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提高饮用水安全:使用机器学习和多传感技术在配水系统中实时预测三甲

Antonio J Aragón-Barroso¹, David Ribes², Francisco Osorio¹

¹Department of Civil Engineering, University of Granada, Dr. Severo Ochoa, s/n, Granada 18071, Spain; Environmental Microbiology Group, Institute of Water Research, University of Granada, C/Ramon y Cajal, 4, Granada 18071, Spain.

Ecotoxicology and environmental safety

|April 29, 2025

概括

这项研究开发了使用多重线性回归来监测饮用水中的三甲 (THM) 的预测模型. 这些模型通过预测水网内的THM的潜在增加,有助于预防健康风险.

科学领域:

环境科学环境科学
水质管理水质管理
公共卫生公共卫生

背景情况:

饮用水中的三甲 (THM) 因其致癌性和致变性特性而对健康构成风险.
在饮用水配送系统 (DWDS) 中有效监测THM对于公共安全至关重要.

研究的目的:

在饮用水分配系统 (DWDS) 中开发三甲 (THMs) 形成的统计预测模型.
创建一个预防工具,用于早期检测潜在的THM在水网内的增加.

主要方法:

利用多重线性回归 (MLR) 来利用水质参数来建模THM的形成:自由残留 (FRC),总有机碳 (TOC),导电性,pH和度.
开发了基于导电水平的两个预测模型,达到0.64和0.47.7的确定系数 (R2).
集成模型算法与实时监控系统使用多传感器设备进行连续数据传输和模型更新.

主要成果:

成功开发和实施了THM形成的两个预测模型.
一个实时监控系统在预测THM水平方面实现了90%的平均准确性.
预测模型作为水质管理的有效预防工具.

结论:

统计建模为预测DWDS中的THM提供了一种可行的方法.
实时监测和预测建模提高了与THMs相关的健康风险的管理和减轻的能力.
开发的系统为确保饮用水安全提供了一个积极的战略.

关键词:

模型模型模型模型模型监控监控监控监控多传感器件是一种多传感器件.预测预测预测在此之前,THM 已经

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