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Friday, June 5
Practice and Applications
Practice and Applications Posters, Part 1
Fri, Jun 5, 10:00 AM - 1:00 PM
TBD
 

Tornado: Classification, Correlation, Prediction (308322)

*Thilini Vasana Mahanama, Texas Tech University 
Dimitri Volchenkov, Texas Tech University 

Keywords: Non-negative matrix factorization, Copula, Long-Short Term Memory Networks

National Oceanic and Atmospheric Administration (NOAA) annually reports around 1,300 tornado events hitting the US soil. Non-negative matrix factorization is used to classify tornado events with the account for property losses. The results of linear regression about that property losses are roughly proportional to the square root of tornado's area has been improved substantially by the copula method. The obtained non-linear correlation coefficients vary with time and location. Long-Short Term Memory networks are used for the prediction of future property losses associated with tornadoes.