Fuzzy Logic Algorithms for Real-Time Fire Hazard Prediction
Annotatsiya
Currently, there is growing interest in using modern technologies such as artificial intelligence (AI), machine learning (ML), big data analytics (Big Data), and geographic information systems (GIS) in fire risk prediction and decision-making processes. As is known, the prevention and effective elimination of emergency situations requires the rapid analysis of information coming from the facility during the current period, the development and implementation of algorithms that provide rapid response to them and facilitate management decisionmaking. The scientific significance of the work lies in the development of models and algorithms that predict the level of fire occurrence and support management decisions to prevent fire hazards. This article focuses on developing algorithms to assist in making management decisions regarding fire risk prediction. Fuzzy models are widely used in fire prediction. A fuzzy model is a mathematical model that takes into account uncertainty rather than being strictly bounded, and is used to describe complex and uncertain systems in real life. This model is closer to the human decision-making process than classical precise mathematical models, because it uses the concepts of “partially true” or “partially false”. In addition, this article presents scientific results using fuzzy logic algorithms to predict the occurrence of fires.