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使用机器学习模型预测水分配系统中的几种消毒副产品
Shakhawat Chowdhury1,2, Karim Asif Sattar3, Syed Masiur Rahman4
1Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia. SChowdhury@kfupm.edu.sa.
Environmental science and pollution research international
|January 20, 2025
概括
机器学习模型准确地预测饮用水分配系统 (WDS) 中的消毒副产品 (DBPs). 这可以减少昂贵的采样,并通过控制DBP来改善人类健康.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 预测建模预测建模
背景情况:
- 饮用水中的消毒副产品 (DBPs) 是一个持续关注的问题.
- 在水分系统 (WDS) 中测量DBP是具有挑战性的,因为可访问性.
- 机器学习 (ML) 提供了在WDS中改进DBP预测的潜力.
研究的目的:
- 开发和评估 ML 模型,用于预测 WDS 中的关键 DBP.
- 用现实数据评估各种ML模型的性能.
主要方法:
- 从113个安大略省供水系统收集了13年的每三个月的DBP数据 (2008-2020).
- 训练并测试了四种ML模型:线性回归器 (LR),随机森林回归器 (RFR),支持矢量回归器 (SVR) 和人工神经网络 (ANN-SV/MV).
- 在训练和测试数据集上使用R平方 (R2) 值评估模型性能.
主要成果:
- 在不同DBP的训练 (0.449-0.993) 和测试 (0.437-0.973) 数据集中实现了高R2值.
- 在预测三甲 (THM),乙酸 (HAA) 和二乙 (DCAN) 方面,ANN-SV模型表现出色.
- SVR模型在N-二甲基胺 (NDMA) 预测方面表现出卓越的性能.
结论:
- 开发的ML模型可靠地预测WDS中的DBP,为传统采样提供了替代方案.
- 这些模型可以在WDS中增强DBP控制策略.
- 改进的DBP预测和控制可以减少人类暴露和相关的健康风险.
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