Analytics and Evolving Landscape of Machine Learning for Emergency Response [chapter]

Rajendra Akerkar Minsung Hong
2019 Zenodo  
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
more » ... f 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 [...]
doi:10.5281/zenodo.5106014 fatcat:ui7kmryflbaljkim4u2ync2dqi