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Tornado Forecast Visualization for Effective Rescue Planning

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IoT and WSN based Smart Cities: A Machine Learning Perspective

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Tornadoes are powerful, highly unpredictable, and destructive weather systems. Due to their short life cycle, there is often little time to warn and take effective rescue measures. This chapter presents a novel system to visualize and communicate the current and predictive path of a spotted tornado and help improve the overall disaster management in light of public safety. We present an adaptive real-time hexagonal grid system that can make use of any form of predictors for tornado occurrence thus far or those that would be developed in future. The system has potential to adapt in real time to the signals from various predictors and can form a basis for issuing warnings and planning rescue measures. As an example, we illustrate a history-based predictor for tornado path.

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Acknowledgements

Acknowledgments are due to Robert Staskowski who initiated this work by his blog [22] after the disaster in Moore, Oklahoma in 2013. John Nelson (now working at ESRI) and Josh Stevens (now GIScientist at NASA’s Earth Observatory) helped brainstorm the idea and encouraged to continue working and develop the idea more. Acknowledgments are due also to IDV Solutions to allow use of their Visual Fusion Visualization engine to help visualize and implement the ideas presented in this article.

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Authors and Affiliations

  1. Vishnu Institute of Technology, Bhimavaram, India

    Abhinav Dayal, Sumit Gupta & Sreenu Ponnada

  2. Karunya Institute of Technology and Sciences, Coimbatore, India

    D. Jude Hemanth

Authors
  1. Abhinav Dayal
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  2. Sumit Gupta
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  3. Sreenu Ponnada
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  4. D. Jude Hemanth
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Correspondence to Abhinav Dayal .

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Editors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Shalli Rani

  2. Intel Corporation, Folsom, CA, USA

    Vyasa Sai

  3. Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, India

    R. Maheswar

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Dayal, A., Gupta, S., Ponnada, S., Hemanth, D.J. (2022). Tornado Forecast Visualization for Effective Rescue Planning. In: Rani, S., Sai, V., Maheswar, R. (eds) IoT and WSN based Smart Cities: A Machine Learning Perspective. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-84182-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-84182-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84181-2

  • Online ISBN: 978-3-030-84182-9

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