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全瑞士空气中的树和草花粉度的时空建模:统计,机器学习和集体方法的比较

Behzad Valipour Shokouhi¹, Kees de Hoogh¹, Regula Gehrig²

¹Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.

Environmental research

|September 21, 2024

概括

一个合体混合模型最好地预测了瑞士每天的树和草花粉度. 像Random Forest这样的非线性机器学习算法表现优于线性模型,改善了流行病学研究的暴露评估.

科学领域:

环境科学环境科学
生物气象学生物气象学
数据科学是数据科学.

背景情况:

在流行病学研究中,统计和机器学习模型对于估计环境压力因素的变性至关重要.
这项研究比较了6个算法,用于在瑞士的树和草花粉度的时空建模.

研究的目的:

评估花粉度建模不同算法的性能,优势和局限性.
开发一个精确的时空模型每日花粉水平在1公里分辨率.

主要方法:

六个算法 (LASSO,Ridge,弹性网,随机森林,XGBoost,ANN) 应用于来自14个地点 (2000-2019年) 的每日花粉数据.
时空预测因素包括气象因素,土地使用,海拔,物种分布和NDVI.
通过使用多重线性回归来组合六个算法,创建了一个集合混合模型.

主要成果:

综合混合模型实现了树 (94.4粒/m3) 和草花粉 (19.7粒/m3) 两种最低的RMSE.
非线性模型 (随机森林,XGBoost,ANNs) 的表现优于线性模型,随机森林显示出最佳的空间和时间性能.
关键预测因素包括全国花粉度,土地使用,天气和NDVI.

结论:

非线性算法有效捕捉复杂的环境因素-花粉度关系.
开发的集体模型为准确的时空花粉暴露评估提供了一个强大的工具.

关键词:

环境压力因素环境压力因素风险投资评估风险投资评估土地使用回归研究机器学习机器学习花粉花粉花粉的花粉时间空间模型.

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