Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques

Author:

Lee Yu-Ju,Hall David,Liu Quan,Liao Wen-Wei,Huang Ming-Chun

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference17 articles.

1. PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning;Asif;Neural Comput. Appl.,2018

2. The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts);Blake,2011

3. Rotation-blended CNNs on a new open dataset for tropical cyclone image-to-intensity regression;Chen,2018

4. A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images;Combinido,2018

5. Coskun, M., Grama, A., Koyuturk, M., 2016. Efficient processing of network proximity queries via Chebyshev acceleration. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1515–1524.

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