使用系统,社区和监管特征预测饮用水风险的创新方法
Liangfei Ye1, Qianqian Dong1, Aaron McCright2
1Texas A&M University, College Station, TX, USA.
概括
预测公共饮用水系统 (PWS) 的风险至关重要. 一项新的研究表明,极端梯度增强 (XGBoost) 有效地利用社区和监管数据预测PWS风险,优于其他模型.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 公共饮用水系统 (PWS) 带来了重大的健康风险和成本.
- 需要强大的预测模型来预防和减轻风险.
- 目前的模型通常依赖于水质和水文数据.
研究的目的:
- 为了比较Logit,支持向量机 (SVM) 和极端梯度增强 (XGBoost) 模型来预测PWS风险.
- 评估PWS特征,社区属性和监管发展对风险预测的影响.
- 引入一种新的方法,重点关注社会决定因素,而不是水质参数.
主要方法:
- 对Logit,SVM和XGBoost机器学习模型进行比较分析.
- 基于公共饮用水系统特征,社区属性和监管发展的风险预测.
- 评估模型性能,特别是针对基于健康的风险.
主要成果:
- 与Logit和SVM模型相比,极端梯度提升 (XGBoost) 显示出更高的性能.
- 社区和监管特征在风险预测中比PWS特征更有影响力.
- 所有模型在预测基于健康的风险方面都表现出较低的有效性.
结论:
- XGBoost为长期PWS风险预测提供了一个具有成本效益和合适的替代方案.
- 使用社区和监管数据的新方法为风险预测提供了有价值的见解.
- 专注于社会决定因素提高了利益相关者的能力,以有效地解决PWS风险.
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