Domain knowledge integration into deep learning for typhoon intensity classification

Abstract In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye d...

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Autores principales:	Maiki Higa, Shinya Tanahara, Yoshitaka Adachi, Natsumi Ishiki, Shin Nakama, Hiroyuki Yamada, Kosuke Ito, Asanobu Kitamoto, Ryota Miyata
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ce72493e0a74592836482ed6f7f41f9
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spelling	oai:doaj.org-article:4ce72493e0a74592836482ed6f7f41f92021-12-02T17:45:17ZDomain knowledge integration into deep learning for typhoon intensity classification10.1038/s41598-021-92286-w2045-2322https://doaj.org/article/4ce72493e0a74592836482ed6f7f41f92021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92286-whttps://doaj.org/toc/2045-2322Abstract In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.Maiki HigaShinya TanaharaYoshitaka AdachiNatsumi IshikiShin NakamaHiroyuki YamadaKosuke ItoAsanobu KitamotoRyota MiyataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maiki Higa Shinya Tanahara Yoshitaka Adachi Natsumi Ishiki Shin Nakama Hiroyuki Yamada Kosuke Ito Asanobu Kitamoto Ryota Miyata Domain knowledge integration into deep learning for typhoon intensity classification
description	Abstract In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.
format	article
author	Maiki Higa Shinya Tanahara Yoshitaka Adachi Natsumi Ishiki Shin Nakama Hiroyuki Yamada Kosuke Ito Asanobu Kitamoto Ryota Miyata
author_facet	Maiki Higa Shinya Tanahara Yoshitaka Adachi Natsumi Ishiki Shin Nakama Hiroyuki Yamada Kosuke Ito Asanobu Kitamoto Ryota Miyata
author_sort	Maiki Higa
title	Domain knowledge integration into deep learning for typhoon intensity classification
title_short	Domain knowledge integration into deep learning for typhoon intensity classification
title_full	Domain knowledge integration into deep learning for typhoon intensity classification
title_fullStr	Domain knowledge integration into deep learning for typhoon intensity classification
title_full_unstemmed	Domain knowledge integration into deep learning for typhoon intensity classification
title_sort	domain knowledge integration into deep learning for typhoon intensity classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ce72493e0a74592836482ed6f7f41f9
work_keys_str_mv	AT maikihiga domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT shinyatanahara domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT yoshitakaadachi domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT natsumiishiki domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT shinnakama domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT hiroyukiyamada domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT kosukeito domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT asanobukitamoto domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification AT ryotamiyata domainknowledgeintegrationintodeeplearningfortyphoonintensityclassification
_version_	1718379584959807488

Domain knowledge integration into deep learning for typhoon intensity classification

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