使用水质参数来通过人工神经网络模型预测基于离子的三甲
Ali Akbar Babaei1,2, Yaser Tahmasebi Birgani1,2, Zeynab Baboli3
1Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Environmental monitoring and assessment
|July 4, 2023
概括
使用人工神经网络研究了饮用水中的潜在致癌物质三甲 (THM). 结果显示,THM水平在某些领域超过标准,突出显示需要监测和控制.
科学领域:
- 环境化学环境化学
- 评估水质水质的评估.
- 公共卫生 公共卫生
背景情况:
- 三甲 (THM) 是化饮用水中的主要消毒副产品.
- THM被归类为潜在的人类致癌物.
- THM形成受到pH,温度和有机物等因素的影响.
研究的目的:
- 用简单的水质参数来评估THM的形成.
- 在五个水分网 (WDNs) 和卡鲁恩河中模拟THM度.
- 评估人工神经网络 (ANN) 在预测THM水平方面的潜力.
主要方法:
- 从五个WDN和卡鲁恩河 (2014年10月 - 2015年9月) 收集了水样.
- 测量了影响THM形成的六个关键水质参数.
- 在THM建模中采用人工神经网络 (ANN) 方法.
主要成果:
- 在WDN中,THM度的差异很大,其中一些超过了伊朗和EPA标准 (例如,Mahshahr,Khorramshahr).
- ANN模型显示了THM测量和预测值 (R2,MSE,RMSE评估) 之间存在很强的相关性.
- 确定了具有需要注意的高THM水平的特定WDNs.
结论:
- 在水源中,ANN显示出对准确估计THM形成的巨大潜力.
- 调查结果强调了在饮用水分配系统中监测THM的重要性.
- 结果为水质管理和风险评估提供了有价值的数据.
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