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基于神经网络的估计,气候对德国的死亡率的影响:应用到故事情节气候模拟
R Schachtschneider1, J Saynisch-Wagner2, A Sánchez-Benítez3
1Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Telegrafenberg, 14473, Potsdam, Germany. reyko.schachtschneider@gfz.de.
Scientific reports
|October 31, 2024
概括
这项研究使用机器学习和气候数据预测了德国未来与热有关的死亡率. 结果显示,由于热浪,夏季死亡人数增加,但由于较温和的条件,冬季死亡人数减少.
科学领域:
- 环境科学环境科学
- 公共卫生 公共卫生
- 气候建模 气候建模
背景情况:
- 气候变化正在增加全球气温.
- 极端高温事件对人类健康构成重大风险,特别是死亡率.
- 准确预测气候相关的健康影响对于公共卫生规划至关重要.
研究的目的:
- 根据未来的气候情景,预测德国与热相关的死亡率.
- 利用机器学习将气候数据与死亡率联系起来.
- 评估预计温度上升对所有原因死亡率的影响.
主要方法:
- 使用回声状态网络 (ESN) 进行预测建模.
- 利用来自气候故事情节模拟的2m温度数据.
- 培训了ESN关于当今气候模型输出和德国所有原因死亡率数据.
主要成果:
- 在ESN成功地预测了未来气候条件 (2K和4K更温暖) 的死亡率.
- 预计夏季死亡率的增加,与更严重的热浪有关.
- 观察到冬季死亡率下降,归因于冬季较温和,呼吸道疾病减少.
结论:
- 机器学习模型可以有效地预测气候驱动的死亡趋势.
- 预计未来的气候变化将增加与高温相关的死亡率,特别是在夏季.
- 虽然温度影响冬季死亡率,但流感等其他因素也起着重要作用.
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