Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Forecast Oriented Classification of Spatio-Temporal Extreme Events

• Main Authors: Zhengzhang Chen, Yusheng Xie, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Wei-keng Liao, Nagiza F. Samatova, Alok Choudhary
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: North Atlantic
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.2595; http://cucis.ece.northwestern.edu/publications/pdf/CheXie13b.pdf

In complex dynamic systems, accurate forecasting of extreme events, such as hurricanes, is a highly underdetermined, yet very important sustainability problem. While physics-based models deserve their own merits, they often provide unreliable predictions for variables highly related to extreme events. In this paper, we propose a new supervised machine learning problem, which we call a forecast oriented classification of spatiotemporal extreme events. We formulate three important real-world extreme event classification tasks, including seasonal forecasting of (a) tropical cyclones in Northern Hemisphere, (b) hurricanes and landfalling hurricanes in North Atlantic, and (c) North African rainfall. Corresponding predictor and predictand data sets are constructed. These data present unique characteristics and challenges that could potentially motivate future Artificial Intelligent and Data Mining research. 1