Barajas, Carlos A.Gobbert, MatthiasWang, Jianwu2022-09-292022-09-292021-10-30Barajas, 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_25https://doi.org/10.1007/978-3-030-71704-9_25http://hdl.handle.net/11603/2592416th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020)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.6 pagesen-USThis 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.UMBC Big Data Analytics LabUMBC High Performance Computing Facility (HPCF)Text