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Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery

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Abstract

Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensity 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 web server 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.

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Acknowledgements

The authors are grateful to Dr. Mark DeMaria and Dr. John Knaff, National Hurricane Center, NOAA, USA for their valuable contribution to the project. AA and MD are funded by the IT Endowment Fund at PIEAS. We will also like to recognize the support by Higher Education Commission (HEC) of the government of Pakistan for establishment of PIEAS Data Science Lab whose computational resources were utilized in this work.

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Authors and Affiliations

  1. PIEAS Data Science Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), PO Nilore, Islamabad, Pakistan

    Muhammad Dawood, Amina Asif & Fayyaz ul Amir Afsar Minhas

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  1. Muhammad Dawood
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  2. Amina Asif
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  3. Fayyaz ul Amir Afsar Minhas
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Correspondence to Fayyaz ul Amir Afsar Minhas.

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Dawood, M., Asif, A. & Minhas, F.u.A.A. Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery. Neural Comput & Applic 32, 9009–9017 (2020). https://doi.org/10.1007/s00521-019-04410-7

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  • DOI: https://doi.org/10.1007/s00521-019-04410-7

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