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饮用水中污染高风险学校的数据稀疏预测:来自美国四个州的例子
Samyukta Shrivatsa1, Gabriel Lobo1, Ashok Gadgil1
1Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA.
预测模型可以识别饮用水中含量不安全的学校,即使数据有限. 这种方法通过减少广泛的测试成本和时间来促进公共卫生.
科学领域:
- 环境健康 环境健康
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 儿童通过饮用水接触会对认知和发育构成风险.
- 在学校进行全面的测试是昂贵和耗时的.
- 以前的研究成功地使用随机森林建模来预测学校饮用水中的污染.
研究的目的:
- 为了评估预测模型的性能如何在较小的训练数据集下降.
- 评估预测建模的有效性,以识别有饮用水中污染风险的学校.
主要方法:
- 随机森林模型在美国四个州的不同比例 (10%至80%) 的学校数据上进行了训练.
- 模型性能使用精度回忆曲线下的面积 (PR AUC) 和接收器操作特征曲线下的面积 (ROC AUC) 来评估.
- 分析了训练集大小对预测准确性的影响.
主要成果:
- 在一些州,只有10%的学校实现了可接受的ROC AUC绩效.
- 所有四个州都显示了可接受的ROC AUC,当至少50%的学校被纳入培训套件时.
- 在纽约和新罕布什尔州,欧几里德距离到最近的受污染学校是重要的预测因素.
结论:
- 预测建模对于识别饮用水中污染风险高的学校是有效的.
- 即使没有关于水水平的完整调查数据,这种方法仍然是有价值的.
- 这些发现支持使用数据驱动方法来优先考虑学校的测试.
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