| Title |
Medium-Term Prediction for Ambulance Demand of Heat Stroke using Weekly Weather Forecast |
| ID_Doc |
43931 |
| Authors |
Nakai, T; Saiki, S; Nakamura, M |
| Title |
Medium-Term Prediction for Ambulance Demand of Heat Stroke using Weekly Weather Forecast |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/IOTSMS53705.2021.9704892 |
| Abstract |
In our joint research with the Kobe City Fire Department, we have been studying the effective use of emergency resources. In our previous research, we developed a prediction model for the number of heat stroke victims in Kobe City by applying machine learning to past weather observation data and emergency dispatch records. It takes a certain period of time to prepare the necessary emergency measures in the field. Therefore, it is necessary to make medium-term forecasts for the next week or so, and the accuracy of such forecasts is not known. In addition, conventional models refer to items of past weather data, which are difficult to obtain from current weather forecasts. Therefore, in this paper, we investigate a new method for predicting the number of heat stroke victims in the medium term by using past weather forecast data. From the proposed method, we know that it is possible to predict the number of heat stroke victims in 7 days by utilizing the actual weather forecast data in Kobe City. |
| Author Keywords |
heat stroke; ambulance; smart city; demand prediction; machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000786996700020 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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