Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network
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Author/Creator ORCID
Date
2021-10-30
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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
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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.