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Abstract

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The article highlights the advantages and disadvantages of fire risk assessment algorithms, taking into account weather conditions, the ecological state of the area, human factors, and other important parameters. The research results can be used to create effective systems aimed at quickly detecting and preventing fire hazards. At the same time, it helps to speed up the response of emergency services, reduce economic losses, and improve environmental protection. This article aims to explore advanced technological approaches to improving fire safety and presents practical solutions.

First Page

60

Last Page

67

References

  1. Mayorov, V.I. (2022). Hardware and software complex “Safe City” as a tool for combating crime. Victimology, 9(4), 445-451.
  2. Koraboshev, O.Z. (2023). Prediction using fire data based on machine learning algorithms. Journal of Chemical Technology Control and Management, (3)111, 57-63.
  3. Hudayberdiev, M.X., Koraboshev, O.Z. (2023). Methods and algorithms for monitoring and prediction of various fire hazardous situations. Journal of Chemical Technology Control and Management, (2)110, 72-79.
  4. Bolov, V.R. (2010). Application of modern technologies, monitoring and prediction methods in providing a crisis management system. Rescue Equipment. Fire Protection. Russian Innovation Systems, (10).
  5. Hudayberdiyev, M.X., Karaboshev, O.Z. (2023). Development of schematic and technical solutions for rapid detection and notification of toxic gases in the air. Fire-Explosion Safety, (3)12, 65-71.
  6. Karaboshev, O.Z. (2023). Innovative approaches to disaster prevention. In Proceedings of the International Scientific and Practical Online Conference “Third Renaissance and Innovative Processes in Uzbekistan”. Andijan. 142-146.
  7. Karaboshev, O.Z. (2023). Issues of prediction fire situations based on intelligent systems. In Proceedings of the Republican Scientific-Practical Conference “The Role of Artificial Intelligence and Digital Technologies in Society”. Karshi. 280-283.
  8. Khasanov, U. (2022). Application of fuzzy methods for solving incorrect problems. In Proceedings of the 1st International Scientific and Practical Conference “Science 119 in the Environment of Rapid Changes”. Brussels, Belgium. 274-277.
  9. Fozilova, M.M., Sotvoldiyev, D., Hasanov, U. (2020). Model of fuzzy knowledge base for the problem of forecasting of agricultural productivity. In Proceedings of the 7th International Scientific and Practical Conference “Challenges in Science of Nowadays”. Washington, USA. 1341-1345.
  10. Rothermel, R.C. (1972). A mathematical model for predicting fire spread in wildland fuels (Research Paper INT–115). USDA Forest Service, Intermountain Forest and Range Experiment Station.

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