Machine Learning-Based Location for Medical Rescue Centers from the Perspective of Low-Carbon

Since the injured can’t get timely rescue when they suffer from sudden disasters, a location model of emergency medical rescue centers from the perspective of low-carbon is proposed, focusing on the distance and time of transporting medical supplies from hospitals to rescue centers. First, taking Shanghai as an example, the K-means clustering algorithm based on machine learning divides Shanghai into four sub-regions, and the straight-line connection method is used to determine the initial alternative sites of each sub-region. Secondly, the entropy weighting method is used to select several final alternative sites from the initial alternative sites in each sub-region. Finally, by considering the transportation cost, carbon emission cost and late arrival penalty cost, the total cost of all hospitals in each sub-region to the rescue center is calculated, and the site with the minimum total cost is determined as the optimal location site of each sub-region, to ensure that each hospital can provide medical resources to the nearest rescue center. Through the feasibility analysis of the optimal location site, the research result can be used as a reference for subsequent medical rescue center location in Shanghai.