Estimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning

Estimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning

Lin Lin Associate Research Scientist

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University of Maryland, College Park

Estimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Lin Lin. 2020. \u201cEstimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning\u201d. Global Journal of Science Frontier Research - H: Environment & Environmental geology GJSFR-H Volume 20 (GJSFR Volume 20 Issue H4).

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GJSFR Volume 20 Issue H4

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Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

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GJSFR-H Classification FOR Code: 059999p

Submission ReceivedAugust 19, 2020
Peer Review Double Blind
Handling Editor
Accepted August 24, 2020
Published October 5, 2020

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v1.2

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October 5, 2020

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