分类机器学习来检测饮用水摄入口的实际重复使用和蓝藻细菌
Emily Clements1, Kyle A Thompson2, Deena Hannoun1
1Southern Nevada Water Authority, 1299 Burkholder Blvd., Henderson, NV 89015, USA.
The Science of the total environment
|July 11, 2024
概括
监督机器学习有效地通过在线传感器检测饮用水摄入处的有害藻类繁殖 (HAB) 和事实上的水再利用 (DFR) 事件. 这项技术可以为水利公司提供早期警报,以便主动调整处理过程.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 机器学习应用 机器学习应用
背景情况:
- 有害的藻类繁殖 (HAB) 和事实上的水再利用 (DFR) 可以将污染物引入饮用水源.
- 对于公共卫生和水处理管理来说,早期检测这些事件在水摄入处至关重要.
研究的目的:
- 开发和评估一种多类监督机器学习 (SML) 分类模型,用于检测生水中的蓝藻细菌 (HAB) 和DFR事件.
- 确定关键的水质参数和最佳的SML模型,以实时检测进水口的事件.
主要方法:
- 利用了6个在线仪器测量14个水质参数的数据.
- 在R中选了caret包中的56个模型,选择了4个 (mda,LogitBoost,bagFDAGCV,xgbTree) 进行优化.
- 基于测试设置准确性和错误警报的模型性能评估.
主要成果:
- 优化的"mda"模型实现了最高的测试设置准确率,为98.09%,具有7个错误警报.
- 鉴定出像物理的光,UVA254和pH值是区分HAB和DFR的关键参数.
- SML成功检测到低水平的藻类事件 (<9000细胞/毫升),并确定了更高的DFR度.
结论:
- 多类SML分类,结合在线仪器仪表,可以有效地检测和区分饮用水摄入处的HAB和DFR事件.
- 这种方法可以促进水利设施的警报系统的开发,使处理过程能够及时调整.
- 该研究证明了SML在主动水质管理和保护饮用水供应方面的潜力.
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