在整个澳大利亚使用可解释的机器学习开发社区特定的每日天气健康风险指数
Zhaoyuan Li1, Rongbin Xu2,3, Wenzhong Huang1
1Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
Environmental science & technology
|November 4, 2025
概括
一个新的天气健康风险指数 (WHRI) 量化了气候因素 (如温度) 对关节健康的风险. 该指数显示了澳大利亚各地死亡风险的显著区域和季节性变化.
科学领域:
- 环境健康 环境健康
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 天气条件显著影响人类健康.
- 对复杂的,相互作用的天气相关健康风险的有效沟通是有限的.
- 量化这些风险对于公共卫生干预至关重要.
研究的目的:
- 开发和验证澳大利亚各地社区的每日天气健康风险指数 (WHRI).
- 量化与多种气象因素相关的联合健康风险.
- 在与天气有关的死亡风险中识别时空空间模式.
主要方法:
- 收集澳大利亚社区的每日死亡率和气象数据 (2009-2019).
- 使用一种可解释的机器学习框架: eXtreme梯度提升 (XGBoost) 与Shapley增量扩展 (SHAP).
- 建立了一个社区特定的每日WHRI.
主要成果:
- 温度是导致死亡风险的主要因素.
- 澳大利亚南部社区的天气相关死亡风险和WHRI高于北部社区.
- WHRI显示出明显的季节性模式,冬季达到峰值,夏季最低.
结论:
- 与天气有关的健康风险显示出显著的时空空间异质性.
- 开发的WHRI有效地捕捉和量化动态风险模式.
- 将WHRI整合到公共卫生仪表板中可以支持针对恶劣天气事件的及时,基于证据的干预措施.
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