Analytics and Evolving Landscape of Machine Learning for Emergency Response

Abstract

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 learning in mass emergency and humanitarian crises is constantly evolving and gaining traction. As a branch of artificial intelligence, machine learning technologies have the huge 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. They have greatly improved the capability and effectiveness of emergency management. In this paper, we review the use of machine learning techniques to support the decision-making processes for the emergency management and discuss their challenges. Additionally, we discuss the challenges and opportunities of the machine learning approaches and intelligent data analysis 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 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. This chapter also focuses on crowd-sourcing approaches with machine learning to achieve better understanding and decision support during an emergency.