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使用机器学习预测小型饮用水流域中受监管和新出现的消毒副产品

Boris Droz¹, Elena Fernández-Pascual¹, Jean O'Dwyer²

¹School of Biological, Earth and Environmental Sciences, University College Cork, Cork T23 TK30, Ireland; Sustainability Institute, Ellen Hutchins Building, University College Cork, Cork T23 XE10, Ireland.

Environment international

|November 22, 2025

概括

机器学习使用溶解有机物 (DOM) 光谱学准确预测饮用水中的有害消毒副产品 (DBPs). 这种方法有助于管理DBP的形成,并确保更安全的供水.

科学领域:

环境化学环境化学
水质管理水质管理
计算科学计算科学

背景情况:

消毒副产品 (DBPs) 由消毒剂和饮用水中的溶解有机物 (DOM) 之间的反应形成.
有害的DBP度对公众健康构成风险.
预测DBP的形成对于有效的水处理和风险管理至关重要.

研究的目的:

探索机器学习 (ML) 模型的应用,用于预测受监管和新兴DBP的形成.
评估DOM光谱变量和水化学参数在ML模型训练中的有用性.
开发一个可扩展的工作流程,以数据驱动的饮用水DBP管理.

主要方法:

在过的原水样本上进行了实验室化实验.
用DOM光谱变量和水化学参数来训练ML模型 (神经网络,包装树,增强回归,SVM).
模型的性能被评估为10个DBP物种的定量预测和二进制存在缺失分类.

主要成果:

ML模型准确预测了10个DBP度 (平均R2=0.86),并分类了5个额外的物种 (准确率为95.6%).
DOM光谱变量,特别是类似的光体和UV-Vis吸收度,是最有影响力的.
包括水化参数,如溶解有机碳 (DOC) 显示只有边际的改善.

结论:

在DOM光谱数据上训练的机器学习模型为预测DBP形成提供了强大的工具.
这种方法为可扩展的,数据驱动的饮用水DBP管理提供了概念验证.
这些发现支持基于风险的管理策略,以确保全球饮用水安全.

关键词:

消毒副产品的形成消毒副产品的形成紫外线视频谱学 UVV光谱学溶解的有机物质是一种有机物质.饮用水饮用水饮用水光激发发射矩阵光谱学光激发发射矩阵光谱学机器学习是机器学习.

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