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. |
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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) |
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Keyword(1) |
heat stroke |
Keyword(2) |
ambulance |
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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 |
6 |
Date of Issue |
2022-02-28 (SS) |
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