Murthy, D., Smith, W.R., Stephens, K.K.: Using a combination of human insights and ‘deep learning’ for real-time disaster communication. Progress in Disaster Science Volume 2 (2019) 100030 ISSN 2590-0617 https://doi.org/10.1016/j.pdisas.2019.100030.
  • Sakurai, M., Murayama, Y.: Information technologies and disaster management – Benefits and issues. Progress in Disaster Science, Volume 2, 100012, (2019) ISSN 2590-0617, https://doi.org/10.1016/j.pdisas.2019.100012.
  • Sinha A., Kumar, P., Rana, N.P., Islam, R. Dwivedi, Y.K.: Impact of internet of things (IoT) in disaster management: a task-technology fit perspective. Annals of Operations Research volume 283 issue 1-2, pages759–794 (2019) https://doi.org/10.1007/s10479-017-2658-1 Talley, J.W.: Disaster management in the digital age. IBM Journal of Research and Development 64(1/2), 1:1-1:5, (2020). doi: 10.1147/JRD.2019.2954412.
  • Tan, L., Guo, J., Mohanarajah, S. Zhou, K.: Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Natural Hazards (2020). https://doi.org/10.1007/s11069-020-04429-3.
  • Tang A., Wen A.: An intelligent simulation system for earthquake disaster assessment. Computers & Geosciences, 35, 871– 879 (2009) doi:10.1016/j.cageo.2008.03.003.
  • Türkiye İstatistik Kurumu, https://data.tuik.gov.tr/, last accessed 2021/05/07.
  • US Geological Survey, https://www.usgs.gov/, last accessed 2021/05/07.
  • Vyas, T., Desai, A.: Information technology for disaster management. PROCEEDINGS OF NATIONAL CONFERENCE INDIACOM-2007 (2007).
  • Westen, C.V.: Remote Sensing For Natural Disaster Management. International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000, (2000).
  • Y. LeCun, B. Boser, JS Denker, D. Henderson, “Backpropagation applied to handwritten zip code recognition”, Neural Comp., 1989, 1, 541–551.
  • Yu M., Yang C., Li Y.: Big data in natural disaster management: a review. Geosciences 8, 165 (2018) doi:10.3390/geosciences8050165.
  • Zhao, L., Li, H., Sun, Y., Huang, R., Hu, Q., Wang, J., Gao, F.: Planning emergency shelters for urban disaster resilience: an integrated location-allocation modeling approach. Sustainability 9, 2098 (2017). https://doi.org/10.3390/su9112098

GÖRÜNTÜ İŞLEME ENTEGRE AFET YÖNETİMİNDE YAPAY ZEKA YÖNTEMİ OLARAK KULLANILABİLİR Mİ?

Yıl 2022, Cilt: 7 Sayı: IMISC2021 Special Issue, 116 - 131, 30.03.2022

Çiğdem Tarhan , Ahmet Selçuk Özgür , İlknur Teke , Murat Komesli

https://doi.org/10.54452/jrb.1025382
Cited By: 1

Öz

Türkiye'de yapı yoğunluğu ve nüfusu yüksek olan şehirler deprem ve diğer doğal afetler sırasında risk taşımaktadır. Acil bir durum (deprem vb.) son-rasında hasar gören evlerin yerini hızlı bir şekilde belirlemek ve bölgesel yoğunluğunu belirlemek zor bir süreçtir. Aynı zamanda kamu ve sivil toplum kuruluşlarının mevcut hasar durumlarına göre kaynakların, afet bölgelerine yönlendirilmesi sürecinde sorunlar yaşanmaktadır. Çalışmanın kapsamı, önerilen sistemdeki uygulama ile yapay zeka tabanlı hasar tespitini hızlı ve etkin bir şekilde gerçekleştirmektir. Böylece hasarlı yapılar konum bilgisi ile veri tabanına kaydedilecek ve afet sonrası gerçekleşecek tüm süreçlere destek verilecektir.

Anahtar Kelimeler

Afet Yönetimi Yapay Zeka Görüntü İşleme Mobil Uygulama.

Kaynakça

  • BBC News, https://www.bbc.com/news/world-europe-54749509, last accessed 2021/05/07.
  • Erkal, T., Değerliyurt, M.: Türkiye’de afet yönetimi. Doğu Coğrafya Dergisi 14 (22), 147-164 (2011).
  • Fujita ve ark., “Damage Detection from Aerial Images via Convolutional Neural Networks”, 2017.
  • Google Colaboratory, https://colab.research.google.com/notebooks/welcome.ipynb?hl=tr, last accessed 2021/05/07.
  • Hoskere V, ve ark., “Towards Automated Post-Earthquake Inspections with Deep Learning-based Condition-Aware Models”, The 7th World Conference on Structural Control and Monitoring, 7WCSCM, July 22-25, 2018, Qingdao, China.
  • Ji, Min, Lanfa Liu, and Manfred Buchroithner. 2018. "Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake" Remote Sensing 10, no. 11: 1689. https://doi.org/10.3390/rs10111689
  • Kalantar, Bahareh, Naonori Ueda, Husam A.H. Al-Najjar, and Alfian A. Halin 2020. "Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images" Remote Sensing 12, no. 21: 3529. https://doi.org/10.3390/rs12213529.
  • Liou J.C., Duclervil S.R. A survey on the effectiveness of the secure software development life cycle models. In: Daimi K., Francia III G. (eds) INNOVATIONS IN CYBERSECURITY EDUCATION. Springer, Cham. https://doi.org/10.1007/978-3-030-50244-7_11 (2020).
  • Macit, İ.: Bütünleşik afet yönetim sistemleri için karar destek sistemi geliştirilmesi: mobil uygulama örneği. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi 2(1):23-41 (2018).
  • Memiş, L., Babaoğlu, C.: Afet yönetimi ve teknoloji.In: Yaman M., Çakır E. (eds.) FARKLI BOYUTLARIYLA AFET YÖNETIMI, Ankara: Nobel, pp. 163-178. (2020).
  • Moe, T.L., Pathranarakul, P.: An integrated approach to natural disaster management public project management and its critical success factors. Disaster Prevention and Management Vol. 15 No. 3, pp. 396-413. (2006) DOI 10.1108/09653560610669882. Nahata D. vd., “Post-Earthquake Assessment of Buildings Using Deep Learning”, 2019.
  • Nunavath, V., Goodwin, M.: The role of artificial intelligence in social media big data analytics for disaster management - initial results of a systematic literature review. 2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM), pp. 1-4 (2018). doi: 10.1109/ICT-DM.2018.8636388.
  • Onat, N.C., Kucukvar, M., Halog, A., Cloutier, S.: Systems thinking for life cycle sustainability assessment: a review of recent developments, applications, and future perspectives. Sustainability 9, 706 (2017) https://doi.org/10.3390/su9050706.
  • Robertson, B.W., Johnson, M., Murthy, D., Smith, W.R., Stephens, K.K.: Using a combination of human insights and ‘deep learning’ for real-time disaster communication. Progress in Disaster Science Volume 2 (2019) 100030 ISSN 2590-0617 https://doi.org/10.1016/j.pdisas.2019.100030.
  • Sakurai, M., Murayama, Y.: Information technologies and disaster management – Benefits and issues. Progress in Disaster Science, Volume 2, 100012, (2019) ISSN 2590-0617, https://doi.org/10.1016/j.pdisas.2019.100012.
  • Sinha A., Kumar, P., Rana, N.P., Islam, R. Dwivedi, Y.K.: Impact of internet of things (IoT) in disaster management: a task-technology fit perspective. Annals of Operations Research volume 283 issue 1-2, pages759–794 (2019) https://doi.org/10.1007/s10479-017-2658-1 Talley, J.W.: Disaster management in the digital age. IBM Journal of Research and Development 64(1/2), 1:1-1:5, (2020). doi: 10.1147/JRD.2019.2954412.
  • Tan, L., Guo, J., Mohanarajah, S. Zhou, K.: Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Natural Hazards (2020). https://doi.org/10.1007/s11069-020-04429-3.
  • Tang A., Wen A.: An intelligent simulation system for earthquake disaster assessment. Computers & Geosciences, 35, 871– 879 (2009) doi:10.1016/j.cageo.2008.03.003.
  • Türkiye İstatistik Kurumu, https://data.tuik.gov.tr/, last accessed 2021/05/07.
  • US Geological Survey, https://www.usgs.gov/, last accessed 2021/05/07.
  • Vyas, T., Desai, A.: Information technology for disaster management. PROCEEDINGS OF NATIONAL CONFERENCE INDIACOM-2007 (2007).
  • Westen, C.V.: Remote Sensing For Natural Disaster Management. International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000, (2000).
  • Y. LeCun, B. Boser, JS Denker, D. Henderson, “Backpropagation applied to handwritten zip code recognition”, Neural Comp., 1989, 1, 541–551.
  • Yu M., Yang C., Li Y.: Big data in natural disaster management: a review. Geosciences 8, 165 (2018) doi:10.3390/geosciences8050165.
  • Zhao, L., Li, H., Sun, Y., Huang, R., Hu, Q., Wang, J., Gao, F.: Planning emergency shelters for urban disaster resilience: an integrated location-allocation modeling approach. Sustainability 9, 2098 (2017). https://doi.org/10.3390/su9112098
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler
Yazarlar

Çiğdem Tarhan 0000-0002-5891-0635

Ahmet Selçuk Özgür 0000-0002-8475-2634

İlknur Teke 0000-0002-6383-4067

Murat Komesli 0000-0002-8240-5540

Erken Görünüm Tarihi 28 Mart 2022
Yayımlanma Tarihi 30 Mart 2022
Gönderilme Tarihi 18 Kasım 2021
Kabul Tarihi 28 Mart 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: IMISC2021 Special Issue

Kaynak Göster

APA Tarhan, Ç., Özgür, A. S., Teke, İ., Komesli, M. (2022). GÖRÜNTÜ İŞLEME ENTEGRE AFET YÖNETİMİNDE YAPAY ZEKA YÖNTEMİ OLARAK KULLANILABİLİR Mİ?. Journal of Research in Business, 7(IMISC2021 Special Issue), 116-131. https://doi.org/10.54452/jrb.1025382

Cited By

A Review of Using Deep Learning Technology in the Built Environment of Disaster Management Phases
Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD)
https://doi.org/10.30785/mbud.1333736