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多重线性回归与随机森林模型之间的比较,用于预测香港的环境噪声及其频率组件水平,使用土地利用回归方法
Chui Hei Wong1, Zhiyuan Li2, Steve Hung Lam Yim3
1Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region.
环境噪音暴露与慢性疾病有关. 土地利用回归 (LUR) 模型,特别是随机森林 (RF),比传统的流行病学研究方法更好地预测噪声水平.
科学领域:
- 环境健康 环境健康
- 声学 声学 在声学上
- 流行病学 流行病学
背景情况:
- 环境噪音暴露与慢性疾病,如心血管问题和认知能力下降有关.
- 传统的声学模型在流行病学研究中缺乏针对不同暴露源的灵活性.
- 土地利用回归 (LUR) 模型为预测环境噪音水平提供了更具适应性的方法.
研究的目的:
- 为了比较多重线性回归 (MLR) 和机器学习算法来开发LUR模型.
- 评估机器学习,特别是随机森林 (RF) 处理预测变量的非线性的能力.
- 评估RF和MLR模型在预测香港环境噪声方面的性能.
主要方法:
- 在102个地点 (2019-2020年) 进行了夏季和冬季A加权相当声压水平 (Leq,24h和Lnight) 的测量.
- 利用噪音参数与交通,人口和土地使用变量一起构建LUR模型.
- 采用多重线性回归 (MLR) 和随机森林 (RF) 算法进行模型开发和比较.
主要成果:
- 随机森林 (RF) 模型在Leq,24h预测方面表现优于MLR (RF R2=0.79与MLR R2=0.70).
- MLR模型在夜间噪声参数上显示出更好的R2,在某些夜间频率误差指标上表现优于RF.
- 两个模型的关键预测因素包括植被,工业区和公共汽车流量.
结论:
- 随机森林 (RF) 模型提供可靠的环境噪声预测,与MLR相提并论.
- 由于RF能够处理异常值和过拟合,因此它是LUR模型开发的有利选择.
- 这些发现支持在未来关于噪音相关健康结果的流行病学研究中使用基于射频的LUR模型.
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