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Related Experiment Video

Updated: Sep 6, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Disaster and Pandemic Management Using Machine Learning: A Survey.

Vinay Chamola1, Vikas Hassija2, Sakshi Gupta2

  • 1Department of Electrical and Electronics Engineering & APPCAIRBirla Institute of Technology and Science at Pilani Pilani 333031 India.

IEEE Internet of Things Journal
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

This review explores machine learning (ML) algorithms for disaster and pandemic management. ML excels at analyzing complex data for prediction, classification, and optimizing responses in crises.

Keywords:
Crowd evacuationdisaster managementhealthcaremachine learning (ML)pandemic managementsocial distancing

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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.