The Role of Artificial Intelligence in Disaster Risk Management

Year 2022, , 401 - 411, 31.10.2022

Nur Sinem Partigöç

https://doi.org/10.17671/gazibtd.1067831

Cited By: 3

Abstract

It is clear that the usual methods do not respond to the problems based on urban activities which are becoming increasingly complicated due to the growth processes observed under the influence of globalization and intensive population mobility. In addition, due to the increase in the number of disasters associated with rapid urbanization processes and global climate change, there has been a significant increase in the problems experienced in the main service areas of cities (environment, health, education, infrastructure, security, etc.). Accordingly, the usage of information technologies effectively become almost an obligation in order to sustain the level of well-being in settlements which have turned into a multi-network in a sustainable manner and to put forward an effective disaster management process. Fom this point, the aim of the study is to emphasize the importance of the usage of Artificial Intelligence (AI) for reducing and/or eliminating possible disaster losses associated with Disaster Risk Management (DRM) processes. The scope of the study includes the role of risk management in AI applications, the advantages and disadvantages of the usage of AI in the disaster risk reduction process and also application examples. The method of the study is the qualitative research method. As a result of research, it can be said that the use of Information and Communication Technologies (ICT) is necessary for DRM which is sustainable, effective in the long term, multi-stakeholder and inter-disciplinary. Moreover, AI plays a critical role in increasing urban resilience.

Keywords

Disaster Risk Management (DRM), Artificial Intelligence (AI), Urbanization, Information and Communication Technologies (ICT), Urban Resilience

Afet Risk Yönetiminde Yapay Zekâ Kullanımının Rolü

Abstract

Küreselleşme etkisi altında gözlenen büyüme süreçleri ve yoğun nüfus hareketliliği nedeniyle gittikçe karmaşıklaşan kentsel faaliyetlerin yarattığı sorunlara alışılagelen yöntemlerin yanıt vermediği açıkça ortadadır. Buna ek olarak, hızlı kentleşme süreçleri ve küresel iklim değişikliğine bağlı olarak yaşanan afet olaylarındaki artış, zaman içerisinde kentlerin temel hizmet alanlarında (çevre, sağlık, eğitim, altyapı, güvenlik, vb.) yaşanan problemleri önemli ölçüde tetiklemektedir. Dolayısıyla, çoklu bir ağa dönüşen yaşam alanlarında toplumun refah düzeyinin sürdürülebilir biçimde devam ettirilebilmesi ve etkin bir afet yönetim sürecinin ortaya konulabilmesi için bilgi teknolojilerinin etkin biçimde kullanılması artık bir zorunluluk haline gelmiştir. Bu noktadan hareketle, çalışmada afet öncesi döneme referans veren Risk Yönetimi alanında olası kayıpların azaltılması ve/veya bertaraf edilmesi konusunda yapay zekâ kullanımının öneminin vurgulanması amaçlanmıştır. Çalışmanın kapsamını yapay zekâ uygulamalarında risk yönetiminin yeri, yapay zekâ kullanımının afet risklerinin azaltılması sürecindeki avantajları ve dezavantajları, uygulama örneklerinin aktarılması gibi konular oluşturmaktadır. Yöntem olarak nitel araştırma yönteminin kullanıldığı çalışmada, yapılan araştırmalar sonucunda denilebilir ki, sürdürülebilir, uzun vadede etkin, çok paydaşlı ve disiplinler arası niteliğe sahip Modern Bütünleşik Afet Yönetim sürecinde Bilgi ve İletişim Teknolojileri (BİT) kullanımının karar alma süreçlerinin temel yapı taşlarından biri haline gelmiştir ve kentsel dirençliliğin arttırılmasında yapay zekâ uygulamaları kritik bir rol oynamaktadır.

Keywords

Afet Risk Yönetimi, Yapay Zeka, Kentleşme, Bilgi ve İletişim Teknolojileri (BİT), Kentsel Dirençlilik.

References

• S.S. Durduran, A. Geymen, “Türkiyede Afet Bilgi Sistemi Çalışmalarının Genel Bir Değerlendirmesi”, 2. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu (UZAL-CBS 2008), 344 – 352, Kayseri, 2008.
• L. Lin, A. Nilsson, J. Sjolin, M. Abrahamsson, H. Tehler, “On the perceived usefulness of risk descriptions for decision-making in disaster risk management”, Reliability Engineering and System Safety, 142, 48–55, 2015.
• E. Örselli, C. Akbay, “Teknoloji ve Kent Yaşamında Dönüşüm: Akıllı Kentler”, Uluslararası Yönetim Akademisi Dergisi, 2 (1), 228-241, 2019.
• C. Harrison, I.A. Donnelly, “A Theory of Smart Cities”, Proceedings of the 55th Annual Meeting of the ISSS - 2011, Hull, UK, 1-15, 2011.
• N. Çağlayan, Ş.I. Satoğlu, E.N. Kapukaya, “Afet Yönetiminde Büyük Veri Ve Veri Analitiği Uygulamaları: Literatür Araştırması”, 7. Ulusal Lojistik ve Tedarik Zinciri Kongresi (ULTZK 2018), Bursa, 2018.
• Internet: Emergency Events Database (EM-DAT), http://emdat.be/sites/default/files/adsr_2016.pdf, 05.01.2022.
• H. Kemper, G. Kemper, “Sensor Fusıon, GIS and AI Technologies for Dısaster Management”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2020, XXIV ISPRS Congress, 2020.
• Y.M. More, “Disaster Management Using Artifıcial Intelligence”, Journal of Xi'an University of Architecture and Technology, Volume XI, Issue XII, Issn No : 1006-7930, 2019.
• L. Tan, J. Guo, S. Mohanarajah, K. Zhou, “Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices”, Natural Hazards, 107, 2389–2417, 2021.
• G. Abhijeet, D. Samir, “Information Based Approach for Disaster Risk Management”, 20th International Symposium on Logistics (ISL 2015), Bologna, Italy, 5-8 Temmuz, 2015.
• R. Corrado, “ICTs and AI-Driven Solutions for Disaster Management”, Cambodia Development Center, 3 (10), 2021.
• W. Sun, P. Bocchini, B.D. Davison, “Applications of artificial intelligence for disaster management”, Natural Hazards, 103, 2631–2689, 2020.
• D. Sürmeli, Yapay Sinir Ağları İle Afet Yönetiminde Sosyal Zarar Görebilirlik Riskinin Belirlenmesi, Sakarya Üniversitesi Sosyal Bilimler Enstitüsü Yüksek Lisans Tezi, Sakarya, 2011.
• T. Yiğitcanlar, K.C. Desouza, L. Butler, F. Roozkhosh, “Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature”, Energies, 13, 1473, 2020.
• L. Memiş, C. Babaoğlu, Afet Yönetimi ve Teknoloji: Farklı Boyutlarıyla Afet Yönetimi (Edt. M. Yaman ve E. Çakır), Nobel Yayınevi, Ankara, Türkiye, 2020.
• World Bank, Machine Learning for Disaster Risk Management, International Bank for Reconstruction and Development/International Development Association, GFDRR, Washington, 2018.
• A. Ayaydın, M. A. Akçayol, “Deep Learning Based Forecasting of Delay on Flights”, Bilişim Teknolojileri Dergisi, 15 (3), 3-5, 2022.
• C. Şen, İ. S. Mert, M. Abubakar, “Büyük Veri Yönetişimi, Bilgi Aramada Sosyal Medya Kullanımı ve T-Yetenek Üzerindeki Etkileri”, Bilişim Teknolojileri Dergisi, 14 (4), 3-5, 2021.
• V. Nunavath, M. Goodwin, “The Role of Artificial Intelligence in Social Media Big Data Analytics for Disaster Management - Initial Results of a Systematic Literature Review”, 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 2018.
• S. Pirasteh, M. Varshosaz, “Geospatial Information Technologies in Support of Disaster Risk Reduction, Mitigation and Resilience: Challenges and Recommendations”, Sustainable Development Goals Connectivity Dilemma, 1st Edition, ImprintCRC Press, 2019.
• F. Peña-Mora, Z.U.H. Aziz, A. Chen, A. Plans, S. Foltz, “Building assessment during disaster response and recovery”, Proceedings of the Institution of Civil Engineers, 161(4), 183–195, 2008.
• F. Alamdar, M. Kalantari, A. Rajabifard, “Towards multi-agency sensor information integration for disaster management”, Comput. Environ. Urban Syst., 56, 68–85, 2016.
• A.Q. Gill, N. Phennel, D. Lane, V.L. Phung, “IoT-enabled emergency information supply chain architecture for elderly people: The Australian context. Information Systems”, Information Systems, 58, 75–86, 2016.
• P.P. Ray, M. Mukherjee, L. Shu, “Internet of Things for Disaster Management: State-of-the-Art and Prospects”, IEEE Access, 5, 18818–18835, 2017.
• N.K. Ray, A.K. Turuk, “A framework for post-disaster communication using wireless ad hoc networks”, Integration, the VLSI Journal, 58(Supplement C), 274–285, 2017.
• Z. Lv, X. Li, K. Choo, “E-government multimedia big data platform for disaster management”, Multimedia Tools and Applications, 1–13, 2017.
• F. Ai, L. K. Comfort, Y. Dong, T. Znati, “A dynamic decision support system based on geographical information and mobile social networks: A model for tsunami risk mitigation in Padang, Indonesia”, Safety Science, 90, 62–74, 2016.
• P.M. Landwehr, W. Wei, M. Kowalchuck, K.M. Carley, “Using tweets to support disaster planning, warning and response”, Safety Science, 90, 33–47, 2016.
• K. Chung, R.C. Park, “P2P cloud network services for IoT based disaster situations information. Peer-to-Peer Networking and Applications”, Peer-to-Peer Networking and Applications, 9 (3), 566–577, 2016.
• G. Deak, K. Curran, J. Condell, E. Asimakopoulou, N. Bessis, “IoTs (Internet of Things) and DfPL (Device-free Passive Localisation) in a disaster management scenario”, Simulation Modeling Practice and Theory, 35, 86–96, 2013.
• W. Wang, C. Hu, N. Chen, C. Xiao, C. Wang, Z. Chen, “Spatio-temporal enabled urban decision-making process modeling and visualization under the cyber-physical environment”, Science China Information Sciences, 58(10), 1–17, 2015.
• L. Yang, S.H. Yang, L. Plotnick, “How the internet of things technology enhances emergency response operations”, Technological Forecasting and Social Change, 80(9), 1854–1867, 2013.
• S. Linardi, “Peer coordination and communication following disaster warnings: An experimental framework”, Safety Science, 90(Supplement C), 24–32, 2016.
• V.K. Neppalli, C. Caragea, A. Squicciarini, A. Tapia, S. Stehle, “Sentiment analysis during Hurricane Sandy in emergency response”, International Journal of Disaster Risk Reduction, 21(Supplement C), 213–222, 2017.
• T. Papadopoulos, A. Gunasekaran, R. Dubey, N. Altay, S.J. Childe, S. Fosso-Wamba, “The role of Big Data in explaining disaster resilience in supply chains for sustainability”, Journal of Cleaner Production, 142(2), 1108–1118, 2017.
• Y. Yao, X. Liu, X. Li, J. Zhang, Z. Liang, K. Mai, Y. Zhang, “Mapping fine-scale population distributions at the building level by integrating multi source geospatial big data”. International Journal of Geographical Information Science, 31(6), 2017.
• Y. Ma, H. Zhang, “Enhancing Knowledge Management and Decision-Making Capability of China’s Emergency Operations Center Using Big Data”, Intelligent Automation and Soft Computing, 24(1), 1-8, 2017.
• C.M. Yeum, S.J. Dyke, J. Ramirez, “Visual data classification in post-event building reconnaissance”, Engineering Structures, 155, 16–24, 2018.
• T. Řezník, V. Lukas, K. Charvát, K. Charvát, Z. Křivánek, M. Kepka, L. Herman, H. Řezníková, “Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing”, ISPRS International Journal of Geo-Information, 6(8), 238, 2017.
• P.J. Tak, K.C. Soo, “A Study on the Construction of City-Gas Smart Disaster Prevention System Based on GIS”. International Journal of Control and Automation, 10, 2017.
• D.A. Griffith, B. Boehmke, R.V. Bradley, B.T. Hazen, A.W. Johnson, “Embedded analytics: improving decision support for humanitarian logistics operations”, Annals of Operations Research, 1–19, 2017.
• Q. Lele, K. Lihua, “Technical Framework Design of Safety Production Information Management Platform for Chemical Industrial Parks Based on Cloud Computing and the Internet of Things”, International Journal of Grid and Distributed Computing, 9(6), 299–314, 2016.
• A. Leiras, I. De Brito, E. Queiroz, T. Bertazzo, H. Yoshida, “Literature review of humanitarian logistics research: trends and challenges', J. Humanitarian Logistic. Supply Chain Manage., 4, 95-130, 2014.
• G. Jain, A. Kulshrestra, N.L. Vyas, “Radio Frequency Identification Technology application for disaster and rescue: a review”, International Archive of Applied Sciences and Technology, 8, 64-73, 2017.
• L. Özdamar, M.A. Ertem, “Models, solutions and enabling technologies in humanitarian logistics”, European Journal of Operational Research, 244(1), 55–65, 2015.
• N. Chen, W. Liu, R. Bai, A. Chen, A., “Application of computational intelligence technologies in emergency management: a literature review”, Artificial Intelligence Review, 1–38, 2017.
• S. Fosso Wamba, S. Akter, A. Edwards, G. Chopin, D. Gnanzou, “How “big data” can make big impact: Findings from a systematic review and a longitudinal case study”, International Journal of Production Economics, 165, 234–246, 2015.
• S. Goswami, S. Chakraborty, S. Ghosh, A. Chakrabarti, B. Chakraborty, "A review on application of data mining techniques to combat natural disasters", Ain Shams Engineering Journal, In press, 2016.
• Su Politikaları Derneği, Yapay Zeka ve Su Yönetimi, Rapor No: 30, Ankara, 2020.
• L. Memiş, C. Babaoğlu, “Acil Durum ve Afet Yönetiminde Süreç Yaklaşımı ve Teknoloji”, Academic Review of Economics and Administrative Sciences, 13(4) 776-791, 2020.
• K. Bingöl, E.A. Akan, H.T. Örmecioğlu, A. Er, “Artificial intelligence applications in earthquake resistant architectural design: Determination of irregular structural systems with deep learning and Image AI method”, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(4), 2197-2209, 2020.
• A. Maskrey, “Revisiting community-based disaster risk management”, Envıronmental Hazards, 10, 42–52, 2011.
• M.L. Carreno, O.D. Cardona, A. H. Barbat, A.H., “Urban seismic risk evaluation: a holistic approach”, Nat. Hazards, 40 (1), 137–172, 2007.
• N. Lantada, M.L. Carreno, N. Jaramillo, “Disaster risk reduction: A decision-making support tool based on morphological analysis”, International Journal of Disaster Risk Reduction, 42, 2020.
• Inter-American Development Bank, “Indicators of Disaster Risk and Risk Management”, Technical Notes, No. IDB-TN-169, 2010.