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Related Concept Videos

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Related Experiment Video

Updated: Sep 29, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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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
PubMed
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.

Keywords:
Disaster impact dataDisaster managementEmergency responseHumanitarian logisticsMachine learningOptimization instances and algorithmsSocioeconomic and geographical data

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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.