使用贝叶斯网络预测城市河口的娱乐用水质量和公共卫生安全
Simon D Lloyd1, Guido Carvajal2, Meredith Campey3
1School of Built Environment, University of New South Wales, NSW, Australia.
Water research
|February 29, 2024
概括
贝叶斯网络准确地预测城市河口的微生物水质和健康风险. 模型显示了可靠的性能,具有足够的数据和可转移结构,有助于安全的娱乐用水使用.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 预测建模预测建模
背景情况:
- 城市河流和河口需要实时微生物水质数据,以确保安全的公共浴.
- 预测建模在数据大小,可转移性和不确定性沟通方面面临挑战.
- 城市河口表现出独特的水文变化,需要专门的建模.
研究的目的:
- 评估贝叶斯网络 (BNs) 以预测城市河口的肠球菌超值和健康风险.
- 评估不同游泳场所之间的BN结构的可转移性.
- 为娱乐用水质量管理提供决策支持.
主要方法:
- 采用贝叶斯网络 (BNs) 来预测肠球菌和评估健康风险.
- 使用k-fold前行验证和独立数据集用于模型验证.
- 分析学习曲线以确定最佳的训练数据大小 (≥400个观察).
主要成果:
- 半原始的BN结构显示出稳定的预测性能和跨站点的可转移性.
- 盐度,阳光照射和先前降雨量是影响预测的关键因素.
- BNs有效量化和可视化了水质,健康风险和不确定性.
结论:
- 贝叶斯网络为预测城市河口的娱乐用水质量提供了一个透明的框架.
- 该研究推进了BN在高不确定性河口环境中的决策支持应用.
- 这些发现支持安全地重新启动城市水道以供公共游泳.
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