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城市内超细颗粒的空间和时空建模:线性,非线性,规范化和机器学习方法的比较
Julien Vachon1, Stéphane Buteau1, Ying Liu2
1Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
The Science of the total environment
|September 26, 2024
概括
与统计模型相比,机器学习,特别是基于树的方法,如XGBoost,显著改善了超细粒子 (UFP) 预测. 空间聚合和细分长度影响了模型性能,突出了未来空气污染建模的关键因素.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 空气质量监测 空气质量监测
背景情况:
- 机器学习 (ML) 提供了增强空气污染预测的潜力.
- 很少有研究全面比较了ML和超细粒子 (UFP) 预测的统计模型.
- 对于环境UFP的最佳时空建模方法的理解有限.
研究的目的:
- 为了比较各种统计和ML方法的空间和空间时间环境UFP建模的预测性能.
- 确定最佳的建模策略,以准确预测UFP度.
主要方法:
- 开发了每日和每年的UFP模型,使用北克市为期一年的移动监控活动的数据.
- 包含262个地理空间和6个气象预测器,测试100米,300米和500米的道路段长度.
- 通过嵌套交叉验证评估了四种统计回归方法和八种ML回归算法 (基于树的,神经网络,SVM,内核).
主要成果:
- 机器学习模型通常优于UFP预测的统计方法.
- 基于树的ML方法,特别是XGBoost,在不同的时间尺度和段长度上表现出卓越的性能.
- 年度模型实现了0.78-0.86的R平方值,而每日模型达到0.47-0.48;空间聚合显著影响了性能.
结论:
- 基于树的ML方法大大提高了空间时间UFP度预测.
- 路段长度和超参数调整是影响模型准确性的关键因素,应在未来的研究中优先考虑.
- 这些发现为开发更有效的UFP空气质量模型提供了宝贵的见解.
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