DEVELOPING DECISION-MAKING MODELS AND ALGORITHMS TO HELP PREVENT EMERGENCIES

The article is devoted to the solution of the scientific issue of decision-making support for the prevention and elimination of the consequences of emergency situations. The relevance of this issue is related to the need to develop a theoretical basis for optimizing the risk of adverse effects on human health and the environment in connection with emergency situations, and a predictive model for the development of emergency situations and their prevention or elimination of their consequences. The optimization of the importance measure of signs for predicting the values of the factors of fire conditions has been carried out. In addition, a regression model of the least squares method was developed based on the information obtained from the methane sensor, which is considered the most important parameter in the occurrence of fire, in relation to one cycle of sampling, in order to predict fire situations and form a training sampling model.