Analytics and Evolving Landscape of Machine Learning for Emergency Response

The advances in information technology have had a profound impact
on emergency management by making unprecedented volumes of data available to
the decision makers. This has resulted in new challenges related to the effective
management of large volumes of data. In this regard, the role of machine learn-
ing in mass emergency and humanitarian crises is constantly evolving and gaining
traction. As a branch of artificial intelligence, machine learning technologies have
the out-standing advantages of self-learning, self-organization, and self-adaptation,
along with simpleness, generality and robustness. Although these technologies do
not perfectly solve issues in emergency management, and have been showed to can
greatly improve the capability and effectiveness of emergency management. The
purpose of this chapter is to discuss a hybrid crowdsourcing and real-time ma-
chine learning approaches to rapidly process large volumes of data for emergency
response in a time-sensitive manner.We review the application of machine learning
techniques to support the decision-making processes for the emergency or crisis
management and discuss their challenges. Additionally, we discuss the challenges
and opportunities of the machine learning approaches and intelligent data analy-
sis to distinct phases of emergency management. Based on the literature review,
we observe a trend to move from narrow in scope, problem-specific applications
of data mining and machine learning to solutions that address a wider spectrum
of problems, such as situational awareness and real-time threat assessment using
diverse streams of data. In particular, this chapter also focuses on crowdsourcing
approaches with machine learning to achieve better understanding and decision
support during a disaster, and we discusses the issues on the approaches in terms
of data analysis. Several examples of the tweet related to emergency are discussed
to more deeply contemplate the issues.