Machine Learning Optimization Algorithms for Clustering Regions and Emergency Management: A Review

Disasters, public health emergencies, and allied humanitarian logistical problems have continued to plague humans for years. In recent years, however, there has been a tremendous increase in the frequency of occurrence of these events. The depot location-allocation problem is vital to ensuring an effective pre-disaster management plan, and techniques for efficiently solving it are of utmost importance to public health emergency planners.

Previously, traditional logistical optimization models and spatial allocation algorithms have been used to provide multi-faceted approaches and solutions to these problems. Machine learning models and related approaches have been suggested as alternative methods for addressing these problems.

This paper presents a review of these attempts from disparate sources, incorporating modern methods of decision-making improvement in dynamic environments through machine learning applications.

A review of existing algorithms for clustering disaster-prone regions and managing rapidly changing disasters was conducted, with a view to developing an updated model to achieve these tasks.