对机器学习预测技术的新见解,以实时评估受便污染影响的石饮用水源的卫生风险
Jaime Fernández-Ortega1, Juan Antonio Barberá1, Bartolomé Andreo1
1Department of Geology and Centre of Hydrogeology (CEHIUMA), Ada Byron Research Building, University of Malaga 29071 Malaga, Spain.
Water research
|December 4, 2025
概括
这项研究引入了一种使用机器学习的新方法,可以实时预测石泉水中的便细菌污染. 这种方法有助于通过提供对潜在健康风险的早期警告来保护饮用水供应.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 水质管理水质管理
背景情况:
- 石灰岩含水层非常容易受到污染,对安全饮用水构成风险.
- 检测便细菌的传统方法是缓慢而昂贵的,延迟了污染警报.
研究的目的:
- 开发一种创新的方法来实时监测饮用水源的水质.
- 评估机器学习模型的潜力,以提前预警石泉的微生物污染.
主要方法:
- 连续监测弹放电,电导率,度和托芬样的光.
- 地下水采样以确定大肠杆菌 (大肠杆菌) 在三年水文学年.
- 测试十个受监督的机器学习模型,从连续的水参数测量中推断卫生风险水平.
主要成果:
- 两个水参数的组合有效预测了两个不同的饮用水源的卫生风险水平.
- 最佳预测参数因特定的水文地质特征和污染物运输而有所不同.
- 高斯过程,神经网络,天真贝叶斯和二次差异分析在区分卫生风险水平方面显示出高准确性.
结论:
- 拟议的方法提供了一个有前途的工具,用于实时评估饮用水供应中的卫生风险.
- 这种方法可以集成到早期预警系统中,以保护公众健康免受岩环境中的微生物威胁.
- 实时监测和机器学习可以显著改善脆弱的石水资源的管理.
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