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    Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network

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    2021-Tornado Storm Data Synthesization using DeepConvolutional Generative Adversarial Network.pdf (2.134Mb)
    Links to Files
    https://link.springer.com/chapter/10.1007/978-3-030-71704-9_25
    Permanent Link
    https://doi.org/10.1007/978-3-030-71704-9_25
    http://hdl.handle.net/11603/25924
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    • UMBC Faculty Collection
    • UMBC Information Systems Department
    • UMBC Mathematics and Statistics Department
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    Author/Creator
    Barajas, Carlos A.
    Gobbert, Matthias
    Wang, Jianwu
    Author/Creator ORCID
    https://orcid.org/0000-0003-1745-2292
    https://orcid.org/0000-0002-9933-1170
    Date
    2021-10-30
    Type of Work
    6 pages
    Text
    journal articles
    conference papers and proceedings
    preprints
    Citation of Original Publication
    Barajas, C.A., Gobbert, M.K., Wang, J. (2021). Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_25
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    UMBC Big Data Analytics Lab
    Abstract
    Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic weather patterns much more rapidly, thus allowing for more timely alerts to the public. A challenge in applying machine learning in tornado prediction is the imbalance between tornadic data and non-tornadic data. To have more balanced data, we created in this work a new data synthesization system to augment tornado storm data by implementing a deep convolutional generative adversarial network (DCGAN) and qualitatively compare its output to natural data.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.