Abstract

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 predict and data sets are constructed. These data present unique characteristics and challenges that could potentially motivate future Artificial Intelligent and Data Mining research.

Forecast Oriented Classification of Spatio-Temporal Extreme Events / 2952 Zhengzhang Chen, Yusheng Xie, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Wei-keng Liao, Nagiza F. Samatova, Alok Choudhary 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 predict and data sets are constructed. These data present unique characteristics and challenges that could potentially motivate future Artificial Intelligent and Data Mining research.