This article is devoted to the development of a set of integrated algorithms that support rapid and well-founded management decision-making in emergency situations, assessing fire risk, predicting its dynamics, and making emergency management decisions. The fire risk assessment module calculates risk indicators using statistical and machine learning methods, while the prediction block is based on physical-empirical fire spread models and time series analysis. The decision support part generates recommendations through multi-criteria optimization. Using real-time scenario simulation, various fire scenarios are compared and the most effective operational measures are selected. The system can be adapted to the conditions of urban infrastructure, industrial facilities, and natural forest fires. The obtained scientific results can be used in emergency services, industrial safety, and “smart city” platforms. The study also explores the possibilities of using artificial intelligence, machine learning, statistical analysis, and expert assessment methods in fire risk monitoring systems. The implementation of algorithms in practice creates the opportunity to increase the effectiveness of fire prevention, reduce the impact of the human factor, and organize rapid action in emergency situations. This work is aimed at scientific research in the field of fire safety management, the development of information systems, and improving decision-making mechanisms based on risk analysis.
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