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Paper Abstract and Keywords
Presentation 2019-02-14 13:20
An attempt of rainfall estimation by monitoring broadcasting satellite combined with machine learning
Ryouichi Nishimura, Byeong-Pyo Jeong, Kazuyoshi Kawasaki, Takashi Takahashi (NICT) SAT2018-79
Abstract (in Japanese) (See Japanese page) 
(in English) Signal level observed by a BS antenna will be attenuated when it rains because of the rain attenuation. Accordingly, it is expected that the precipitation estimation is possible by measuring the attenuation of BS signal. This study is an attempt to estimate precipitation by means of machine learning using BS signal levels sampled at every one second. Simultaneous monitoring of precipitation and BS signal at the same location for 30 days reveals that there is diurnal variation in the observed BS signal level. Therefore, this diurnal variation is first estimated by means of long short-term memory (LSTM) method, which is one of recurrent neural network (RNN), to mitigate its influence. After that, deep neural network (DNN) is applied to estimate precipitation. Training and evaluation of the neural network using the data showed a modest agreement in terms of whether it rains or not when accumulated over a relatively long period such as an hour. It is also shown that variations in minutes are difficult to estimate by the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Rain attenuation / LSTM / DNN / Diurnal variation / AMeDAS / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 442, SAT2018-79, pp. 123-128, Feb. 2019.
Paper # SAT2018-79 
Date of Issue 2019-02-06 (SAT) 
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 SAT2018-79

Conference Information
Committee SANE SAT  
Conference Date 2019-02-13 - 2019-02-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Tanegashima Island 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Satellite Application and General 
Paper Information
Registration To SAT 
Conference Code 2019-02-SANE-SAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An attempt of rainfall estimation by monitoring broadcasting satellite combined with machine learning 
Sub Title (in English)  
Keyword(1) Rain attenuation  
Keyword(2) LSTM  
Keyword(3) DNN  
Keyword(4) Diurnal variation  
Keyword(5) AMeDAS  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryouichi Nishimura  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Byeong-Pyo Jeong  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Kazuyoshi Kawasaki  
3rd Author's Affiliation National Institute of Information and Communications Technology (NICT)
4th Author's Name Takashi Takahashi  
4th Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2019-02-14 13:20:00 
Presentation Time 25 minutes 
Registration for SAT 
Paper # SAT2018-79 
Volume (vol) vol.118 
Number (no) no.442 
Page pp.123-128 
#Pages 6 
Date of Issue 2019-02-06 (SAT) 

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