Determination of fire evacuation parameters in higher education institutions with inclusive groups using machine learning methods

Abstract

The object of the study is evacuation parameters from institutions of higher education with inclusive education, in particular, speed, flow density and the proportion of participants using crutches and wheelchairs. The relevance of the work is confirmed by the rapid development of inclusive education in Ukraine and abroad. This requires adaptation of existing models for calculating the evacuation duration for use in educational institutions with inclusive education. A way to solve this problem is to present the speed of movement of people during evacuation as a function not only of the flow density, but also of the participants with reduced mobility percentage. In the work machine learning methods, in particular, a linear regression model and an artificial neural network are used to establish the relationship between movement speed, flow density, the proportion of participants using crutches and the proportion of participants moving in wheelchairs. It has been proven that the use of the obtained dependence makes it possible to increase the accuracy of calculating the duration of evacuation from educational institutions with inclusive education by 14%. This makes it possible to use an individual approach in standardizing fire safety requirements for evacuation routes and exits in higher education institutions with inclusive groups