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A Review of Using Deep Learning Technology in the Built Environment of Disaster Management Phases

Year 2024, Volume: 9 Issue: Special Issue, 201 - 218, 06.02.2024

Gizem Sünbül Asena Soyluk

https://doi.org/10.30785/mbud.1333736

Abstract

Türkiye is a country on the Alpine-Himalayan earthquake zone and needs an effective disaster management plan, with its geography experiencing severe seismic activities. In this respect, natural disaster risks can be reduced by using developing artificial intelligence technology and deep learning applications in the mitigation, preparedness, response, and recovery phases that constitute the disaster management plan. This study examines deep learning models, application areas, deep learning layers and libraries used, and how deep learning can be used in the four stages of disaster management through study examples in the literature. The study aims to examine the use of deep learning in architecture and disaster management phases based on the earthquake factor as a result of the literature review. As a result, when studies on deep learning are examined, disaster management studies closely related to the discipline of architecture are mainly in the response phase. However, the discipline of architecture plays an important role at every stage of disaster management. In this respect, as holistic studies and applications related to deep learning, architectural science, and effective disaster management increase, the loss of life and property due to disasters, especially earthquakes, will decrease. The study carried out is thought to be an important guide for future research.

Keywords

Artificial intelligence deep learning disaster management earthquake architecture

Thanks

National and international research and publication ethics have been complied with in the article. Ethics committee approval was not required in the study.

References

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  • Baran Ergül, D., Varol Malkoçoğlu, A. B. & Acun Özgünler, S. (2022). Mimari tasarım karar verme süreçlerinde yapay zeka tabanlı bulanık mantık sistemlerinin değerlendirilmesi. Journal of Architectural Sciences and Applications, 7 (2), 878-899. DOI: 10.30785/mbud.1117910
  • Bingöl, K., Er Akan, A., Örmecioğlu, H. T. & Er, A. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210. DOI: 10.17341/gazimmfd.647981
  • Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
  • Chaoxian, L., Haigang, S. & Shan, Z. (2023). Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models, International Journal of Remote Sensing, 44:22, 6954-6980, DOI: 10.1080/01431161.2023.2277163
  • Chaudhuri, N. & Bose, I. (2020). Exploring the role of deep neural networks for post-disaster decision support. Decision Support Systems, 130, 113234.
  • Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M. ... & Zhang, Z. (2015). Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274.
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  • Debus, B., Parastar, H., Harrington, P. & Kirsanov, D. (2021). Deep learning in analytical chemistry. TrAC Trends in Analytical Chemistry, 145, 116459.
  • Dixit, M., Tiwari, A., Pathak, H. & Astya, R. (2018, October). An overview of deep learning architectures, libraries and its applications areas. In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (pp. 293-297). IEEE.
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  • Isin, A. & Ozdalili, S. (2017). Cardiac arrhythmia detection using deep learning. Procedia Computer Science, 120, 268-275.
  • İnik, Ö. & Ülker, E. (2017) Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104
  • Karcı, M. & Şahin, İ. (2022). Derin öğrenme yöntemleri kullanılarak deprem tahmini gerçekleştirilmesi. Artificial Intelligence Studies, 5(1), 23-34
  • Kaynar, O., Görmez, Y. & Işık, Y. E. (2016). Oto Kodlayıcı Tabanlı Derin Öğrenme Makinaları ile Spam Tespiti. 3. Uluslararası Yönetim Bilişim Sistemleri Konferansı, İzmir, 44.
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  • Lettieri, E., Masella, C. & Radaelli, G. (2009). Disaster management: findings from a systematic review. Disaster Prevention and Management: An International Journal, 18(2), 117-136. https://doi.org/10.1108/09653560910953207
  • Li, X., Caragea, D., Zhang, H. & Imran, M. (2018). Localizing and quantifying damage in social media images. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 194-201). IEEE.
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  • McEntire, D. A. (2009). Emergency Management in the United States: Disasters Experienced, Lessons Learned, and Recommendations for the Future. Comparative Emergency Management: Understanding Disaster Policies, Organizations, and Initiatives from Around the World. http://training.fema.gov/EMIWeb/edu/CompEmMgmtBookProject.asp. Federal Emergency Management Agency: Emmitsburg, MD.
  • Mishra, B., Garg, D., Narang, P. & Mishra, V. (2020). Drone-surveillance for search and rescue in natural disaster. Computer Communications, 156, 1-10.
  • Nabian, M. A., & Meidani, H. (2018). Deep learning for accelerated seismic reliability analysis of transportation networks. Computer‐Aided Civil and Infrastructure Engineering, 33(6), 443-458.
  • Nguyen, D. T., Ofli, F., Imran, M., & Mitra, P. (2017). Damage assessment from social media imagery data during disasters. In Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017 (pp. 569-576).
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  • Safalı, Y. & Avaroğlu, E. (2021). Derin öğrenme ile yüz tanıma ve duygu analizi. Avrupa Bilim ve Teknoloji Dergisi, (31), 764-770. DOI: 10.31590/ejosat.1010450
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  • Şeker, A., Diri, B. & Balık, H. H. (2017). Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64
  • Tarhan, Ç., Özgür, A. S., Teke, İ. & Komesli, M. (2022). Görüntü işleme entegre afet yönetiminde yapay zeka yöntemi olarak kullanılabilir mi?. Journal of Research in Business, IMISC 2021 Special Issue, 116-131. DOI: 10.54452/jrb.1025382
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Yapılı Çevrede Afet Yönetimi Aşamalarında Derin Öğrenme Teknolojisinin Kullanımına İlişkin Bir İnceleme

Year 2024, Volume: 9 Issue: Special Issue, 201 - 218, 06.02.2024

Gizem Sünbül Asena Soyluk

https://doi.org/10.30785/mbud.1333736

Abstract

Türkiye Alp Himalaya deprem kuşağı üzerinde olan ve şiddetli sismik aktivitelerin yaşandığı coğrafyası ile etkin afet yönetim planı olması gereken bir ülkedir. Bu açıdan, gelişen yapay zeka teknolojisi ve derin öğrenme uygulamaları kullanılarak afet yönetim planını oluşturan risk ve zarar azaltma, hazırlık, müdahale ve iyileştirme evrelerinde doğal afet riskleri azaltılabilir. Bu çalışmada, derin öğrenme modelleri, uygulama alanları, derin öğrenme katmanları ve kullanılan kütüphaneler incelenerek, literatürde yapılmış çalışma örnekleri üzerinden derin öğrenmenin afet yönetiminin dört aşamasında nasıl kullanılabileceği irdelenmiştir. Çalışmanın amacı yapılan literatür taramasının sonucunda deprem faktörü baz alınarak mimarlık ve afet yönetimi aşamalarında derin öğrenmenin kullanımını incelemektir. Sonuç olarak derin öğrenme ile ilgili çalışmalar incelendiğinde, mimarlık disiplini ile yakın ilişkili olan afet yönetimi çalışmaları en çok müdahale aşamasında bulunmaktadır. Oysaki mimarlık disiplini afet yönetiminin her aşamasında önemli görevler almaktadır. Bu açıdan derin öğrenme, mimarlık bilimi ve etkin afet yönetimi ile ilgili bütünsel çalışmalar ve uygulamalar arttıkça, afetler özellikle deprem nedeniyle yaşanacak can ve mal kayıpları azalacaktır. Yapılan çalışmanın ilerideki araştırmalar için önemli bir klavuz niteliği taşıdığı düşünülmektedir.

Keywords

Yapay zekâ derin öğrenme afet yönetimi deprem mimarlık

References

  • Alimovski, E. (2019). Derin öğrenmeye dayalı güçlü yüz tanıma sistemi için gan ile veri çoğaltma (Master's thesis). İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
  • Amin, M. S. & Ahn, H. (2021). Earthquake disaster avoidance learning system using deep learning. Cognitive Systems Research, 66, 221-235.
  • Baran Ergül, D., Varol Malkoçoğlu, A. B. & Acun Özgünler, S. (2022). Mimari tasarım karar verme süreçlerinde yapay zeka tabanlı bulanık mantık sistemlerinin değerlendirilmesi. Journal of Architectural Sciences and Applications, 7 (2), 878-899. DOI: 10.30785/mbud.1117910
  • Bingöl, K., Er Akan, A., Örmecioğlu, H. T. & Er, A. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210. DOI: 10.17341/gazimmfd.647981
  • Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
  • Chaoxian, L., Haigang, S. & Shan, Z. (2023). Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models, International Journal of Remote Sensing, 44:22, 6954-6980, DOI: 10.1080/01431161.2023.2277163
  • Chaudhuri, N. & Bose, I. (2020). Exploring the role of deep neural networks for post-disaster decision support. Decision Support Systems, 130, 113234.
  • Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M. ... & Zhang, Z. (2015). Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274.
  • CloudTime Talk. (2022). Deep learning (derin öğrenme) nedir. Access address (12.06.2023): https://cloudtalktime.com/deep-learning-derin-ogrenme-nedir/
  • Dahl, R., Norouzi, M. & Shlens, J. (2017). Pixel recursive super resolution. In Proceedings of the IEEE international Conference on Computer Vision (pp. 5439-5448).
  • Debus, B., Parastar, H., Harrington, P. & Kirsanov, D. (2021). Deep learning in analytical chemistry. TrAC Trends in Analytical Chemistry, 145, 116459.
  • Dixit, M., Tiwari, A., Pathak, H. & Astya, R. (2018, October). An overview of deep learning architectures, libraries and its applications areas. In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (pp. 293-297). IEEE.
  • Doğan, F. & Türkoğlu, İ. (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445. DOI: 10.24012/dumf.411130
  • Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T. & Philbrick, K. (2017). Toolkits and libraries for deep learning. Journal of Digital Imaging, 30, 400-405. https://doi.org/10.1007/s10278-017-9965-6
  • Fischer, A & Igel, C. (2012). An Introduction to Restricted Boltzmann Machines. L. Alvarez et al. (Eds). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 17th Iberoamerican Congress, CIARP 2012 Buenos Aires, Argentina, September 2012 Proceedings. (pp. 14-36). Springer Berlin Heidelberg.
  • Francoeur, B. (2023). 4 phases of disaster management explained (the easy way). Access address (17.07.2023): https://home.akitabox.com/blog/4-phases-of-disaster-management/
  • Gültekin, B. (2022). Betonarme yapılarda yapısal ve yapısal olmayan hasarların derin öğrenme ile tespiti (Master's thesis). Konya Teknik Üniversitesi, Konya.
  • Gündüz, G. & Cedimoğlu, İ. H. (2019). Derin öğrenme algoritmalarını kullanarak görüntüden cinsiyet tahmini. Sakarya University Journal of Computer and Information Sciences, 2(1), 9-17.
  • Harsha, A. (2018). Ai vs machine learning vs deep learning. Access address (12.06.2023): https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-learning/
  • Hung, C. (2023). Deep Learning in Biomedical Informatics. Zheng, Y. & Wu, Z. (Eds). Intelligent Nanotechnology: Merging Nanoscience and Artificial Intelligence (p.307-329). Amsterdam: Elsevier.
  • Isin, A. & Ozdalili, S. (2017). Cardiac arrhythmia detection using deep learning. Procedia Computer Science, 120, 268-275.
  • İnik, Ö. & Ülker, E. (2017) Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104
  • Karcı, M. & Şahin, İ. (2022). Derin öğrenme yöntemleri kullanılarak deprem tahmini gerçekleştirilmesi. Artificial Intelligence Studies, 5(1), 23-34
  • Kaynar, O., Görmez, Y. & Işık, Y. E. (2016). Oto Kodlayıcı Tabanlı Derin Öğrenme Makinaları ile Spam Tespiti. 3. Uluslararası Yönetim Bilişim Sistemleri Konferansı, İzmir, 44.
  • Larsson, G., Maire, M. & Shakhnarovich, G. (2016). Learning Representations for Automatic Colorization. B. Leibe et al. (Eds). Computer Vision – ECCV 2016. (p. 577-593). Springer, Cham.
  • Lettieri, E., Masella, C. & Radaelli, G. (2009). Disaster management: findings from a systematic review. Disaster Prevention and Management: An International Journal, 18(2), 117-136. https://doi.org/10.1108/09653560910953207
  • Li, X., Caragea, D., Zhang, H. & Imran, M. (2018). Localizing and quantifying damage in social media images. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 194-201). IEEE.
  • Maraş, E. E. & Sarıyıldız, H. İ. (2023). İHA ile derin öğrenme algoritmaları kullanılarak hasarlı yapıların tespit edilmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 23(2), 427-437. DOI: 10.35414/akufemubid.1171393
  • McEntire, D. A. (2009). Emergency Management in the United States: Disasters Experienced, Lessons Learned, and Recommendations for the Future. Comparative Emergency Management: Understanding Disaster Policies, Organizations, and Initiatives from Around the World. http://training.fema.gov/EMIWeb/edu/CompEmMgmtBookProject.asp. Federal Emergency Management Agency: Emmitsburg, MD.
  • Mishra, B., Garg, D., Narang, P. & Mishra, V. (2020). Drone-surveillance for search and rescue in natural disaster. Computer Communications, 156, 1-10.
  • Nabian, M. A., & Meidani, H. (2018). Deep learning for accelerated seismic reliability analysis of transportation networks. Computer‐Aided Civil and Infrastructure Engineering, 33(6), 443-458.
  • Nguyen, D. T., Ofli, F., Imran, M., & Mitra, P. (2017). Damage assessment from social media imagery data during disasters. In Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017 (pp. 569-576).
  • NVIDIA. (2015). NVIDIA interactive deep learning GPU training system. Access address (15.06.2023): https://developer.nvidia.com/digits.
  • Özgür, S. N. & Bozkurt Keser, S. (2021). Meme kanseri tümörlerinin derin öğrenme algoritmaları ile sınıflandırılması. Türk Doğa ve Fen Dergisi, 10(2), 212-222. DOI: 10.46810/tdfd.957618
  • Patel, K. (2019). Convolutional Neural Networks — a beginner’s guide. Access address (12.06.2023): https://towardsdatascience.com/convolution-neural-networks-a-beginners-guide-implementing-a-mnist- hand-written-digit-8aa60330d022
  • Pogrebnyakov, N. & Maldonado, E. (2017). Identifying emergency stages in facebook posts of police departments with convolutional and recurrent neural networks and support vector machines. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4343-4352). IEEE.
  • Rao, M. N. (2023). A comparative analysis of deep learning frameworks and libraries. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 337-342.
  • Robertson, B. W., Johnson, M., Murthy, D., Smith, W. R. & Stephens, K. K. (2019). Using a combination of human insights and ‘deep learning’for real-time disaster communication. Progress in Disaster Science, 2, 100030.
  • Safalı, Y. & Avaroğlu, E. (2021). Derin öğrenme ile yüz tanıma ve duygu analizi. Avrupa Bilim ve Teknoloji Dergisi, (31), 764-770. DOI: 10.31590/ejosat.1010450
  • Saleem, M. H., Potgieter, J. & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468
  • Shrestha, A. & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 53040- 53065. DOI: 10.1109/ACCESS.2019.2912200.
  • Song, X., Shibasaki, R., Yuan, N. J., Xie, X., Li, T., & Adachi, R. (2017). DeepMob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data. ACM Transactions on Information Systems (TOIS), 35(4), 1-19.
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Slakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15(1), 1929- 1958
  • Sun, W., Bocchini, P. & Davison, B. D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards, 103(3), 2631-2689.
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There are 55 citations in total.

Details

Primary Language English
Subjects Architectural Science and Technology
Journal Section Research Articles
Authors

Gizem Sünbül 0009-0002-1673-1097

Asena Soyluk 0000-0002-6905-4774

Early Pub Date February 6, 2024
Publication Date February 6, 2024
Submission Date July 27, 2023
Published in Issue Year 2024 Volume: 9 Issue: Special Issue

Cite

APA Sünbül, G., & Soyluk, A. (2024). A Review of Using Deep Learning Technology in the Built Environment of Disaster Management Phases. Journal of Architectural Sciences and Applications, 9(Special Issue), 201-218. https://doi.org/10.30785/mbud.1333736
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