| Presentation | 2022-03-08 Applying a medium-term prediction method for the number of heat stroke victims to medium-sized local governments Tetsuya Nakai, Sachio Saiki, Masahide Nakamura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | heat stroke / ambulance / smartcity / demand prediction / machine learning |
| Paper # | SS2021-68 |
| Date of Issue | 2022-02-28 (SS) |
| Conference Information | |
| Committee | SS |
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| Conference Date | 2022/3/7(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Science etc. |
| Chair | Takashi Kobayashi(Tokyo Inst. of Tech.) |
| Vice Chair | Kozo Okano(Shinshu Univ.) |
| Secretary | Kozo Okano(Hiroshima City Univ.) |
| Assistant | Shinpei Ogata(Shinshu Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Software Science |
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| Language | JPN |
| 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 |
| 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.) |
| Date | 2022-03-08 |
| Paper # | SS2021-68 |
| Volume (vol) | vol.121 |
| Number (no) | SS-416 |
| Page | pp.pp.157-162(SS), |
| #Pages | 6 |
| Date of Issue | 2022-02-28 (SS) |