GENERALIZED NET MODEL OF THE DISASTER PROTECTION ASSESSMENT PROCESS USING ARTIFICIAL INTELLIGENCE

Authors

  • Rosen Iliev Bulgarian Defence Institute “Prof. Tsvetan Lazarov”, Sofia, Bulgaria
  • Svilen Neykov Bulgarian Defence Institute “Prof. Tsvetan Lazarov”, Sofia, Bulgaria https://orcid.org/0009-0005-3975-2781

DOI:

https://doi.org/10.68302/std2026.vol3.142

Keywords:

AI, crisis, generalized net, model

Abstract

The paper presents an original generalized net model (GN-model) for using artificial intelligence in assessing the protection of the population and infrastructure in disasters. It includes assessments of basic components such as population, infrastructure, personnel and procedures using intuitionistic fuzzy logic. A simulation of the GN-model was conducted in the GN IDE simulation environment and some of the results obtained are presented. Possible applications of the model for improving the procedures for protecting the population and infrastructure in disasters and accidents are described.

Supporting Agencies

This work was supported by the NSP “Security and defence” program, which has received funding from the Ministry of Education and Science of the Republic of Bulgaria under the grant agreement no. Д01-74/19.05.2022.

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Author Biographies

  • Rosen Iliev, Bulgarian Defence Institute “Prof. Tsvetan Lazarov”, Sofia, Bulgaria

    "Development C4I systems" department in Bulgarian Defence Institute, proffesor

  • Svilen Neykov, Bulgarian Defence Institute “Prof. Tsvetan Lazarov”, Sofia, Bulgaria

    "Development C4I systems" department in Bulgarian Defence Institute, PhD-student

References

[1] V. Tselkov, and N. Stoianov. E-net models of a software system for web pages security. Mathematics and mathematical education, Sofia, Bulgaria, BAS, 2003.

[2] P., Boyanov, Using a specialized software for comprehensive monitoring the suspicious states in computer networks, a refereed Journal Scientific and Applied Research (Licensed in EBSCO, USA), Konstantin Preslavsky University Press, ISSN 1314-6289, vol. 6, 2014, pp. 148-154.

[3] Hr. Hristov, P. Boyanov, and T. Trifonov. Approaches to identify vulnerabilities in the security system of the social organization and computer resources, a refereed Journal Scientific and Applied Research (Licensed in EBSCO, USA), Konstantin Preslavsky University Press, ISSN 1314-6289, vol. 5, 2014, pp. 101-107.

[4] A. Kolev, and P. Nikolova, Instrumental Equipment for Cyber-attack Prevention, Information & Security: An International Journal 47, no. 3 (2020): 285-299. https://doi.org/10.11610/isij.4720

[5] "What is AI-powered damage detection in drone inspections?," FlyNex FAQ. [Online]. Available: https://www.flynex.io/en/faq-items/what-is-ai-powered-damage-detection-in-drone-inspections/

[6] "CNN Dimov," IICT-BAS Publications. [Online]. Available: https://sdp.iict.bas.bg/publ/CNN_Dimov.pdf

[7] "Article Title," arXiv preprint arXiv:2510.21063, 2025. https://doi.org/10.48550/arXiv.2510.21063

[8] "Article Title," arXiv preprint arXiv:2510.21063, 2025. [Online]. Available: https://arxiv.org/abs/2510.21063

[9] IEEE Xplore Document. [Online]. Available: https://ieeexplore.ieee.org/document/11101106

[10] ScienceDirect Article. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0045794925001191

[11] MDPI Remote Sensing Article. [Online]. Available: https://www.mdpi.com/2072-4292/17/15/2668

[12] "Drone digital twins for structural damage assessment," Anvil Post. [Online]. Available: https://anvil.so/post/drone-digital-twins-for-structural-damage-assessment

[13] "Article Title," arXiv preprint arXiv:2510.21063, 2025. https://doi.org/10.48550/arXiv.2510.21063

[14] R. P. Westerholt, P. Resch, and A. Zipf, "A statistical approach to the use of social media data in disaster management," Sci. Rep., vol. 11, no. 1, p. 14231, Jul. 2021 Available: https://nature.com

[15] J. R. Curran and S. Clark, "Language-independent NER using a maximum entropy combined model," in Proc. ACL Workshop on Computational Learned Lexical Semantics, Stanford, CA, USA, 2014. [Online]. Available: stanford.edu

[16] S. Behl, A. Rao, S. Aggarwal, S. Chadha, and H. S. Pannu, "Resource optimization for disaster management using Twitter data processing," Int. J. Disaster Risk Reduct., vol. 34, pp. 29–42, Mar. 2019. Available: https://www.sciencedirect.com/science/article/pii/S2212420918306630

[17] UN OCHA, "Artificial Intelligence for Disaster Response: Current Trends and Future Perspectives," ReliefWeb Report, Oct. 2021. [Online]. Available: reliefweb.int.

[18] Y. S. Bagloee, M. Sarvi, and M. Patriksson, "A Reinforcement Learning Application for Real-Time Emergency Resource Allocation," IEEE Trans. Intell. Transp. Syst., vol. 23, no. 10, pp. 18234-18245, Oct. 2022 Available: https://ieee.org

[19] X. Liang, et al., "Deep Reinforcement Learning for Dynamic Resource Allocation in Disaster Response Networks," in Proc. IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1-6. doi: 10.1109/GLOBECOM38437.2019.9013567

[20] K. T. Atanassov, Intuitionistic Fuzzy Sets, Heidelberg, Germany: Physica-Verlag, 1999. Available: https://springer.com

[21] K. T. Atanassov, On Generalized Nets, Singapore: World Scientific, 1991. Available: https://worldscientific.com

[22] V. Bureva, S. Sotirov, and E. Sotirova, "Generalized Net Model of Disaster Management with Intuitionistic Fuzzy Predicates," in Proc. 21st Int. Conf. on Intuitionistic Fuzzy Sets (ICIFS), Sofia, Bulgaria, 2017, pp. 64-70.

[23] M. Biçer and V. K. Akram, "Multi-Agent Based Path Planning for Emergency Evacuation in Smart Cities," in Proc. IEEE 13th Int. Conf. on Semantic Computing (ICSC), Newport Beach, CA, USA, 2019, pp. 412-417.

[24] X. Chen and Y. Zhan, "Agent-Based Modeling and Simulation of Urban Evacuation under Disaster Scenarios," IEEE Access, vol. 8, pp. 21563-21575, 2020. Available: https://ieee.org

[25] F. S. Hsieh, "Collaborative Disaster Management Based on Multi-Agent Systems," in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Bari, Italy, 2019, pp. 2454–2459. doi: 10.1109/SMC.2019.8914364

[26] Manchev A. I. “Ammunition Supply In Multinational Joint Operations”, In: International Scientific Conference – Hemus-2020, pp. I-111--118, ISSN 1213-2916, Plovdiv, Bulgaria, 2020

[27] Ivanov V. V. Manchev A. I. "Crises And Some Possibilities For Their Successful Management", In: Proceedings of the International Scientific Conference - ARTDef, (29-30 June2021), pp.I-86--93, ISSN 2815-2581 Varna, Bulgaria, 2021

[28] Ivanov V. V., Manchev A. I. “Opportunities For Improving The National Security System Of The Republic Of Bulgaria”, In: Proceedings of the International Scientific Conference – Hemus - 2022 (2-3 June 2022), pp. VI-123--133, ISSN 1213-2916, Sofia, Bulgaria, 2022.

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Published

17.09.2026

Issue

Vol. 3 (2026): Proceedings of the International Conference on Secure Systems Design and Technology Development.

Section

INFORMATION TECHNOLOGY

License

Copyright (c) 2026 Rosen Iliev, Svilen Neykov

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. Iliev and S. Neykov, “GENERALIZED NET MODEL OF THE DISASTER PROTECTION ASSESSMENT PROCESS USING ARTIFICIAL INTELLIGENCE”, SysTechDev, vol. 3, pp. 99–105, Sep. 2026, doi: 10.68302/std2026.vol3.142.