Journal of multidisciplinary healthcare · 2026
Machine Learning Optimization Algorithms for Clustering Regions and Emergency Management: A Review.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Akwafuo Sampson, Fnu Prachi, Quezada Daniel
Аннотация
PROBLEM: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.