使用人工神经网络预测供水中的消毒副产品 (DBPs) 在一个真实的供水网络中使用人工神经网络
F Khan1, M F R Zuthi1, M S Rahman2
1Department of Civil Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.
Ecotoxicology and environmental safety
|August 3, 2025
概括
这项研究引入了一种人工神经网络 (ANN) 模型,用于预测饮用水中的消毒副产品 (DBPs). 该模型使用来自孟加拉国Chattogram供水网的水质数据准确预测总三甲 (TTHMs).
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 计算建模 计算建模
背景情况:
- 消毒副产品 (DBP) 是饮用水中的一个问题.
- 在水源中化物存在可能导致和-DBP的形成.
- 准确预测DBP对于公共卫生和水资源管理至关重要.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型,用于预测供水中的消毒副产品.
- 将管道距离和自由等新参数纳入DBP预测模型.
- 评估模型的准确性和稳定性,使用来自沿海城市水分网络的真实数据.
主要方法:
- 基于辐射基函数 (RBF) 的人工神经网络 (ANN) 模型的开发.
- 使用查特格拉姆水分网络 (WDN) 的120个数据点.
- 纳入九个水质参数,包括管道距离和自由 (I−),以预测总三甲 (TTHMs) 和它们的物种.
主要成果:
- RBF-ANN模型表现出良好的可预测性,回归系数 (R2) 在0.81到0.87.8之间.
- 优化的模型配置 (20个神经元,60个扩散) 提高了预测准确性.
- 十倍的交叉验证证实了该模型的稳定性和通用化能力.
- 使用所有九个输入参数产生了最准确的TTHM度预测.
结论:
- 开发的RBF-ANN模型对预测实际供水系统中的DBPs有效,特别是在化物丰富的环境中.
- 管道距离和自由是DBP预测的有价值参数.
- 该模型的预测能力随着数据量增加而提高,突出其可扩展性.
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