FUNCTIONAL MODULES OF THE SYSTEM FOR PREDICTING FIRE RISK OBJECTS
- Keywords:
- Fire safety, prediction, model, artificial intelligence, machine learning methods, evaluation, parameters, control, decision making.
- Abstract
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This article analyzes the functional modules of a predictive system aimed at early detection and assessment of fire hazard objects. The study examines in detail the main components of the system, including data collection, processing, risk assessment, model building, and visualization modules. It also highlights effective methods for determining fire hazard using sensors, remote sensing data, and artificial intelligence algorithms. The proposed system allows for real-time monitoring, risk forecasting, and rapid decision-making. As a result, it helps prevent fires, reduce their negative consequences, and increase the effectiveness of security.
- References
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1.T.Nurmukhamedov, M.Hudayberdiev, O.Koraboshev, S.Sodikov, K.Hudayberdiev. Algorithms and methods of using intelligent systems in fire safety // Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing and Security (ICABCS 2023), Gr. Noida, UP, India, 24 - 25 February 2023. - DOI: 10.1201/9781032684994-98.
2. M.I.Ozerova. Development of the management telecommunication systems mechanism with problem of the high loading // Zhigalov I.E., Novikov I.A. Scientific enquiry in the contemporary world: theoretical basiсs and innovative approach. FL, USA, L&L Publishing, Vol. 4, “Technical Sciences”, 2012, pp. 20-23.
3.D.V.Tupikov. The base of knowledge for operative management of enterprises with explosion- and fire-risks / D.V.Tupikov // Bringing Science to Life: Proceedings of the International Internet Conference 2013. Saratov. SSTU, 2013, pp. 65-66.
4.A.V.Demin. Deep Learning of Adaptive Control Systems Based on a Logical-probabilistic Approach. The Bulletin of Irkutsk State University. Series Mathematics, 2021, Vol. 38, pp. 65-83. (in Russian) https://doi.org/10.26516/1997-7670.2021.38.65.
5.S.V.Gudin Problems of fire risk management on the territory of oil refining facilities using modern software products. Fire and explosion safety, №12(24), 2015. pp. 40-45.
6.B.A.Mavlyankariev, A.Kh.Kuldashev, B.B.Khatamov, A.Yu.Pen. Methodology for reducing the risks of fire development based on dialectical analysis of situations. Fires and emergencies: prevention, liquidation, M., №3, 2016. pp. 56-60.
7.M.X.Hudayberdiev, O.Z.Koraboshev. Methods and algorithms for monitoring and prediction of various fire hazardous situations // Journal of Chemical technology control and management. – Tashkent, 2023, №2 (110). pp. 72-79.
8.O.Z.Koraboshev. Prediction using fire data based on machine learning algorithms. // Journal of Chemical technology control and management. – Tashkent, 2023, №3 (111) pp. 57-63.
9.D.S.Korolev, A.V.Kalach. Prediction based on molecular descriptors and artificial neural networks as a way to eliminate the formation of a flammable environment. Fires and emergencies: prevention, elimination. 2016. pp. 68-72.
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- Published
- 2026-05-10
- Issue
- Vol. 2 No. 5 (2026)
- Section
- Articles
- License
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This work is licensed under a Creative Commons Attribution 4.0 International License.
