Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network

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dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorGobbert, Matthias
dc.contributor.authorWang, Jianwu
dc.date.accessioned2022-09-29T14:42:24Z
dc.date.available2022-09-29T14:42:24Z
dc.date.issued2021-10-30
dc.description16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020)
dc.description.abstractPredicting 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.en_US
dc.description.sponsorshipThis work is supported in part by the U.S. National Science Foundation under the CyberTraining (OAC–1730250) and MRI (OAC–1726023) programs. The hardware used in the computational studies is part of the UMBC High Perfor- mance Computing Facility (HPCF). Co-author Carlos A. Barajas was supported as HPCF RA as well as as CyberTraining RA.en_US
dc.description.urihttps://link.springer.com/chapter/10.1007/978-3-030-71704-9_25en_US
dc.format.extent6 pagesen_US
dc.genrejournal articlesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2fdeo-ura9
dc.identifier.citationBarajas, 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_25en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-71704-9_25
dc.identifier.urihttp://hdl.handle.net/11603/25924
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsThis 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.en_US
dc.subjectUMBC Big Data Analytics Laben_US
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleTornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Networken_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-1745-2292en_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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