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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1 University of Maryland

GJSFR Volume 20 Issue H4

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for 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
Pg. 5- 15

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Journal Specifications

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

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v1.2

Issue date

October 5, 2020

Language

English

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