The Library
PHURIE : hurricane intensity estimation from infrared satellite imagery using machine learning
Tools
Tools
Tools
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
|
PDF
WRAP-PHURIE- hurricane-intensity-infrared-Minhas-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1966Kb) | Preview |
Official URL: http://dx.doi.org/10.1007/s00521-018-3874-6
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 > 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: |
|
||||||
| DOI: | 10.1007/s00521-018-3874-6 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | 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 | ||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
