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Paper Abstract and Keywords
Presentation 2022-03-08 15:55
Applying a medium-term prediction method for the number of heat stroke victims to medium-sized local governments
Tetsuya Nakai (Kobe Univ.), Sachio Saiki (Kochi tech.), Masahide Nakamura (Kobe Univ.) SS2021-68
Abstract (in Japanese) (See Japanese page) 
(in English) In our previous research, we proposed a prediction method of the number of heat stroke victims for the next week based on the weekly weather forecast.
In this paper, we evaluate the possibility of using this method in Sanda City, Hyogo Prefecture, a medium-sized local government.
We developed a prediction model for Sanda City using the daily number of heat stroke victims in Sanda City and historical weather data.
Using the prediction model, we were able to predict the occurrence of heat stroke in 71.3% of the cases.
In addition, we developed a web application, HSP (HeatStroke-Prediction), which automatically displays the prediction results.
As a result of using HSP at the Kobe City Fire Department, they gave us feedback that HSP can be used to publicize heat stroke prevention measures based on numerical evidence.
Keyword (in Japanese) (See Japanese page) 
(in English) heat stroke / ambulance / smartcity / demand prediction / machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 416, SS2021-68, pp. 157-162, March 2022.
Paper # SS2021-68 
Date of Issue 2022-02-28 (SS) 
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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF SS2021-68

Conference Information
Committee SS  
Conference Date 2022-03-07 - 2022-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Software Science etc. 
Paper Information
Registration To SS 
Conference Code 2022-03-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Applying a medium-term prediction method for the number of heat stroke victims to medium-sized local governments 
Sub Title (in English)  
Keyword(1) heat stroke  
Keyword(2) ambulance  
Keyword(3) smartcity  
Keyword(4) demand prediction  
Keyword(5) machine learning  
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1st Author's Name Tetsuya Nakai  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Sachio Saiki  
2nd Author's Affiliation Kochi University of Technology (Kochi tech.)
3rd Author's Name Masahide Nakamura  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2022-03-08 15:55:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2021-68 
Volume (vol) vol.121 
Number (no) no.416 
Page pp.157-162 
#Pages
Date of Issue 2022-02-28 (SS) 


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