Analysis of fire rescue problems and fire station siting based on fitting algorithms

Abstract

This paper provides an in-depth study of the fire rescue problem. Based on the number of calls to police at the site in previous years, monthly calls to police in 2022 were predicted through polynomial fitting, cubic spline interpolation, and a two-tone spline interpolation model to analyze and determine the best analytical model. The level of demand for fire stations at the site was measured by using the population density of the site and the number of emergencies per unit time, plus the distance to the nearest fire station at the site to measure its risk level, and finally the cost of building the fire station was considered to measure its composite index, and the site of the fire station was determined based on the composite index. And the grey prediction algorithm as well as the Topsis evaluation algorithm predicts the composite evaluation index for the next 9 years in order to determine where to build the fire station. This study will enable more efficient and uniform fire rescue and more rapid rescue operations to maximize the efficiency of rescue. The relevant data to reasonably predict the various types of emergencies that may occur in the future around the world is an important reference in the issue of fire station location.