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PHURIE : hurricane intensity estimation from infrared satellite imagery using machine learning

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Asif, Amina, Dawood, Muhammad, Jan, Bismillah, Khurshid, Javaid, DeMaria, Mark and Minhas, Fayyaz ul Amir Afsar (2018) PHURIE : hurricane intensity estimation from infrared satellite imagery using machine learning. Neural Computing and Applications . doi:10.1007/s00521-018-3874-6 ISSN 0941-0643.

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Official URL: http://dx.doi.org/10.1007/s00521-018-3874-6

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Abstract

Automated prediction of hurricane intensity from satellite infrared imagery is a challenging problem with implications in weather forecasting and disaster planning. In this work, a novel machine learning-based method for estimation of intensity or maximum sustained wind speed of tropical cyclones over their life cycle is presented. The approach is based on a support vector regression model over novel statistical features of infrared images of a hurricane. Specifically, the features characterize the degree of uniformity in various temperature bands of a hurricane. Performance of several machine learning methods such as ordinary least squares regression, backpropagation neural networks and XGBoost regression has been compared using these features under different experimental setups for the task. Kernelized support vector regression resulted in the lowest prediction error between true and predicted hurricane intensities (approximately 10 knots or 18.5 km/h), which is better than previously proposed techniques and comparable to SATCON consensus. The performance of the proposed scheme has also been analyzed with respect to errors in annotation of center of the hurricane and aircraft reconnaissance data. The source code and webserver implementation of the proposed method called PHURIE (PIEAS HURricane Intensity Estimator) is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#PHURIE.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QC Physics
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Hurricanes -- Forecasting -- Mathematical models, Cyclones—Tropics, Machine learning -- Mathematical models, Remote sensing
Journal or Publication Title: Neural Computing and Applications
Publisher: Springer
ISSN: 0941-0643
Official Date: 19 November 2018
Dates:
DateEvent
19 November 2018Available
9 November 2018Accepted
DOI: 10.1007/s00521-018-3874-6
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-018-3874-6
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 5 November 2019
Date of first compliant Open Access: 19 November 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Information Technology and Telecommunication Endowment FundPakistan Institute of Engineering and Applied Scienceshttp://www.pieas.edu.pk/

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