通过基于土地使用的机器学习模型评估湿球球体温度的长期和高时空分辨率
Chin-Yu Hsu1,2, Pei-Yi Wong3, Yinq-Rong Chern4
1Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan.
Journal of exposure science & environmental epidemiology
|December 16, 2023
概括
新的基于土地使用的空间机器学习 (LBSM) 模型准确预测湿球球体温度 (WBGT) 的变化. 这种热应激指标现在可以在精细分辨率下绘制,帮助未来的生产力损失评估.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 地理空间分析的研究.
背景情况:
- 全球气温上升和城市变暖加剧了热浪,影响了人类健康和劳动生产率.
- 准确评估温度变化对于理解这些影响至关重要.
研究的目的:
- 引入基于土地使用的新型空间机器学习 (LBSM) 模型,用于预测湿球球体温度 (WBGT) 的时空变化.
- 评估室内和室外环境中的热舒适度和热应力,特别是台湾主要岛屿.
主要方法:
- 用气象站的温度,湿度和太阳辐射数据计算了2001-2019年的WBGT.
- 开发了使用WBGT估计作为依赖变量的时空预测模型.
- 在非线性变量选择和机器学习算法中使用了SHapley添加式扩展 (SHAP),以提高模型性能.
主要成果:
- 包括土地使用/土地覆盖变量在内的LBSM模型取得了出色的表现,R2高达0.99.
- 模型解释了98% (工作时间) 和99% (中午时间) 的时空WBGT变化.
- 不包括温度的模型仍然解释了94% (工作时间) 和96% (中午时间) 的变化.
结论:
- 开发了一种新的LBSM方法,用于高分辨率 (50m x 50m) 的时空WBGT预测.
- 解决了缺乏用于热应力影响研究的时空空间数据库的问题.
- 方法论可以支持未来研究因全球变暖和城市热岛而导致的劳动生产率损失.
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