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与受体模型相比,使用机器学习追踪消毒副产品前体的污染源

Yuan Xiao¹, Shunjun Ma², Shumin Yang¹

¹College of Environmental Science & Engineering, Shanghai East Hospital, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Tongji University, 1239 Siping Road, Shanghai 200092, China.

The Science of the total environment

|January 6, 2024

概括

微生物和运输来源对消毒副产品前体有着显著的贡献. 一个新的混合模型 (FCM-SVR) 准确地识别和分配水资源中的这些污染源.

科学领域:

环境化学环境化学
水质管理水质管理

背景情况:

备用水资源的有效管理需要识别和分配污染源.
消毒副产品 (DBP) 是危险的,在饮用水处理过程中形成,但它们的来源分配方法尚不发达.

研究的目的:

调查有助于消毒副产品形成潜力 (DBPFPs) 的溶解有机物 (DOM) 的来源分配.
评估一种新的混合模型 (FCM-SVR) 以准确地分配源和预测DBPFPs.

主要方法:

利用并行因子分析 (PARAFAC) 和斯皮尔曼相关性用于初始来源识别.
采用混合模糊C-Means和支向量回归 (FCM-SVR) 模型进行源分配,并与传统的受体模型进行比较.
应用标准化和规范化作为FCM-SVR模型的唯一预处理步骤.

主要成果:

该FCM-SVR模型表现出了优秀的概括能力与最小的预处理.
微生物来源 (28.1%) 和航运海洋来源 (21.2%) 被确定为DBPFPs的主要贡献者.
FCM-SVR模型实现了与受体模型相比或超过的预测准确性,溶解有机碳 (DOC) 的高R2为0.884.

结论:

结合的FCM-SVR和PARAFAC方法在追踪DBPFP方面非常有效,提供优越的来源识别,分配和预测准确性.
该模型为跟踪有机化合物提供了一个有希望的工具,并为管理水资源和减轻DBP形成提供了有价值的见解.
该研究为DBP的源分配提供了一种新有效的方法,解决了当前水质管理策略中的差距.

关键词:

模糊的集群算法模糊的集群算法机器学习是机器学习.平行因子分析的同时,因子分析的同时.来源分配的分配方式支持向量回归的支持向量回归

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