Investigation of Framework Conditions for AI-Driven Population Warning and Preparedness Systems in Digital-Generation Crisis Communication
Absztrakt
This study examines the feasibility framework conditions of a locally deployed (on-premise), artificial intelligence-based civil protection population information and preparedness system, with particular focus on the specific crisis communication needs of digitally active Generation Y and Z, as well as the test-environment implementation of online educational models. The system aligns with the objectives of Hungary's Artificial Intelligence Strategy 2025–2030, which identifies the extension of AI applications to public services and civil protection as a priority, while acknowledging that science and technology are advancing at such a pace that certain elements of the strategy already require continuous updating at the very moment of their formulation.
Hivatkozások
BM OKF Scientific Council for Disaster Management (2025). New perspectives for population preparedness using artificial intelligence [Prize-winning study summary]. Ministry of Interior, National Directorate General for Disaster Management. [Online]. Available: https://www.katasztrofavedelem.hu/application/uploads/documents/2026-02/86850.pdf (2026.09.10.)
Kiss, G. & Kozma, G. (2024). Communication habits of digital generations in the workplace. Jel-Kép: Kommunikáció, Közvélemény, Média, 2024(1). [Online]. Available: https://www.jelkep.eu/pdf/2024/1/JelKep_2024_1__Kiss_Kozma_Georgina.pdf (2026.09.10.)
Petruska, F. (2025). Connection areas between artificial intelligence and national defence, with particular attention to regulation. Defence-Security Regulatory and Governance Studies, 2025(10). [Online]. Available: https://hhk.uni-nke.hu/document/hhk-uni-nke-hu/VBSZK_MT_2025_10 (2026.09.10.)
DRIVER+ Consortium (2019). DRIVER+ project in a nutshell. European Commission, 7th Framework Programme. [Online]. Available: https://www.driver-project.eu/driver-project/(2026.09.10.)
EC Research and Innovation (2021). The ultimate platform to handle extreme weather events [beAWARE project]. European Commission. [Online]. Available: https://projects.research-and-innovation.ec.europa.eu/en/projects/success-stories/all/ultimate-platform-handle-extreme-weather-events (2026.09.10.)
SecurIT (2023). AI Disaster Emergency Com. SecurIT Project, European Commission. [Online]. Available: https://securit-project.eu/funded-projects/ai-disaster-emergency-com/ (2026.09.10.)
Kis, M., Bódi, A. & Számadó, R. (2025). The process and risks of domestic implementation of NIS2. Hadmérnök [Military Engineering], 19(3), 165–182. [Online]. Available: https://doi.org/10.32567/hm.2024.3.10 (2026.09.10.)
Kirovné Rácz, R. & Scholtz, E. (2025). Application of artificial intelligence and machine learning algorithms in defence against hydrological disasters. Hadmérnök [Military Engineering], 20(4), 125–147. [Online]. Available: https://doi.org/10.32567/hm.2025.4.8 (2026.09.10.)
Vásárhelyi, Ö. (2024). The future of Hungary's cybersecurity in the light of the EU NIS2 Directive. Nemzetbiztonsági Szemle [National Security Review], 12(3), 18–35. [Online]. Available: https://doi.org/10.32561/nsz.2024.3.2 (2026.09.10.)
Guo, S. et al. (2023). Ensemble Reinforcement Learning: A Survey. arXiv:2303.02618. [Online]. Available: https://arxiv.org/abs/2303.02618 (2026.09.10.)
Sutton, R.S. & Barto, A.G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0262039246. [Online]. Available: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf (2026.09.10.)
Alqithami, S. (2025). Leveraging Public Sentiment for Resource Coordination in Disaster Response: A Multiagent Framework. arXiv:2505.03979. [Online]. Available: https://arxiv.org/pdf/2505.03979 (2026.09.10.)
Mnih, V. et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. [Online]. Available: https://doi.org/10.1038/nature14236 (2026.09.10.)
Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942–1948. [Online]. Available: https://doi.org/10.1109/ICNN.1995.488968 (2026.09.10.)
Guan, M., Feng, Z., Jiang, S. & Zhou, W. (2024). Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks. Entropy, 26(12), 1071. [Online]. Available: https://doi.org/10.3390/e26121071 (2026.09.10.)
Dorigo, M. & Stützle, T. (2004). Ant Colony Optimization. MIT Press. ISBN: 978-0262042192. [Online]. Available: https://mitpress.mit.edu/9780262042192/ant-colony-optimization/(2026.09.10.)
Muhammad, A. & Wu, T. (2023). Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm. Open Geosciences, 15(1), 20220512. [Online]. Available: https://doi.org/10.1515/geo-2022-0512 (2026.09.10.)
Brassai, S.T. (2019). Neurális hálózatok és fuzzy logika [Neural Networks and Fuzzy Logic]. Kolozsvár: Scientia Kiadó. ISBN: 978-606-975-007-1
Riano-Briceno, G. et al. (2013). Comparison of ANFIS and ANN model to categorize patients in the emergency department. PMC/NCBI. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776083/(2026.09.10.)
ScienceDirect (2025). Advancements in data-driven evolving fuzzy and neuro-fuzzy control: A comprehensive survey. Engineering Applications of Artificial Intelligence. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197625000739 (2026.09.10.)
Liu, Y., Zhang, K., Li, Y. et al. (2024). Towards trustworthy large language models for disaster management. Patterns, 5(6), 100940. [Online]. Available: https://doi.org/10.1016/j.patter.2024.100940 (2026.09.10.)
Xia, Y., Dittler, D., Jazdi, N., Chen, H. & Weyrich, M. (2024). LLM experiments with simulation: Large language model multi-agent system for simulation model parametrization in digital twins. IEEE ETFA 2024. [Online]. Available: https://doi.org/10.1109/ETFA61700.2024.10710961 (2026.09.10.)
Xie, Z. et al. (2025). Towards Intelligent Emergency Management: A Scenario-Learning-Decision Framework Enabled by Large Language Models. Mathematics, MDPI. [Online]. Available: https://doi.org/10.3390/math13010000 (2026.09.10.)
Al-Shamrani, S. & Hasan, R. (2025). CrisisGuideGPT: An Agentic-AI for Crisis Response Planning. 2025 IEEE World Forum on Public Safety Technology (WF-PST), 200–205. [Online]. Available: https://doi.org/10.1109/WF-PST65083.2025.00037 (2026.09.10.)
Yang, L. et al. (2025). Leveraging Large Language Models for Enhanced Digital Twin Modeling. arXiv:2503.02167. [Online]. Available: https://arxiv.org/abs/2503.02167 (2026.09.10.)
Sudanthiran, A. et al. (2025). Disaster Management in the Era of Agentic AI Systems: A Vision for the Future. arXiv:2510.16034. [Online]. Available: https://arxiv.org/abs/2510.16034 (2026.09.10.)
Harang, P. (2024). How efficient are LLM tokenisers on Hungarian text? Medium. [Online]. Available: https://medium.com/@harangpeter/mennyire-hat%C3%A9kony-az-llm-ek-tokeniz%C3%A1l%C3%B3ja-magyar-sz%C3%B6vegen-ce8481e29f93 (2026.09.10.)
Farquhar, S., Kossen, J., Kuhn, L. & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630, 625–630. [Online]. Available: https://doi.org/10.1038/s41586-024-07421-0 (2026.09.10.)
Xie, Z. & Sun, Z. (2025). Mitigating Hallucination in RAG via Ensemble Voting [VOTE-RAG]. arXiv:2603.27253. [Online]. Available: https://arxiv.org/pdf/2603.27253 (2026.09.10.)
George Mason University (2024). Transforming emergency response training through AI and interactive games. [Online]. Available: https://www.gmu.edu/news/2024-12/transforming-emergency-response-training-through-ai-and-interactive-games (2026.09.10.)
Gurcan, O. (2025). LLM-augmented agent-based modelling for social simulations: Challenges and opportunities. arXiv:2405.06700. [Online]. Available: https://arxiv.org/abs/2405.06700 (2026.09.10.)
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