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Analytics and Evolving Landscape of Machine Learning for Emergency Response

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Machine Learning Paradigms

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 1))

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.

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Notes

  1. 1.

    During Hurricane Sandy in 2012: http://www.cbsnews.com/news/social-media-a-news-source-and-toolduring-superstorm-sandy/.

  2. 2.

    https://twitter.com/NevaKey9/status/262860388083843072.

  3. 3.

    https://twitter.com/OccupySandyNJ/status/269183900113326082.

  4. 4.

    https://twitter.com/FEMASandy/status/269180520993259520.

  5. 5.

    https://twitter.com/saflaher/status/598247877295644674.

  6. 6.

    https://www.youtube.com/watch?v=aNZiLYEr6to.

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Acknowledgements

The work is funded from the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) grant through INTPART programme.

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    Minsung Hong & Rajendra Akerkar

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  4. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, NSW, Australia

    Lakhmi C. Jain

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Hong, M., Akerkar, R. (2019). Analytics and Evolving Landscape of Machine Learning for Emergency Response. In: Tsihrintzis, G., Virvou, M., Sakkopoulos, E., Jain, L. (eds) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_11

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