探索统计和机器学习技术,以确定影响室内度的因素
1"Constantin Cosma" Radon Laboratory (LiRaCC), Faculty of Environmental Science and Engineering, "Babeş-Bolyai" University, Fântânele Street, no. 30, Cluj-Napoca, Romania.
The Science of the total environment
|September 14, 2023
概括
这项研究确定了影响罗马尼亚室内度 (IRC) 的关键因素. 房子的特点,如地下室和施工期,显著影响水平,这对于公共卫生风险评估至关重要.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 地质科学 地质科学
背景情况:
- 是一种具有致癌作用的放射性气体,主要通过室内暴露,对健康构成重大风险.
- 室内度 (IRC) 受各种环境和结构因素的影响.
- 了解这些因素对于减轻与暴露相关的公共卫生风险至关重要.
研究的目的:
- 调查地质,地面和房屋特征对罗马尼亚室内度 (IRC) 的综合影响.
- 确定影响室内水平的主要预测因素.
- 为了比较不同的统计和机器学习模型在预测IRC方面的表现.
主要方法:
- 在罗马尼亚各地进行了3132次被动测量.
- 使用单变量统计,人工神经网络和随机森林回归器 (RFR) 进行分析.
- 利用后勤回归和随机森林分类器来对IRC进行二进制分类.
主要成果:
- 随机森林回归器 (RFR) 模型显示了连续度 (R2=0.14,RMSE=0.83) 的最佳性能.
- 对于离散的IRC (中位数为115 Bq/m3),后勤回归实现了0.61的AUC-ROC,随机森林分类器实现了0.62.
- 确定的关键预测因素包括地下室的存在,施工期,高度和地板类型.
结论:
- 地质,地形和房屋特征是室内度的重要决定因素.
- 机器学习模型,特别是RFR,提供了可靠的方法来预测水平.
- 识别地下室存在和施工期等主要预测因素可以为有针对性的减策略提供信息.
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