Extended Abstract (136K)3.2
Probabilistic Turbulence Prediction using Random Forests
Zhengzheng Li, The University of Oklahoma, Norman, OK; and T. A. Supinie
The predicting turbulence problems for this AI contest containing measurement from aircraft, satellite, ground-based radar and numerical weather model. According to the peak_edr value, scenarios can be classified into two class types: moderate-or-greater turbulence or no turbulence. Random Forest is notorious being effective on weakly correlated features, thus is adopted in this study. Unlike normal Random Forests having each tree in the forest to vote, a new technique of combining results from the forest is proposed to generate probabilistic forecast, which is the contest requirement. Preliminary results show that the BSS value can reach 0.4 for the proposed algorithm.
Session 3, Third Annual AMS Artificial Intelligence Forecasting Contest: Methods and Results
Wednesday, 20 January 2010, 10:30 AM-12:00 PM, B204
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