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

Area of Science:

• Operations Research
• Public Health
• Computer Science

Background:

• Humanitarian logistical problems and public health emergencies are increasing in frequency.
• Effective pre-disaster management relies on solving the depot location-allocation problem.
• Traditional optimization models have limitations in dynamic disaster environments.

Purpose of the Study:

• To review existing methods for disaster logistics and emergency planning.
• To explore the application of machine learning in improving decision-making for dynamic disaster environments.
• To identify needs for updated models in disaster management.

Main Methods:

• Literature review of traditional logistical optimization and spatial allocation algorithms.
• Review of machine learning models and approaches for disaster management.
• Analysis of algorithms for clustering disaster-prone regions and managing rapidly changing disasters.

Main Results:

• Traditional methods have been augmented by machine learning approaches.
• Machine learning offers potential for improved decision-making in dynamic disaster scenarios.
• Existing algorithms for disaster region clustering and management were evaluated.

Conclusions:

• Machine learning applications are crucial for enhancing disaster preparedness and response.
• There is a need for updated models that integrate machine learning for dynamic disaster management.
• Further research can lead to more effective humanitarian logistical solutions.