Artificial Intelligence-Powered Resource Allocation During Disasters: A
Scoping Review of Machine Learning Algorithms
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.