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Brazilian disaster datasets and real-world instances for optimization and machine learning
Rafaela Veloso1,2, Juliana Cespedes2, Aakil Caunhye3
1Aeronautics Institute of Technology, São José dos Campos-SP, Brazil.
Data in Brief
|March 21, 2022
Summary
This study provides extensive Brazilian disaster data (2003-2021) and optimization models for disaster management. The datasets cover 9 disaster types, affected populations, and socioeconomic factors across 5,402 municipalities.
Area of Science:
- Disaster Science and Management
- Operations Research
- Data Science
Background:
- Comprehensive disaster data is crucial for effective disaster management and humanitarian logistics.
- Existing datasets often lack the detail or real-world applicability needed for robust optimization modeling.
- Brazil faces diverse natural disasters, necessitating detailed data for preparedness and response.
Purpose of the Study:
- To present a comprehensive dataset of Brazilian disasters from 2003 to 2021.
- To develop real-world optimization instances for disaster management problems based on this data.
- To facilitate research in disaster response, humanitarian logistics, and applied machine learning.
Main Methods:
- Data collection from government and institutional reports.
- Data consolidation and summarization using Excel and Python.
- Development of optimization instances for problems like facility location and location-allocation.
Main Results:
- Creation of a detailed dataset covering 9 disaster types across 5,402 Brazilian municipalities over 18 years.
- Inclusion of geographical, demographic, and socioeconomic data for each municipality.
- Generation of two real-world instances for the location-allocation problem.
Conclusions:
- The presented dataset and optimization instances offer a valuable resource for disaster research and practice.
- This work supports the application of machine learning and operations research techniques to real-world disaster scenarios.
- Availability of such comprehensive data and instances is rare and significantly advances the field.
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