|
Proceedings of International Conference on Applied Innovation in IT 2025/06/27, Volume 13, Issue 2, pp.189-195 Algorithms of a Digital Fire Prediction and Suppression SystemTolaniddin Nurmukhamedov, Oybek Koraboshev and Javlon GulyamovAbstract: This article analyses the algorithms of digital systems created to predict fire hazards and effectively eliminate them. Natural and man-made fires are one of the factors that cause great damage to human life, ecology and the economy. Fire risk prediction algorithms analyze environmental conditions such as weather patterns, wind speed, temperature, and humidity to assess potential fire hazards. These algorithms determine the level of risk in real time, predict possible fire situations and provide early warnings. In particular, the introduction of analytical models based on artificial intelligence significantly increases the accuracy of predictions. Fire suppression algorithms, on the other hand, allow for rapid coordination of actions after a fire is detected, optimal allocation of resources, automation of evacuation plans and effective management of emergency services. These algorithms embody complex solutions that include digital maps, real-time data exchange and inter-system integration. Therefore, the use of modern digital technologies and algorithmic approaches in ensuring fire safety is of urgent importance. This article examines the basic principles of fire prediction algorithms, namely, methods for predicting the level of risk based on factors such as weather data, air humidity, temperature, wind speed and plant dryness. It also analyses the mechanisms for determining the probability of a fire using artificial intelligence and machine learning models (for example, Random Forest, Neural networks). In addition, algorithms for quickly eliminating a fire after it is detected are considered, including optimal resource management, automation of evacuation plans, and the possibility of integrating drones and IoT devices into the system. To increase the efficiency of the system, algorithms based on real-time monitoring and digital maps are recommended. The results of the research work reveal the practical importance of advanced algorithms in firefighting and contribute to the development of digital approaches in the field of fire safety. Keywords: Machine Learning, Artificial Intelligence, Pattern Recognition, Fire Risks, Model, Kalman Filter, Optimal Management, Algorithm, Prediction. DOI: 10.25673/120437 Download: PDF References:
|
Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
ISSN 2199-8876
Publisher: Edition Hochschule Anhalt
Location: Anhalt University of Applied Sciences
Email: leiterin.hsb@hs-anhalt.de
Phone: +49 (0) 3496 67 5611
Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.
