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Artificial Intelligence-Powered Resource Allocation During Disasters: A Scoping Review of Machine Learning Algorithms
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  • Nina M. Martin ,
  • Lisa Poirier ,
  • Andrew J. Rosenblum ,
  • Melissa M. Reznar ,
  • Daniel J. Barnett
Nina M. Martin
Johns Hopkins Bloomberg School of Public Health

Corresponding Author:[email protected]

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Lisa Poirier
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Andrew J. Rosenblum
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Melissa M. Reznar
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Daniel J. Barnett
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Abstract

Background: The quick detection of needs and resources during a disaster can save lives. Twitter is a reliable information source during disasters and has been studied using machine learning for situational awareness. There are many methods to utilize machine learning to detect needs and resource availability via Twitter, but the most common and accurate methods remain unclear. This scoping review addresses this gap.
Methods: Keywords were defined within the concepts of machine learning, disasters, and classifying needs and resources. After the database searches, PRISMA guidelines were followed to perform a partnered, two-round scoping literature review.
Results: 42 articles met the inclusion criteria for analysis. Geographically, the largest portion of the studies took place in the United States (24.5%), followed by Nepal (18.4%), and India (16.3%). The most studied disaster type was earthquake (25.6%), followed by hurricane (16.7%). While there was no consensus on best methods, the most used algorithms included neural networks using different types of word embeddings to optimize performance. None of the tools were ready to be used directly by aid organizations or policymakers.
Conclusion: Machine learning tools for resource allocation are needed to provide timely assistance to those in need during disasters. This review indicates a need for additional research regarding a consensus on best practices for algorithm model selection, benchmarking datasets, crisis lexicons, word embedding techniques and evaluation methods. These tools have a high potential for improving real-time emergency management across all disaster phases, especially as disasters of all kinds become more and more frequent.