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Article

Atlantic Hurricane Activity Prediction: A Machine Learning Approach

1
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-8208, USA
2
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695-8208, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Sunil Kumar Jha
Received: 28 February 2021 / Revised: 29 March 2021 / Accepted: 1 April 2021 / Published: 3 April 2021
Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance. View Full-Text
Keywords: hurricanes; tropical cyclones; fusion networks; weather forecast hurricanes; tropical cyclones; fusion networks; weather forecast
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MDPI and ACS Style

Asthana, T.; Krim, H.; Sun, X.; Roheda, S.; Xie, L. Atlantic Hurricane Activity Prediction: A Machine Learning Approach. Atmosphere 2021, 12, 455. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12040455

AMA Style

Asthana T, Krim H, Sun X, Roheda S, Xie L. Atlantic Hurricane Activity Prediction: A Machine Learning Approach. Atmosphere. 2021; 12(4):455. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12040455

Chicago/Turabian Style

Asthana, Tanmay, Hamid Krim, Xia Sun, Siddharth Roheda, and Lian Xie. 2021. "Atlantic Hurricane Activity Prediction: A Machine Learning Approach" Atmosphere 12, no. 4: 455. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12040455

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