Nagiza Samatova NCSU/ORNL
Fred Semazzi NCSU
- Develop predictive forecasting methodology for climate extremes (e.g., hurricanes, droughts, rainfalls)
- Devise scalable algorithms for predictive mining of large-scale climate complex networks
- Provide mechanistic insights about the key factors contributing to extreme events variability
- Demonstrate high predictive skill for North Atlantic seasonal hurricane activity
- Provide policy makers more reliable information on seasonal climate extremes
- Scalable large-scale graph mining algorithms of broader applicability (e.g., bioenergy)
- Advance our understanding of the mechanisms that influence hurricane variability and behavior
- International impact managing meningitis epidemic outbreaks driven by climate extremes
15 percent more accurate forecast of seasonal hurricane activity
"Novel data-driven methods promise to excel beyond the traditional methods in climate prediction tools"
Z. Chen, W. Hendrix, H. Guan, I. Tetteh, A. Choudhary, F. Semazzi, N. Samatova, "Discovery of extreme events-related communities in contrasting groups of physical system networks," Data Mining and Knowledge Discovery, 27(2), p. 225-258, 2012.