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Deep-PHURIE : deep learning based hurricane intensity estimation from infrared satellite imagery
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Dawood, Muhammad, Asif, Amina and Minhas, Fayyaz ul Amir Afsar (2019) Deep-PHURIE : deep learning based hurricane intensity estimation from infrared satellite imagery. Neural Computing and Applications . doi:10.1007/s00521-019-04410-7 ISSN 0941-0643.
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WRAP-Deep-PHURIE-deep-learning-hurricane-intensity-satellite-imagery-Minhasf-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1079Kb) | Preview |
Official URL: http://dx.doi.org/10.1007/s00521-019-04410-7
Abstract
Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensities is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root mean squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A webserver implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE.
| Item Type: | Journal Article | ||||||
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| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Hurricanes -- Mathematical models, Remote-sensing images, Machine learning | ||||||
| Journal or Publication Title: | Neural Computing and Applications | ||||||
| Publisher: | Springer | ||||||
| ISSN: | 0941-0643 | ||||||
| Official Date: | 9 August 2019 | ||||||
| Dates: |
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| DOI: | 10.1007/s00521-019-04410-7 | ||||||
| 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/s00521-019-04410-7 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 4 November 2019 | ||||||
| Date of first compliant Open Access: | 9 August 2020 | ||||||
| RIOXX Funder/Project Grant: |
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