Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network
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2021-10-30Type of Work
6 pagesText
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_25Rights
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.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.