透视的水质分析和预测模型通过机器学习在双源饮用水分配系统的建立
Huiping Li1, Baiqin Zhou2, Xiaoyan Xu3
1Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
Environmental research
|February 18, 2024
概括
双源饮用水系统在液压连接处显示出显著的水质变化. 三甲 (THMs) 在这些地区达到峰值,需要用于安全水资源管理的预测建模.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 分析化学 分析化学
背景情况:
- 双源饮用水分配系统 (DWDS) 对大城市越来越重要.
- 在DWDS中的液压连接带来了复杂的水质挑战.
研究的目的:
- 分析双源DWDS的液压连接处的水质变化.
- 确定影响三甲 (THM) 形成的因素.
- 在DWDS中开发THM的预测模型.
主要方法:
- 在双源DWDS液压交叉点进行一年的水质采样和分析.
- 平行因子 (PARAFAC) 和高性能尺寸排除色谱 (HPSEC) 用于有机物质分析.
- 机器学习 (ML) 模型,包括SHAP分析,用于THM预测.
主要成果:
- 在10-12公里之间观察到度,pH,UV254,DOC,残留和THM的显著变化.
- 剩余减少,而THM在液压接口区域达到峰值.
- 与THM水平相关的托芬类物质和氨基酸.
- 机器学习模型在THM预测方面实现了R2>0.8,确定了关键影响因素.
结论:
- 液压接口区域 (10-12公里) 对双源DWDS的水质变化至关重要.
- 了解有机物动态对于管理THM形成至关重要.
- 数据驱动的模型为预测和管理复杂的水分系统中的THM提供了有效的工具.
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