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Paper Abstract and Keywords
Presentation 2021-01-21 12:05
Examination of precipitation estimation using atmospheric variables
Takanori Ito, Motoki Amagasaki, Kei Ishida, Masato Kiyama, Masahiro Iida (GSST Kumamoto University) NC2020-34
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we developed a model for SR using ConvLSTM to improve the resolution of precipitation data.
In the related work, SISR using SRCNN, it is difficult to recover local values for precipitation data.
In this study, we propose a method that adds atmospheric variables to the low-resolution precipitation data and treats it as a time series data.
The proposed model is based on ConvLSTM, which treats images as time series.
In this evaluation, we compared the proposed model with the high-resolution precipitation data generated by SRCNN using the evaluation indices RMSE (Root Mean Square Error) and CC (Correlation Coefficient).
The results show that the proposed model is 0.93 times more accurate in terms of RMSE and 13.25 times more accurate in terms of correlation coefficient for high-resolution precipitation data.
Keyword (in Japanese) (See Japanese page) 
(in English) Precipitation / Super Resolution / Convolutional Neural Network / Long Short Term Memory / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 331, NC2020-34, pp. 13-17, Jan. 2021.
Paper # NC2020-34 
Date of Issue 2021-01-14 (NC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2020-34

Conference Information
Committee NC NLP  
Conference Date 2021-01-21 - 2021-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) NC，NLP 
Paper Information
Registration To NC 
Conference Code 2021-01-NC-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Examination of precipitation estimation using atmospheric variables 
Sub Title (in English)  
Keyword(1) Precipitation  
Keyword(2) Super Resolution  
Keyword(3) Convolutional Neural Network  
Keyword(4) Long Short Term Memory  
Keyword(5)  
Keyword(6)  
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Keyword(8)  
1st Author's Name Takanori Ito  
1st Author's Affiliation Graduate School of Science and Technology Kumamoto University (GSST Kumamoto University)
2nd Author's Name Motoki Amagasaki  
2nd Author's Affiliation Graduate School of Science and Technology Kumamoto University (GSST Kumamoto University)
3rd Author's Name Kei Ishida  
3rd Author's Affiliation Graduate School of Science and Technology Kumamoto University (GSST Kumamoto University)
4th Author's Name Masato Kiyama  
4th Author's Affiliation Graduate School of Science and Technology Kumamoto University (GSST Kumamoto University)
5th Author's Name Masahiro Iida  
5th Author's Affiliation Graduate School of Science and Technology Kumamoto University (GSST Kumamoto University)
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Speaker Author-1 
Date Time 2021-01-21 12:05:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2020-34 
Volume (vol) vol.120 
Number (no) no.331 
Page pp.13-17 
#Pages 5 
Date of Issue 2021-01-14 (NC) 

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