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Applications of Decision Tree Analytics on Semi-Structured North Atlantic Tropical Cyclone Forecasts

Applications of Decision Tree Analytics on Semi-Structured North Atlantic Tropical Cyclone Forecasts

Skylar Hernandez, Caroline Howard, Richard Livingood, Cynthia Calongne
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 23
ISSN: 1941-6253|EISSN: 1941-6261|EISBN13: 9781522565017|DOI: 10.4018/IJSKD.2019040103
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MLA

Hernandez, Skylar, et al. "Applications of Decision Tree Analytics on Semi-Structured North Atlantic Tropical Cyclone Forecasts." IJSKD vol.11, no.2 2019: pp.31-53. http://doi.org/10.4018/IJSKD.2019040103

APA

Hernandez, S., Howard, C., Livingood, R., & Calongne, C. (2019). Applications of Decision Tree Analytics on Semi-Structured North Atlantic Tropical Cyclone Forecasts. International Journal of Sociotechnology and Knowledge Development (IJSKD), 11(2), 31-53. http://doi.org/10.4018/IJSKD.2019040103

Chicago

Hernandez, Skylar, et al. "Applications of Decision Tree Analytics on Semi-Structured North Atlantic Tropical Cyclone Forecasts," International Journal of Sociotechnology and Knowledge Development (IJSKD) 11, no.2: 31-53. http://doi.org/10.4018/IJSKD.2019040103

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Abstract

This interdisciplinary quantitative study examines how a text mining technique that is widely used to understand financial market forecasts could also help in understanding North Atlantic Tropical Cyclone (TC) forecasts. TCs are a destructive circulation of thunderstorms over a surface low-pressure center. The C4.5 decision tree algorithm has been used successfully to aid in the understanding of financial market forecasts with accuracy rates greater than 55%. This study has examined the use of the C4.5 decision tree algorithm on a 15-year period of the National Hurricane Centers five-day TC forecasts to see if the algorithm could provide a statistically significant value to improving the overall TC forecast accuracy. Improvements in the overall TC forecast accuracy can aid in providing those impacted by a TC adequate early, relevant, and lifesaving TC watches and warnings. This study has helped identify key weather pattern components that have significant information gain, which can help both researchers and practitioners prioritize projects that could help improve TC forecasts.

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