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A Systematic Classification Investigation of Rapid Intensification of Atlantic Tropical Cyclones with the SHIPS Database

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  • 1 Department of Geography and Geoinformation Science, and Center for Earth Observing and Space Research, College of Science, George Mason University, Fairfax, Virginia
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Abstract

In hopes of better understanding the rapid intensification (RI) of tropical cyclones, the classification technique as a data mining process is used in this mining experiment. The mining results are expected to increase accurate forecasting abilities for RI through exhaustive data distillation. In this work, the Statistical Hurricane Intensity Prediction Scheme (SHIPS) database for the Atlantic basin during the period 1982–2009 is used as the data source and the Waikato Environment for Knowledge Analysis (WEKA) software is used for various classifier implementations. As in most classification applications, accuracies in model building with training data may be high. However, accuracies with testing data usually deteriorate. Various special steps are carried out in an effort to improve the accuracy. These steps include setting the cost parameters for overcoming the unbalanced RI samples, temporal averages of variable values for more accurate environmental estimation, feature filtering for irrelevant feature removal, and subset feature selections. The best performance measures of the training results are above 90% for probability of detection (POD) with 10%–20% false alarm ratios (FARs) for cases of RI within 24 h. However, the performance on the testing data is not as good. The reported RI forecasting accuracies in this work are lower than the goals set by NOAA in their Hurricane Forecast Improvement Project. Nevertheless, this work sheds light on the future direction of RI investigations using data mining techniques. Many more studies are needed before we can fully understand the potential and/or limitations of data mining techniques in RI investigations.

Corresponding author address: R. Yang, Dept. of Geography and Geoinformation Science, MS 6C3, College of Science, George Mason University, 4400 University Dr., Fairfax, VA 22030-4444. E-mail: ryang@gmu.edu

Abstract

In hopes of better understanding the rapid intensification (RI) of tropical cyclones, the classification technique as a data mining process is used in this mining experiment. The mining results are expected to increase accurate forecasting abilities for RI through exhaustive data distillation. In this work, the Statistical Hurricane Intensity Prediction Scheme (SHIPS) database for the Atlantic basin during the period 1982–2009 is used as the data source and the Waikato Environment for Knowledge Analysis (WEKA) software is used for various classifier implementations. As in most classification applications, accuracies in model building with training data may be high. However, accuracies with testing data usually deteriorate. Various special steps are carried out in an effort to improve the accuracy. These steps include setting the cost parameters for overcoming the unbalanced RI samples, temporal averages of variable values for more accurate environmental estimation, feature filtering for irrelevant feature removal, and subset feature selections. The best performance measures of the training results are above 90% for probability of detection (POD) with 10%–20% false alarm ratios (FARs) for cases of RI within 24 h. However, the performance on the testing data is not as good. The reported RI forecasting accuracies in this work are lower than the goals set by NOAA in their Hurricane Forecast Improvement Project. Nevertheless, this work sheds light on the future direction of RI investigations using data mining techniques. Many more studies are needed before we can fully understand the potential and/or limitations of data mining techniques in RI investigations.

Corresponding author address: R. Yang, Dept. of Geography and Geoinformation Science, MS 6C3, College of Science, George Mason University, 4400 University Dr., Fairfax, VA 22030-4444. E-mail: ryang@gmu.edu
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