Disaster and Pandemic Management Using Machine Learning: A Survey.

Area of Science:

• Computer Science
• Public Health
• Data Science

Background:

• Disasters and pandemics pose significant global risks, necessitating advanced management strategies.
• Existing technologies like IoT, UAVs, and 5G are employed, but data analysis remains a challenge.
• Machine learning (ML) offers powerful tools for handling large, multidimensional datasets inherent in disaster and pandemic scenarios.

Purpose of the Study:

• To provide a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management.
• To present a tutorial on ML algorithms and their application in these critical fields.
• To discuss the integration of ML with other technologies and identify future research directions.

Main Methods:

• Literature review of current machine learning algorithms.
• Analysis of ML applications in disaster prediction, evacuation planning, and social media analysis.
• Review of ML in pandemic forecasting, spread monitoring, and disease diagnosis.
• Exploration of combining ML with technologies like IoT, UAVs, and satellite systems.

Main Results:

• Machine learning algorithms are highly effective for recognition, classification, and prediction tasks in disaster and pandemic management.
• ML aids in crucial functions such as determining evacuation routes, analyzing social media for situational awareness, and post-disaster assessment.
• ML significantly enhances pandemic management through prediction, real-time spread monitoring, and diagnostic support.
• Integration of ML with various technologies amplifies their effectiveness in managing crises.

Conclusions:

• Machine learning algorithms are indispensable tools for modern disaster and pandemic management.
• Combining ML with emerging technologies presents a powerful approach to mitigate risks and improve response.
• Further research is needed to address challenges and explore novel ML applications in this domain.