Ke Deng, Takahashi Kiyoshi, Takakura Jun'ya, Takara Kaoru, Kamranzad Bahareh
Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Yoshida-Nakaadachi 1, Sakyo-ku, Kyoto 606-8306, Japan.
Center for Social & Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan.
Sci Total Environ. 2023 May 15;873:162283. doi: 10.1016/j.scitotenv.2023.162283. Epub 2023 Feb 19.
Researchers agree that there is substantial evidence of an increasing trend in both the frequency and duration of extreme temperature events. Increasing extreme temperature events will place more pressure on public health and emergency medical resources, and societies will need to find effective and reliable solutions to adapt to hotter summers. This study developed an effective method to predict the number of daily heat-related ambulance calls. Both national- and regional-level models were developed to evaluate the performance of machine-learning-based methods on heat-related ambulance call prediction. The national model showed a high prediction accuracy and can be applied over most regions, while the regional model showed extremely high prediction accuracy in each corresponding region and reliable accuracy in special cases. We found that the introduction of heatwave features, including accumulated heat stress, heat acclimatization, and optimal temperature, significantly improved prediction accuracy. The adjusted coefficient of determination (adjusted R) of the national model improved from 0.9061 to 0.9659 by including these features, and the adjusted R of the regional model also improved from 0.9102 to 0.9860. Furthermore, we used five bias-corrected global climate models (GCMs) to forecast the total number of summer heat-related ambulance calls under three different future climate scenarios nationally and regionally. Our analysis demonstrated that, at the end of the 21st century, the total number of heat-related ambulance calls in Japan will reach approximately 250,000 per year (nearly four times the current amount) under SSP-5.85. Our results suggest that disaster management agencies can use this highly accurate model to forecast potential high emergency medical resource burden caused by extreme heat events, allowing them to raise and improve public awareness and prepare countermeasures in advance. The method proposed in Japan in this paper can be applied to other countries that have relevant data and weather information systems.
研究人员一致认为，有大量证据表明极端温度事件的频率和持续时间呈上升趋势。极端温度事件的增加将给公共卫生和应急医疗资源带来更大压力，社会需要找到有效且可靠的解决方案来适应更炎热的夏季。本研究开发了一种有效的方法来预测每日与热相关的救护车呼叫数量。我们建立了国家和区域层面的模型，以评估基于机器学习的方法在与热相关的救护车呼叫预测方面的性能。国家模型显示出较高的预测准确性，可应用于大多数地区，而区域模型在每个相应地区显示出极高的预测准确性，在特殊情况下也具有可靠的准确性。我们发现，引入热浪特征，包括累积热应激、热适应和最佳温度，显著提高了预测准确性。通过纳入这些特征，国家模型的调整决定系数（调整后的R）从0.9061提高到了0.9659，区域模型的调整后R也从0.9102提高到了0.9860。此外，我们使用了五个偏差校正的全球气候模型（GCMs），在国家和区域层面预测三种不同未来气候情景下夏季与热相关的救护车呼叫总数。我们的分析表明，在21世纪末，在共享社会经济路径（SSP）-5.85情景下，日本与热相关的救护车呼叫总数每年将达到约25万次（几乎是目前数量的四倍）。我们的结果表明，灾害管理机构可以使用这个高度准确的模型来预测极端高温事件可能造成的高应急医疗资源负担，从而提高公众意识并提前准备应对措施。本文在日本提出的方法可应用于其他拥有相关数据和气象信息系统的国家。
